diff --git a/MANIFEST.in b/MANIFEST.in index 388882df0c3701780dd6371bc91887356a7bca40..4c16c09d588d39906e1fa581c901cd9fcd7778c8 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,8 +1,7 @@ include LICENSE.txt include README.md -recursive-include ppocr/utils *.txt utility.py character.py check.py -recursive-include ppocr/data/det *.py +recursive-include ppocr/utils *.txt utility.py logging.py +recursive-include ppocr/data/ *.py recursive-include ppocr/postprocess *.py -recursive-include ppocr/postprocess/lanms *.* -recursive-include tools/infer *.py +recursive-include tools/infer *.py \ No newline at end of file diff --git a/configs/cls/cls_mv3.yml b/configs/cls/cls_mv3.yml index c2b171590cfe2d6308bbee16e534bfc21099309e..b165bc4830f01f0e63c43b1d6a9635e432dc7605 100644 --- a/configs/cls/cls_mv3.yml +++ b/configs/cls/cls_mv3.yml @@ -8,7 +8,6 @@ Global: # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [0, 1000] # if pretrained_model is saved in static mode, load_static_weights must set to True - load_static_weights: True cal_metric_during_train: True pretrained_model: checkpoints: diff --git a/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml b/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml new file mode 100644 index 0000000000000000000000000000000000000000..275c71b97d21e6b168ef6aafae67eb2eb91c6f2b --- /dev/null +++ b/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml @@ -0,0 +1,134 @@ +Global: + use_gpu: true + epoch_num: 1200 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/ch_db_mv3/ + save_epoch_step: 1200 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [3000, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + model_type: det + algorithm: DB + Transform: + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + disable_se: True + Neck: + name: DBFPN + out_channels: 96 + Head: + name: DBHead + k: 50 + +Loss: + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Cosine + learning_rate: 0.001 + warmup_epoch: 2 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [960, 960] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: +# image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 diff --git a/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml new file mode 100644 index 0000000000000000000000000000000000000000..e34d94490fbfa81452a7ff507f6568e5ac29e3b7 --- /dev/null +++ b/configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml @@ -0,0 +1,133 @@ +Global: + use_gpu: true + epoch_num: 1200 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/ch_db_res18/ + save_epoch_step: 1200 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [3000, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet18_vd_pretrained + checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + model_type: det + algorithm: DB + Transform: + Backbone: + name: ResNet + layers: 18 + disable_se: True + Neck: + name: DBFPN + out_channels: 256 + Head: + name: DBHead + k: 50 + +Loss: + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + name: Cosine + learning_rate: 0.001 + warmup_epoch: 2 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.6 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [960, 960] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: +# image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 diff --git a/configs/det/det_mv3_db.yml b/configs/det/det_mv3_db.yml index b55ff99be91bfea795d4b023edcc57e95b97ecc2..640f3a205b1fd3ec7fe19d5c6b6e3aef9ddf3968 100644 --- a/configs/det/det_mv3_db.yml +++ b/configs/det/det_mv3_db.yml @@ -45,9 +45,7 @@ Optimizer: beta1: 0.9 beta2: 0.999 lr: -# name: Cosine learning_rate: 0.001 -# warmup_epoch: 0 regularizer: name: 'L2' factor: 0 diff --git a/configs/det/det_mv3_east.yml b/configs/det/det_mv3_east.yml new file mode 100644 index 0000000000000000000000000000000000000000..05581a761cc91abce6d1e83bcdd7aacef1349148 --- /dev/null +++ b/configs/det/det_mv3_east.yml @@ -0,0 +1,111 @@ +Global: + use_gpu: true + epoch_num: 10000 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/east_mv3/ + save_epoch_step: 1000 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [4000, 5000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: + save_res_path: ./output/det_east/predicts_east.txt + +Architecture: + model_type: det + algorithm: EAST + Transform: + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: EASTFPN + model_name: small + Head: + name: EASTHead + model_name: small + +Loss: + name: EASTLoss + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + # name: Cosine + learning_rate: 0.001 + # warmup_epoch: 0 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: EASTPostProcess + score_thresh: 0.8 + cover_thresh: 0.1 + nms_thresh: 0.2 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - EASTProcessTrain: + image_shape: [512, 512] + background_ratio: 0.125 + min_crop_side_ratio: 0.1 + min_text_size: 10 + - KeepKeys: + keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 16 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 2400 + limit_type: max + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 \ No newline at end of file diff --git a/configs/det/det_r50_vd_db.yml b/configs/det/det_r50_vd_db.yml new file mode 100644 index 0000000000000000000000000000000000000000..491983f57a59f0a4105712743a69a56c8212a0e9 --- /dev/null +++ b/configs/det/det_r50_vd_db.yml @@ -0,0 +1,130 @@ +Global: + use_gpu: true + epoch_num: 1200 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/det_rc/det_r50_vd/ + save_epoch_step: 1200 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [5000,4000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_en/img_10.jpg + save_res_path: ./output/det_db/predicts_db.txt + +Architecture: + model_type: det + algorithm: DB + Transform: + Backbone: + name: ResNet + layers: 50 + Neck: + name: DBFPN + out_channels: 256 + Head: + name: DBHead + k: 50 + +Loss: + name: DBLoss + balance_loss: true + main_loss_type: DiceLoss + alpha: 5 + beta: 10 + ohem_ratio: 3 + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.001 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: DBPostProcess + thresh: 0.3 + box_thresh: 0.7 + max_candidates: 1000 + unclip_ratio: 1.5 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [0.5] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - IaaAugment: + augmenter_args: + - { 'type': Fliplr, 'args': { 'p': 0.5 } } + - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } + - { 'type': Resize, 'args': { 'size': [0.5, 3] } } + - EastRandomCropData: + size: [640, 640] + max_tries: 50 + keep_ratio: true + - MakeBorderMap: + shrink_ratio: 0.4 + thresh_min: 0.3 + thresh_max: 0.7 + - MakeShrinkMap: + shrink_ratio: 0.4 + min_text_size: 8 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list + loader: + shuffle: True + drop_last: False + batch_size_per_card: 16 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + image_shape: [736, 1280] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 8 \ No newline at end of file diff --git a/configs/det/det_r50_vd_east.yml b/configs/det/det_r50_vd_east.yml new file mode 100644 index 0000000000000000000000000000000000000000..b8fe55d4ac5473f6c0392820f5b1f651448bdddd --- /dev/null +++ b/configs/det/det_r50_vd_east.yml @@ -0,0 +1,110 @@ +Global: + use_gpu: true + epoch_num: 10000 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/east_r50_vd/ + save_epoch_step: 1000 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [4000, 5000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/ + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: + save_res_path: ./output/det_east/predicts_east.txt + +Architecture: + model_type: det + algorithm: EAST + Transform: + Backbone: + name: ResNet + layers: 50 + Neck: + name: EASTFPN + model_name: large + Head: + name: EASTHead + model_name: large + +Loss: + name: EASTLoss + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + # name: Cosine + learning_rate: 0.001 + # warmup_epoch: 0 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: EASTPostProcess + score_thresh: 0.8 + cover_thresh: 0.1 + nms_thresh: 0.2 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt + ratio_list: [1.0] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - EASTProcessTrain: + image_shape: [512, 512] + background_ratio: 0.125 + min_crop_side_ratio: 0.1 + min_text_size: 10 + - KeepKeys: + keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + limit_side_len: 2400 + limit_type: max + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 \ No newline at end of file diff --git a/configs/det/det_r50_vd_sast_icdar15.yml b/configs/det/det_r50_vd_sast_icdar15.yml new file mode 100644 index 0000000000000000000000000000000000000000..7ca93cecec3dbf7152c1d509c4d8ca614dec388f --- /dev/null +++ b/configs/det/det_r50_vd_sast_icdar15.yml @@ -0,0 +1,110 @@ +Global: + use_gpu: true + epoch_num: 5000 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/sast_r50_vd_ic15/ + save_epoch_step: 1000 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [4000, 5000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: + save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt + +Architecture: + model_type: det + algorithm: SAST + Transform: + Backbone: + name: ResNet_SAST + layers: 50 + Neck: + name: SASTFPN + with_cab: True + Head: + name: SASTHead + +Loss: + name: SASTLoss + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + # name: Cosine + learning_rate: 0.001 + # warmup_epoch: 0 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: SASTPostProcess + score_thresh: 0.5 + sample_pts_num: 2 + nms_thresh: 0.2 + expand_scale: 1.0 + shrink_ratio_of_width: 0.3 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_path: [./train_data/art_latin_icdar_14pt/train_no_tt_test/train_label_json.txt, ./train_data/total_text_icdar_14pt/train_label_json.txt] + data_ratio_list: [0.5, 0.5] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - SASTProcessTrain: + image_shape: [512, 512] + min_crop_side_ratio: 0.3 + min_crop_size: 24 + min_text_size: 4 + max_text_size: 512 + - KeepKeys: + keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 4 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/icdar2015/text_localization/ + label_file_list: + - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + resize_long: 1536 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 \ No newline at end of file diff --git a/configs/det/det_r50_vd_sast_totaltext.yml b/configs/det/det_r50_vd_sast_totaltext.yml new file mode 100644 index 0000000000000000000000000000000000000000..a9a037c8bd940d04d08d0dae3a90139ead68cdba --- /dev/null +++ b/configs/det/det_r50_vd_sast_totaltext.yml @@ -0,0 +1,109 @@ +Global: + use_gpu: true + epoch_num: 5000 + log_smooth_window: 20 + print_batch_step: 2 + save_model_dir: ./output/sast_r50_vd_tt/ + save_epoch_step: 1000 + # evaluation is run every 5000 iterations after the 4000th iteration + eval_batch_step: [4000, 5000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + load_static_weights: True + cal_metric_during_train: False + pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/ + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: + save_res_path: ./output/sast_r50_vd_tt/predicts_sast.txt + +Architecture: + model_type: det + algorithm: SAST + Transform: + Backbone: + name: ResNet_SAST + layers: 50 + Neck: + name: SASTFPN + with_cab: True + Head: + name: SASTHead + +Loss: + name: SASTLoss + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + # name: Cosine + learning_rate: 0.001 + # warmup_epoch: 0 + regularizer: + name: 'L2' + factor: 0 + +PostProcess: + name: SASTPostProcess + score_thresh: 0.5 + sample_pts_num: 6 + nms_thresh: 0.2 + expand_scale: 1.2 + shrink_ratio_of_width: 0.2 + +Metric: + name: DetMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + label_file_list: [./train_data/icdar2013/train_label_json.txt, ./train_data/icdar2015/train_label_json.txt, ./train_data/icdar17_mlt_latin/train_label_json.txt, ./train_data/coco_text_icdar_4pts/train_label_json.txt] + ratio_list: [0.1, 0.45, 0.3, 0.15] + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - SASTProcessTrain: + image_shape: [512, 512] + min_crop_side_ratio: 0.3 + min_crop_size: 24 + min_text_size: 4 + max_text_size: 512 + - KeepKeys: + keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order + loader: + shuffle: True + drop_last: False + batch_size_per_card: 4 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/ + label_file_list: + - ./train_data/total_text_icdar_14pt/test_label_json.txt + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - DetLabelEncode: # Class handling label + - DetResizeForTest: + resize_long: 768 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + keep_keys: ['image', 'shape', 'polys', 'ignore_tags'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 1 # must be 1 + num_workers: 2 \ No newline at end of file diff --git a/configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yaml b/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yaml similarity index 97% rename from configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yaml rename to configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yaml index 6d53ce8b04003612f10ed6abf3866474261cc812..1db3e1cb8633f03f91d1d44064a19f7661e57b12 100644 --- a/configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yaml +++ b/configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yaml @@ -3,7 +3,7 @@ Global: epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 - save_model_dir: ./output/rec_chinese_common_v1.1 + save_model_dir: ./output/rec_chinese_common_v2.0 save_epoch_step: 3 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [0, 2000] diff --git a/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yaml b/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yaml similarity index 96% rename from configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yaml rename to configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yaml index 94a22e5c6e738429939d8df7b9d1c556abba7f6c..dc9d650f30f3d1086616a81c27aaf5db389a1fe7 100644 --- a/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yaml +++ b/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yaml @@ -3,7 +3,7 @@ Global: epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 - save_model_dir: ./output/rec_chinese_lite_v1.1 + save_model_dir: ./output/rec_chinese_lite_v2.0 save_epoch_step: 3 # evaluation is run every 5000 iterations after the 4000th iteration eval_batch_step: [0, 2000] @@ -19,7 +19,7 @@ Global: character_type: ch max_text_length: 25 infer_mode: False - use_space_char: False + use_space_char: True Optimizer: diff --git a/configs/rec/multi_language/rec_en_number_lite_train.yml b/configs/rec/multi_language/rec_en_number_lite_train.yml index 70d825e61ab48029382426b0ec56b4a1a4ac439d..cee0512114fe9d488004a71cf6f0a0409822a4b5 100644 --- a/configs/rec/multi_language/rec_en_number_lite_train.yml +++ b/configs/rec/multi_language/rec_en_number_lite_train.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 @@ -15,7 +15,7 @@ Global: use_visualdl: False infer_img: # for data or label process - character_dict_path: ppocr/utils/dict/ic15_dict.txt + character_dict_path: ppocr/utils/dict/en_dict.txt character_type: ch max_text_length: 25 infer_mode: False diff --git a/configs/rec/multi_language/rec_french_lite_train.yml b/configs/rec/multi_language/rec_french_lite_train.yml index 0e8f4eb3a803e8958cf38f0b55fca25e7204bd9c..63378d38a0d31fc77c33173e0ed864f28c5c3a8b 100644 --- a/configs/rec/multi_language/rec_french_lite_train.yml +++ b/configs/rec/multi_language/rec_french_lite_train.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 @@ -9,9 +9,9 @@ Global: eval_batch_step: [0, 2000] # if pretrained_model is saved in static mode, load_static_weights must set to True cal_metric_during_train: True - pretrained_model: + pretrained_model: checkpoints: - save_inference_dir: + save_inference_dir: use_visualdl: False infer_img: # for data or label process @@ -19,7 +19,7 @@ Global: character_type: french max_text_length: 25 infer_mode: False - use_space_char: True + use_space_char: False Optimizer: diff --git a/configs/rec/multi_language/rec_german_lite_train.yml b/configs/rec/multi_language/rec_german_lite_train.yml index 9978a21e1e8c70e44e6fd670eca305d045a919e9..1651510c5e4597e82298135d2f6c64aa747cf961 100644 --- a/configs/rec/multi_language/rec_german_lite_train.yml +++ b/configs/rec/multi_language/rec_german_lite_train.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 @@ -19,7 +19,7 @@ Global: character_type: german max_text_length: 25 infer_mode: False - use_space_char: True + use_space_char: False Optimizer: diff --git a/configs/rec/multi_language/rec_japan_lite_train.yml b/configs/rec/multi_language/rec_japan_lite_train.yml index 938d377e5d6497bea72fa27b0adcd3d0d691d431..bb47584edbc70f68d8d2d89dced3ec9b12f0e1cb 100644 --- a/configs/rec/multi_language/rec_japan_lite_train.yml +++ b/configs/rec/multi_language/rec_japan_lite_train.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 @@ -19,7 +19,7 @@ Global: character_type: japan max_text_length: 25 infer_mode: False - use_space_char: True + use_space_char: False Optimizer: diff --git a/configs/rec/multi_language/rec_korean_lite_train.yml b/configs/rec/multi_language/rec_korean_lite_train.yml index 7b070c449e9511e353b05877eaf217d0b286b47f..77f15524f78cd7f1c3dcf4988960e718422f5d89 100644 --- a/configs/rec/multi_language/rec_korean_lite_train.yml +++ b/configs/rec/multi_language/rec_korean_lite_train.yml @@ -1,5 +1,5 @@ Global: - use_gpu: true + use_gpu: True epoch_num: 500 log_smooth_window: 20 print_batch_step: 10 @@ -19,7 +19,7 @@ Global: character_type: korean max_text_length: 25 infer_mode: False - use_space_char: True + use_space_char: False Optimizer: diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml index def72375142ccf9f0988c0821d041b837442f3d0..38f1e8691e6056ada01a2d5c19f70955e8117498 100644 --- a/configs/rec/rec_mv3_none_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml @@ -5,7 +5,7 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec/mv3_none_bilstm_ctc/ save_epoch_step: 3 - # evaluation is run every 5000 iterations after the 4000th iteration + # evaluation is run every 2000 iterations eval_batch_step: [0, 2000] # if pretrained_model is saved in static mode, load_static_weights must set to True cal_metric_during_train: True @@ -13,7 +13,7 @@ Global: checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/imgs_words/ch/word_1.jpg + infer_img: doc/imgs_words_en/word_10.png # for data or label process character_dict_path: character_type: en @@ -21,7 +21,6 @@ Global: infer_mode: False use_space_char: False - Optimizer: name: Adam beta1: 0.9 diff --git a/configs/rec/rec_mv3_none_none_ctc.yml b/configs/rec/rec_mv3_none_none_ctc.yml new file mode 100644 index 0000000000000000000000000000000000000000..33079ad48c94c217ef86ef3f245492a540559350 --- /dev/null +++ b/configs/rec/rec_mv3_none_none_ctc.yml @@ -0,0 +1,95 @@ +Global: + use_gpu: True + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/mv3_none_none_ctc/ + save_epoch_step: 3 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + infer_mode: False + use_space_char: False + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: Rosetta + Transform: + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: SequenceEncoder + encoder_type: reshape + Head: + name: CTCHead + fc_decay: 0.0004 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 8 diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml new file mode 100644 index 0000000000000000000000000000000000000000..08f68939d4f1e6de1c3688652bd86f6556a43384 --- /dev/null +++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml @@ -0,0 +1,100 @@ +Global: + use_gpu: true + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/mv3_tps_bilstm_ctc/ + save_epoch_step: 3 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + infer_mode: False + use_space_char: False + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: STARNet + Transform: + name: TPS + num_fiducial: 20 + loc_lr: 0.1 + model_name: small + Backbone: + name: MobileNetV3 + scale: 0.5 + model_name: large + Neck: + name: SequenceEncoder + encoder_type: rnn + hidden_size: 96 + Head: + name: CTCHead + fc_decay: 0.0004 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 4 diff --git a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml index 74937138aac1ae155ce8754d861d21a85b35e031..4ad2ff89ef1e72c58c426670742bc2ada27cfc4a 100644 --- a/configs/rec/rec_r34_vd_none_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_none_bilstm_ctc.yml @@ -5,7 +5,7 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec/r34_vd_none_bilstm_ctc/ save_epoch_step: 3 - # evaluation is run every 5000 iterations after the 4000th iteration + # evaluation is run every 2000 iterations eval_batch_step: [0, 2000] # if pretrained_model is saved in static mode, load_static_weights must set to True cal_metric_during_train: True @@ -13,7 +13,7 @@ Global: checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/imgs_words/ch/word_1.jpg + infer_img: doc/imgs_words_en/word_10.png # for data or label process character_dict_path: character_type: en @@ -21,7 +21,6 @@ Global: infer_mode: False use_space_char: False - Optimizer: name: Adam beta1: 0.9 @@ -71,7 +70,7 @@ Train: - KeepKeys: keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order loader: - shuffle: False + shuffle: True batch_size_per_card: 256 drop_last: True num_workers: 8 diff --git a/configs/rec/rec_r34_vd_none_none_ctc.yml b/configs/rec/rec_r34_vd_none_none_ctc.yml new file mode 100644 index 0000000000000000000000000000000000000000..9c1eeb304f41d46e49cee350e5d659dd1e0c8b0e --- /dev/null +++ b/configs/rec/rec_r34_vd_none_none_ctc.yml @@ -0,0 +1,93 @@ +Global: + use_gpu: true + epoch_num: 72 + log_smooth_window: 20 + print_batch_step: 10 + save_model_dir: ./output/rec/r34_vd_none_none_ctc/ + save_epoch_step: 3 + # evaluation is run every 2000 iterations + eval_batch_step: [0, 2000] + # if pretrained_model is saved in static mode, load_static_weights must set to True + cal_metric_during_train: True + pretrained_model: + checkpoints: + save_inference_dir: + use_visualdl: False + infer_img: doc/imgs_words_en/word_10.png + # for data or label process + character_dict_path: + character_type: en + max_text_length: 25 + infer_mode: False + use_space_char: False + +Optimizer: + name: Adam + beta1: 0.9 + beta2: 0.999 + lr: + learning_rate: 0.0005 + regularizer: + name: 'L2' + factor: 0 + +Architecture: + model_type: rec + algorithm: Rosetta + Backbone: + name: ResNet + layers: 34 + Neck: + name: SequenceEncoder + encoder_type: reshape + Head: + name: CTCHead + fc_decay: 0.0004 + +Loss: + name: CTCLoss + +PostProcess: + name: CTCLabelDecode + +Metric: + name: RecMetric + main_indicator: acc + +Train: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/training/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: True + batch_size_per_card: 256 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: LMDBDateSet + data_dir: ./train_data/data_lmdb_release/validation/ + transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - CTCLabelEncode: # Class handling label + - RecResizeImg: + image_shape: [3, 32, 100] + - KeepKeys: + keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order + loader: + shuffle: False + drop_last: False + batch_size_per_card: 256 + num_workers: 4 diff --git a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml index 269f1e411776d591610082cff2900ca6fc621752..aeded4926a6d09cf30210f2d348d2933461a06b1 100644 --- a/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml +++ b/configs/rec/rec_r34_vd_tps_bilstm_ctc.yml @@ -5,7 +5,7 @@ Global: print_batch_step: 10 save_model_dir: ./output/rec/r34_vd_tps_bilstm_ctc/ save_epoch_step: 3 - # evaluation is run every 5000 iterations after the 4000th iteration + # evaluation is run every 2000 iterations eval_batch_step: [0, 2000] # if pretrained_model is saved in static mode, load_static_weights must set to True cal_metric_during_train: True @@ -13,7 +13,7 @@ Global: checkpoints: save_inference_dir: use_visualdl: False - infer_img: doc/imgs_words/ch/word_1.jpg + infer_img: doc/imgs_words_en/word_10.png # for data or label process character_dict_path: character_type: en @@ -21,7 +21,6 @@ Global: infer_mode: False use_space_char: False - Optimizer: name: Adam beta1: 0.9 @@ -34,7 +33,7 @@ Optimizer: Architecture: model_type: rec - algorithm: CRNN + algorithm: STARNet Transform: name: TPS num_fiducial: 20 diff --git a/deploy/cpp_infer/src/ocr_cls.cpp b/deploy/cpp_infer/src/ocr_cls.cpp index 40debaa7835d3174627f8b0528abba673c6e3d86..fed2023f9f111294a07a9c841f4843404bbd9af2 100644 --- a/deploy/cpp_infer/src/ocr_cls.cpp +++ b/deploy/cpp_infer/src/ocr_cls.cpp @@ -81,7 +81,8 @@ cv::Mat Classifier::Run(cv::Mat &img) { void Classifier::LoadModel(const std::string &model_dir) { AnalysisConfig config; - config.SetModel(model_dir + "/model", model_dir + "/params"); + config.SetModel(model_dir + "/inference.pdmodel", + model_dir + "/inference.pdiparams"); if (this->use_gpu_) { config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); diff --git a/deploy/cpp_infer/src/ocr_det.cpp b/deploy/cpp_infer/src/ocr_det.cpp index 1e1aaa1bf04d5f0bb80163d39d3a74f0312ae5af..e253f9cc89810f4d1adfca5be5186220a873d1a2 100644 --- a/deploy/cpp_infer/src/ocr_det.cpp +++ b/deploy/cpp_infer/src/ocr_det.cpp @@ -18,7 +18,8 @@ namespace PaddleOCR { void DBDetector::LoadModel(const std::string &model_dir) { AnalysisConfig config; - config.SetModel(model_dir + "/model", model_dir + "/params"); + config.SetModel(model_dir + "/inference.pdmodel", + model_dir + "/inference.pdiparams"); if (this->use_gpu_) { config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp index 009b6b75f53230c781dee285774da369ae10ce4b..d4deb5a17fc47427eb92cda02c270d268cfcafc7 100644 --- a/deploy/cpp_infer/src/ocr_rec.cpp +++ b/deploy/cpp_infer/src/ocr_rec.cpp @@ -103,7 +103,8 @@ void CRNNRecognizer::Run(std::vector>> boxes, void CRNNRecognizer::LoadModel(const std::string &model_dir) { AnalysisConfig config; - config.SetModel(model_dir + "/model", model_dir + "/params"); + config.SetModel(model_dir + "/inference.pdmodel", + model_dir + "/inference.pdiparams"); if (this->use_gpu_) { config.EnableUseGpu(this->gpu_mem_, this->gpu_id_); diff --git a/deploy/docker/hubserving/README.md b/deploy/docker/hubserving/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d4db277ffbeaf1efba18c0caef550404e08f2e85 --- /dev/null +++ b/deploy/docker/hubserving/README.md @@ -0,0 +1,54 @@ +English | [简体中文](README_cn.md) + +## Introduction +Many users hope package the PaddleOCR service into a docker image, so that it can be quickly released and used in the docker or k8s environment. + +This page provides some standardized code to achieve this goal. You can quickly publish the PaddleOCR project into a callable Restful API service through the following steps. (At present, the deployment based on the HubServing mode is implemented first, and author plans to increase the deployment of the PaddleServing mode in the futrue) + +## 1. Prerequisites + +You need to install the following basic components first： +a. Docker +b. Graphics driver and CUDA 10.0+（GPU） +c. NVIDIA Container Toolkit（GPU，Docker 19.03+ can skip this） +d. cuDNN 7.6+（GPU） + +## 2. Build Image +a. Goto Dockerfile directory（ps：Need to distinguish between cpu and gpu version, the following takes cpu as an example, gpu version needs to replace the keyword） +``` +cd deploy/docker/hubserving/cpu +``` +c. Build image +``` +docker build -t paddleocr:cpu . +``` + +## 3. Start container +a. CPU version +``` +sudo docker run -dp 8868:8868 --name paddle_ocr paddleocr:cpu +``` +b. GPU version (base on NVIDIA Container Toolkit) +``` +sudo nvidia-docker run -dp 8868:8868 --name paddle_ocr paddleocr:gpu +``` +c. GPU version (Docker 19.03++) +``` +sudo docker run -dp 8868:8868 --gpus all --name paddle_ocr paddleocr:gpu +``` +d. Check service status（If you can see the following statement then it means completed：Successfully installed ocr_system && Running on http://0.0.0.0:8868/） +``` +docker logs -f paddle_ocr +``` + +## 4. Test +a. Calculate the Base64 encoding of the picture to be recognized (if you just test, you can use a free online tool, like：https://freeonlinetools24.com/base64-image/） +b. Post a service request（sample request in sample_request.txt） + +``` +curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"Input image Base64 encode(need to delete the code 'data:image/jpg;base64,'）\"]}" http://localhost:8868/predict/ocr_system +``` +c. Get resposne（If the call is successful, the following result will be returned） +``` +{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"} +``` diff --git a/deploy/docker/hubserving/README_cn.md b/deploy/docker/hubserving/README_cn.md new file mode 100644 index 0000000000000000000000000000000000000000..046903c4c7d80327ef9e8537e735d3b87104dddf --- /dev/null +++ b/deploy/docker/hubserving/README_cn.md @@ -0,0 +1,53 @@ +[English](README.md) | 简体中文 + +## Docker化部署服务 +在日常项目应用中，相信大家一般都会希望能通过Docker技术，把PaddleOCR服务打包成一个镜像，以便在Docker或k8s环境里，快速发布上线使用。 + +本文将提供一些标准化的代码来实现这样的目标。大家通过如下步骤可以把PaddleOCR项目快速发布成可调用的Restful API服务。（目前暂时先实现了基于HubServing模式的部署，后续作者计划增加PaddleServing模式的部署） + +## 1.实施前提准备 + +需要先完成如下基本组件的安装： +a. Docker环境 +b. 显卡驱动和CUDA 10.0+（GPU） +c. NVIDIA Container Toolkit（GPU，Docker 19.03以上版本可以跳过此步） +d. cuDNN 7.6+（GPU） + +## 2.制作镜像 +a.切换至Dockerfile目录（注：需要区分cpu或gpu版本，下文以cpu为例，gpu版本需要替换一下关键字即可） +``` +cd deploy/docker/hubserving/cpu +``` +c.生成镜像 +``` +docker build -t paddleocr:cpu . +``` + +## 3.启动Docker容器 +a. CPU 版本 +``` +sudo docker run -dp 8868:8868 --name paddle_ocr paddleocr:cpu +``` +b. GPU 版本 (通过NVIDIA Container Toolkit) +``` +sudo nvidia-docker run -dp 8868:8868 --name paddle_ocr paddleocr:gpu +``` +c. GPU 版本 (Docker 19.03以上版本，可以直接用如下命令) +``` +sudo docker run -dp 8868:8869 --gpus all --name paddle_ocr paddleocr:gpu +``` +d. 检查服务运行情况（出现：Successfully installed ocr_system和Running on http://0.0.0.0:8868 等信息，表示运行成功） +``` +docker logs -f paddle_ocr +``` + +## 4.测试服务 +a. 计算待识别图片的Base64编码（如果只是测试一下效果，可以通过免费的在线工具实现，如：http://tool.chinaz.com/tools/imgtobase/） +b. 发送服务请求（可参见sample_request.txt中的值） +``` +curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"填入图片Base64编码(需要删除'data:image/jpg;base64,'）\"]}" http://localhost:8868/predict/ocr_system +``` +c. 返回结果（如果调用成功，会返回如下结果） +``` +{"msg":"","results":[[{"confidence":0.8403433561325073,"text":"约定","text_region":[[345,377],[641,390],[634,540],[339,528]]},{"confidence":0.8131805658340454,"text":"最终相遇","text_region":[[356,532],[624,530],[624,596],[356,598]]}]],"status":"0"} +``` diff --git a/deploy/docker/hubserving/cpu/Dockerfile b/deploy/docker/hubserving/cpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..342f1e819a24721719566b9f3cfc81666a225b9b --- /dev/null +++ b/deploy/docker/hubserving/cpu/Dockerfile @@ -0,0 +1,32 @@ +# Version: 1.0.0 +FROM hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev + +# PaddleOCR base on Python3.7 +RUN pip3.7 install --upgrade pip -i https://mirror.baidu.com/pypi/simple + +RUN python3.7 -m pip install paddlepaddle==2.0.0rc0 -i https://mirror.baidu.com/pypi/simple + +RUN pip3.7 install paddlehub --upgrade -i https://mirror.baidu.com/pypi/simple + +RUN git clone https://github.com/PaddlePaddle/PaddleOCR.git /PaddleOCR + +WORKDIR /PaddleOCR + +RUN pip3.7 install -r requirements.txt -i https://mirror.baidu.com/pypi/simple + +RUN mkdir -p /PaddleOCR/inference/ +# Download orc detect model(light version). if you want to change normal version, you can change ch_ppocr_mobile_v1.1_det_infer to ch_ppocr_server_v1.1_det_infer, also remember change det_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file} -C /PaddleOCR/inference/ + +# Download direction classifier(light version). If you want to change normal version, you can change ch_ppocr_mobile_v1.1_cls_infer to ch_ppocr_mobile_v1.1_cls_infer, also remember change cls_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file}.tar -C /PaddleOCR/inference/ + +# Download orc recognition model(light version). If you want to change normal version, you can change ch_ppocr_mobile_v1.1_rec_infer to ch_ppocr_server_v1.1_rec_infer, also remember change rec_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file}.tar -C /PaddleOCR/inference/ + +EXPOSE 8868 + +CMD ["/bin/bash","-c","hub install deploy/hubserving/ocr_system/ && hub serving start -m ocr_system"] \ No newline at end of file diff --git a/deploy/docker/hubserving/gpu/Dockerfile b/deploy/docker/hubserving/gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..222d053d953f64d2b7f2d2c0b975ba7169426d92 --- /dev/null +++ b/deploy/docker/hubserving/gpu/Dockerfile @@ -0,0 +1,32 @@ +# Version: 1.0.0 +FROM hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda10.0-cudnn7-dev + +# PaddleOCR base on Python3.7 +RUN pip3.7 install --upgrade pip -i https://mirror.baidu.com/pypi/simple + +RUN python3.7 -m pip install paddlepaddle-gpu==2.0.0rc0 -i https://mirror.baidu.com/pypi/simple + +RUN pip3.7 install paddlehub --upgrade -i https://mirror.baidu.com/pypi/simple + +RUN git clone https://github.com/PaddlePaddle/PaddleOCR.git /PaddleOCR + +WORKDIR /PaddleOCR + +RUN pip3.7 install -r requirements.txt -i https://mirror.baidu.com/pypi/simple + +RUN mkdir -p /PaddleOCR/inference/ +# Download orc detect model(light version). if you want to change normal version, you can change ch_ppocr_mobile_v1.1_det_infer to ch_ppocr_server_v1.1_det_infer, also remember change det_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file}.tar -C /PaddleOCR/inference/ + +# Download direction classifier(light version). If you want to change normal version, you can change ch_ppocr_mobile_v1.1_cls_infer to ch_ppocr_mobile_v1.1_cls_infer, also remember change cls_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file} -C /PaddleOCR/inference/ + +# Download orc recognition model(light version). If you want to change normal version, you can change ch_ppocr_mobile_v1.1_rec_infer to ch_ppocr_server_v1.1_rec_infer, also remember change rec_model_dir in deploy/hubserving/ocr_system/params.py） +ADD {link} /PaddleOCR/inference/ +RUN tar xf /PaddleOCR/inference/{file}.tar -C /PaddleOCR/inference/ + +EXPOSE 8868 + +CMD ["/bin/bash","-c","hub install deploy/hubserving/ocr_system/ && hub serving start -m ocr_system"] \ No newline at end of file diff --git a/deploy/docker/hubserving/sample_request.txt b/deploy/docker/hubserving/sample_request.txt new file mode 100644 index 0000000000000000000000000000000000000000..ec2b25b1f54cb7d7cdaeaff6162810a5772442c6 --- /dev/null +++ b/deploy/docker/hubserving/sample_request.txt @@ -0,0 +1 @@ +curl -H "Content-Type:application/json" -X POST --data "{\"images\": [\"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\"]}" http://localhost:8866/predict/ocr_system \ No newline at end of file diff --git a/deploy/hubserving/ocr_cls/__init__.py b/deploy/hubserving/ocr_cls/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/deploy/hubserving/ocr_cls/config.json b/deploy/hubserving/ocr_cls/config.json new file mode 100644 index 0000000000000000000000000000000000000000..2ced861b57efe3b9a426c129ff8a3f56e9d83a60 --- /dev/null +++ b/deploy/hubserving/ocr_cls/config.json @@ -0,0 +1,15 @@ +{ + "modules_info": { + "ocr_cls": { + "init_args": { + "version": "1.0.0", + "use_gpu": true + }, + "predict_args": { + } + } + }, + "port": 8866, + "use_multiprocess": false, + "workers": 2 +} diff --git a/deploy/hubserving/ocr_cls/module.py b/deploy/hubserving/ocr_cls/module.py new file mode 100644 index 0000000000000000000000000000000000000000..1b91580ca37027e512a73138270d051497af5b89 --- /dev/null +++ b/deploy/hubserving/ocr_cls/module.py @@ -0,0 +1,121 @@ +# -*- coding:utf-8 -*- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +sys.path.insert(0, ".") + +from paddlehub.common.logger import logger +from paddlehub.module.module import moduleinfo, runnable, serving +import cv2 +import paddlehub as hub + +from tools.infer.utility import base64_to_cv2 +from tools.infer.predict_cls import TextClassifier + + +@moduleinfo( + name="ocr_cls", + version="1.0.0", + summary="ocr recognition service", + author="paddle-dev", + author_email="paddle-dev@baidu.com", + type="cv/text_recognition") +class OCRCls(hub.Module): + def _initialize(self, use_gpu=False, enable_mkldnn=False): + """ + initialize with the necessary elements + """ + from ocr_cls.params import read_params + cfg = read_params() + + cfg.use_gpu = use_gpu + if use_gpu: + try: + _places = os.environ["CUDA_VISIBLE_DEVICES"] + int(_places[0]) + print("use gpu: ", use_gpu) + print("CUDA_VISIBLE_DEVICES: ", _places) + cfg.gpu_mem = 8000 + except: + raise RuntimeError( + "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id." + ) + cfg.ir_optim = True + cfg.enable_mkldnn = enable_mkldnn + + self.text_classifier = TextClassifier(cfg) + + def read_images(self, paths=[]): + images = [] + for img_path in paths: + assert os.path.isfile( + img_path), "The {} isn't a valid file.".format(img_path) + img = cv2.imread(img_path) + if img is None: + logger.info("error in loading image:{}".format(img_path)) + continue + images.append(img) + return images + + def predict(self, images=[], paths=[]): + """ + Get the text angle in the predicted images. + Args: + images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths + paths (list[str]): The paths of images. If paths not images + Returns: + res (list): The result of text detection box and save path of images. + """ + + if images != [] and isinstance(images, list) and paths == []: + predicted_data = images + elif images == [] and isinstance(paths, list) and paths != []: + predicted_data = self.read_images(paths) + else: + raise TypeError("The input data is inconsistent with expectations.") + + assert predicted_data != [], "There is not any image to be predicted. Please check the input data." + + img_list = [] + for img in predicted_data: + if img is None: + continue + img_list.append(img) + + rec_res_final = [] + try: + img_list, cls_res, predict_time = self.text_classifier(img_list) + for dno in range(len(cls_res)): + angle, score = cls_res[dno] + rec_res_final.append({ + 'angle': angle, + 'confidence': float(score), + }) + except Exception as e: + print(e) + return [[]] + + return [rec_res_final] + + @serving + def serving_method(self, images, **kwargs): + """ + Run as a service. + """ + images_decode = [base64_to_cv2(image) for image in images] + results = self.predict(images_decode, **kwargs) + return results + + +if __name__ == '__main__': + ocr = OCRCls() + image_path = [ + './doc/imgs_words/ch/word_1.jpg', + './doc/imgs_words/ch/word_2.jpg', + './doc/imgs_words/ch/word_3.jpg', + ] + res = ocr.predict(paths=image_path) + print(res) diff --git a/deploy/hubserving/ocr_cls/params.py b/deploy/hubserving/ocr_cls/params.py new file mode 100644 index 0000000000000000000000000000000000000000..bcdb2d6e3800c0ba7897b71f0b0999cafdc223af --- /dev/null +++ b/deploy/hubserving/ocr_cls/params.py @@ -0,0 +1,24 @@ +# -*- coding:utf-8 -*- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +class Config(object): + pass + + +def read_params(): + cfg = Config() + + #params for text classifier + cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/" + cfg.cls_image_shape = "3, 48, 192" + cfg.label_list = ['0', '180'] + cfg.cls_batch_num = 30 + cfg.cls_thresh = 0.9 + + cfg.use_zero_copy_run = False + cfg.use_pdserving = False + + return cfg diff --git a/deploy/hubserving/ocr_det/config.json b/deploy/hubserving/ocr_det/config.json index c8ef055e05470b8011db7a59782d2edc8c123782..6080d1c53ad065f47739a77e95bd15f792c63da0 100644 --- a/deploy/hubserving/ocr_det/config.json +++ b/deploy/hubserving/ocr_det/config.json @@ -9,7 +9,7 @@ } } }, - "port": 8866, + "port": 8865, "use_multiprocess": false, "workers": 2 } diff --git a/deploy/hubserving/ocr_det/module.py b/deploy/hubserving/ocr_det/module.py index be74306dacf4a3648e3227f11227e6399e6ed2eb..5f7bd6c473884b80fbe4d24088808444633f100d 100644 --- a/deploy/hubserving/ocr_det/module.py +++ b/deploy/hubserving/ocr_det/module.py @@ -3,20 +3,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import argparse -import ast -import copy -import math import os -import time +import sys +sys.path.insert(0, ".") -from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor from paddlehub.common.logger import logger from paddlehub.module.module import moduleinfo, runnable, serving -from PIL import Image import cv2 import numpy as np -import paddle.fluid as fluid import paddlehub as hub from tools.infer.utility import base64_to_cv2 @@ -67,9 +61,7 @@ class OCRDet(hub.Module): images.append(img) return images - def predict(self, - images=[], - paths=[]): + def predict(self, images=[], paths=[]): """ Get the text box in the predicted images. Args: @@ -87,7 +79,7 @@ class OCRDet(hub.Module): raise TypeError("The input data is inconsistent with expectations.") assert predicted_data != [], "There is not any image to be predicted. Please check the input data." - + all_results = [] for img in predicted_data: if img is None: @@ -99,11 +91,9 @@ class OCRDet(hub.Module): rec_res_final = [] for dno in range(len(dt_boxes)): - rec_res_final.append( - { - 'text_region': dt_boxes[dno].astype(np.int).tolist() - } - ) + rec_res_final.append({ + 'text_region': dt_boxes[dno].astype(np.int).tolist() + }) all_results.append(rec_res_final) return all_results @@ -116,7 +106,7 @@ class OCRDet(hub.Module): results = self.predict(images_decode, **kwargs) return results - + if __name__ == '__main__': ocr = OCRDet() image_path = [ @@ -124,4 +114,4 @@ if __name__ == '__main__': './doc/imgs/12.jpg', ] res = ocr.predict(paths=image_path) - print(res) \ No newline at end of file + print(res) diff --git a/deploy/hubserving/ocr_det/params.py b/deploy/hubserving/ocr_det/params.py index e88ab45c7bb548ef971465d4aaefb30d247ab17f..4d4a9fc27b727034d8185c82dad3e542659fd463 100644 --- a/deploy/hubserving/ocr_det/params.py +++ b/deploy/hubserving/ocr_det/params.py @@ -10,16 +10,17 @@ class Config(object): def read_params(): cfg = Config() - + #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_det_mv3_db/" - cfg.det_max_side_len = 960 + cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/" + cfg.det_limit_side_len = 960 + cfg.det_limit_type = 'max' #DB parmas - cfg.det_db_thresh =0.3 - cfg.det_db_box_thresh =0.5 - cfg.det_db_unclip_ratio =2.0 + cfg.det_db_thresh = 0.3 + cfg.det_db_box_thresh = 0.5 + cfg.det_db_unclip_ratio = 2.0 # #EAST parmas # cfg.det_east_score_thresh = 0.8 @@ -37,5 +38,6 @@ def read_params(): # cfg.use_space_char = True cfg.use_zero_copy_run = False + cfg.use_pdserving = False return cfg diff --git a/deploy/hubserving/ocr_rec/module.py b/deploy/hubserving/ocr_rec/module.py index 846f5437fe3b0a5136bff6c902481f888558d594..41a42104a81b736eeca346737d2dcefe3d728ef8 100644 --- a/deploy/hubserving/ocr_rec/module.py +++ b/deploy/hubserving/ocr_rec/module.py @@ -3,20 +3,13 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import argparse -import ast -import copy -import math import os -import time +import sys +sys.path.insert(0, ".") -from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor from paddlehub.common.logger import logger from paddlehub.module.module import moduleinfo, runnable, serving -from PIL import Image import cv2 -import numpy as np -import paddle.fluid as fluid import paddlehub as hub from tools.infer.utility import base64_to_cv2 @@ -67,9 +60,7 @@ class OCRRec(hub.Module): images.append(img) return images - def predict(self, - images=[], - paths=[]): + def predict(self, images=[], paths=[]): """ Get the text box in the predicted images. Args: @@ -87,31 +78,28 @@ class OCRRec(hub.Module): raise TypeError("The input data is inconsistent with expectations.") assert predicted_data != [], "There is not any image to be predicted. Please check the input data." - + img_list = [] for img in predicted_data: if img is None: continue img_list.append(img) - + rec_res_final = [] try: rec_res, predict_time = self.text_recognizer(img_list) for dno in range(len(rec_res)): text, score = rec_res[dno] - rec_res_final.append( - { - 'text': text, - 'confidence': float(score), - } - ) + rec_res_final.append({ + 'text': text, + 'confidence': float(score), + }) except Exception as e: print(e) return [[]] return [rec_res_final] - @serving def serving_method(self, images, **kwargs): """ @@ -121,7 +109,7 @@ class OCRRec(hub.Module): results = self.predict(images_decode, **kwargs) return results - + if __name__ == '__main__': ocr = OCRRec() image_path = [ @@ -130,4 +118,4 @@ if __name__ == '__main__': './doc/imgs_words/ch/word_3.jpg', ] res = ocr.predict(paths=image_path) - print(res) \ No newline at end of file + print(res) diff --git a/deploy/hubserving/ocr_rec/params.py b/deploy/hubserving/ocr_rec/params.py index 59772e2163d1d5f8279dee85432b5bf93502914e..6f428ecb2686afa5ff66b84d963d1c2175b9cee2 100644 --- a/deploy/hubserving/ocr_rec/params.py +++ b/deploy/hubserving/ocr_rec/params.py @@ -10,25 +10,10 @@ class Config(object): def read_params(): cfg = Config() - - # #params for text detector - # cfg.det_algorithm = "DB" - # cfg.det_model_dir = "./inference/ch_det_mv3_db/" - # cfg.det_max_side_len = 960 - - # #DB parmas - # cfg.det_db_thresh =0.3 - # cfg.det_db_box_thresh =0.5 - # cfg.det_db_unclip_ratio =2.0 - - # #EAST parmas - # cfg.det_east_score_thresh = 0.8 - # cfg.det_east_cover_thresh = 0.1 - # cfg.det_east_nms_thresh = 0.2 #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/" + cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v1.1_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' @@ -39,5 +24,6 @@ def read_params(): cfg.use_space_char = True cfg.use_zero_copy_run = False + cfg.use_pdserving = False return cfg diff --git a/deploy/hubserving/ocr_system/module.py b/deploy/hubserving/ocr_system/module.py index cb526e1185d8eb623af84ba2451ff38523bbf642..7f3617330bc87ac77a150af9e40dc125d3bfd7de 100644 --- a/deploy/hubserving/ocr_system/module.py +++ b/deploy/hubserving/ocr_system/module.py @@ -3,20 +3,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import argparse -import ast -import copy -import math import os +import sys +sys.path.insert(0, ".") + import time -from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor from paddlehub.common.logger import logger from paddlehub.module.module import moduleinfo, runnable, serving -from PIL import Image import cv2 import numpy as np -import paddle.fluid as fluid import paddlehub as hub from tools.infer.utility import base64_to_cv2 @@ -52,7 +48,7 @@ class OCRSystem(hub.Module): ) cfg.ir_optim = True cfg.enable_mkldnn = enable_mkldnn - + self.text_sys = TextSystem(cfg) def read_images(self, paths=[]): @@ -67,9 +63,7 @@ class OCRSystem(hub.Module): images.append(img) return images - def predict(self, - images=[], - paths=[]): + def predict(self, images=[], paths=[]): """ Get the chinese texts in the predicted images. Args: @@ -104,13 +98,11 @@ class OCRSystem(hub.Module): for dno in range(dt_num): text, score = rec_res[dno] - rec_res_final.append( - { - 'text': text, - 'confidence': float(score), - 'text_region': dt_boxes[dno].astype(np.int).tolist() - } - ) + rec_res_final.append({ + 'text': text, + 'confidence': float(score), + 'text_region': dt_boxes[dno].astype(np.int).tolist() + }) all_results.append(rec_res_final) return all_results @@ -123,7 +115,7 @@ class OCRSystem(hub.Module): results = self.predict(images_decode, **kwargs) return results - + if __name__ == '__main__': ocr = OCRSystem() image_path = [ @@ -131,4 +123,4 @@ if __name__ == '__main__': './doc/imgs/12.jpg', ] res = ocr.predict(paths=image_path) - print(res) \ No newline at end of file + print(res) diff --git a/deploy/hubserving/ocr_system/params.py b/deploy/hubserving/ocr_system/params.py index 0ff56d37d50b30b09bb13b529a48a260dfe8f84a..1f6a07bcc0167e90564edab9c4719b9192233b4c 100644 --- a/deploy/hubserving/ocr_system/params.py +++ b/deploy/hubserving/ocr_system/params.py @@ -10,16 +10,17 @@ class Config(object): def read_params(): cfg = Config() - + #params for text detector cfg.det_algorithm = "DB" - cfg.det_model_dir = "./inference/ch_det_mv3_db/" - cfg.det_max_side_len = 960 + cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/" + cfg.det_limit_side_len = 960 + cfg.det_limit_type = 'max' #DB parmas - cfg.det_db_thresh =0.3 - cfg.det_db_box_thresh =0.5 - cfg.det_db_unclip_ratio =2.0 + cfg.det_db_thresh = 0.3 + cfg.det_db_box_thresh = 0.5 + cfg.det_db_unclip_ratio = 2.0 #EAST parmas cfg.det_east_score_thresh = 0.8 @@ -28,7 +29,7 @@ def read_params(): #params for text recognizer cfg.rec_algorithm = "CRNN" - cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/" + cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v1.1_rec_infer/" cfg.rec_image_shape = "3, 32, 320" cfg.rec_char_type = 'ch' @@ -38,6 +39,15 @@ def read_params(): cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt" cfg.use_space_char = True + #params for text classifier + cfg.use_angle_cls = True + cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/" + cfg.cls_image_shape = "3, 48, 192" + cfg.label_list = ['0', '180'] + cfg.cls_batch_num = 30 + cfg.cls_thresh = 0.9 + cfg.use_zero_copy_run = False + cfg.use_pdserving = False return cfg diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..f64bd372569f12ea52214e3e89927df0c859a17f --- /dev/null +++ b/deploy/hubserving/readme.md @@ -0,0 +1,193 @@ +[English](readme_en.md) | 简体中文 + +PaddleOCR提供2种服务部署方式： +- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，按照本教程使用； +- 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，使用方法参考[文档](../../deploy/pdserving/readme.md)。 + +# 基于PaddleHub Serving的服务部署 + +hubserving服务部署目录下包括检测、识别、2阶段串联三种服务包，请根据需求选择相应的服务包进行安装和启动。目录结构如下： +``` +deploy/hubserving/ + └─ ocr_cls 分类模块服务包 + └─ ocr_det 检测模块服务包 + └─ ocr_rec 识别模块服务包 + └─ ocr_system 检测+识别串联服务包 +``` + +每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下： +``` +deploy/hubserving/ocr_system/ + └─ __init__.py 空文件，必选 + └─ config.json 配置文件，可选，使用配置启动服务时作为参数传入 + └─ module.py 主模块，必选，包含服务的完整逻辑 + └─ params.py 参数文件，必选，包含模型路径、前后处理参数等参数 +``` + +## 快速启动服务 +以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。 +### 1. 准备环境 +```shell +# 安装paddlehub +pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +### 2. 下载推理模型 +安装服务模块前，需要准备推理模型并放到正确路径。默认使用的是v1.1版的超轻量模型，默认模型路径为： +``` +检测模型：./inference/ch_ppocr_mobile_v1.1_det_infer/ +识别模型：./inference/ch_ppocr_mobile_v1.1_rec_infer/ +方向分类器：./inference/ch_ppocr_mobile_v1.1_cls_infer/ +``` + +**模型路径可在`params.py`中查看和修改。** 更多模型可以从PaddleOCR提供的[模型库](../../doc/doc_ch/models_list.md)下载，也可以替换成自己训练转换好的模型。 + +### 3. 安装服务模块 +PaddleOCR提供3种服务模块，根据需要安装所需模块。 + +* 在Linux环境下，安装示例如下： +```shell +# 安装检测服务模块： +hub install deploy/hubserving/ocr_det/ + +# 或，安装分类服务模块： +hub install deploy/hubserving/ocr_cls/ + +# 或，安装识别服务模块： +hub install deploy/hubserving/ocr_rec/ + +# 或，安装检测+识别串联服务模块： +hub install deploy/hubserving/ocr_system/ +``` + +* 在Windows环境下(文件夹的分隔符为`\`)，安装示例如下： +```shell +# 安装检测服务模块： +hub install deploy\hubserving\ocr_det\ + +# 或，安装分类服务模块： +hub install deploy\hubserving\ocr_cls\ + +# 或，安装识别服务模块： +hub install deploy\hubserving\ocr_rec\ + +# 或，安装检测+识别串联服务模块： +hub install deploy\hubserving\ocr_system\ +``` + +### 4. 启动服务 +#### 方式1. 命令行命令启动（仅支持CPU） +**启动命令：** +```shell +$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \ + --port XXXX \ + --use_multiprocess \ + --workers \ +``` + +**参数：** + +|参数|用途| +|-|-| +|--modules/-m|PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出
*`当不指定Version时，默认选择最新版本`*| +|--port/-p|服务端口，默认为8866| +|--use_multiprocess|是否启用并发方式，默认为单进程方式，推荐多核CPU机器使用此方式
*`Windows操作系统只支持单进程方式`*| +|--workers|在并发方式下指定的并发任务数，默认为`2*cpu_count-1`，其中`cpu_count`为CPU核数| + +如启动串联服务： ```hub serving start -m ocr_system``` + +这样就完成了一个服务化API的部署，使用默认端口号8866。 + +#### 方式2. 配置文件启动（支持CPU、GPU） +**启动命令：** +```hub serving start -c config.json``` + +其中，`config.json`格式如下： +```python +{ + "modules_info": { + "ocr_system": { + "init_args": { + "version": "1.0.0", + "use_gpu": true + }, + "predict_args": { + } + } + }, + "port": 8868, + "use_multiprocess": false, + "workers": 2 +} +``` + +- `init_args`中的可配参数与`module.py`中的`_initialize`函数接口一致。其中，**当`use_gpu`为`true`时，表示使用GPU启动服务**。 +- `predict_args`中的可配参数与`module.py`中的`predict`函数接口一致。 + +**注意:** +- 使用配置文件启动服务时，其他参数会被忽略。 +- 如果使用GPU预测(即，`use_gpu`置为`true`)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。 +- **`use_gpu`不可与`use_multiprocess`同时为`true`**。 + +如，使用GPU 3号卡启动串联服务： +```shell +export CUDA_VISIBLE_DEVICES=3 +hub serving start -c deploy/hubserving/ocr_system/config.json +``` + +## 发送预测请求 +配置好服务端，可使用以下命令发送预测请求，获取预测结果: + +```python tools/test_hubserving.py server_url image_path``` + +需要给脚本传递2个参数： +- **server_url**：服务地址，格式为 +`http://[ip_address]:[port]/predict/[module_name]` +例如，如果使用配置文件启动分类，检测、识别，检测+分类+识别3阶段服务，那么发送请求的url将分别是： +`http://127.0.0.1:8865/predict/ocr_det` +`http://127.0.0.1:8866/predict/ocr_cls` +`http://127.0.0.1:8867/predict/ocr_rec` +`http://127.0.0.1:8868/predict/ocr_system` +- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径 + +访问示例： +```python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/``` + +## 返回结果格式说明 +返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下： + +|字段名称|数据类型|意义| +|----|----|----| +|angle|str|文本角度| +|text|str|文本内容| +|confidence|float| 文本识别置信度或文本角度分类置信度| +|text_region|list|文本位置坐标| + +不同模块返回的字段不同，如，文本识别服务模块返回结果不含`text_region`字段，具体信息如下： + +| 字段名/模块名 | ocr_det | ocr_cls | ocr_rec | ocr_system | +| ---- | ---- | ---- | ---- | ---- | +|angle| | ✔ | | ✔ | +|text| | |✔|✔| +|confidence| |✔ |✔|✔| +|text_region| ✔| | |✔ | + +**说明：** 如果需要增加、删除、修改返回字段，可在相应模块的`module.py`文件中进行修改，完整流程参考下一节自定义修改服务模块。 + +## 自定义修改服务模块 +如果需要修改服务逻辑，你一般需要操作以下步骤（以修改`ocr_system`为例）： + +- 1、 停止服务 +```hub serving stop --port/-p XXXX``` + +- 2、 到相应的`module.py`和`params.py`等文件中根据实际需求修改代码。 +例如，如果需要替换部署服务所用模型，则需要到`params.py`中修改模型路径参数`det_model_dir`和`rec_model_dir`，如果需要关闭文本方向分类器，则将参数`use_angle_cls`置为`False`，当然，同时可能还需要修改其他相关参数，请根据实际情况修改调试。 **强烈建议修改后先直接运行`module.py`调试，能正确运行预测后再启动服务测试。** + +- 3、 卸载旧服务包 +```hub uninstall ocr_system``` + +- 4、 安装修改后的新服务包 +```hub install deploy/hubserving/ocr_system/``` + +- 5、重新启动服务 +```hub serving start -m ocr_system``` diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md new file mode 100644 index 0000000000000000000000000000000000000000..c6cf53413bc3eac45f933fead66356d1491cc60c --- /dev/null +++ b/deploy/hubserving/readme_en.md @@ -0,0 +1,204 @@ +English | [简体中文](readme.md) + +PaddleOCR provides 2 service deployment methods: +- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial. +- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/readme.md) for usage. + +# Service deployment based on PaddleHub Serving + +The hubserving service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Please select the corresponding service package to install and start service according to your needs. The directory is as follows: +``` +deploy/hubserving/ + └─ ocr_det detection module service package + └─ ocr_cls angle class module service package + └─ ocr_rec recognition module service package + └─ ocr_system two-stage series connection service package +``` + +Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows: +``` +deploy/hubserving/ocr_system/ + └─ __init__.py Empty file, required + └─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service + └─ module.py Main module file, required, contains the complete logic of the service + └─ params.py Parameter file, required, including parameters such as model path, pre- and post-processing parameters +``` + +## Quick start service +The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path. + +### 1. Prepare the environment +```shell +# Install paddlehub +pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +### 2. Download inference model +Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default model path is: +``` +detection model: ./inference/ch_ppocr_mobile_v1.1_det_infer/ +recognition model: ./inference/ch_ppocr_mobile_v1.1_rec_infer/ +text direction classifier: ./inference/ch_ppocr_mobile_v1.1_cls_infer/ +``` + +**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself. + +### 3. Install Service Module +PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs. + +* On Linux platform, the examples are as follows. +```shell +# Install the detection service module: +hub install deploy/hubserving/ocr_det/ + +# Or, install the angle class service module: +hub install deploy/hubserving/ocr_cls/ + +# Or, install the recognition service module: +hub install deploy/hubserving/ocr_rec/ + +# Or, install the 2-stage series service module: +hub install deploy/hubserving/ocr_system/ +``` + +* On Windows platform, the examples are as follows. +```shell +# Install the detection service module: +hub install deploy\hubserving\ocr_det\ + +# Or, install the angle class service module: +hub install deploy\hubserving\ocr_cls\ + +# Or, install the recognition service module: +hub install deploy\hubserving\ocr_rec\ + +# Or, install the 2-stage series service module: +hub install deploy\hubserving\ocr_system\ +``` + +### 4. Start service +#### Way 1. Start with command line parameters (CPU only) + +**start command：** +```shell +$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \ + --port XXXX \ + --use_multiprocess \ + --workers \ +``` +**parameters：** + +|parameters|usage| +|-|-| +|--modules/-m|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs
*`When Version is not specified, the latest version is selected by default`*| +|--port/-p|Service port, default is 8866| +|--use_multiprocess|Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines
*`Windows operating system only supports single-process mode`*| +|--workers|The number of concurrent tasks specified in concurrent mode, the default is `2*cpu_count-1`, where `cpu_count` is the number of CPU cores| + +For example, start the 2-stage series service: +```shell +hub serving start -m ocr_system +``` + +This completes the deployment of a service API, using the default port number 8866. + +#### Way 2. Start with configuration file（CPU、GPU） +**start command：** +```shell +hub serving start --config/-c config.json +``` +Wherein, the format of `config.json` is as follows: +```python +{ + "modules_info": { + "ocr_system": { + "init_args": { + "version": "1.0.0", + "use_gpu": true + }, + "predict_args": { + } + } + }, + "port": 8868, + "use_multiprocess": false, + "workers": 2 +} +``` +- The configurable parameters in `init_args` are consistent with the `_initialize` function interface in `module.py`. Among them, **when `use_gpu` is `true`, it means that the GPU is used to start the service**. +- The configurable parameters in `predict_args` are consistent with the `predict` function interface in `module.py`. + +**Note:** +- When using the configuration file to start the service, other parameters will be ignored. +- If you use GPU prediction (that is, `use_gpu` is set to `true`), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as: ```export CUDA_VISIBLE_DEVICES=0```, otherwise you do not need to set it. +- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.** + +For example, use GPU card No. 3 to start the 2-stage series service: +```shell +export CUDA_VISIBLE_DEVICES=3 +hub serving start -c deploy/hubserving/ocr_system/config.json +``` + +## Send prediction requests +After the service starts, you can use the following command to send a prediction request to obtain the prediction result: +```shell +python tools/test_hubserving.py server_url image_path +``` + +Two parameters need to be passed to the script: +- **server_url**：service address，format of which is +`http://[ip_address]:[port]/predict/[module_name]` +For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective `server_url` would be: +`http://127.0.0.1:8865/predict/ocr_det` +`http://127.0.0.1:8866/predict/ocr_cls` +`http://127.0.0.1:8867/predict/ocr_rec` +`http://127.0.0.1:8868/predict/ocr_system` +- **image_path**：Test image path, can be a single image path or an image directory path + +**Eg.** +```shell +python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/ +``` + +## Returned result format +The returned result is a list. Each item in the list is a dict. The dict may contain three fields. The information is as follows: + +|field name|data type|description| +|----|----|----| +|angle|str|angle| +|text|str|text content| +|confidence|float|text recognition confidence| +|text_region|list|text location coordinates| + +The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain `text_region`. The details are as follows: + +| field name/module name | ocr_det | ocr_cls | ocr_rec | ocr_system | +| ---- | ---- | ---- | ---- | ---- | +|angle| | ✔ | | ✔ | +|text| | |✔|✔| +|confidence| |✔ |✔|✔| +|text_region| ✔| | |✔ | + +**Note：** If you need to add, delete or modify the returned fields, you can modify the file `module.py` of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section. + +## User defined service module modification +If you need to modify the service logic, the following steps are generally required (take the modification of `ocr_system` for example): + +- 1. Stop service +```shell +hub serving stop --port/-p XXXX +``` +- 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs. +For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. If you want to turn off the text direction classifier, set the parameter `use_angle_cls` to `False`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test. +- 3. Uninstall old service module +```shell +hub uninstall ocr_system +``` +- 4. Install modified service module +```shell +hub install deploy/hubserving/ocr_system/ +``` +- 5. Restart service +```shell +hub serving start -m ocr_system +``` diff --git a/doc/doc_ch/add_new_algorithm.md b/doc/doc_ch/add_new_algorithm.md new file mode 100644 index 0000000000000000000000000000000000000000..37159c801a5ebc83c3b855a70ea772ba641e5297 --- /dev/null +++ b/doc/doc_ch/add_new_algorithm.md @@ -0,0 +1,303 @@ +# 添加新算法 + +PaddleOCR将一个算法分解为以下几个部分，并对各部分进行模块化处理，方便快速组合出新的算法。 + +* 数据加载和处理 +* 网络 +* 后处理 +* 损失函数 +* 指标评估 +* 优化器 + +下面将分别对每个部分进行介绍，并介绍如何在该部分里添加新算法所需模块。 + +## 数据加载和处理 + +数据加载和处理由不同的模块(module)组成，其完成了图片的读取、数据增强和label的制作。这一部分在[ppocr/data](../../ppocr/data)下。 各个文件及文件夹作用说明如下: + +```bash +ppocr/data/ +├── imaug # 图片的读取、数据增强和label制作相关的文件 +│ ├── label_ops.py # 对label进行变换的modules +│ ├── operators.py # 对image进行变换的modules +│ ├──..... +├── __init__.py +├── lmdb_dataset.py # 读取lmdb的数据集的dataset +└── simple_dataset.py # 读取以`image_path\tgt`形式保存的数据集的dataset +``` + +PaddleOCR内置了大量图像操作相关模块，对于没有没有内置的模块可通过如下步骤添加: + +1. 在 [ppocr/data/imaug](../../ppocr/data/imaug) 文件夹下新建文件，如my_module.py。 +2. 在 my_module.py 文件内添加相关代码，示例代码如下: + +```python +class MyModule: + def __init__(self, *args, **kwargs): + # your init code + pass + + def __call__(self, data): + img = data['image'] + label = data['label'] + # your process code + + data['image'] = img + data['label'] = label + return data +``` + +3. 在 [ppocr/data/imaug/\__init\__.py](../../ppocr/data/imaug/__init__.py) 文件内导入添加的模块。 + +数据处理的所有处理步骤由不同的模块顺序执行而成，在config文件中按照列表的形式组合并执行。如: + +```yaml +# angle class data process +transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - MyModule: + args1: args1 + args2: args2 + - KeepKeys: + keep_keys: [ 'image', 'label' ] # dataloader will return list in this order +``` + +## 网络 + +网络部分完成了网络的组网操作，PaddleOCR将网络划分为四部分，这一部分在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones-> +necks->heads)依次通过这四个部分。 + +```bash +├── architectures # 网络的组网代码 +├── transforms # 网络的图像变换模块 +├── backbones # 网络的特征提取模块 +├── necks # 网络的特征增强模块 +└── heads # 网络的输出模块 +``` + +PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的常用模块，对于没有内置的模块可通过如下步骤添加，四个部分添加步骤一致，以backbones为例: + +1. 在 [ppocr/modeling/backbones](../../ppocr/modeling/backbones) 文件夹下新建文件，如my_backbone.py。 +2. 在 my_backbone.py 文件内添加相关代码，示例代码如下: + +```python +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class MyBackbone(nn.Layer): + def __init__(self, *args, **kwargs): + super(MyBackbone, self).__init__() + # your init code + self.conv = nn.xxxx + + def forward(self, inputs): + # your necwork forward + y = self.conv(inputs) + return y +``` + +3. 在 [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py)文件内导入添加的模块。 + +在完成网络的四部分模块添加之后，只需要配置文件中进行配置即可使用，如: + +```yaml +Architecture: + model_type: rec + algorithm: CRNN + Transform: + name: MyTransform + args1: args1 + args2: args2 + Backbone: + name: MyBackbone + args1: args1 + Neck: + name: MyNeck + args1: args1 + Head: + name: MyHead + args1: args1 +``` + +## 后处理 + +后处理实现解码网络输出获得文本框或者识别到的文字。这一部分在[ppocr/postprocess](../../ppocr/postprocess)下。 +PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的后处理模块，对于没有内置的组件可通过如下步骤添加: + +1. 在 [ppocr/postprocess](../../ppocr/postprocess) 文件夹下新建文件，如 my_postprocess.py。 +2. 在 my_postprocess.py 文件内添加相关代码，示例代码如下: + +```python +import paddle + + +class MyPostProcess: + def __init__(self, *args, **kwargs): + # your init code + pass + + def __call__(self, preds, label=None, *args, **kwargs): + if isinstance(preds, paddle.Tensor): + preds = preds.numpy() + # you preds decode code + preds = self.decode_preds(preds) + if label is None: + return preds + # you label decode code + label = self.decode_label(label) + return preds, label + + def decode_preds(self, preds): + # you preds decode code + pass + + def decode_label(self, preds): + # you label decode code + pass +``` + +3. 在 [ppocr/postprocess/\__init\__.py](../../ppocr/postprocess/__init__.py)文件内导入添加的模块。 + +在后处理模块添加之后，只需要配置文件中进行配置即可使用，如: + +```yaml +PostProcess: + name: MyPostProcess + args1: args1 + args2: args2 +``` + +## 损失函数 + +损失函数用于计算网络输出和label之间的距离。这一部分在[ppocr/losses](../../ppocr/losses)下。 +PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的损失函数模块，对于没有内置的模块可通过如下步骤添加: + +1. 在 [ppocr/losses](../../ppocr/losses) 文件夹下新建文件，如 my_loss.py。 +2. 在 my_loss.py 文件内添加相关代码，示例代码如下: + +```python +import paddle +from paddle import nn + + +class MyLoss(nn.Layer): + def __init__(self, **kwargs): + super(MyLoss, self).__init__() + # you init code + pass + + def __call__(self, predicts, batch): + label = batch[1] + # your loss code + loss = self.loss(input=predicts, label=label) + return {'loss': loss} +``` + +3. 在 [ppocr/losses/\__init\__.py](../../ppocr/losses/__init__.py)文件内导入添加的模块。 + +在损失函数添加之后，只需要配置文件中进行配置即可使用，如: + +```yaml +Loss: + name: MyLoss + args1: args1 + args2: args2 +``` + +## 指标评估 + +指标评估用于计算网络在当前batch上的性能。这一部分在[ppocr/metrics](../../ppocr/metrics)下。 PaddleOCR内置了检测，分类和识别等算法相关的指标评估模块，对于没有内置的模块可通过如下步骤添加: + +1. 在 [ppocr/metrics](../../ppocr/metrics) 文件夹下新建文件，如my_metric.py。 +2. 在 my_metric.py 文件内添加相关代码，示例代码如下: + +```python + +class MyMetric(object): + def __init__(self, main_indicator='acc', **kwargs): + # main_indicator is used for select best model + self.main_indicator = main_indicator + self.reset() + + def __call__(self, preds, batch, *args, **kwargs): + # preds is out of postprocess + # batch is out of dataloader + labels = batch[1] + cur_correct_num = 0 + cur_all_num = 0 + # you metric code + self.correct_num += cur_correct_num + self.all_num += cur_all_num + return {'acc': cur_correct_num / cur_all_num, } + + def get_metric(self): + """ + return metircs { + 'acc': 0, + 'norm_edit_dis': 0, + } + """ + acc = self.correct_num / self.all_num + self.reset() + return {'acc': acc} + + def reset(self): + # reset metric + self.correct_num = 0 + self.all_num = 0 + +``` + +3. 在 [ppocr/metrics/\__init\__.py](../../ppocr/metrics/__init__.py)文件内导入添加的模块。 + +在指标评估模块添加之后，只需要配置文件中进行配置即可使用，如: + +```yaml +Metric: + name: MyMetric + main_indicator: acc +``` + +## 优化器 + +优化器用于训练网络。优化器内部还包含了网络正则化和学习率衰减模块。 这一部分在[ppocr/optimizer](../../ppocr/optimizer)下。 PaddleOCR内置了`Momentum`,`Adam` +和`RMSProp`等常用的优化器模块，`Linear`,`Cosine`,`Step`和`Piecewise`等常用的正则化模块与`L1Decay`和`L2Decay`等常用的学习率衰减模块。 +对于没有内置的模块可通过如下步骤添加，以`optimizer`为例: + +1. 在 [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) 文件内创建自己的优化器，示例代码如下: + +```python +from paddle import optimizer as optim + + +class MyOptim(object): + def __init__(self, learning_rate=0.001, *args, **kwargs): + self.learning_rate = learning_rate + + def __call__(self, parameters): + # It is recommended to wrap the built-in optimizer of paddle + opt = optim.XXX( + learning_rate=self.learning_rate, + parameters=parameters) + return opt + +``` + +在优化器模块添加之后，只需要配置文件中进行配置即可使用，如: + +```yaml +Optimizer: + name: MyOptim + args1: args1 + args2: args2 + lr: + name: Cosine + learning_rate: 0.001 + regularizer: + name: 'L2' + factor: 0 +``` \ No newline at end of file diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 01570f30c6381592cd87e7d07b1407bbd3b1bcb0..440b392227182f50396f0e66ca8250bc6bfc1c0d 100644 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -41,8 +41,8 @@ PaddleOCR基于动态图开源的文本识别算法列表： - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐） - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) - [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) -- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) -- [ ] SRN([paper](https://arxiv.org/abs/2003.12294)) +- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon +- [ ] SRN([paper](https://arxiv.org/abs/2003.12294)) coming soon 参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： @@ -55,4 +55,5 @@ PaddleOCR基于动态图开源的文本识别算法列表： |STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](link)| |STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](link)| + PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。 diff --git a/doc/doc_ch/config.md b/doc/doc_ch/config.md index f9c664d4ea38e2e52dc76bfb5b63d9a515b106a7..2cc502cadf5101c4321ca7543647dd90ea7e0466 100644 --- a/doc/doc_ch/config.md +++ b/doc/doc_ch/config.md @@ -1,4 +1,4 @@ -# 可选参数列表 +## 可选参数列表 以下列表可以通过`--help`查看 @@ -8,65 +8,115 @@ | -o | ALL | 设置配置文件里的参数内容 | None | 使用-o配置相较于-c选择的配置文件具有更高的优先级。例如：`-o Global.use_gpu=false` | -## 配置文件 Global 参数介绍 +## 配置文件参数介绍 以 `rec_chinese_lite_train_v1.1.yml ` 为例 - +### Global | 字段 | 用途 | 默认值 | 备注 | | :----------------------: | :---------------------: | :--------------: | :--------------------: | -| algorithm | 设置算法 | 与配置文件同步 | 选择模型，支持模型请参考[简介](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/README.md) | -| use_gpu | 设置代码运行场所 | true | \ | -| epoch_num | 最大训练epoch数 | 3000 | \ | +| use_gpu | 设置代码是否在gpu运行 | true | \ | +| epoch_num | 最大训练epoch数 | 500 | \ | | log_smooth_window | 滑动窗口大小 | 20 | \ | | print_batch_step | 设置打印log间隔 | 10 | \ | | save_model_dir | 设置模型保存路径 | output/{算法名称} | \ | | save_epoch_step | 设置模型保存间隔 | 3 | \ | | eval_batch_step | 设置模型评估间隔 | 2000 或 [1000, 2000] | 2000 表示每2000次迭代评估一次，[1000， 2000]表示从1000次迭代开始，每2000次评估一次 | -|train_batch_size_per_card | 设置训练时单卡batch size | 256 | \ | -| test_batch_size_per_card | 设置评估时单卡batch size | 256 | \ | -| image_shape | 设置输入图片尺寸 | [3, 32, 100] | \ | +| cal_metric_during_train | 设置是否在训练过程中评估指标，此时评估的是模型在当前batch下的指标 | true | \ | +| load_static_weights | 设置预训练模型是否是静态图模式保存(目前仅检测算法需要) | true | \ | +| pretrained_model | 设置加载预训练模型路径 | ./pretrain_models/CRNN/best_accuracy | \ | +| checkpoints | 加载模型参数路径 | None | 用于中断后加载参数继续训练 | +| use_visualdl | 设置是否启用visualdl进行可视化log展示 | False | [教程地址](https://www.paddlepaddle.org.cn/paddle/visualdl) | +| infer_img | 设置预测图像路径或文件夹路径 | ./infer_img | \| +| character_dict_path | 设置字典路径 | ./ppocr/utils/ppocr_keys_v1.txt | \ | | max_text_length | 设置文本最大长度 | 25 | \ | | character_type | 设置字符类型 | ch | en/ch, en时将使用默认dict，ch时使用自定义dict| -| character_dict_path | 设置字典路径 | ./ppocr/utils/ic15_dict.txt | \ | -| loss_type | 设置 loss 类型 | ctc | 支持两种loss： ctc / attention | -| distort | 设置是否使用数据增强 | false | 设置为true时，将在训练时随机进行扰动，支持的扰动操作可阅读[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) | -| use_space_char | 设置是否识别空格 | false | 仅在 character_type=ch 时支持空格 | +| use_space_char | 设置是否识别空格 | True | 仅在 character_type=ch 时支持空格 | | label_list | 设置方向分类器支持的角度 | ['0','180'] | 仅在方向分类器中生效 | -| average_window | ModelAverage优化器中的窗口长度计算比例 | 0.15 | 目前仅应用与SRN | -| max_average_window | 平均值计算窗口长度的最大值 | 15625 | 推荐设置为一轮训练中mini-batchs的数目| -| min_average_window | 平均值计算窗口长度的最小值 | 10000 | \ | -| reader_yml | 设置reader配置文件 | ./configs/rec/rec_icdar15_reader.yml | \ | -| pretrain_weights | 加载预训练模型路径 | ./pretrain_models/CRNN/best_accuracy | \ | -| checkpoints | 加载模型参数路径 | None | 用于中断后加载参数继续训练 | -| save_inference_dir | inference model 保存路径 | None | 用于保存inference model | +| save_res_path | 设置检测模型的结果保存地址 | ./output/det_db/predicts_db.txt | 仅在检测模型中生效 | -## 配置文件 Reader 系列参数介绍 +### Optimizer ([ppocr/optimizer](../../ppocr/optimizer)) -以 `rec_chinese_reader.yml` 为例 +| 字段 | 用途 | 默认值 | 备注 | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | 优化器类名 | Adam | 目前支持`Momentum`,`Adam`,`RMSProp`, 见[ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) | +| beta1 | 设置一阶矩估计的指数衰减率 | 0.9 | \ | +| beta2 | 设置二阶矩估计的指数衰减率 | 0.999 | \ | +| **lr** | 设置学习率decay方式 | - | \ | +| name | 学习率decay类名 | Cosine | 目前支持`Linear`,`Cosine`,`Step`,`Piecewise`, 见[ppocr/optimizer/learning_rate.py](../../ppocr/optimizer/learning_rate.py) | +| learning_rate | 基础学习率 | 0.001 | \ | +| **regularizer** | 设置网络正则化方式 | - | \ | +| name | 正则化类名 | L2 | 目前支持`L1`,`L2`, 见[ppocr/optimizer/regularizer.py](../../ppocr/optimizer/regularizer.py) | +| factor | 学习率衰减系数 | 0.00004 | \ | -| 字段 | 用途 | 默认值 | 备注 | -| :----------------------: | :---------------------: | :--------------: | :--------------------: | -| reader_function | 选择数据读取方式 | ppocr.data.rec.dataset_traversal,SimpleReader | 支持SimpleReader / LMDBReader 两种数据读取方式 | -| num_workers | 设置数据读取线程数 | 8 | \ | -| img_set_dir | 数据集路径 | ./train_data | \ | -| label_file_path | 数据标签路径 | ./train_data/rec_gt_train.txt| \ | -| infer_img | 预测图像文件夹路径 | ./infer_img | \| -## 配置文件 Optimizer 系列参数介绍 +### Architecture ([ppocr/modeling](../../ppocr/modeling)) +在ppocr中，网络被划分为Transform,Backbone,Neck和Head四个阶段 + +| 字段 | 用途 | 默认值 | 备注 | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| model_type | 网络类型 | rec | 目前支持`rec`,`det`,`cls` | +| algorithm | 模型名称 | CRNN | 支持列表见[algorithm_overview](./algorithm_overview.md) | +| **Transform** | 设置变换方式 | - | 目前仅rec类型的算法支持, 具体见[ppocr/modeling/transform](../../ppocr/modeling/transform) | +| name | 变换方式类名 | TPS | 目前支持`TPS` | +| num_fiducial | TPS控制点数 | 20 | 上下边各十个 | +| loc_lr | 定位网络学习率 | 0.1 | \ | +| model_name | 定位网络大小 | small | 目前支持`small`,`large` | +| **Backbone** | 设置网络backbone类名 | - | 具体见[ppocr/modeling/backbones](../../ppocr/modeling/backbones) | +| name | backbone类名 | ResNet | 目前支持`MobileNetV3`,`ResNet` | +| layers | resnet层数 | 34 | 支持18,34,50,101,152,200 | +| model_name | MobileNetV3 网络大小 | small | 支持`small`,`large` | +| **Neck** | 设置网络neck | - | 具体见[ppocr/modeling/necks](../../ppocr/modeling/necks) | +| name | neck类名 | SequenceEncoder | 目前支持`SequenceEncoder`,`DBFPN` | +| encoder_type | SequenceEncoder编码器类型 | rnn | 支持`reshape`,`fc`,`rnn` | +| hidden_size | rnn内部单元数 | 48 | \ | +| out_channels | DBFPN输出通道数 | 256 | \ | +| **Head** | 设置网络Head | - | 具体见[ppocr/modeling/heads](../../ppocr/modeling/heads) | +| name | head类名 | CTCHead | 目前支持`CTCHead`,`DBHead`,`ClsHead` | +| fc_decay | CTCHead正则化系数 | 0.0004 | \ | +| k | DBHead二值化系数 | 50 | \ | +| class_dim | ClsHead输出分类数 | 2 | \ | + -以 `rec_icdar15_train.yml` 为例 +### Loss ([ppocr/losses](../../ppocr/losses)) + +| 字段 | 用途 | 默认值 | 备注 | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | 网络loss类名 | CTCLoss | 目前支持`CTCLoss`,`DBLoss`,`ClsLoss` | +| balance_loss | DBLossloss中是否对正负样本数量进行均衡(使用OHEM) | True | \ | +| ohem_ratio | DBLossloss中的OHEM的负正样本比例 | 3 | \ | +| main_loss_type | DBLossloss中shrink_map所采用的的loss | DiceLoss | 支持`DiceLoss`,`BCELoss` | +| alpha | DBLossloss中shrink_map_loss的系数 | 5 | \ | +| beta | DBLossloss中threshold_map_loss的系数 | 10 | \ | + +### PostProcess ([ppocr/postprocess](../../ppocr/postprocess)) + +| 字段 | 用途 | 默认值 | 备注 | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | 后处理类名 | CTCLabelDecode | 目前支持`CTCLoss`,`AttnLabelDecode`,`DBPostProcess`,`ClsPostProcess` | +| thresh | DBPostProcess中分割图进行二值化的阈值 | 0.3 | \ | +| box_thresh | DBPostProcess中对输出框进行过滤的阈值，低于此阈值的框不会输出 | 0.7 | \ | +| max_candidates | DBPostProcess中输出的最大文本框数量 | 1000 | | +| unclip_ratio | DBPostProcess中对文本框进行放大的比例 | 2.0 | \ | + +### Metric ([ppocr/metrics](../../ppocr/metrics)) + +| 字段 | 用途 | 默认值 | 备注 | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | 指标评估方法名称 | CTCLabelDecode | 目前支持`DetMetric`,`RecMetric`,`ClsMetric` | +| main_indicator | 主要指标,用于选取最优模型 | acc | 对于检测方法为hmean，识别和分类方法为acc | +### Dataset ([ppocr/data](../../ppocr/data)) | 字段 | 用途 | 默认值 | 备注 | | :---------------------: | :---------------------: | :--------------: | :--------------------: | -| function | 选择优化器 | pocr.optimizer,AdamDecay | 目前只支持Adam方式 | -| base_lr | 设置初始学习率 | 0.0005 | \ | -| beta1 | 设置一阶矩估计的指数衰减率 | 0.9 | \ | -| beta2 | 设置二阶矩估计的指数衰减率 | 0.999 | \ | -| decay | 是否使用decay | \ | \ | -| function(decay) | 设置decay方式 | - | 目前支持cosine_decay, cosine_decay_warmup与piecewise_decay | -| step_each_epoch | 每个epoch包含多少次迭代, cosine_decay/cosine_decay_warmup时有效 | 20 | 计算方式：total_image_num / (batch_size_per_card * card_size) | -| total_epoch | 总共迭代多少个epoch, cosine_decay/cosine_decay_warmup时有效 | 1000 | 与Global.epoch_num 一致 | -| warmup_minibatch | 线性warmup的迭代次数, cosine_decay_warmup时有效 | 1000 | \ | -| boundaries | 学习率下降时的迭代次数间隔, piecewise_decay时有效 | - | 参数为列表形式 | -| decay_rate | 学习率衰减系数, piecewise_decay时有效 | - | \ | +| **dataset** | 每次迭代返回一个样本 | - | - | +| name | dataset类名 | SimpleDataSet | 目前支持`SimpleDataSet`和`LMDBDateSet` | +| data_dir | 数据集图片存放路径 | ./train_data | \ | +| label_file_list | 数据标签路径 | ["./train_data/train_list.txt"] | dataset为LMDBDateSet时不需要此参数 | +| ratio_list | 数据集的比例 | [1.0] | 若label_file_list中有两个train_list，且ratio_list为[0.4,0.6]，则从train_list1中采样40%，从train_list2中采样60%组合整个dataset | +| transforms | 对图片和标签进行变换的方法列表 | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | 见[ppocr/data/imaug](../../ppocr/data/imaug) | +| **loader** | dataloader相关 | - | | +| shuffle | 每个epoch是否将数据集顺序打乱 | True | \ | +| batch_size_per_card | 训练时单卡batch size | 256 | \ | +| drop_last | 是否丢弃因数据集样本数不能被 batch_size 整除而产生的最后一个不完整的mini-batch | True | \ | +| num_workers | 用于加载数据的子进程个数，若为0即为不开启子进程，在主进程中进行数据加载 | 8 | \ | \ No newline at end of file diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index 0432695a3fbb31f04122c4134490ff465e445b4c..dfd84cccbab18cd543038d676a21cd67a79dfc28 100644 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -1,11 +1,11 @@ # 基于Python预测引擎推理 -inference 模型（`fluid.io.save_inference_model`保存的模型） +inference 模型（`paddle.jit.save`保存的模型） 一般是模型训练完成后保存的固化模型，多用于预测部署。训练过程中保存的模型是checkpoints模型，保存的是模型的参数，多用于恢复训练等。 -与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合与实际系统集成。更详细的介绍请参考文档[分类预测框架](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md). +与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合与实际系统集成。 -接下来首先介绍如何将训练的模型转换成inference模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。 +接下来首先介绍如何将训练的模型转换成inference模型，然后将依次介绍文本检测、文本角度分类器、文本识别以及三者串联基于预测引擎推理。 - [一、训练模型转inference模型](#训练模型转inference模型) @@ -23,9 +23,8 @@ inference 模型（`fluid.io.save_inference_model`保存的模型） - [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理) - [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理) - [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理) - - [4. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理) - - [5. 自定义文本识别字典的推理](#自定义文本识别字典的推理) - - [6. 多语言模型的推理](#多语言模型的推理) + - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理) + - [5. 多语言模型的推理](#多语言模型的推理) - [四、方向分类模型推理](#方向识别模型推理) - [1. 方向分类模型推理](#方向分类模型推理) @@ -42,24 +41,25 @@ inference 模型（`fluid.io.save_inference_model`保存的模型） 下载超轻量级中文检测模型： ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_det_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/ ``` 上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成inference模型只需要运行如下命令： ``` -# -c后面设置训练算法的yml配置文件 -# -o配置可选参数 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 +# -c 后面设置训练算法的yml配置文件 +# -o 配置可选参数 +# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 +# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/ +python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/ ``` -转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`、`Global.save_inference_dir`参数。 -其中`Global.checkpoints`指向训练中保存的模型参数文件，`Global.save_inference_dir`是生成的inference模型要保存的目录。 -转换成功后，在`save_inference_dir`目录下有两个文件： +转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`参数，其指向训练中保存的模型参数文件。 +转换成功后，在模型保存目录下有三个文件： ``` inference/det_db/ - └─ model 检测inference模型的program文件 - └─ params 检测inference模型的参数文件 + ├── inference.pdiparams # 检测inference模型的参数文件 + ├── inference.pdiparams.info # 检测inference模型的参数信息，可忽略 + └── inference.pdmodel # 检测inference模型的program文件 ``` @@ -67,27 +67,28 @@ inference/det_db/ 下载超轻量中文识别模型： ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_rec_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/ ``` 识别模型转inference模型与检测的方式相同，如下： ``` -# -c后面设置训练算法的yml配置文件 -# -o配置可选参数 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 +# -c 后面设置训练算法的yml配置文件 +# -o 配置可选参数 +# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 +# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_rec_train/best_accuracy \ - Global.save_inference_dir=./inference/rec_crnn/ +python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/ ``` **注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。 -转换成功后，在目录下有两个文件： +转换成功后，在目录下有三个文件： ``` /inference/rec_crnn/ - └─ model 识别inference模型的program文件 - └─ params 识别inference模型的参数文件 + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 ``` @@ -95,25 +96,26 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_trai 下载方向分类模型： ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_cls_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/ ``` 方向分类模型转inference模型与检测的方式相同，如下： ``` -# -c后面设置训练算法的yml配置文件 -# -o配置可选参数 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 +# -c 后面设置训练算法的yml配置文件 +# -o 配置可选参数 +# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。 +# Global.load_static_weights 参数需要设置为 False。 # Global.save_inference_dir参数设置转换的模型将保存的地址。 -python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_cls_train/best_accuracy \ - Global.save_inference_dir=./inference/cls/ +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/ ``` -转换成功后，在目录下有两个文件： +转换成功后，在目录下有三个文件： ``` /inference/cls/ - └─ model 识别inference模型的program文件 - └─ params 识别inference模型的参数文件 + ├── inference.pdiparams # 分类inference模型的参数文件 + ├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略 + └── inference.pdmodel # 分类inference模型的program文件 ``` @@ -134,10 +136,12 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di ![](../imgs_results/det_res_2.jpg) -通过设置参数`det_max_side_len`的大小，改变检测算法中图片规范化的最大值。当图片的长宽都小于`det_max_side_len`，则使用原图预测，否则将图片等比例缩放到最大值，进行预测。该参数默认设置为`det_max_side_len=960`。 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以执行如下命令： +通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制限，`limit_type=max`为限制长边长度<`det_limit_side_len`，`limit_type=min`为限制短边长度>`det_limit_side_len`, +图片不满足限制条件时(`limit_type=max`时长边长度>`det_limit_side_len`或`limit_type=min`时短边长度<`det_limit_side_len`)，将对图片进行等比例缩放。 +该参数默认设置为`limit_type='max',det_max_side_len=960`。 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以执行如下命令： ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1200 ``` 如果想使用CPU进行预测，执行命令如下 @@ -148,14 +152,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di ### 2. DB文本检测模型推理 -首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar))，可以使用如下命令进行转换： +首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换： ``` -# -c后面设置训练算法的yml配置文件 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 -# Global.save_inference_dir参数设置转换的模型将保存的地址。 - -python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db" +python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db ``` DB文本检测模型推理，可以执行如下命令： @@ -173,14 +173,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ ### 3. EAST文本检测模型推理 -首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar))，可以使用如下命令进行转换： +首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换： ``` -# -c后面设置训练算法的yml配置文件 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 -# Global.save_inference_dir参数设置转换的模型将保存的地址。 - -python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east" +python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east ``` **EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令： @@ -198,9 +194,10 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img ### 4. SAST文本检测模型推理 #### (1). 四边形文本检测模型（ICDAR2015） -首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar))，可以使用如下命令进行转换： +首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](link))，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15" +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 + ``` **SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令： ``` @@ -211,10 +208,11 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ![](../imgs_results/det_res_img_10_sast.jpg) #### (2). 弯曲文本检测模型（Total-Text） -首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar))，可以使用如下命令进行转换： +首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换： ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt" +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt + ``` **SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，**可以执行如下命令： @@ -253,33 +251,30 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695] ### 2. 基于CTC损失的识别模型推理 -我们以STAR-Net为例，介绍基于CTC损失的识别模型推理。 CRNN和Rosetta使用方式类似，不用设置识别算法参数rec_algorithm。 +我们以 CRNN 为例，介绍基于CTC损失的识别模型推理。 Rosetta 使用方式类似，不用设置识别算法参数rec_algorithm。 -首先将STAR-Net文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，使用MJSynth和SynthText两个英文文本识别合成数据集训练 -的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar))，可以使用如下命令进行转换： +首先将 Rosetta 文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，使用MJSynth和SynthText两个英文文本识别合成数据集训练 +的模型为例（[模型下载地址](link))，可以使用如下命令进行转换： ``` -# -c后面设置训练算法的yml配置文件 -# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。 -# Global.save_inference_dir参数设置转换的模型将保存的地址。 +python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn -python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.checkpoints="./models/rec_r34_vd_tps_bilstm_ctc/best_accuracy" Global.save_inference_dir="./inference/starnet" ``` STAR-Net文本识别模型推理，可以执行如下命令： ``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en" +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_type="en" ``` ### 3. 基于Attention损失的识别模型推理 基于Attention损失的识别模型与ctc不同，需要额外设置识别算法参数 --rec_algorithm="RARE" - RARE 文本识别模型推理，可以执行如下命令： ``` python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE" + ``` ![](../imgs_words_en/word_336.png) @@ -298,21 +293,8 @@ Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555] self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" dict_character = list(self.character_str) ``` - -### 4. 基于SRN损失的识别模型推理 - -基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。 同时需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256" - -``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \ - --rec_model_dir="./inference/srn/" \ - --rec_image_shape="1, 64, 256" \ - --rec_char_type="en" \ - --rec_algorithm="SRN" -``` - -### 5. 自定义文本识别字典的推理 +### 4. 自定义文本识别字典的推理 如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径 ``` @@ -320,7 +302,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ``` -### 6. 多语言模型的推理 +### 5. 多语言模型的推理 如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果， 需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别： @@ -350,11 +332,14 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" - python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/" ``` -![](../imgs_words/ch/word_4.jpg) +![](../imgs_words/ch/word_1.jpg) 执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下： -Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963] +``` +infer_img: doc/imgs_words/ch/word_1.jpg + result: ('0', 0.9998784) +``` ## 五、文本检测、方向分类和文字识别串联推理 diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md index 71be1e89dc561630315337ef11c52289e0756c00..91b1af78835707fc37494c847a74f7c1f30191bf 100644 --- a/doc/doc_ch/recognition.md +++ b/doc/doc_ch/recognition.md @@ -142,9 +142,8 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起， - 添加空格类别 -如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `true`。 +如果希望支持识别"空格"类别, 请将yml文件中的 `use_space_char` 字段设置为 `True`。 -**注意：`use_space_char` 仅在 `character_type=ch` 时生效** ### 启动训练 @@ -167,10 +166,9 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* ``` -# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号 -export CUDA_VISIBLE_DEVICES=0,1,2,3 +# GPU训练 支持单卡，多卡训练，通过--gpus参数指定卡号 # 训练icdar15英文数据 并将训练日志保存为 tain_rec.log -python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml ``` - 数据增强 @@ -195,8 +193,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t | 配置文件 | 算法名称 | backbone | trans | seq | pred | | :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | -| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | -| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc | +| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | +| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc | | rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | | rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc | | rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc | @@ -210,39 +208,69 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc | | rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn | -训练中文数据，推荐使用[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件： +训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件： -以 `rec_mv3_none_none_ctc.yml` 为例： +以 `rec_chinese_lite_train_v2.0.yml` 为例： ``` Global: ... - # 修改 image_shape 以适应长文本 - image_shape: [3, 32, 320] - ... + # 添加自定义字典，如修改字典请将路径指向新字典 + character_dict_path: ppocr/utils/ppocr_keys_v1.txt # 修改字符类型 character_type: ch - # 添加自定义字典，如修改字典请将路径指向新字典 - character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt - # 训练时添加数据增强 - distort: true - # 识别空格 - use_space_char: true - ... - # 修改reader类型 - reader_yml: ./configs/rec/rec_chinese_reader.yml ... + # 识别空格 + use_space_char: True -... Optimizer: ... # 添加学习率衰减策略 - decay: - function: cosine_decay - # 每个 epoch 包含 iter 数 - step_each_epoch: 20 - # 总共训练epoch数 - total_epoch: 1000 + lr: + name: Cosine + learning_rate: 0.001 + ... + +... + +Train: + dataset: + # 数据集格式，支持LMDBDateSet以及SimpleDataSet + name: SimpleDataSet + # 数据集路径 + data_dir: ./train_data/ + # 训练集标签文件 + label_file_list: ["./train_data/train_list.txt"] + transforms: + ... + - RecResizeImg: + # 修改 image_shape 以适应长文本 + image_shape: [3, 32, 320] + ... + loader: + ... + # 单卡训练的batch_size + batch_size_per_card: 256 + ... + +Eval: + dataset: + # 数据集格式，支持LMDBDateSet以及SimpleDataSet + name: SimpleDataSet + # 数据集路径 + data_dir: ./train_data + # 验证集标签文件 + label_file_list: ["./train_data/val_list.txt"] + transforms: + ... + - RecResizeImg: + # 修改 image_shape 以适应长文本 + image_shape: [3, 32, 320] + ... + loader: + # 单卡验证的batch_size + batch_size_per_card: 256 + ... ``` **注意，预测/评估时的配置文件请务必与训练一致。** @@ -270,39 +298,41 @@ Global: ... # 添加自定义字典，如修改字典请将路径指向新字典 character_dict_path: ./ppocr/utils/dict/french_dict.txt - # 训练时添加数据增强 - distort: true - # 识别空格 - use_space_char: true - ... - # 修改reader类型 - reader_yml: ./configs/rec/multi_languages/rec_french_reader.yml - ... -... -``` - -同时需要修改数据读取文件 `rec_french_reader.yml`： - -``` -TrainReader: ... - # 修改训练数据存放的目录名 - img_set_dir: ./train_data - # 修改 label 文件名称 - label_file_path: ./train_data/french_train.txt + # 识别空格 + use_space_char: True ... + +Train: + dataset: + # 数据集格式，支持LMDBDateSet以及SimpleDataSet + name: SimpleDataSet + # 数据集路径 + data_dir: ./train_data/ + # 训练集标签文件 + label_file_list: ["./train_data/french_train.txt"] + ... + +Eval: + dataset: + # 数据集格式，支持LMDBDateSet以及SimpleDataSet + name: SimpleDataSet + # 数据集路径 + data_dir: ./train_data + # 验证集标签文件 + label_file_list: ["./train_data/french_val.txt"] + ... ``` ### 评估 -评估数据集可以通过 `configs/rec/rec_icdar15_reader.yml` 修改EvalReader中的 `label_file_path` 设置。 +评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。 *注意* 评估时必须确保配置文件中 infer_img 字段为空 ``` -export CUDA_VISIBLE_DEVICES=0 # GPU 评估， Global.checkpoints 为待测权重 -python3 tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` @@ -332,12 +362,12 @@ infer_img: doc/imgs_words/en/word_1.png word : joint ``` -预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml` 完成了中文模型的训练， +预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml` 完成了中文模型的训练， 您可以使用如下命令进行中文模型预测。 ``` # 预测中文结果 -python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg +python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg ``` 预测图片： diff --git a/doc/doc_ch/tree.md b/doc/doc_ch/tree.md index ba8503d119a27b2cac34b593243caa68404b95ec..511860869188020ccfe7531db55ecc5008ccb56b 100644 --- a/doc/doc_ch/tree.md +++ b/doc/doc_ch/tree.md @@ -4,38 +4,34 @@ PaddleOCR 的整体目录结构介绍如下： ``` PaddleOCR -├── configs // 配置文件，可通过yml文件选择模型结构并修改超参 -│ ├── cls // 方向分类器相关配置文件 -│ │ ├── cls_mv3.yml // 训练配置相关，包括骨干网络、head、loss、优化器 -│ │ └── cls_reader.yml // 数据读取相关，数据读取方式、数据存储路径 -│ ├── det // 检测相关配置文件 -│ │ ├── det_db_icdar15_reader.yml // 数据读取 -│ │ ├── det_mv3_db.yml // 训练配置 +├── configs // 配置文件，可通过 yml 文件选择模型结构并修改超参 +│ ├── cls // 方向分类器相关配置文件 +│ │ ├── cls_mv3.yml // 训练配置相关，包括骨干网络、head、loss、优化器和数据 +│ ├── det // 检测相关配置文件 +│ │ ├── det_mv3_db.yml // 训练配置 │ │ ... -│ └── rec // 识别相关配置文件 -│ ├── rec_benchmark_reader.yml // LMDB 格式数据读取相关 -│ ├── rec_chinese_common_train.yml // 通用中文训练配置 -│ ├── rec_icdar15_reader.yml // simple 数据读取相关，包括数据读取函数、数据路径、标签文件 +│ └── rec // 识别相关配置文件 +│ ├── rec_mv3_none_bilstm_ctc.yml // crnn 训练配置 │ ... -├── deploy // 部署相关 -│ ├── android_demo // android_demo +├── deploy // 部署相关 +│ ├── android_demo // android_demo │ │ ... -│ ├── cpp_infer // C++ infer -│ │ ├── CMakeLists.txt // Cmake 文件 -│ │ ├── docs // 说明文档 +│ ├── cpp_infer // C++ infer +│ │ ├── CMakeLists.txt // Cmake 文件 +│ │ ├── docs // 说明文档 │ │ │ └── windows_vs2019_build.md -│ │ ├── include // 头文件 -│ │ │ ├── clipper.h // clipper 库 -│ │ │ ├── config.h // 预测配置 -│ │ │ ├── ocr_cls.h // 方向分类器 -│ │ │ ├── ocr_det.h // 文字检测 -│ │ │ ├── ocr_rec.h // 文字识别 -│ │ │ ├── postprocess_op.h // 检测后处理 -│ │ │ ├── preprocess_op.h // 检测预处理 -│ │ │ └── utility.h // 工具 -│ │ ├── readme.md // 说明文档 +│ │ ├── include // 头文件 +│ │ │ ├── clipper.h // clipper 库 +│ │ │ ├── config.h // 预测配置 +│ │ │ ├── ocr_cls.h // 方向分类器 +│ │ │ ├── ocr_det.h // 文字检测 +│ │ │ ├── ocr_rec.h // 文字识别 +│ │ │ ├── postprocess_op.h // 检测后处理 +│ │ │ ├── preprocess_op.h // 检测预处理 +│ │ │ └── utility.h // 工具 +│ │ ├── readme.md // 说明文档 │ │ ├── ... -│ │ ├── src // 源文件 +│ │ ├── src // 源文件 │ │ │ ├── clipper.cpp │ │ │ ├── config.cpp │ │ │ ├── main.cpp @@ -45,10 +41,10 @@ PaddleOCR │ │ │ ├── postprocess_op.cpp │ │ │ ├── preprocess_op.cpp │ │ │ └── utility.cpp -│ │ └── tools // 编译、执行脚本 -│ │ ├── build.sh // 编译脚本 -│ │ ├── config.txt // 配置文件 -│ │ └── run.sh // 测试启动脚本 +│ │ └── tools // 编译、执行脚本 +│ │ ├── build.sh // 编译脚本 +│ │ ├── config.txt // 配置文件 +│ │ └── run.sh // 测试启动脚本 │ ├── docker │ │ └── hubserving │ │ ├── cpu @@ -58,151 +54,163 @@ PaddleOCR │ │ ├── README_cn.md │ │ ├── README.md │ │ └── sample_request.txt -│ ├── hubserving // hubserving -│ │ ├── ocr_det // 文字检测 -│ │ │ ├── config.json // serving 配置 +│ ├── hubserving // hubserving +│ │ ├── ocr_cls // 方向分类器 +│ │ │ ├── config.json // serving 配置 │ │ │ ├── __init__.py -│ │ │ ├── module.py // 预测模型 -│ │ │ └── params.py // 预测参数 -│ │ ├── ocr_rec // 文字识别 +│ │ │ ├── module.py // 预测模型 +│ │ │ └── params.py // 预测参数 +│ │ ├── ocr_det // 文字检测 +│ │ │ ├── config.json // serving 配置 +│ │ │ ├── __init__.py +│ │ │ ├── module.py // 预测模型 +│ │ │ └── params.py // 预测参数 +│ │ ├── ocr_rec // 文字识别 │ │ │ ├── config.json │ │ │ ├── __init__.py │ │ │ ├── module.py │ │ │ └── params.py -│ │ └── ocr_system // 系统预测 +│ │ └── ocr_system // 系统预测 │ │ ├── config.json │ │ ├── __init__.py │ │ ├── module.py │ │ └── params.py -│ ├── imgs // 预测图片 +│ ├── imgs // 预测图片 │ │ ├── cpp_infer_pred_12.png │ │ └── demo.png -│ ├── ios_demo // ios demo +│ ├── ios_demo // ios demo │ │ ... -│ ├── lite // lite 部署 -│ │ ├── cls_process.cc // 方向分类器数据处理 +│ ├── lite // lite 部署 +│ │ ├── cls_process.cc // 方向分类器数据处理 │ │ ├── cls_process.h -│ │ ├── config.txt // 检测配置参数 -│ │ ├── crnn_process.cc // crnn数据处理 +│ │ ├── config.txt // 检测配置参数 +│ │ ├── crnn_process.cc // crnn 数据处理 │ │ ├── crnn_process.h -│ │ ├── db_post_process.cc // db数据处理 +│ │ ├── db_post_process.cc // db 数据处理 │ │ ├── db_post_process.h -│ │ ├── Makefile // 编译文件 -│ │ ├── ocr_db_crnn.cc // 串联预测 -│ │ ├── prepare.sh // 数据准备 -│ │ ├── readme.md // 说明文档 +│ │ ├── Makefile // 编译文件 +│ │ ├── ocr_db_crnn.cc // 串联预测 +│ │ ├── prepare.sh // 数据准备 +│ │ ├── readme.md // 说明文档 │ │ ... -│ ├── pdserving // pdserving 部署 -│ │ ├── det_local_server.py // 检测 快速版，部署方便预测速度快 -│ │ ├── det_web_server.py // 检测 完整版，稳定性高分布式部署 -│ │ ├── ocr_local_server.py // 检测+识别 快速版 -│ │ ├── ocr_web_client.py // 客户端 -│ │ ├── ocr_web_server.py // 检测+识别 完整版 -│ │ ├── readme.md // 说明文档 -│ │ ├── rec_local_server.py // 识别 快速版 -│ │ └── rec_web_server.py // 识别 完整版 +│ ├── pdserving // pdserving 部署 +│ │ ├── det_local_server.py // 检测 快速版，部署方便预测速度快 +│ │ ├── det_web_server.py // 检测 完整版，稳定性高分布式部署 +│ │ ├── ocr_local_server.py // 检测+识别 快速版 +│ │ ├── ocr_web_client.py // 客户端 +│ │ ├── ocr_web_server.py // 检测+识别 完整版 +│ │ ├── readme.md // 说明文档 +│ │ ├── rec_local_server.py // 识别 快速版 +│ │ └── rec_web_server.py // 识别 完整版 │ └── slim -│ └── quantization // 量化相关 -│ ├── export_model.py // 导出模型 -│ ├── quant.py // 量化 -│ └── README.md // 说明文档 -├── doc // 文档教程 +│ └── quantization // 量化相关 +│ ├── export_model.py // 导出模型 +│ ├── quant.py // 量化 +│ └── README.md // 说明文档 +├── doc // 文档教程 │ ... -├── paddleocr.py -├── ppocr // 网络核心代码 -│ ├── data // 数据处理 -│ │ ├── cls // 方向分类器 -│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch -│ │ │ └── randaugment.py // 随机数据增广操作 -│ │ ├── det // 检测 -│ │ │ ├── data_augment.py // 数据增广操作 -│ │ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，读取数据并组成batch -│ │ │ ├── db_process.py // db 数据处理 -│ │ │ ├── east_process.py // east 数据处理 -│ │ │ ├── make_border_map.py // 生成边界图 -│ │ │ ├── make_shrink_map.py // 生成收缩图 -│ │ │ ├── random_crop_data.py // 随机切割 -│ │ │ └── sast_process.py // sast 数据处理 -│ │ ├── reader_main.py // 数据读取器主函数 -│ │ └── rec // 识别 -│ │ ├── dataset_traversal.py // 数据传输，定义数据读取器，包含 LMDB_Reader 和 Simple_Reader -│ │ └── img_tools.py // 数据处理相关，包括数据归一化、扰动 -│ ├── __init__.py -│ ├── modeling // 组网相关 -│ │ ├── architectures // 模型架构，定义模型所需的各个模块 -│ │ │ ├── cls_model.py // 方向分类器 -│ │ │ ├── det_model.py // 检测 -│ │ │ └── rec_model.py // 识别 -│ │ ├── backbones // 骨干网络 -│ │ │ ├── det_mobilenet_v3.py // 检测 mobilenet_v3 -│ │ │ ├── det_resnet_vd.py -│ │ │ ├── det_resnet_vd_sast.py -│ │ │ ├── rec_mobilenet_v3.py // 识别 mobilenet_v3 -│ │ │ ├── rec_resnet_fpn.py -│ │ │ └── rec_resnet_vd.py -│ │ ├── common_functions.py // 公共函数 -│ │ ├── heads // 头函数 -│ │ │ ├── cls_head.py // 分类头 -│ │ │ ├── det_db_head.py // db 检测头 -│ │ │ ├── det_east_head.py // east 检测头 -│ │ │ ├── det_sast_head.py // sast 检测头 -│ │ │ ├── rec_attention_head.py // 识别 attention -│ │ │ ├── rec_ctc_head.py // 识别 ctc -│ │ │ ├── rec_seq_encoder.py // 识别 序列编码 -│ │ │ ├── rec_srn_all_head.py // 识别 srn 相关 -│ │ │ └── self_attention // srn attention -│ │ │ └── model.py -│ │ ├── losses // 损失函数 -│ │ │ ├── cls_loss.py // 方向分类器损失函数 -│ │ │ ├── det_basic_loss.py // 检测基础loss -│ │ │ ├── det_db_loss.py // DB loss -│ │ │ ├── det_east_loss.py // EAST loss -│ │ │ ├── det_sast_loss.py // SAST loss -│ │ │ ├── rec_attention_loss.py // attention loss -│ │ │ ├── rec_ctc_loss.py // ctc loss -│ │ │ └── rec_srn_loss.py // srn loss -│ │ └── stns // 空间变换网络 -│ │ └── tps.py // TPS 变换 -│ ├── optimizer.py // 优化器 -│ ├── postprocess // 后处理 -│ │ ├── db_postprocess.py // DB 后处理 -│ │ ├── east_postprocess.py // East 后处理 -│ │ ├── lanms // lanms 相关 -│ │ │ ... -│ │ ├── locality_aware_nms.py // nms -│ │ └── sast_postprocess.py // sast 后处理 -│ └── utils // 工具 -│ ├── character.py // 字符处理，包括对文本的编码和解码，计算预测准确率 -│ ├── check.py // 参数加载检查 -│ ├── ic15_dict.txt // 英文数字字典，区分大小写 -│ ├── ppocr_keys_v1.txt // 中文字典，用于训练中文模型 -│ ├── save_load.py // 模型保存和加载函数 -│ ├── stats.py // 统计 -│ └── utility.py // 工具函数，包含输入参数是否合法等相关检查工具 -├── README_en.md // 说明文档 -├── README.md -├── requirments.txt // 安装依赖 -├── setup.py // whl包打包脚本 -└── tools // 启动工具 - ├── eval.py // 评估函数 - ├── eval_utils // 评估工具 - │ ├── eval_cls_utils.py // 分类相关 - │ ├── eval_det_iou.py // 检测 iou 相关 - │ ├── eval_det_utils.py // 检测相关 - │ ├── eval_rec_utils.py // 识别相关 - │ └── __init__.py - ├── export_model.py // 导出 infer 模型 - ├── infer // 基于预测引擎预测 - │ ├── predict_cls.py - │ ├── predict_det.py - │ ├── predict_rec.py - │ ├── predict_system.py - │ └── utility.py - ├── infer_cls.py // 基于训练引擎 预测分类 - ├── infer_det.py // 基于训练引擎 预测检测 - ├── infer_rec.py // 基于训练引擎 预测识别 - ├── program.py // 整体流程 - ├── test_hubserving.py - └── train.py // 启动训练 - -``` +├── ppocr // 网络核心代码 +│ ├── data // 数据处理 +│ │ ├── imaug // 图片和 label 处理代码 +│ │ │ ├── text_image_aug // 文本识别的 tia 数据扩充 +│ │ │ │ ├── __init__.py +│ │ │ │ ├── augment.py // tia_distort,tia_stretch 和 tia_perspective 的代码 +│ │ │ │ ├── warp_mls.py +│ │ │ ├── __init__.py +│ │ │ ├── east_process.py // EAST 算法的数据处理步骤 +│ │ │ ├── make_border_map.py // 生成边界图 +│ │ │ ├── make_shrink_map.py // 生成收缩图 +│ │ │ ├── operators.py // 图像基本操作，如读取和归一化 +│ │ │ ├── randaugment.py // 随机数据增广操作 +│ │ │ ├── random_crop_data.py // 随机裁剪 +│ │ │ ├── rec_img_aug.py // 文本识别的数据扩充 +│ │ │ └── sast_process.py // SAST 算法的数据处理步骤 +│ │ ├── __init__.py // 构造 dataloader 相关代码 +│ │ ├── lmdb_dataset.py // 读取lmdb数据集的 dataset +│ │ ├── simple_dataset.py // 读取文本格式存储数据集的 dataset +│ ├── losses // 损失函数 +│ │ ├── __init__.py // 构造 loss 相关代码 +│ │ ├── cls_loss.py // 方向分类器 loss +│ │ ├── det_basic_loss.py // 检测基础 loss +│ │ ├── det_db_loss.py // DB loss +│ │ ├── det_east_loss.py // EAST loss +│ │ ├── det_sast_loss.py // SAST loss +│ │ ├── rec_ctc_loss.py // CTC loss +│ │ ├── rec_att_loss.py // Attention loss +│ ├── metrics // 评估指标 +│ │ ├── __init__.py // 构造 metric 相关代码 +│ │ ├── cls_metric.py // 方向分类器 metric +│ │ ├── det_metric.py // 检测 metric + │ ├── eval_det_iou.py // 检测 iou 相关 +│ │ ├── rec_metric.py // 识别 metric +│ ├── modeling // 组网相关 +│ │ ├── architectures // 网络 +│ │ │ ├── __init__.py // 构造 model 相关代码 +│ │ │ ├── base_model.py // 组网代码 +│ │ ├── backbones // 骨干网络 +│ │ │ ├── __init__.py // 构造 backbone 相关代码 +│ │ │ ├── det_mobilenet_v3.py // 检测 mobilenet_v3 +│ │ │ ├── det_resnet_vd.py // 检测 resnet +│ │ │ ├── det_resnet_vd_sast.py // 检测 SAST算法的resnet backbone +│ │ │ ├── rec_mobilenet_v3.py // 识别 mobilenet_v3 +│ │ │ └── rec_resnet_vd.py // 识别 resnet +│ │ ├── necks // 颈函数 +│ │ │ ├── __init__.py // 构造 neck 相关代码 +│ │ │ ├── db_fpn.py // 标准 fpn 网络 +│ │ │ ├── east_fpn.py // EAST 算法的 fpn 网络 +│ │ │ ├── sast_fpn.py // SAST 算法的 fpn 网络 +│ │ │ ├── rnn.py // 识别 序列编码 +│ │ ├── heads // 头函数 +│ │ │ ├── __init__.py // 构造 head 相关代码 +│ │ │ ├── cls_head.py // 方向分类器 分类头 +│ │ │ ├── det_db_head.py // DB 检测头 +│ │ │ ├── det_east_head.py // EAST 检测头 +│ │ │ ├── det_sast_head.py // SAST 检测头 +│ │ │ ├── rec_ctc_head.py // 识别 ctc +│ │ │ ├── rec_att_head.py // 识别 attention +│ │ ├── transforms // 图像变换 +│ │ │ ├── __init__.py // 构造 transform 相关代码 +│ │ │ └── tps.py // TPS 变换 +│ ├── optimizer // 优化器 +│ │ ├── __init__.py // 构造 optimizer 相关代码 +│ │ └── learning_rate.py // 学习率衰减 +│ │ └── optimizer.py // 优化器 +│ │ └── regularizer.py // 网络正则化 +│ ├── postprocess // 后处理 +│ │ ├── cls_postprocess.py // 方向分类器 后处理 +│ │ ├── db_postprocess.py // DB 后处理 +│ │ ├── east_postprocess.py // EAST 后处理 +│ │ ├── locality_aware_nms.py // NMS +│ │ ├── rec_postprocess.py // 识别网络 后处理 +│ │ └── sast_postprocess.py // SAST 后处理 +│ └── utils // 工具 +│ ├── dict // 小语种字典 +│ .... +│ ├── ic15_dict.txt // 英文数字字典，区分大小写 +│ ├── ppocr_keys_v1.txt // 中文字典，用于训练中文模型 +│ ├── logging.py // logger +│ ├── save_load.py // 模型保存和加载函数 +│ ├── stats.py // 统计 +│ └── utility.py // 工具函数 +├── tools +│ ├── eval.py // 评估函数 +│ ├── export_model.py // 导出 inference 模型 +│ ├── infer // 基于预测引擎预测 +│ │ ├── predict_cls.py +│ │ ├── predict_det.py +│ │ ├── predict_rec.py +│ │ ├── predict_system.py +│ │ └── utility.py +│ ├── infer_cls.py // 基于训练引擎 预测分类 +│ ├── infer_det.py // 基于训练引擎 预测检测 +│ ├── infer_rec.py // 基于训练引擎 预测识别 +│ ├── program.py // 整体流程 +│ ├── test_hubserving.py +│ └── train.py // 启动训练 +├── paddleocr.py +├── README_ch.md // 中文说明文档 +├── README_en.md // 英文说明文档 +├── README.md // 主页说明文档 +├── requirments.txt // 安装依赖 +├── setup.py // whl包打包脚本 +├── train.sh // 启动训练脚本 \ No newline at end of file diff --git a/doc/doc_ch/whl.md b/doc/doc_ch/whl.md index 1b04a9a8a967f39516db0c0f1be3e3842a87278b..c51f32778f0e6e5069cb8d45e1632263b68569ad 100644 --- a/doc/doc_ch/whl.md +++ b/doc/doc_ch/whl.md @@ -261,6 +261,61 @@ im_show.save('result.jpg') paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true ``` +### 使用网络图片或者numpy数组作为输入 + +1. 网络图片 + +代码使用 +```python +from paddleocr import PaddleOCR, draw_ocr +# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换 +# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。 +ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory +img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg' +result = ocr.ocr(img_path, cls=True) +for line in result: + print(line) + +# 显示结果 +from PIL import Image +image = Image.open(img_path).convert('RGB') +boxes = [line[0] for line in result] +txts = [line[1][0] for line in result] +scores = [line[1][1] for line in result] +im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf') +im_show = Image.fromarray(im_show) +im_show.save('result.jpg') +``` +命令行模式 +```bash +paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true +``` + +2. numpy数组 +仅通过代码使用时支持numpy数组作为输入 +```python +from paddleocr import PaddleOCR, draw_ocr +# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换 +# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。 +ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory +img_path = 'PaddleOCR/doc/imgs/11.jpg' +img = cv2.imread(img_path) +# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消 +result = ocr.ocr(img_path, cls=True) +for line in result: + print(line) + +# 显示结果 +from PIL import Image +image = Image.open(img_path).convert('RGB') +boxes = [line[0] for line in result] +txts = [line[1][0] for line in result] +scores = [line[1][1] for line in result] +im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf') +im_show = Image.fromarray(im_show) +im_show.save('result.jpg') +``` + ## 参数说明 | 字段 | 说明 | 默认值 | @@ -285,6 +340,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ | max_text_length | 识别算法能识别的最大文字长度 | 25 | | rec_char_dict_path | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径 | ./ppocr/utils/ppocr_keys_v1.txt | | use_space_char | 是否识别空格 | TRUE | +| drop_score | 对输出按照分数(来自于识别模型)进行过滤，低于此分数的不返回 | 0.5 | | use_angle_cls | 是否加载分类模型 | FALSE | | cls_model_dir | 分类模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/cls`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None | | cls_image_shape | 分类算法的输入图片尺寸 | "3, 48, 192" | @@ -295,4 +351,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ | lang | 模型语言类型,目前支持 中文(ch)和英文(en) | ch | | det | 前向时使用启动检测 | TRUE | | rec | 前向时是否启动识别 | TRUE | -| cls | 前向时是否启动分类 | FALSE | +| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE | diff --git a/doc/doc_en/add_new_algorithm_en.md b/doc/doc_en/add_new_algorithm_en.md new file mode 100644 index 0000000000000000000000000000000000000000..48b505b6994a99e66ce38ad2a59d47975534663b --- /dev/null +++ b/doc/doc_en/add_new_algorithm_en.md @@ -0,0 +1,304 @@ +# Add new algorithm + +PaddleOCR decomposes an algorithm into the following parts, and modularizes each part to make it more convenient to develop new algorithms. + +* Data loading and processing +* Network +* Post-processing +* Loss +* Metric +* Optimizer + +The following will introduce each part separately, and introduce how to add the modules required for the new algorithm. + + +## Data loading and processing + +Data loading and processing are composed of different modules, which complete the image reading, data augment and label production. This part is under [ppocr/data](../../ppocr/data). The explanation of each file and folder are as follows: + +```bash +ppocr/data/ +├── imaug # Scripts for image reading, data augment and label production +│ ├── label_ops.py # Modules that transform the label +│ ├── operators.py # Modules that transform the image +│ ├──..... +├── __init__.py +├── lmdb_dataset.py # The dataset that reads the lmdb +└── simple_dataset.py # Read the dataset saved in the form of `image_path\tgt` +``` + +PaddleOCR has a large number of built-in image operation related modules. For modules that are not built-in, you can add them through the following steps: + +1. Create a new file under the [ppocr/data/imaug](../../ppocr/data/imaug) folder, such as my_module.py. +2. Add code in the my_module.py file, the sample code is as follows: + +```python +class MyModule: + def __init__(self, *args, **kwargs): + # your init code + pass + + def __call__(self, data): + img = data['image'] + label = data['label'] + # your process code + + data['image'] = img + data['label'] = label + return data +``` + +3. Import the added module in the [ppocr/data/imaug/\__init\__.py](../../ppocr/data/imaug/__init__.py) file. + +All different modules of data processing are executed by sequence, combined and executed in the form of a list in the config file. Such as: + +```yaml +# angle class data process +transforms: + - DecodeImage: # load image + img_mode: BGR + channel_first: False + - MyModule: + args1: args1 + args2: args2 + - KeepKeys: + keep_keys: [ 'image', 'label' ] # dataloader will return list in this order +``` + +## Network + +The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones-> +necks->heads). + +```bash +├── architectures # Code for building network +├── transforms # Image Transformation Module +├── backbones # Feature extraction module +├── necks # Feature enhancement module +└── heads # Output module +``` + +PaddleOCR has built-in commonly used modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For modules that do not have built-in, you can add them through the following steps, the four parts are added in the same steps, take backbones as an example: + +1. Create a new file under the [ppocr/modeling/backbones](../../ppocr/modeling/backbones) folder, such as my_backbone.py. +2. Add code in the my_backbone.py file, the sample code is as follows: + +```python +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +class MyBackbone(nn.Layer): + def __init__(self, *args, **kwargs): + super(MyBackbone, self).__init__() + # your init code + self.conv = nn.xxxx + + def forward(self, inputs): + # your necwork forward + y = self.conv(inputs) + return y +``` + +3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py) file. + +After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as: + +```yaml +Architecture: + model_type: rec + algorithm: CRNN + Transform: + name: MyTransform + args1: args1 + args2: args2 + Backbone: + name: MyBackbone + args1: args1 + Neck: + name: MyNeck + args1: args1 + Head: + name: MyHead + args1: args1 +``` + +## Post-processing + +Post-processing realizes decoding network output to obtain text box or recognized text. This part is under [ppocr/postprocess](../../ppocr/postprocess). +PaddleOCR has built-in post-processing modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For components that are not built-in, they can be added through the following steps: + +1. Create a new file under the [ppocr/postprocess](../../ppocr/postprocess) folder, such as my_postprocess.py. +2. Add code in the my_postprocess.py file, the sample code is as follows: + +```python +import paddle + + +class MyPostProcess: + def __init__(self, *args, **kwargs): + # your init code + pass + + def __call__(self, preds, label=None, *args, **kwargs): + if isinstance(preds, paddle.Tensor): + preds = preds.numpy() + # you preds decode code + preds = self.decode_preds(preds) + if label is None: + return preds + # you label decode code + label = self.decode_label(label) + return preds, label + + def decode_preds(self, preds): + # you preds decode code + pass + + def decode_label(self, preds): + # you label decode code + pass +``` + +3. Import the added module in the [ppocr/postprocess/\__init\__.py](../../ppocr/postprocess/__init__.py) file. + +After the post-processing module is added, you only need to configure it in the configuration file to use, such as: + +```yaml +PostProcess: + name: MyPostProcess + args1: args1 + args2: args2 +``` + +## Loss + +The loss function is used to calculate the distance between the network output and the label. This part is under [ppocr/losses](../../ppocr/losses). +PaddleOCR has built-in loss function modules related to algorithms such as DB, EAST, SAST, CRNN and Attention. For modules that do not have built-in modules, you can add them through the following steps: + +1. Create a new file in the [ppocr/losses](../../ppocr/losses) folder, such as my_loss.py. +2. Add code in the my_loss.py file, the sample code is as follows: + +```python +import paddle +from paddle import nn + + +class MyLoss(nn.Layer): + def __init__(self, **kwargs): + super(MyLoss, self).__init__() + # you init code + pass + + def __call__(self, predicts, batch): + label = batch[1] + # your loss code + loss = self.loss(input=predicts, label=label) + return {'loss': loss} +``` + +3. Import the added module in the [ppocr/losses/\__init\__.py](../../ppocr/losses/__init__.py) file. + +After the loss function module is added, you only need to configure it in the configuration file to use it, such as: + +```yaml +Loss: + name: MyLoss + args1: args1 + args2: args2 +``` + +## Metric + +Metric is used to calculate the performance of the network on the current batch. This part is under [ppocr/metrics](../../ppocr/metrics). PaddleOCR has built-in evaluation modules related to algorithms such as detection, classification and recognition. For modules that do not have built-in modules, you can add them through the following steps: + +1. Create a new file under the [ppocr/metrics](../../ppocr/metrics) folder, such as my_metric.py. +2. Add code in the my_metric.py file, the sample code is as follows: + +```python + +class MyMetric(object): + def __init__(self, main_indicator='acc', **kwargs): + # main_indicator is used for select best model + self.main_indicator = main_indicator + self.reset() + + def __call__(self, preds, batch, *args, **kwargs): + # preds is out of postprocess + # batch is out of dataloader + labels = batch[1] + cur_correct_num = 0 + cur_all_num = 0 + # you metric code + self.correct_num += cur_correct_num + self.all_num += cur_all_num + return {'acc': cur_correct_num / cur_all_num, } + + def get_metric(self): + """ + return metircs { + 'acc': 0, + 'norm_edit_dis': 0, + } + """ + acc = self.correct_num / self.all_num + self.reset() + return {'acc': acc} + + def reset(self): + # reset metric + self.correct_num = 0 + self.all_num = 0 + +``` + +3. Import the added module in the [ppocr/metrics/\__init\__.py](../../ppocr/metrics/__init__.py) file. + +After the metric module is added, you only need to configure it in the configuration file to use it, such as: + +```yaml +Metric: + name: MyMetric + main_indicator: acc +``` + +## 优化器 + +The optimizer is used to train the network. The optimizer also contains network regularization and learning rate decay modules. This part is under [ppocr/optimizer](../../ppocr/optimizer). PaddleOCR has built-in +Commonly used optimizer modules such as `Momentum`, `Adam` and `RMSProp`, common regularization modules such as `Linear`, `Cosine`, `Step` and `Piecewise`, and common learning rate decay modules such as `L1Decay` and `L2Decay`. +Modules without built-in can be added through the following steps, take `optimizer` as an example: + +1. Create your own optimizer in the [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) file, the sample code is as follows: + +```python +from paddle import optimizer as optim + + +class MyOptim(object): + def __init__(self, learning_rate=0.001, *args, **kwargs): + self.learning_rate = learning_rate + + def __call__(self, parameters): + # It is recommended to wrap the built-in optimizer of paddle + opt = optim.XXX( + learning_rate=self.learning_rate, + parameters=parameters) + return opt + +``` + +After the optimizer module is added, you only need to configure it in the configuration file to use, such as: + +```yaml +Optimizer: + name: MyOptim + args1: args1 + args2: args2 + lr: + name: Cosine + learning_rate: 0.001 + regularizer: + name: 'L2' + factor: 0 +``` \ No newline at end of file diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index 998ef08f5d6b67f06971de1d62914df02284b9e2..532ebd90cf149813acc9ad929840e1611766f652 100644 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -42,8 +42,8 @@ PaddleOCR open-source text recognition algorithms list: - [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) - [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) -- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) -- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research) +- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon +- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research) coming soon Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: @@ -56,4 +56,5 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r |STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)| |STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)| + Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md) diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md index 722da6620fd03912c48a47679e7c13a23f15752e..574bb41b6b1735271f9c794b856c5efb32db424f 100644 --- a/doc/doc_en/config_en.md +++ b/doc/doc_en/config_en.md @@ -1,69 +1,121 @@ -# OPTIONAL PARAMETERS LIST +## Optional parameter list -The following list can be viewed via `--help` +The following list can be viewed through `--help` | FLAG | Supported script | Use | Defaults | Note | | :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: | -| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** | -| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: `-o Global.use_gpu=false` | - +| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** | +| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false | ## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE -Take `rec_chinese_lite_train_v1.1.yml` as an example - +Take rec_chinese_lite_train_v1.1.yml as an example +### Global -| Parameter | Use | Default | Note | +| Parameter | Use | Defaults | Note | | :----------------------: | :---------------------: | :--------------: | :--------------------: | -| algorithm | Select algorithm to use | Synchronize with configuration file | For selecting model, please refer to the supported model [list](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/README_en.md) | -| use_gpu | Set using GPU or not | true | \ | -| epoch_num | Maximum training epoch number | 3000 | \ | +| use_gpu | Set using GPU or not | true | \ | +| epoch_num | Maximum training epoch number | 500 | \ | | log_smooth_window | Sliding window size | 20 | \ | | print_batch_step | Set print log interval | 10 | \ | -| save_model_dir | Set model save path | output/{model_name} | \ | +| save_model_dir | Set model save path | output/{算法名称} | \ | | save_epoch_step | Set model save interval | 3 | \ | -| eval_batch_step | Set the model evaluation interval |2000 or [1000, 2000] |runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration | -|train_batch_size_per_card | Set the batch size during training | 256 | \ | -| test_batch_size_per_card | Set the batch size during testing | 256 | \ | -| image_shape | Set input image size | [3, 32, 100] | \ | -| max_text_length | Set the maximum text length | 25 | \ | -| character_type | Set character type | ch | en/ch, the default dict will be used for en, and the custom dict will be used for ch| -| character_dict_path | Set dictionary path | ./ppocr/utils/ic15_dict.txt | \ | -| loss_type | Set loss type | ctc | Supports two types of loss: ctc / attention | -| distort | Set use distort | false | Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) | -| use_space_char | Wether to recognize space | false | Only support in character_type=ch mode | - label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in the direction classifier | -| reader_yml | Set the reader configuration file | ./configs/rec/rec_icdar15_reader.yml | \ | -| pretrain_weights | Load pre-trained model path | ./pretrain_models/CRNN/best_accuracy | \ | -| checkpoints | Load saved model path | None | Used to load saved parameters to continue training after interruption | -| save_inference_dir | path to save model for inference | None | Use to save inference model | - -## INTRODUCTION TO READER PARAMETERS OF CONFIGURATION FILE - -Take `rec_chinese_reader.yml` as an example: - -| Parameter | Use | Default | Note | -| :----------------------: | :---------------------: | :--------------: | :--------------------: | -| reader_function | Select data reading method | ppocr.data.rec.dataset_traversal,SimpleReader | Support two data reading methods: SimpleReader / LMDBReader | -| num_workers | Set the number of data reading threads | 8 | \ | -| img_set_dir | Image folder path | ./train_data | \ | -| label_file_path | Groundtruth file path | ./train_data/rec_gt_train.txt| \ | -| infer_img | Result folder path | ./infer_img | \| +| eval_batch_step | Set the model evaluation interval | 2000 or [1000, 2000] | runing evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration | +| cal_metric_during_train | Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated | true | \ | +| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ | +| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ | +| checkpoints | set model parameter path | None | Used to load parameters after interruption to continue training| +| use_visualdl | Set whether to enable visualdl for visual log display | False | [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) | +| infer_img | Set inference image path or folder path | ./infer_img | \| +| character_dict_path | Set dictionary path | ./ppocr/utils/ppocr_keys_v1.txt | \ | +| max_text_length | Set the maximum length of text | 25 | \ | +| character_type | Set character type | ch | en/ch, the default dict will be used for en, and the custom dict will be used for ch | +| use_space_char | Set whether to recognize spaces | True | Only support in character_type=ch mode | +| label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in angle classifier model | +| save_res_path | Set the save address of the test model results | ./output/det_db/predicts_db.txt | Only valid in the text detection model | + +### Optimizer ([ppocr/optimizer](../../ppocr/optimizer)) + +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | Optimizer class name | Adam | Currently supports`Momentum`,`Adam`,`RMSProp`, see [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) | +| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ | +| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ | +| **lr** | Set the learning rate decay method | - | \ | +| name | Learning rate decay class name | Cosine | Currently supports`Linear`,`Cosine`,`Step`,`Piecewise`, see[ppocr/optimizer/learning_rate.py](../../ppocr/optimizer/learning_rate.py) | +| learning_rate | Set the base learning rate | 0.001 | \ | +| **regularizer** | Set network regularization method | - | \ | +| name | Regularizer class name | L2 | Currently support`L1`,`L2`, see[ppocr/optimizer/regularizer.py](../../ppocr/optimizer/regularizer.py) | +| factor | Learning rate decay coefficient | 0.00004 | \ | + + +### Architecture ([ppocr/modeling](../../ppocr/modeling)) +In ppocr, the network is divided into four stages: Transform, Backbone, Neck and Head + +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` | +| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview.md) for the support list | +| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details | +| name | Transformation class name | TPS | Currently supports `TPS` | +| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom | +| loc_lr | Localization network learning rate | 0.1 | \ | +| model_name | Localization network size | small | Currently support`small`,`large` | +| **Backbone** | Set the network backbone class name | - | see [ppocr/modeling/backbones](../../ppocr/modeling/backbones) | +| name | backbone class name | ResNet | Currently support`MobileNetV3`,`ResNet` | +| layers | resnet layers | 34 | Currently support18,34,50,101,152,200 | +| model_name | MobileNetV3 network size | small | Currently support`small`,`large` | +| **Neck** | Set network neck | - | see[ppocr/modeling/necks](../../ppocr/modeling/necks) | +| name | neck class name | SequenceEncoder | Currently support`SequenceEncoder`,`DBFPN` | +| encoder_type | SequenceEncoder encoder type | rnn | Currently support`reshape`,`fc`,`rnn` | +| hidden_size | rnn number of internal units | 48 | \ | +| out_channels | Number of DBFPN output channels | 256 | \ | +| **Head** | Set the network head | - | see[ppocr/modeling/heads](../../ppocr/modeling/heads) | +| name | head class name | CTCHead | Currently support`CTCHead`,`DBHead`,`ClsHead` | +| fc_decay | CTCHead regularization coefficient | 0.0004 | \ | +| k | DBHead binarization coefficient | 50 | \ | +| class_dim | ClsHead output category number | 2 | \ | + + +### Loss ([ppocr/losses](../../ppocr/losses)) + +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | loss class name | CTCLoss | Currently support`CTCLoss`,`DBLoss`,`ClsLoss` | +| balance_loss | Whether to balance the number of positive and negative samples in DBLossloss (using OHEM) | True | \ | +| ohem_ratio | The negative and positive sample ratio of OHEM in DBLossloss | 3 | \ | +| main_loss_type | The loss used by shrink_map in DBLossloss | DiceLoss | Currently support`DiceLoss`,`BCELoss` | +| alpha | The coefficient of shrink_map_loss in DBLossloss | 5 | \ | +| beta | The coefficient of threshold_map_loss in DBLossloss | 10 | \ | -## INTRODUCTION TO OPTIMIZER PARAMETERS OF CONFIGURATION FILE +### PostProcess ([ppocr/postprocess](../../ppocr/postprocess)) -Take `rec_icdar15_train.yml` as an example: +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | Post-processing class name | CTCLabelDecode | Currently support`CTCLoss`,`AttnLabelDecode`,`DBPostProcess`,`ClsPostProcess` | +| thresh | The threshold for binarization of the segmentation map in DBPostProcess | 0.3 | \ | +| box_thresh | The threshold for filtering output boxes in DBPostProcess. Boxes below this threshold will not be output | 0.7 | \ | +| max_candidates | The maximum number of text boxes output in DBPostProcess | 1000 | | +| unclip_ratio | The unclip ratio of the text box in DBPostProcess | 2.0 | \ | + +### Metric ([ppocr/metrics](../../ppocr/metrics)) + +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| name | Metric method name | CTCLabelDecode | Currently support`DetMetric`,`RecMetric`,`ClsMetric` | +| main_indicator | Main indicators, used to select the best model | acc | For the detection method is hmean, the recognition and classification method is acc | -| Parameter | Use | Default | None | +### Dataset ([ppocr/data](../../ppocr/data)) +| Parameter | Use | Defaults | Note | | :---------------------: | :---------------------: | :--------------: | :--------------------: | -| function | Select Optimizer function | pocr.optimizer,AdamDecay | Only support Adam | -| base_lr | Set the base lr | 0.0005 | \ | -| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ | -| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ | -| decay | Whether to use decay | \ | \ | -| function(decay) | Set the decay function | cosine_decay | Support cosine_decay, cosine_decay_warmup and piecewise_decay | -| step_each_epoch | The number of steps in an epoch. Used in cosine_decay/cosine_decay_warmup | 20 | Calculation: total_image_num / (batch_size_per_card * card_size) | -| total_epoch | The number of epochs. Used in cosine_decay/cosine_decay_warmup | 1000 | Consistent with Global.epoch_num | -| warmup_minibatch | Number of steps for linear warmup. Used in cosine_decay_warmup | 1000 | \ | -| boundaries | The step intervals to reduce learning rate. Used in piecewise_decay | - | The format is list | -| decay_rate | Learning rate decay rate. Used in piecewise_decay | - | \ | +| **dataset** | Return one sample per iteration | - | - | +| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDateSet` | +| data_dir | Image folder path | ./train_data | \ | +| label_file_list | Groundtruth file path | ["./train_data/train_list.txt"] | This parameter is not required when dataset is LMDBDateSet | +| ratio_list | Ratio of data set | [1.0] | If there are two train_lists in label_file_list and ratio_list is [0.4,0.6], 40% will be sampled from train_list1, and 60% will be sampled from train_list2 to combine the entire dataset | +| transforms | List of methods to transform images and labels | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | see[ppocr/data/imaug](../../ppocr/data/imaug) | +| **loader** | dataloader related | - | | +| shuffle | Does each epoch disrupt the order of the data set | True | \ | +| batch_size_per_card | Single card batch size during training | 256 | \ | +| drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ | +| num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ | \ No newline at end of file diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 609b65fa55a743acd72407a34288afc793885d3c..ac1b634de453de15a11751488fe4059defd746e6 100644 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -1,13 +1,13 @@ # Reasoning based on Python prediction engine -The inference model (the model saved by `fluid.io.save_inference_model`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment. +The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment. The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training. Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md). -Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model. +Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model. - [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT) - [Convert detection model to inference model](#Convert_detection_model) @@ -26,9 +26,8 @@ Next, we first introduce how to convert a trained model into an inference model, - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION) - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION) - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION) - - [4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION) - - [5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS) - - [6. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE) + - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS) + - [5. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE) - [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) @@ -44,26 +43,27 @@ Next, we first introduce how to convert a trained model into an inference model, Download the lightweight Chinese detection model: ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_det_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/ ``` The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command: ``` # -c Set the training algorithm yml configuration file # -o Set optional parameters -# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.save_inference_dir Set the address where the converted model will be saved. +# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. +# Global.load_static_weights needs to be set to False +# Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/ +python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/ ``` -When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file. -`Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved. -After the conversion is successful, there are two files in the `save_inference_dir` directory: +When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` parameter in the configuration file. +After the conversion is successful, there are three files in the model save directory: ``` inference/det_db/ - └─ model Check the program file of inference model - └─ params Check the parameter file of the inference model + ├── inference.pdiparams # The parameter file of detection inference model + ├── inference.pdiparams.info # The parameter information of detection inference model, which can be ignored + └── inference.pdmodel # The program file of detection inference model ``` @@ -71,26 +71,28 @@ inference/det_db/ Download the lightweight Chinese recognition model: ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar && tar xf ch_ppocr_mobile_v1.1_rec_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/ ``` The recognition model is converted to the inference model in the same way as the detection, as follows: ``` # -c Set the training algorithm yml configuration file # -o Set optional parameters -# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.save_inference_dir Set the address where the converted model will be saved. +# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. +# Global.load_static_weights needs to be set to False +# Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_rec_train/best_accuracy \ +python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/ ``` If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path. -After the conversion is successful, there are two files in the directory: +After the conversion is successful, there are three files in the model save directory: ``` -/inference/rec_crnn/ - └─ model Identify the saved model files - └─ params Identify the parameter files of the inference model +inference/det_db/ + ├── inference.pdiparams # The parameter file of recognition inference model + ├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored + └── inference.pdmodel # The program file of recognition model ``` @@ -98,25 +100,26 @@ After the conversion is successful, there are two files in the directory: Download the angle classification model: ``` -wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_cls_train.tar -C ./ch_lite/ +wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/ ``` The angle classification model is converted to the inference model in the same way as the detection, as follows: ``` # -c Set the training algorithm yml configuration file # -o Set optional parameters -# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# Global.save_inference_dir Set the address where the converted model will be saved. +# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. +# Global.load_static_weights needs to be set to False +# Global.save_inference_dir Set the address where the converted model will be saved. -python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_cls_train/best_accuracy \ - Global.save_inference_dir=./inference/cls/ +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/ ``` After the conversion is successful, there are two files in the directory: ``` -/inference/cls/ - └─ model Identify the saved model files - └─ params Identify the parameter files of the inference model +inference/det_db/ + ├── inference.pdiparams # The parameter file of angle class inference model + ├── inference.pdiparams.info # The parameter information of angle class inference model, which can be ignored + └── inference.pdmodel # The program file of angle class model ``` @@ -139,10 +142,12 @@ The visual text detection results are saved to the ./inference_results folder by ![](../imgs_results/det_res_2.jpg) -By setting the size of the parameter `det_max_side_len`, the maximum value of picture normalization in the detection algorithm is changed. When the length and width of the picture are less than det_max_side_len, the original picture is used for prediction, otherwise the picture is scaled to the maximum value for prediction. This parameter is set to det_max_side_len=960 by default. If the resolution of the input picture is relatively large and you want to use a larger resolution for prediction, you can execute the following command: +The size of the image is limited by the parameters `limit_type` and `det_limit_side_len`, `limit_type=max` is to limit the length of the long side <`det_limit_side_len`, and `limit_type=min` is to limit the length of the short side>`det_limit_side_len`, +When the picture does not meet the restriction conditions (for `limit_type=max`and long side >`det_limit_side_len` or for `min` and short side <`det_limit_side_len`), the image will be scaled proportionally. +This parameter is set to `limit_type='max', det_max_side_len=960` by default. If the resolution of the input picture is relatively large, and you want to use a larger resolution prediction, you can execute the following command: ``` -python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200 +python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1200 ``` If you want to use the CPU for prediction, execute the command as follows @@ -153,14 +158,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di ### 2. DB TEXT DETECTION MODEL INFERENCE -First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)), you can use the following command to convert: +First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert: ``` -# Set the yml configuration file of the training algorithm after -c -# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# The Global.save_inference_dir parameter sets the address where the converted model will be saved. - -python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db" +python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db ``` DB text detection model inference, you can execute the following command: @@ -178,16 +179,11 @@ The visualized text detection results are saved to the `./inference_results` fol ### 3. EAST TEXT DETECTION MODEL INFERENCE -First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)), you can use the following command to convert: +First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert: ``` -# Set the yml configuration file of the training algorithm after -c -# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# The Global.save_inference_dir parameter sets the address where the converted model will be saved. - -python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east" +python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east ``` - **For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command: ``` @@ -204,10 +200,10 @@ The visualized text detection results are saved to the `./inference_results` fol ### 4. SAST TEXT DETECTION MODEL INFERENCE #### (1). Quadrangle text detection model (ICDAR2015) -First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)), you can use the following command to convert: +First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15" +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 ``` **For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command: @@ -224,7 +220,7 @@ The visualized text detection results are saved to the `./inference_results` fol First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert: ``` -python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt" +python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt ``` **For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command: @@ -264,19 +260,15 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695] ### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE -Taking STAR-Net as an example, we introduce the recognition model inference based on CTC loss. CRNN and Rosetta are used in a similar way, by setting the recognition algorithm parameter `rec_algorithm`. +Taking CRNN as an example, we introduce the recognition model inference based on CTC loss. Rosetta and Star-Net are used in a similar way, No need to set the recognition algorithm parameter rec_algorithm. -First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)). It can be converted as follow: +First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](link)). It can be converted as follow: ``` -# Set the yml configuration file of the training algorithm after -c -# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. -# The Global.save_inference_dir parameter sets the address where the converted model will be saved. - -python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.checkpoints="./models/rec_r34_vd_tps_bilstm_ctc/best_accuracy" Global.save_inference_dir="./inference/starnet" +python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn ``` -For STAR-Net text recognition model inference, execute the following commands: +For CRNN text recognition model inference, execute the following commands: ``` python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en" @@ -286,7 +278,11 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE ![](../imgs_words_en/word_336.png) +The recognition model based on Attention loss is different from ctc, and additional recognition algorithm parameters need to be set --rec_algorithm="RARE" After executing the command, the recognition result of the above image is as follows: +```bash +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE" +``` Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555] @@ -301,23 +297,8 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" dict_character = list(self.character_str) ``` - -### 4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE - -The recognition model based on SRN requires additional setting of the recognition algorithm parameter --rec_algorithm="SRN". -At the same time, it is necessary to ensure that the predicted shape is consistent with the training, such as: --rec_image_shape="1, 64, 256" - -``` -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \ - --rec_model_dir="./inference/srn/" \ - --rec_image_shape="1, 64, 256" \ - --rec_char_type="en" \ - --rec_algorithm="SRN" -``` - - -### 5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY +### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict. ``` @@ -325,7 +306,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ``` -### 6. MULTILINGAUL MODEL INFERENCE +### 5. MULTILINGAUL MODEL INFERENCE If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition: @@ -357,12 +338,14 @@ For angle classification model inference, you can execute the following commands python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/" ``` -![](../imgs_words/ch/word_4.jpg) +![](../imgs_words_en/word_10.png) After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen. -Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963] - +``` +infer_img: doc/imgs_words_en/word_10.png + result: ('0', 0.9999995) +``` ## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 41b00c52a7780d02c144c251553f427e5b875e5e..f9849321d3ac792accf02cac260f16daf030e755 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -135,7 +135,7 @@ If you need to customize dic file, please add character_dict_path field in confi - Add space category -If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `true`. +If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`. **Note: use_space_char only takes effect when character_type=ch** @@ -158,10 +158,9 @@ tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar Start training: ``` -# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES -export CUDA_VISIBLE_DEVICES=0,1,2,3 +# GPU training Support single card and multi-card training, specify the card number through --gpus # Training icdar15 English data and saving the log as train_rec.log -python3 tools/train.py -c configs/rec/rec_icdar15_train.yml 2>&1 | tee train_rec.log +python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml ``` - Data Augmentation @@ -184,8 +183,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend | Configuration file | Algorithm | backbone | trans | seq | pred | | :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | -| [rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | -| [rec_chinese_common_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_common_train_v1.1.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc | +| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | +| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) | CRNN | ResNet34_vd | None | BiLSTM | ctc | | rec_chinese_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | | rec_chinese_common_train.yml | CRNN | ResNet34_vd | None | BiLSTM | ctc | | rec_icdar15_train.yml | CRNN | Mobilenet_v3 large 0.5 | None | BiLSTM | ctc | @@ -199,39 +198,69 @@ If the evaluation set is large, the test will be time-consuming. It is recommend | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc | For training Chinese data, it is recommended to use -训练中文数据，推荐使用[rec_chinese_lite_train_v1.1.yml](../../configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file: +[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file: co -Take `rec_mv3_none_none_ctc.yml` as an example: +Take `rec_chinese_lite_train_v2.0.yml` as an example: ``` Global: ... - # Modify image_shape to fit long text - image_shape: [3, 32, 320] - ... + # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary + character_dict_path: ppocr/utils/ppocr_keys_v1.txt # Modify character type character_type: ch - # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary - character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt ... - # Modify reader type - reader_yml: ./configs/rec/rec_chinese_reader.yml - # Whether to use data augmentation - distort: true # Whether to recognize spaces - use_space_char: true - ... + use_space_char: True -... Optimizer: ... # Add learning rate decay strategy - decay: - function: cosine_decay - # Each epoch contains iter number - step_each_epoch: 20 - # Total epoch number - total_epoch: 1000 + lr: + name: Cosine + learning_rate: 0.001 + ... + +... + +Train: + dataset: + # Type of dataset，we support LMDBDateSet and SimpleDataSet + name: SimpleDataSet + # Path of dataset + data_dir: ./train_data/ + # Path of train list + label_file_list: ["./train_data/train_list.txt"] + transforms: + ... + - RecResizeImg: + # Modify image_shape to fit long text + image_shape: [3, 32, 320] + ... + loader: + ... + # Train batch_size for Single card + batch_size_per_card: 256 + ... + +Eval: + dataset: + # Type of dataset，we support LMDBDateSet and SimpleDataSet + name: SimpleDataSet + # Path of dataset + data_dir: ./train_data + # Path of eval list + label_file_list: ["./train_data/val_list.txt"] + transforms: + ... + - RecResizeImg: + # Modify image_shape to fit long text + image_shape: [3, 32, 320] + ... + loader: + # Eval batch_size for Single card + batch_size_per_card: 256 + ... ``` **Note that the configuration file for prediction/evaluation must be consistent with the training.** @@ -257,18 +286,33 @@ Take `rec_french_lite_train` as an example: ``` Global: ... - # Add a custom dictionary, if you modify the dictionary - # please point the path to the new dictionary + # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary character_dict_path: ./ppocr/utils/dict/french_dict.txt - # Add data augmentation during training - distort: true - # Identify spaces - use_space_char: true - ... - # Modify reader type - reader_yml: ./configs/rec/multi_languages/rec_french_reader.yml ... + # Whether to recognize spaces + use_space_char: True + ... + +Train: + dataset: + # Type of dataset，we support LMDBDateSet and SimpleDataSet + name: SimpleDataSet + # Path of dataset + data_dir: ./train_data/ + # Path of train list + label_file_list: ["./train_data/french_train.txt"] + ... + +Eval: + dataset: + # Type of dataset，we support LMDBDateSet and SimpleDataSet + name: SimpleDataSet + # Path of dataset + data_dir: ./train_data + # Path of eval list + label_file_list: ["./train_data/french_val.txt"] + ... ``` @@ -277,9 +321,8 @@ Global: The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader. ``` -export CUDA_VISIBLE_DEVICES=0 # GPU evaluation, Global.checkpoints is the weight to be tested -python3 tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy +python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` @@ -294,7 +337,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci ``` # Predict English results -python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg +python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg ``` Input image: @@ -309,11 +352,11 @@ infer_img: doc/imgs_words/en/word_1.png word : joint ``` -The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml`, you can use the following command to predict the Chinese model: +The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml`, you can use the following command to predict the Chinese model: ``` # Predict Chinese results -python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg +python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg ``` Input image: diff --git a/doc/doc_en/tree_en.md b/doc/doc_en/tree_en.md index 8f05de7e76b243bdcc6358bfea4e153e769dd3d5..be2f5733d3c30af8c83f1072cd0aea07865bacc3 100644 --- a/doc/doc_en/tree_en.md +++ b/doc/doc_en/tree_en.md @@ -2,40 +2,37 @@ The overall directory structure of PaddleOCR is introduced as follows: + ``` PaddleOCR -├── configs // configuration file, you can select model structure and modify hyperparameters through yml file -│ ├── cls // Related configuration files of direction classifier -│ │ ├── cls_mv3.yml // training configuration related, including backbone network, head, loss, optimizer -│ │ └── cls_reader.yml // Data reading related, data reading method, data storage path -│ ├── det // Detection related configuration files -│ │ ├── det_db_icdar15_reader.yml // data read -│ │ ├── det_mv3_db.yml // training configuration +├── configs // Configuration file, you can config the model structure and modify the hyperparameters through the yml file +│ ├── cls // Angle classifier config files +│ │ ├── cls_mv3.yml // Training config, including backbone network, head, loss, optimizer and data +│ ├── det // Text detection config files +│ │ ├── det_mv3_db.yml // Training config │ │ ... -│ └── rec // Identify related configuration files -│ ├── rec_benchmark_reader.yml // LMDB format data reading related -│ ├── rec_chinese_common_train.yml // General Chinese training configuration -│ ├── rec_icdar15_reader.yml // simple data reading related, including data reading function, data path, label file +│ └── rec // Text recognition config files +│ ├── rec_mv3_none_bilstm_ctc.yml // CRNN config │ ... -├── deploy // deployment related -│ ├── android_demo // android_demo +├── deploy // Depoly +│ ├── android_demo // Android demo │ │ ... -│ ├── cpp_infer // C++ infer -│ │ ├── CMakeLists.txt // Cmake file -│ │ ├── docs // documentation +│ ├── cpp_infer // C++ infer +│ │ ├── CMakeLists.txt // Cmake file +│ │ ├── docs // Docs │ │ │ └── windows_vs2019_build.md -│ │ ├── include -│ │ │ ├── clipper.h // clipper library -│ │ │ ├── config.h // infer configuration -│ │ │ ├── ocr_cls.h // direction classifier -│ │ │ ├── ocr_det.h // text detection -│ │ │ ├── ocr_rec.h // text recognition -│ │ │ ├── postprocess_op.h // postprocess after detection -│ │ │ ├── preprocess_op.h // preprocess detection -│ │ │ └── utility.h // tools -│ │ ├── readme.md // documentation +│ │ ├── include // Head Files +│ │ │ ├── clipper.h // clipper +│ │ │ ├── config.h // Inference config +│ │ │ ├── ocr_cls.h // Angle class +│ │ │ ├── ocr_det.h // Text detection +│ │ │ ├── ocr_rec.h // Text recognition +│ │ │ ├── postprocess_op.h // Post-processing +│ │ │ ├── preprocess_op.h // Pre-processing +│ │ │ └── utility.h // tools +│ │ ├── readme.md // Documentation │ │ ├── ... -│ │ ├── src // source file +│ │ ├── src // Source code files │ │ │ ├── clipper.cpp │ │ │ ├── config.cpp │ │ │ ├── main.cpp @@ -45,10 +42,10 @@ PaddleOCR │ │ │ ├── postprocess_op.cpp │ │ │ ├── preprocess_op.cpp │ │ │ └── utility.cpp -│ │ └── tools // compile and execute script -│ │ ├── build.sh // compile script -│ │ ├── config.txt // configuration file -│ │ └── run.sh // Test startup script +│ │ └── tools // Compile and execute script +│ │ ├── build.sh // Compile script +│ │ ├── config.txt // Config file +│ │ └── run.sh // Execute script │ ├── docker │ │ └── hubserving │ │ ├── cpu @@ -58,151 +55,165 @@ PaddleOCR │ │ ├── README_cn.md │ │ ├── README.md │ │ └── sample_request.txt -│ ├── hubserving // hubserving -│ │ ├── ocr_det // text detection -│ │ │ ├── config.json // serving configuration +│ ├── hubserving // hubserving +│ │ ├── ocr_cls // Angle class +│ │ │ ├── config.json // Serving config +│ │ │ ├── __init__.py +│ │ │ ├── module.py // Model +│ │ │ └── params.py // Parameters +│ │ ├── ocr_det // Text detection +│ │ │ ├── config.json // serving config │ │ │ ├── __init__.py -│ │ │ ├── module.py // prediction model -│ │ │ └── params.py // prediction parameters -│ │ ├── ocr_rec // text recognition +│ │ │ ├── module.py // Model +│ │ │ └── params.py // Parameters +│ │ ├── ocr_rec // Text recognition │ │ │ ├── config.json │ │ │ ├── __init__.py │ │ │ ├── module.py │ │ │ └── params.py -│ │ └── ocr_system // system forecast +│ │ └── ocr_system // Inference System │ │ ├── config.json │ │ ├── __init__.py │ │ ├── module.py │ │ └── params.py -│ ├── imgs // prediction picture +│ ├── imgs // Inference images │ │ ├── cpp_infer_pred_12.png │ │ └── demo.png -│ ├── ios_demo // ios demo +│ ├── ios_demo // IOS demo │ │ ... -│ ├── lite // lite deployment -│ │ ├── cls_process.cc // direction classifier data processing +│ ├── lite // Lite depoly +│ │ ├── cls_process.cc // Pre-process for angle class │ │ ├── cls_process.h -│ │ ├── config.txt // check configuration parameters -│ │ ├── crnn_process.cc // crnn data processing +│ │ ├── config.txt // Config file +│ │ ├── crnn_process.cc // Pre-process for CRNN │ │ ├── crnn_process.h -│ │ ├── db_post_process.cc // db data processing +│ │ ├── db_post_process.cc // Pre-process for DB │ │ ├── db_post_process.h -│ │ ├── Makefile // compile file -│ │ ├── ocr_db_crnn.cc // series prediction -│ │ ├── prepare.sh // data preparation -│ │ ├── readme.md // documentation +│ │ ├── Makefile // Compile file +│ │ ├── ocr_db_crnn.cc // Inference system +│ │ ├── prepare.sh // Prepare bash script +│ │ ├── readme.md // Documentation │ │ ... -│ ├── pdserving // pdserving deployment -│ │ ├── det_local_server.py // fast detection version, easy deployment and fast prediction -│ │ ├── det_web_server.py // Full version of detection, high stability and distributed deployment -│ │ ├── ocr_local_server.py // detection + identification quick version -│ │ ├── ocr_web_client.py // client -│ │ ├── ocr_web_server.py // detection + identification full version -│ │ ├── readme.md // documentation -│ │ ├── rec_local_server.py // recognize quick version -│ │ └── rec_web_server.py // Identify the full version +│ ├── pdserving // Pdserving depoly +│ │ ├── det_local_server.py // Text detection fast version, easy to deploy and fast to predict +│ │ ├── det_web_server.py // Text detection full version, high stability distributed deployment +│ │ ├── ocr_local_server.py // Text detection + recognition fast version +│ │ ├── ocr_web_client.py // client +│ │ ├── ocr_web_server.py // Text detection + recognition full version +│ │ ├── readme.md // Documentation +│ │ ├── rec_local_server.py // Text recognition fast version +│ │ └── rec_web_server.py // Text recognition full version │ └── slim -│ └── quantization // quantization related -│ ├── export_model.py // export model -│ ├── quant.py // quantization -│ └── README.md // Documentation -├── doc // Documentation tutorial +│ └── quantization // Quantization +│ ├── export_model.py // Export model +│ ├── quant.py // Quantization script +│ └── README.md // Documentation +├── doc // Documentation and Tutorials │ ... -├── paddleocr.py -├── ppocr // network core code -│ ├── data // data processing -│ │ ├── cls // direction classifier -│ │ │ ├── dataset_traversal.py // Data transmission, define data reader, read data and form batch -│ │ │ └── randaugment.py // Random data augmentation operation -│ │ ├── det // detection -│ │ │ ├── data_augment.py // data augmentation operation -│ │ │ ├── dataset_traversal.py // Data transmission, define data reader, read data and form batch -│ │ │ ├── db_process.py // db data processing -│ │ │ ├── east_process.py // east data processing -│ │ │ ├── make_border_map.py // Generate boundary map -│ │ │ ├── make_shrink_map.py // Generate shrink map -│ │ │ ├── random_crop_data.py // random crop -│ │ │ └── sast_process.py // sast data processing -│ │ ├── reader_main.py // main function of data reader -│ │ └── rec // recognation -│ │ ├── dataset_traversal.py // Data transmission, define data reader, including LMDB_Reader and Simple_Reader -│ │ └── img_tools.py // Data processing related, including data normalization and disturbance -│ ├── __init__.py -│ ├── modeling // networking related -│ │ ├── architectures // Model architecture, which defines the various modules required by the model -│ │ │ ├── cls_model.py // direction classifier -│ │ │ ├── det_model.py // detection -│ │ │ └── rec_model.py // recognition -│ │ ├── backbones // backbone network -│ │ │ ├── det_mobilenet_v3.py // detect mobilenet_v3 -│ │ │ ├── det_resnet_vd.py -│ │ │ ├── det_resnet_vd_sast.py -│ │ │ ├── rec_mobilenet_v3.py // recognize mobilenet_v3 -│ │ │ ├── rec_resnet_fpn.py -│ │ │ └── rec_resnet_vd.py -│ │ ├── common_functions.py // common functions -│ │ ├── heads -│ │ │ ├── cls_head.py // class header -│ │ │ ├── det_db_head.py // db detection head -│ │ │ ├── det_east_head.py // east detection head -│ │ │ ├── det_sast_head.py // sast detection head -│ │ │ ├── rec_attention_head.py // recognition attention -│ │ │ ├── rec_ctc_head.py // recognition ctc -│ │ │ ├── rec_seq_encoder.py // recognition sequence code -│ │ │ ├── rec_srn_all_head.py // srn related -│ │ │ └── self_attention // srn attention -│ │ │ └── model.py -│ │ ├── losses // loss function -│ │ │ ├── cls_loss.py // Directional classifier loss function -│ │ │ ├── det_basic_loss.py // detect basic loss -│ │ │ ├── det_db_loss.py // DB loss -│ │ │ ├── det_east_loss.py // EAST loss -│ │ │ ├── det_sast_loss.py // SAST loss -│ │ │ ├── rec_attention_loss.py // attention loss -│ │ │ ├── rec_ctc_loss.py // ctc loss -│ │ │ └── rec_srn_loss.py // srn loss -│ │ └── stns // Spatial transformation network -│ │ └── tps.py // TPS conversion -│ ├── optimizer.py // optimizer -│ ├── postprocess // post-processing -│ │ ├── db_postprocess.py // DB postprocess -│ │ ├── east_postprocess.py // East postprocess -│ │ ├── lanms // lanms related -│ │ │ ... -│ │ ├── locality_aware_nms.py // nms -│ │ └── sast_postprocess.py // sast post-processing -│ └── utils // tools -│ ├── character.py // Character processing, including text encoding and decoding, and calculation of prediction accuracy -│ ├── check.py // parameter loading check -│ ├── ic15_dict.txt // English number dictionary, case sensitive -│ ├── ppocr_keys_v1.txt // Chinese dictionary, used to train Chinese models -│ ├── save_load.py // model save and load function -│ ├── stats.py // Statistics -│ └── utility.py // Tool functions, including related check tools such as whether the input parameters are legal -├── README_en.md // documentation -├── README.md -├── requirments.txt // installation dependencies -├── setup.py // whl package packaging script -└── tools // start tool - ├── eval.py // evaluation function - ├── eval_utils // evaluation tools - │ ├── eval_cls_utils.py // category related - │ ├── eval_det_iou.py // detect iou related - │ ├── eval_det_utils.py // detection related - │ ├── eval_rec_utils.py // recognition related - │ └── __init__.py - ├── export_model.py // export infer model - ├── infer // Forecast based on prediction engine - │ ├── predict_cls.py - │ ├── predict_det.py - │ ├── predict_rec.py - │ ├── predict_system.py - │ └── utility.py - ├── infer_cls.py // Predict classification based on training engine - ├── infer_det.py // Predictive detection based on training engine - ├── infer_rec.py // Predictive recognition based on training engine - ├── program.py // overall process - ├── test_hubserving.py - └── train.py // start training - -``` +├── ppocr // Core code +│ ├── data // Data processing +│ │ ├── imaug // Image and label processing code +│ │ │ ├── text_image_aug // Tia data augment for text recognition +│ │ │ │ ├── __init__.py +│ │ │ │ ├── augment.py // Tia_distort,tia_stretch and tia_perspective +│ │ │ │ ├── warp_mls.py +│ │ │ ├── __init__.py +│ │ │ ├── east_process.py // Data processing steps of EAST algorithm +│ │ │ ├── iaa_augment.py // Data augmentation operations +│ │ │ ├── label_ops.py // label encode operations +│ │ │ ├── make_border_map.py // Generate boundary map +│ │ │ ├── make_shrink_map.py // Generate shrink graph +│ │ │ ├── operators.py // Basic image operations, such as reading and normalization +│ │ │ ├── randaugment.py // Random data augmentation operation +│ │ │ ├── random_crop_data.py // Random crop +│ │ │ ├── rec_img_aug.py // Data augmentation for text recognition +│ │ │ └── sast_process.py // Data processing steps of SAST algorithm +│ │ ├── __init__.py // Construct dataloader code +│ │ ├── lmdb_dataset.py // Read lmdb dataset +│ │ ├── simple_dataset.py // Read the dataset stored in text format +│ ├── losses // Loss function +│ │ ├── __init__.py // Construct loss code +│ │ ├── cls_loss.py // Angle class loss +│ │ ├── det_basic_loss.py // Text detection basic loss +│ │ ├── det_db_loss.py // DB loss +│ │ ├── det_east_loss.py // EAST loss +│ │ ├── det_sast_loss.py // SAST loss +│ │ ├── rec_ctc_loss.py // CTC loss +│ │ ├── rec_att_loss.py // Attention loss +│ ├── metrics // Metrics +│ │ ├── __init__.py // Construct metric code +│ │ ├── cls_metric.py // Angle class metric +│ │ ├── det_metric.py // Text detection metric + │ ├── eval_det_iou.py // Text detection iou code +│ │ ├── rec_metric.py // Text recognition metric +│ ├── modeling // Network +│ │ ├── architectures // Architecture +│ │ │ ├── __init__.py // Construct model code +│ │ │ ├── base_model.py // Base model +│ │ ├── backbones // backbones +│ │ │ ├── __init__.py // Construct backbone code +│ │ │ ├── det_mobilenet_v3.py // Text detection mobilenet_v3 +│ │ │ ├── det_resnet_vd.py // Text detection resnet +│ │ │ ├── det_resnet_vd_sast.py // Text detection resnet backbone of the SAST algorithm +│ │ │ ├── rec_mobilenet_v3.py // Text recognition mobilenet_v3 +│ │ │ └── rec_resnet_vd.py // Text recognition resnet +│ │ ├── necks // Necks +│ │ │ ├── __init__.py // Construct neck code +│ │ │ ├── db_fpn.py // Standard fpn +│ │ │ ├── east_fpn.py // EAST algorithm fpn network +│ │ │ ├── sast_fpn.py // SAST algorithm fpn network +│ │ │ ├── rnn.py // Character recognition sequence encoding +│ │ ├── heads // Heads +│ │ │ ├── __init__.py // Construct head code +│ │ │ ├── cls_head.py // Angle class head +│ │ │ ├── det_db_head.py // DB head +│ │ │ ├── det_east_head.py // EAST head +│ │ │ ├── det_sast_head.py // SAST head +│ │ │ ├── rec_ctc_head.py // CTC head +│ │ │ ├── rec_att_head.py // Attention head +│ │ ├── transforms // Transforms +│ │ │ ├── __init__.py // Construct transform code +│ │ │ └── tps.py // TPS transform +│ ├── optimizer // Optimizer +│ │ ├── __init__.py // Construct optimizer code +│ │ └── learning_rate.py // Learning rate decay +│ │ └── optimizer.py // Optimizer +│ │ └── regularizer.py // Network regularization +│ ├── postprocess // Post-processing +│ │ ├── cls_postprocess.py // Angle class post-processing +│ │ ├── db_postprocess.py // DB post-processing +│ │ ├── east_postprocess.py // EAST post-processing +│ │ ├── locality_aware_nms.py // NMS +│ │ ├── rec_postprocess.py // Text recognition post-processing +│ │ └── sast_postprocess.py // SAST post-processing +│ └── utils // utils +│ ├── dict // Minor language dictionary +│ .... +│ ├── ic15_dict.txt // English number dictionary, case sensitive +│ ├── ppocr_keys_v1.txt // Chinese dictionary for training Chinese models +│ ├── logging.py // logger +│ ├── save_load.py // Model saving and loading functions +│ ├── stats.py // Training status statistics +│ └── utility.py // Utility function +├── tools +│ ├── eval.py // Evaluation function +│ ├── export_model.py // Export inference model +│ ├── infer // Inference based on Inference engine +│ │ ├── predict_cls.py +│ │ ├── predict_det.py +│ │ ├── predict_rec.py +│ │ ├── predict_system.py +│ │ └── utility.py +│ ├── infer_cls.py // Angle classification inference based on training engine +│ ├── infer_det.py // Text detection inference based on training engine +│ ├── infer_rec.py // Text recognition inference based on training engine +│ ├── program.py // Inference system +│ ├── test_hubserving.py +│ └── train.py // Start training script +├── paddleocr.py +├── README_ch.md // Chinese documentation +├── README_en.md // English documentation +├── README.md // Home page documentation +├── requirments.txt // Requirments +├── setup.py // Whl package packaging script +├── train.sh // Start training bash script \ No newline at end of file diff --git a/doc/doc_en/whl_en.md b/doc/doc_en/whl_en.md index ffbced346f7a3f661f382b5f2d826c20fef2c012..c25999d4d5c57ee272de20dd4b5e47ebf41abd52 100644 --- a/doc/doc_en/whl_en.md +++ b/doc/doc_en/whl_en.md @@ -271,6 +271,59 @@ im_show.save('result.jpg') paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true ``` +### Use web images or numpy array as input + +1. Web image + +Use by code +```python +from paddleocr import PaddleOCR, draw_ocr +ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory +img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg' +result = ocr.ocr(img_path, cls=True) +for line in result: + print(line) + +# show result +from PIL import Image +image = Image.open(img_path).convert('RGB') +boxes = [line[0] for line in result] +txts = [line[1][0] for line in result] +scores = [line[1][1] for line in result] +im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf') +im_show = Image.fromarray(im_show) +im_show.save('result.jpg') +``` +Use by command line +```bash +paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true +``` + +2. Numpy array +Support numpy array as input only when used by code + +```python +from paddleocr import PaddleOCR, draw_ocr +ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory +img_path = 'PaddleOCR/doc/imgs/11.jpg' +img = cv2.imread(img_path) +# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), If your own training model supports grayscale images, you can uncomment this line +result = ocr.ocr(img_path, cls=True) +for line in result: + print(line) + +# show result +from PIL import Image +image = Image.open(img_path).convert('RGB') +boxes = [line[0] for line in result] +txts = [line[1][0] for line in result] +scores = [line[1][1] for line in result] +im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf') +im_show = Image.fromarray(im_show) +im_show.save('result.jpg') +``` + + ## Parameter Description | Parameter | Description | Default value | @@ -295,6 +348,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ | max_text_length | The maximum text length that the recognition algorithm can recognize | 25 | | rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt | | use_space_char | Whether to recognize spaces | TRUE | +| drop_score | Filter the output by score (from the recognition model), and those below this score will not be returned | 0.5 | | use_angle_cls | Whether to load classification model | FALSE | | cls_model_dir | the classification inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/cls`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None | | cls_image_shape | image shape of classification algorithm | "3,48,192" | @@ -305,4 +359,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_ | lang | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported | ch | | det | Enable detction when `ppocr.ocr` func exec | TRUE | | rec | Enable recognition when `ppocr.ocr` func exec | TRUE | -| cls | Enable classification when `ppocr.ocr` func exec | FALSE | +| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE | diff --git a/paddleocr.py b/paddleocr.py index d3d73cb1b92cb2228fafb4e0efa36ab13207a4b3..17306e79fe3c94e7f885de408a6c0f6c060a67da 100644 --- a/paddleocr.py +++ b/paddleocr.py @@ -26,17 +26,50 @@ import requests from tqdm import tqdm from tools.infer import predict_system -from ppocr.utils.utility import initial_logger +from ppocr.utils.logging import get_logger -logger = initial_logger() +logger = get_logger() from ppocr.utils.utility import check_and_read_gif, get_image_file_list __all__ = ['PaddleOCR'] -model_params = { - 'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar', - 'rec': - 'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar', +model_urls = { + 'det': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar', + 'rec': { + 'ch': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/ppocr_keys_v1.txt' + }, + 'en': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/ic15_dict.txt' + }, + 'french': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/french_dict.txt' + }, + 'german': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/german_dict.txt' + }, + 'korean': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/korean_dict.txt' + }, + 'japan': { + 'url': + 'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar', + 'dict_path': './ppocr/utils/dict/japan_dict.txt' + } + }, + 'cls': + 'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar' } SUPPORT_DET_MODEL = ['DB'] @@ -54,8 +87,8 @@ def download_with_progressbar(url, save_path): progress_bar.update(len(data)) file.write(data) progress_bar.close() - if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes: - logger.error("ERROR, something went wrong") + if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: + logger.error("Something went wrong while downloading models") sys.exit(0) @@ -63,7 +96,7 @@ def maybe_download(model_storage_directory, url): # using custom model if not os.path.exists(os.path.join( model_storage_directory, 'model')) or not os.path.exists( - os.path.join(model_storage_directory, 'params')): + os.path.join(model_storage_directory, 'params')): tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) print('download {} to {}'.format(url, tmp_path)) os.makedirs(model_storage_directory, exist_ok=True) @@ -84,53 +117,102 @@ def maybe_download(model_storage_directory, url): os.remove(tmp_path) -def parse_args(): +def parse_args(mMain=True, add_help=True): import argparse def str2bool(v): return v.lower() in ("true", "t", "1") - parser = argparse.ArgumentParser() - # params for prediction engine - parser.add_argument("--use_gpu", type=str2bool, default=True) - parser.add_argument("--ir_optim", type=str2bool, default=True) - parser.add_argument("--use_tensorrt", type=str2bool, default=False) - parser.add_argument("--gpu_mem", type=int, default=8000) - - # params for text detector - parser.add_argument("--image_dir", type=str) - parser.add_argument("--det_algorithm", type=str, default='DB') - parser.add_argument("--det_model_dir", type=str, default=None) - parser.add_argument("--det_max_side_len", type=float, default=960) - - # DB parmas - parser.add_argument("--det_db_thresh", type=float, default=0.3) - parser.add_argument("--det_db_box_thresh", type=float, default=0.5) - parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0) - - # EAST parmas - parser.add_argument("--det_east_score_thresh", type=float, default=0.8) - parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) - parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) - - # params for text recognizer - parser.add_argument("--rec_algorithm", type=str, default='CRNN') - parser.add_argument("--rec_model_dir", type=str, default=None) - parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320") - parser.add_argument("--rec_char_type", type=str, default='ch') - parser.add_argument("--rec_batch_num", type=int, default=30) - parser.add_argument("--max_text_length", type=int, default=25) - parser.add_argument( - "--rec_char_dict_path", - type=str, - default="./ppocr/utils/ppocr_keys_v1.txt") - parser.add_argument("--use_space_char", type=bool, default=True) - parser.add_argument("--enable_mkldnn", type=bool, default=False) - - parser.add_argument("--det", type=str2bool, default=True) - parser.add_argument("--rec", type=str2bool, default=True) - parser.add_argument("--use_zero_copy_run", type=bool, default=False) - return parser.parse_args() + if mMain: + parser = argparse.ArgumentParser(add_help=add_help) + # params for prediction engine + parser.add_argument("--use_gpu", type=str2bool, default=True) + parser.add_argument("--ir_optim", type=str2bool, default=True) + parser.add_argument("--use_tensorrt", type=str2bool, default=False) + parser.add_argument("--gpu_mem", type=int, default=8000) + + # params for text detector + parser.add_argument("--image_dir", type=str) + parser.add_argument("--det_algorithm", type=str, default='DB') + parser.add_argument("--det_model_dir", type=str, default=None) + parser.add_argument("--det_limit_side_len", type=float, default=960) + parser.add_argument("--det_limit_type", type=str, default='max') + + # DB parmas + parser.add_argument("--det_db_thresh", type=float, default=0.3) + parser.add_argument("--det_db_box_thresh", type=float, default=0.5) + parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0) + + # EAST parmas + parser.add_argument("--det_east_score_thresh", type=float, default=0.8) + parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) + parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) + + # params for text recognizer + parser.add_argument("--rec_algorithm", type=str, default='CRNN') + parser.add_argument("--rec_model_dir", type=str, default=None) + parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320") + parser.add_argument("--rec_char_type", type=str, default='ch') + parser.add_argument("--rec_batch_num", type=int, default=30) + parser.add_argument("--max_text_length", type=int, default=25) + parser.add_argument("--rec_char_dict_path", type=str, default=None) + parser.add_argument("--use_space_char", type=bool, default=True) + parser.add_argument("--drop_score", type=float, default=0.5) + + # params for text classifier + parser.add_argument("--cls_model_dir", type=str, default=None) + parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192") + parser.add_argument("--label_list", type=list, default=['0', '180']) + parser.add_argument("--cls_batch_num", type=int, default=30) + parser.add_argument("--cls_thresh", type=float, default=0.9) + + parser.add_argument("--enable_mkldnn", type=bool, default=False) + parser.add_argument("--use_zero_copy_run", type=bool, default=False) + parser.add_argument("--use_pdserving", type=str2bool, default=False) + + parser.add_argument("--lang", type=str, default='ch') + parser.add_argument("--det", type=str2bool, default=True) + parser.add_argument("--rec", type=str2bool, default=True) + parser.add_argument("--use_angle_cls", type=str2bool, default=False) + return parser.parse_args() + else: + return argparse.Namespace(use_gpu=True, + ir_optim=True, + use_tensorrt=False, + gpu_mem=8000, + image_dir='', + det_algorithm='DB', + det_model_dir=None, + det_limit_side_len=960, + det_limit_type='max', + det_db_thresh=0.3, + det_db_box_thresh=0.5, + det_db_unclip_ratio=2.0, + det_east_score_thresh=0.8, + det_east_cover_thresh=0.1, + det_east_nms_thresh=0.2, + rec_algorithm='CRNN', + rec_model_dir=None, + rec_image_shape="3, 32, 320", + rec_char_type='ch', + rec_batch_num=30, + max_text_length=25, + rec_char_dict_path=None, + use_space_char=True, + drop_score=0.5, + cls_model_dir=None, + cls_image_shape="3, 48, 192", + label_list=['0', '180'], + cls_batch_num=30, + cls_thresh=0.9, + enable_mkldnn=False, + use_zero_copy_run=False, + use_pdserving=False, + lang='ch', + det=True, + rec=True, + use_angle_cls=False + ) class PaddleOCR(predict_system.TextSystem): @@ -140,18 +222,31 @@ class PaddleOCR(predict_system.TextSystem): args: **kwargs: other params show in paddleocr --help """ - postprocess_params = parse_args() + postprocess_params = parse_args(mMain=False, add_help=False) postprocess_params.__dict__.update(**kwargs) + self.use_angle_cls = postprocess_params.use_angle_cls + lang = postprocess_params.lang + assert lang in model_urls[ + 'rec'], 'param lang must in {}, but got {}'.format( + model_urls['rec'].keys(), lang) + if postprocess_params.rec_char_dict_path is None: + postprocess_params.rec_char_dict_path = model_urls['rec'][lang][ + 'dict_path'] # init model dir if postprocess_params.det_model_dir is None: postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det') if postprocess_params.rec_model_dir is None: - postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec') + postprocess_params.rec_model_dir = os.path.join( + BASE_DIR, 'rec/{}'.format(lang)) + if postprocess_params.cls_model_dir is None: + postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls') print(postprocess_params) # download model - maybe_download(postprocess_params.det_model_dir, model_params['det']) - maybe_download(postprocess_params.rec_model_dir, model_params['rec']) + maybe_download(postprocess_params.det_model_dir, model_urls['det']) + maybe_download(postprocess_params.rec_model_dir, + model_urls['rec'][lang]['url']) + maybe_download(postprocess_params.cls_model_dir, model_urls['cls']) if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL: logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL)) @@ -166,7 +261,7 @@ class PaddleOCR(predict_system.TextSystem): # init det_model and rec_model super().__init__(postprocess_params) - def ocr(self, img, det=True, rec=True): + def ocr(self, img, det=True, rec=True, cls=False): """ ocr with paddleocr args： @@ -175,7 +270,16 @@ class PaddleOCR(predict_system.TextSystem): rec: use text recognition or not, if false, only det will be exec. default is True """ assert isinstance(img, (np.ndarray, list, str)) + if isinstance(img, list) and det == True: + logger.error('When input a list of images, det must be false') + exit(0) + + self.use_angle_cls = cls if isinstance(img, str): + # download net image + if img.startswith('http'): + download_with_progressbar(img, 'tmp.jpg') + img = 'tmp.jpg' image_file = img img, flag = check_and_read_gif(image_file) if not flag: @@ -183,6 +287,8 @@ class PaddleOCR(predict_system.TextSystem): if img is None: logger.error("error in loading image:{}".format(image_file)) return None + if isinstance(img, np.ndarray) and len(img.shape) == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) if det and rec: dt_boxes, rec_res = self.__call__(img) return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)] @@ -194,20 +300,34 @@ class PaddleOCR(predict_system.TextSystem): else: if not isinstance(img, list): img = [img] + if self.use_angle_cls: + img, cls_res, elapse = self.text_classifier(img) + if not rec: + return cls_res rec_res, elapse = self.text_recognizer(img) return rec_res def main(): - # for com - args = parse_args() - image_file_list = get_image_file_list(args.image_dir) + # for cmd + args = parse_args(mMain=True) + image_dir = args.image_dir + if image_dir.startswith('http'): + download_with_progressbar(image_dir, 'tmp.jpg') + image_file_list = ['tmp.jpg'] + else: + image_file_list = get_image_file_list(args.image_dir) if len(image_file_list) == 0: logger.error('no images find in {}'.format(args.image_dir)) return - ocr_engine = PaddleOCR() + + ocr_engine = PaddleOCR(**(args.__dict__)) for img_path in image_file_list: - print(img_path) - result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec) - for line in result: - print(line) \ No newline at end of file + logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10)) + result = ocr_engine.ocr(img_path, + det=args.det, + rec=args.rec, + cls=args.use_angle_cls) + if result is not None: + for line in result: + logger.info(line) diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index fd143b53202a6116df90a1df2b75b254a73bd5f5..6ea4dd8ed6d0f58fbee3362e8eb82a0eda65e812 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -26,6 +26,9 @@ from .randaugment import RandAugment from .operators import * from .label_ops import * +from .east_process import * +from .sast_process import * + def transform(data, ops=None): """ transform """ diff --git a/ppocr/data/imaug/east_process.py b/ppocr/data/imaug/east_process.py new file mode 100644 index 0000000000000000000000000000000000000000..b1d7a5e51939af981dd62c269c930f4bf9ba4179 --- /dev/null +++ b/ppocr/data/imaug/east_process.py @@ -0,0 +1,439 @@ +#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +import math +import cv2 +import numpy as np +import json +import sys +import os + +__all__ = ['EASTProcessTrain'] + + +class EASTProcessTrain(object): + def __init__(self, + image_shape = [512, 512], + background_ratio = 0.125, + min_crop_side_ratio = 0.1, + min_text_size = 10, + **kwargs): + self.input_size = image_shape[1] + self.random_scale = np.array([0.5, 1, 2.0, 3.0]) + self.background_ratio = background_ratio + self.min_crop_side_ratio = min_crop_side_ratio + self.min_text_size = min_text_size + + def preprocess(self, im): + input_size = self.input_size + im_shape = im.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + im_scale = float(input_size) / float(im_size_max) + im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale) + img_mean = [0.485, 0.456, 0.406] + img_std = [0.229, 0.224, 0.225] + # im = im[:, :, ::-1].astype(np.float32) + im = im / 255 + im -= img_mean + im /= img_std + new_h, new_w, _ = im.shape + im_padded = np.zeros((input_size, input_size, 3), dtype=np.float32) + im_padded[:new_h, :new_w, :] = im + im_padded = im_padded.transpose((2, 0, 1)) + im_padded = im_padded[np.newaxis, :] + return im_padded, im_scale + + def rotate_im_poly(self, im, text_polys): + """ + rotate image with 90 / 180 / 270 degre + """ + im_w, im_h = im.shape[1], im.shape[0] + dst_im = im.copy() + dst_polys = [] + rand_degree_ratio = np.random.rand() + rand_degree_cnt = 1 + if 0.333 < rand_degree_ratio < 0.666: + rand_degree_cnt = 2 + elif rand_degree_ratio > 0.666: + rand_degree_cnt = 3 + for i in range(rand_degree_cnt): + dst_im = np.rot90(dst_im) + rot_degree = -90 * rand_degree_cnt + rot_angle = rot_degree * math.pi / 180.0 + n_poly = text_polys.shape[0] + cx, cy = 0.5 * im_w, 0.5 * im_h + ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0] + for i in range(n_poly): + wordBB = text_polys[i] + poly = [] + for j in range(4): + sx, sy = wordBB[j][0], wordBB[j][1] + dx = math.cos(rot_angle) * (sx - cx)\ + - math.sin(rot_angle) * (sy - cy) + ncx + dy = math.sin(rot_angle) * (sx - cx)\ + + math.cos(rot_angle) * (sy - cy) + ncy + poly.append([dx, dy]) + dst_polys.append(poly) + dst_polys = np.array(dst_polys, dtype=np.float32) + return dst_im, dst_polys + + def polygon_area(self, poly): + """ + compute area of a polygon + :param poly: + :return: + """ + edge = [(poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]), + (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]), + (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]), + (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])] + return np.sum(edge) / 2. + + def check_and_validate_polys(self, polys, tags, img_height, img_width): + """ + check so that the text poly is in the same direction, + and also filter some invalid polygons + :param polys: + :param tags: + :return: + """ + h, w = img_height, img_width + if polys.shape[0] == 0: + return polys + polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1) + polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1) + + validated_polys = [] + validated_tags = [] + for poly, tag in zip(polys, tags): + p_area = self.polygon_area(poly) + #invalid poly + if abs(p_area) < 1: + continue + if p_area > 0: + #'poly in wrong direction' + if not tag: + tag = True #reversed cases should be ignore + poly = poly[(0, 3, 2, 1), :] + validated_polys.append(poly) + validated_tags.append(tag) + return np.array(validated_polys), np.array(validated_tags) + + def draw_img_polys(self, img, polys): + if len(img.shape) == 4: + img = np.squeeze(img, axis=0) + if img.shape[0] == 3: + img = img.transpose((1, 2, 0)) + img[:, :, 2] += 123.68 + img[:, :, 1] += 116.78 + img[:, :, 0] += 103.94 + cv2.imwrite("tmp.jpg", img) + img = cv2.imread("tmp.jpg") + for box in polys: + box = box.astype(np.int32).reshape((-1, 1, 2)) + cv2.polylines(img, [box], True, color=(255, 255, 0), thickness=2) + import random + ino = random.randint(0, 100) + cv2.imwrite("tmp_%d.jpg" % ino, img) + return + + def shrink_poly(self, poly, r): + """ + fit a poly inside the origin poly, maybe bugs here... + used for generate the score map + :param poly: the text poly + :param r: r in the paper + :return: the shrinked poly + """ + # shrink ratio + R = 0.3 + # find the longer pair + dist0 = np.linalg.norm(poly[0] - poly[1]) + dist1 = np.linalg.norm(poly[2] - poly[3]) + dist2 = np.linalg.norm(poly[0] - poly[3]) + dist3 = np.linalg.norm(poly[1] - poly[2]) + if dist0 + dist1 > dist2 + dist3: + # first move (p0, p1), (p2, p3), then (p0, p3), (p1, p2) + ## p0, p1 + theta = np.arctan2((poly[1][1] - poly[0][1]), + (poly[1][0] - poly[0][0])) + poly[0][0] += R * r[0] * np.cos(theta) + poly[0][1] += R * r[0] * np.sin(theta) + poly[1][0] -= R * r[1] * np.cos(theta) + poly[1][1] -= R * r[1] * np.sin(theta) + ## p2, p3 + theta = np.arctan2((poly[2][1] - poly[3][1]), + (poly[2][0] - poly[3][0])) + poly[3][0] += R * r[3] * np.cos(theta) + poly[3][1] += R * r[3] * np.sin(theta) + poly[2][0] -= R * r[2] * np.cos(theta) + poly[2][1] -= R * r[2] * np.sin(theta) + ## p0, p3 + theta = np.arctan2((poly[3][0] - poly[0][0]), + (poly[3][1] - poly[0][1])) + poly[0][0] += R * r[0] * np.sin(theta) + poly[0][1] += R * r[0] * np.cos(theta) + poly[3][0] -= R * r[3] * np.sin(theta) + poly[3][1] -= R * r[3] * np.cos(theta) + ## p1, p2 + theta = np.arctan2((poly[2][0] - poly[1][0]), + (poly[2][1] - poly[1][1])) + poly[1][0] += R * r[1] * np.sin(theta) + poly[1][1] += R * r[1] * np.cos(theta) + poly[2][0] -= R * r[2] * np.sin(theta) + poly[2][1] -= R * r[2] * np.cos(theta) + else: + ## p0, p3 + # print poly + theta = np.arctan2((poly[3][0] - poly[0][0]), + (poly[3][1] - poly[0][1])) + poly[0][0] += R * r[0] * np.sin(theta) + poly[0][1] += R * r[0] * np.cos(theta) + poly[3][0] -= R * r[3] * np.sin(theta) + poly[3][1] -= R * r[3] * np.cos(theta) + ## p1, p2 + theta = np.arctan2((poly[2][0] - poly[1][0]), + (poly[2][1] - poly[1][1])) + poly[1][0] += R * r[1] * np.sin(theta) + poly[1][1] += R * r[1] * np.cos(theta) + poly[2][0] -= R * r[2] * np.sin(theta) + poly[2][1] -= R * r[2] * np.cos(theta) + ## p0, p1 + theta = np.arctan2((poly[1][1] - poly[0][1]), + (poly[1][0] - poly[0][0])) + poly[0][0] += R * r[0] * np.cos(theta) + poly[0][1] += R * r[0] * np.sin(theta) + poly[1][0] -= R * r[1] * np.cos(theta) + poly[1][1] -= R * r[1] * np.sin(theta) + ## p2, p3 + theta = np.arctan2((poly[2][1] - poly[3][1]), + (poly[2][0] - poly[3][0])) + poly[3][0] += R * r[3] * np.cos(theta) + poly[3][1] += R * r[3] * np.sin(theta) + poly[2][0] -= R * r[2] * np.cos(theta) + poly[2][1] -= R * r[2] * np.sin(theta) + return poly + + def generate_quad(self, im_size, polys, tags): + """ + Generate quadrangle. + """ + h, w = im_size + poly_mask = np.zeros((h, w), dtype=np.uint8) + score_map = np.zeros((h, w), dtype=np.uint8) + # (x1, y1, ..., x4, y4, short_edge_norm) + geo_map = np.zeros((h, w, 9), dtype=np.float32) + # mask used during traning, to ignore some hard areas + training_mask = np.ones((h, w), dtype=np.uint8) + for poly_idx, poly_tag in enumerate(zip(polys, tags)): + poly = poly_tag[0] + tag = poly_tag[1] + + r = [None, None, None, None] + for i in range(4): + dist1 = np.linalg.norm(poly[i] - poly[(i + 1) % 4]) + dist2 = np.linalg.norm(poly[i] - poly[(i - 1) % 4]) + r[i] = min(dist1, dist2) + # score map + shrinked_poly = self.shrink_poly( + poly.copy(), r).astype(np.int32)[np.newaxis, :, :] + cv2.fillPoly(score_map, shrinked_poly, 1) + cv2.fillPoly(poly_mask, shrinked_poly, poly_idx + 1) + # if the poly is too small, then ignore it during training + poly_h = min( + np.linalg.norm(poly[0] - poly[3]), + np.linalg.norm(poly[1] - poly[2])) + poly_w = min( + np.linalg.norm(poly[0] - poly[1]), + np.linalg.norm(poly[2] - poly[3])) + if min(poly_h, poly_w) < self.min_text_size: + cv2.fillPoly(training_mask, + poly.astype(np.int32)[np.newaxis, :, :], 0) + + if tag: + cv2.fillPoly(training_mask, + poly.astype(np.int32)[np.newaxis, :, :], 0) + + xy_in_poly = np.argwhere(poly_mask == (poly_idx + 1)) + # geo map. + y_in_poly = xy_in_poly[:, 0] + x_in_poly = xy_in_poly[:, 1] + poly[:, 0] = np.minimum(np.maximum(poly[:, 0], 0), w) + poly[:, 1] = np.minimum(np.maximum(poly[:, 1], 0), h) + for pno in range(4): + geo_channel_beg = pno * 2 + geo_map[y_in_poly, x_in_poly, geo_channel_beg] =\ + x_in_poly - poly[pno, 0] + geo_map[y_in_poly, x_in_poly, geo_channel_beg+1] =\ + y_in_poly - poly[pno, 1] + geo_map[y_in_poly, x_in_poly, 8] = \ + 1.0 / max(min(poly_h, poly_w), 1.0) + return score_map, geo_map, training_mask + + def crop_area(self, + im, + polys, + tags, + crop_background=False, + max_tries=50): + """ + make random crop from the input image + :param im: + :param polys: + :param tags: + :param crop_background: + :param max_tries: + :return: + """ + h, w, _ = im.shape + pad_h = h // 10 + pad_w = w // 10 + h_array = np.zeros((h + pad_h * 2), dtype=np.int32) + w_array = np.zeros((w + pad_w * 2), dtype=np.int32) + for poly in polys: + poly = np.round(poly, decimals=0).astype(np.int32) + minx = np.min(poly[:, 0]) + maxx = np.max(poly[:, 0]) + w_array[minx + pad_w:maxx + pad_w] = 1 + miny = np.min(poly[:, 1]) + maxy = np.max(poly[:, 1]) + h_array[miny + pad_h:maxy + pad_h] = 1 + # ensure the cropped area not across a text + h_axis = np.where(h_array == 0)[0] + w_axis = np.where(w_array == 0)[0] + if len(h_axis) == 0 or len(w_axis) == 0: + return im, polys, tags + + for i in range(max_tries): + xx = np.random.choice(w_axis, size=2) + xmin = np.min(xx) - pad_w + xmax = np.max(xx) - pad_w + xmin = np.clip(xmin, 0, w - 1) + xmax = np.clip(xmax, 0, w - 1) + yy = np.random.choice(h_axis, size=2) + ymin = np.min(yy) - pad_h + ymax = np.max(yy) - pad_h + ymin = np.clip(ymin, 0, h - 1) + ymax = np.clip(ymax, 0, h - 1) + if xmax - xmin < self.min_crop_side_ratio * w or \ + ymax - ymin < self.min_crop_side_ratio * h: + # area too small + continue + if polys.shape[0] != 0: + poly_axis_in_area = (polys[:, :, 0] >= xmin)\ + & (polys[:, :, 0] <= xmax)\ + & (polys[:, :, 1] >= ymin)\ + & (polys[:, :, 1] <= ymax) + selected_polys = np.where( + np.sum(poly_axis_in_area, axis=1) == 4)[0] + else: + selected_polys = [] + + if len(selected_polys) == 0: + # no text in this area + if crop_background: + im = im[ymin:ymax + 1, xmin:xmax + 1, :] + polys = [] + tags = [] + return im, polys, tags + else: + continue + + im = im[ymin:ymax + 1, xmin:xmax + 1, :] + polys = polys[selected_polys] + tags = tags[selected_polys] + polys[:, :, 0] -= xmin + polys[:, :, 1] -= ymin + return im, polys, tags + return im, polys, tags + + def crop_background_infor(self, im, text_polys, text_tags): + im, text_polys, text_tags = self.crop_area( + im, text_polys, text_tags, crop_background=True) + + if len(text_polys) > 0: + return None + # pad and resize image + input_size = self.input_size + im, ratio = self.preprocess(im) + score_map = np.zeros((input_size, input_size), dtype=np.float32) + geo_map = np.zeros((input_size, input_size, 9), dtype=np.float32) + training_mask = np.ones((input_size, input_size), dtype=np.float32) + return im, score_map, geo_map, training_mask + + def crop_foreground_infor(self, im, text_polys, text_tags): + im, text_polys, text_tags = self.crop_area( + im, text_polys, text_tags, crop_background=False) + + if text_polys.shape[0] == 0: + return None + #continue for all ignore case + if np.sum((text_tags * 1.0)) >= text_tags.size: + return None + # pad and resize image + input_size = self.input_size + im, ratio = self.preprocess(im) + text_polys[:, :, 0] *= ratio + text_polys[:, :, 1] *= ratio + _, _, new_h, new_w = im.shape + # print(im.shape) + # self.draw_img_polys(im, text_polys) + score_map, geo_map, training_mask = self.generate_quad( + (new_h, new_w), text_polys, text_tags) + return im, score_map, geo_map, training_mask + + def __call__(self, data): + im = data['image'] + text_polys = data['polys'] + text_tags = data['ignore_tags'] + if im is None: + return None + if text_polys.shape[0] == 0: + return None + + #add rotate cases + if np.random.rand() < 0.5: + im, text_polys = self.rotate_im_poly(im, text_polys) + h, w, _ = im.shape + text_polys, text_tags = self.check_and_validate_polys(text_polys, + text_tags, h, w) + if text_polys.shape[0] == 0: + return None + + # random scale this image + rd_scale = np.random.choice(self.random_scale) + im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale) + text_polys *= rd_scale + if np.random.rand() < self.background_ratio: + outs = self.crop_background_infor(im, text_polys, text_tags) + else: + outs = self.crop_foreground_infor(im, text_polys, text_tags) + + if outs is None: + return None + im, score_map, geo_map, training_mask = outs + score_map = score_map[np.newaxis, ::4, ::4].astype(np.float32) + geo_map = np.swapaxes(geo_map, 1, 2) + geo_map = np.swapaxes(geo_map, 1, 0) + geo_map = geo_map[:, ::4, ::4].astype(np.float32) + training_mask = training_mask[np.newaxis, ::4, ::4] + training_mask = training_mask.astype(np.float32) + + data['image'] = im[0] + data['score_map'] = score_map + data['geo_map'] = geo_map + data['training_mask'] = training_mask + # print(im.shape, score_map.shape, geo_map.shape, training_mask.shape) + return data \ No newline at end of file diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 0b1d46c62404978563d24698018ff24d2d552f2f..af3308a553b40b61fb206a8970038e91b718c56f 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -52,6 +52,7 @@ class DetLabelEncode(object): txt_tags.append(True) else: txt_tags.append(False) + boxes = self.expand_points_num(boxes) boxes = np.array(boxes, dtype=np.float32) txt_tags = np.array(txt_tags, dtype=np.bool) @@ -70,6 +71,17 @@ class DetLabelEncode(object): rect[3] = pts[np.argmax(diff)] return rect + def expand_points_num(self, boxes): + max_points_num = 0 + for box in boxes: + if len(box) > max_points_num: + max_points_num = len(box) + ex_boxes = [] + for box in boxes: + ex_box = box + [box[-1]] * (max_points_num - len(box)) + ex_boxes.append(ex_box) + return ex_boxes + class BaseRecLabelEncode(object): """ Convert between text-label and text-index """ @@ -83,7 +95,7 @@ class BaseRecLabelEncode(object): 'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean' ] assert character_type in support_character_type, "Only {} are supported now but get {}".format( - support_character_type, self.character_str) + support_character_type, character_type) self.max_text_len = max_text_length if character_type == "en": diff --git a/ppocr/data/imaug/operators.py b/ppocr/data/imaug/operators.py index 74b60de4258d7420847a9533f24b4d7da9306e17..57cd3b4b172cc52d073999a007fe5a9a343c9186 100644 --- a/ppocr/data/imaug/operators.py +++ b/ppocr/data/imaug/operators.py @@ -42,6 +42,8 @@ class DecodeImage(object): img) > 0, "invalid input 'img' in DecodeImage" img = np.frombuffer(img, dtype='uint8') img = cv2.imdecode(img, 1) + if img is None: + return None if self.img_mode == 'GRAY': img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) elif self.img_mode == 'RGB': @@ -120,26 +122,37 @@ class DetResizeForTest(object): if 'limit_side_len' in kwargs: self.limit_side_len = kwargs['limit_side_len'] self.limit_type = kwargs.get('limit_type', 'min') + if 'resize_long' in kwargs: + self.resize_type = 2 + self.resize_long = kwargs.get('resize_long', 960) else: self.limit_side_len = 736 self.limit_type = 'min' def __call__(self, data): img = data['image'] + src_h, src_w, _ = img.shape if self.resize_type == 0: - img, shape = self.resize_image_type0(img) + # img, shape = self.resize_image_type0(img) + img, [ratio_h, ratio_w] = self.resize_image_type0(img) + elif self.resize_type == 2: + img, [ratio_h, ratio_w] = self.resize_image_type2(img) else: - img, shape = self.resize_image_type1(img) + # img, shape = self.resize_image_type1(img) + img, [ratio_h, ratio_w] = self.resize_image_type1(img) data['image'] = img - data['shape'] = shape + data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w]) return data def resize_image_type1(self, img): resize_h, resize_w = self.image_shape ori_h, ori_w = img.shape[:2] # (h, w, c) + ratio_h = float(resize_h) / ori_h + ratio_w = float(resize_w) / ori_w img = cv2.resize(img, (int(resize_w), int(resize_h))) - return img, np.array([ori_h, ori_w]) + # return img, np.array([ori_h, ori_w]) + return img, [ratio_h, ratio_w] def resize_image_type0(self, img): """ @@ -182,4 +195,31 @@ class DetResizeForTest(object): except: print(img.shape, resize_w, resize_h) sys.exit(0) - return img, np.array([h, w]) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + # return img, np.array([h, w]) + return img, [ratio_h, ratio_w] + + def resize_image_type2(self, img): + h, w, _ = img.shape + + resize_w = w + resize_h = h + + # Fix the longer side + if resize_h > resize_w: + ratio = float(self.resize_long) / resize_h + else: + ratio = float(self.resize_long) / resize_w + + resize_h = int(resize_h * ratio) + resize_w = int(resize_w * ratio) + + max_stride = 128 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + img = cv2.resize(img, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + + return img, [ratio_h, ratio_w] diff --git a/ppocr/data/imaug/sast_process.py b/ppocr/data/imaug/sast_process.py new file mode 100644 index 0000000000000000000000000000000000000000..b8d6ff89eed5e5061f6f07def25d4fef53125810 --- /dev/null +++ b/ppocr/data/imaug/sast_process.py @@ -0,0 +1,689 @@ +#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + +import math +import cv2 +import numpy as np +import json +import sys +import os + +__all__ = ['SASTProcessTrain'] + + +class SASTProcessTrain(object): + def __init__(self, + image_shape = [512, 512], + min_crop_size = 24, + min_crop_side_ratio = 0.3, + min_text_size = 10, + max_text_size = 512, + **kwargs): + self.input_size = image_shape[1] + self.min_crop_size = min_crop_size + self.min_crop_side_ratio = min_crop_side_ratio + self.min_text_size = min_text_size + self.max_text_size = max_text_size + + def quad_area(self, poly): + """ + compute area of a polygon + :param poly: + :return: + """ + edge = [ + (poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]), + (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]), + (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]), + (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1]) + ] + return np.sum(edge) / 2. + + def gen_quad_from_poly(self, poly): + """ + Generate min area quad from poly. + """ + point_num = poly.shape[0] + min_area_quad = np.zeros((4, 2), dtype=np.float32) + if True: + rect = cv2.minAreaRect(poly.astype(np.int32)) # (center (x,y), (width, height), angle of rotation) + center_point = rect[0] + box = np.array(cv2.boxPoints(rect)) + + first_point_idx = 0 + min_dist = 1e4 + for i in range(4): + dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ + np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ + np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ + np.linalg.norm(box[(i + 3) % 4] - poly[-1]) + if dist < min_dist: + min_dist = dist + first_point_idx = i + for i in range(4): + min_area_quad[i] = box[(first_point_idx + i) % 4] + + return min_area_quad + + def check_and_validate_polys(self, polys, tags, xxx_todo_changeme): + """ + check so that the text poly is in the same direction, + and also filter some invalid polygons + :param polys: + :param tags: + :return: + """ + (h, w) = xxx_todo_changeme + if polys.shape[0] == 0: + return polys, np.array([]), np.array([]) + polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1) + polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1) + + validated_polys = [] + validated_tags = [] + hv_tags = [] + for poly, tag in zip(polys, tags): + quad = self.gen_quad_from_poly(poly) + p_area = self.quad_area(quad) + if abs(p_area) < 1: + print('invalid poly') + continue + if p_area > 0: + if tag == False: + print('poly in wrong direction') + tag = True # reversed cases should be ignore + poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1), :] + quad = quad[(0, 3, 2, 1), :] + + len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[3] - quad[2]) + len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2]) + hv_tag = 1 + + if len_w * 2.0 < len_h: + hv_tag = 0 + + validated_polys.append(poly) + validated_tags.append(tag) + hv_tags.append(hv_tag) + return np.array(validated_polys), np.array(validated_tags), np.array(hv_tags) + + def crop_area(self, im, polys, tags, hv_tags, crop_background=False, max_tries=25): + """ + make random crop from the input image + :param im: + :param polys: + :param tags: + :param crop_background: + :param max_tries: 50 -> 25 + :return: + """ + h, w, _ = im.shape + pad_h = h // 10 + pad_w = w // 10 + h_array = np.zeros((h + pad_h * 2), dtype=np.int32) + w_array = np.zeros((w + pad_w * 2), dtype=np.int32) + for poly in polys: + poly = np.round(poly, decimals=0).astype(np.int32) + minx = np.min(poly[:, 0]) + maxx = np.max(poly[:, 0]) + w_array[minx + pad_w: maxx + pad_w] = 1 + miny = np.min(poly[:, 1]) + maxy = np.max(poly[:, 1]) + h_array[miny + pad_h: maxy + pad_h] = 1 + # ensure the cropped area not across a text + h_axis = np.where(h_array == 0)[0] + w_axis = np.where(w_array == 0)[0] + if len(h_axis) == 0 or len(w_axis) == 0: + return im, polys, tags, hv_tags + for i in range(max_tries): + xx = np.random.choice(w_axis, size=2) + xmin = np.min(xx) - pad_w + xmax = np.max(xx) - pad_w + xmin = np.clip(xmin, 0, w - 1) + xmax = np.clip(xmax, 0, w - 1) + yy = np.random.choice(h_axis, size=2) + ymin = np.min(yy) - pad_h + ymax = np.max(yy) - pad_h + ymin = np.clip(ymin, 0, h - 1) + ymax = np.clip(ymax, 0, h - 1) + # if xmax - xmin < ARGS.min_crop_side_ratio * w or \ + # ymax - ymin < ARGS.min_crop_side_ratio * h: + if xmax - xmin < self.min_crop_size or \ + ymax - ymin < self.min_crop_size: + # area too small + continue + if polys.shape[0] != 0: + poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \ + & (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax) + selected_polys = np.where(np.sum(poly_axis_in_area, axis=1) == 4)[0] + else: + selected_polys = [] + if len(selected_polys) == 0: + # no text in this area + if crop_background: + return im[ymin : ymax + 1, xmin : xmax + 1, :], \ + polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], txts + else: + continue + im = im[ymin: ymax + 1, xmin: xmax + 1, :] + polys = polys[selected_polys] + tags = tags[selected_polys] + hv_tags = hv_tags[selected_polys] + polys[:, :, 0] -= xmin + polys[:, :, 1] -= ymin + return im, polys, tags, hv_tags + + return im, polys, tags, hv_tags + + def generate_direction_map(self, poly_quads, direction_map): + """ + """ + width_list = [] + height_list = [] + for quad in poly_quads: + quad_w = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0 + quad_h = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])) / 2.0 + width_list.append(quad_w) + height_list.append(quad_h) + norm_width = max(sum(width_list) / (len(width_list) + 1e-6), 1.0) + average_height = max(sum(height_list) / (len(height_list) + 1e-6), 1.0) + + for quad in poly_quads: + direct_vector_full = ((quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0 + direct_vector = direct_vector_full / (np.linalg.norm(direct_vector_full) + 1e-6) * norm_width + direction_label = tuple(map(float, [direct_vector[0], direct_vector[1], 1.0 / (average_height + 1e-6)])) + cv2.fillPoly(direction_map, quad.round().astype(np.int32)[np.newaxis, :, :], direction_label) + return direction_map + + def calculate_average_height(self, poly_quads): + """ + """ + height_list = [] + for quad in poly_quads: + quad_h = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])) / 2.0 + height_list.append(quad_h) + average_height = max(sum(height_list) / len(height_list), 1.0) + return average_height + + def generate_tcl_label(self, hw, polys, tags, ds_ratio, + tcl_ratio=0.3, shrink_ratio_of_width=0.15): + """ + Generate polygon. + """ + h, w = hw + h, w = int(h * ds_ratio), int(w * ds_ratio) + polys = polys * ds_ratio + + score_map = np.zeros((h, w,), dtype=np.float32) + tbo_map = np.zeros((h, w, 5), dtype=np.float32) + training_mask = np.ones((h, w,), dtype=np.float32) + direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape([1, 1, 3]).astype(np.float32) + + for poly_idx, poly_tag in enumerate(zip(polys, tags)): + poly = poly_tag[0] + tag = poly_tag[1] + + # generate min_area_quad + min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly) + min_area_quad_h = 0.5 * (np.linalg.norm(min_area_quad[0] - min_area_quad[3]) + + np.linalg.norm(min_area_quad[1] - min_area_quad[2])) + min_area_quad_w = 0.5 * (np.linalg.norm(min_area_quad[0] - min_area_quad[1]) + + np.linalg.norm(min_area_quad[2] - min_area_quad[3])) + + if min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio \ + or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio: + continue + + if tag: + # continue + cv2.fillPoly(training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0.15) + else: + tcl_poly = self.poly2tcl(poly, tcl_ratio) + tcl_quads = self.poly2quads(tcl_poly) + poly_quads = self.poly2quads(poly) + # stcl map + stcl_quads, quad_index = self.shrink_poly_along_width(tcl_quads, shrink_ratio_of_width=shrink_ratio_of_width, + expand_height_ratio=1.0 / tcl_ratio) + # generate tcl map + cv2.fillPoly(score_map, np.round(stcl_quads).astype(np.int32), 1.0) + + # generate tbo map + for idx, quad in enumerate(stcl_quads): + quad_mask = np.zeros((h, w), dtype=np.float32) + quad_mask = cv2.fillPoly(quad_mask, np.round(quad[np.newaxis, :, :]).astype(np.int32), 1.0) + tbo_map = self.gen_quad_tbo(poly_quads[quad_index[idx]], quad_mask, tbo_map) + return score_map, tbo_map, training_mask + + def generate_tvo_and_tco(self, hw, polys, tags, tcl_ratio=0.3, ds_ratio=0.25): + """ + Generate tcl map, tvo map and tbo map. + """ + h, w = hw + h, w = int(h * ds_ratio), int(w * ds_ratio) + polys = polys * ds_ratio + poly_mask = np.zeros((h, w), dtype=np.float32) + + tvo_map = np.ones((9, h, w), dtype=np.float32) + tvo_map[0:-1:2] = np.tile(np.arange(0, w), (h, 1)) + tvo_map[1:-1:2] = np.tile(np.arange(0, w), (h, 1)).T + poly_tv_xy_map = np.zeros((8, h, w), dtype=np.float32) + + # tco map + tco_map = np.ones((3, h, w), dtype=np.float32) + tco_map[0] = np.tile(np.arange(0, w), (h, 1)) + tco_map[1] = np.tile(np.arange(0, w), (h, 1)).T + poly_tc_xy_map = np.zeros((2, h, w), dtype=np.float32) + + poly_short_edge_map = np.ones((h, w), dtype=np.float32) + + for poly, poly_tag in zip(polys, tags): + + if poly_tag == True: + continue + + # adjust point order for vertical poly + poly = self.adjust_point(poly) + + # generate min_area_quad + min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly) + min_area_quad_h = 0.5 * (np.linalg.norm(min_area_quad[0] - min_area_quad[3]) + + np.linalg.norm(min_area_quad[1] - min_area_quad[2])) + min_area_quad_w = 0.5 * (np.linalg.norm(min_area_quad[0] - min_area_quad[1]) + + np.linalg.norm(min_area_quad[2] - min_area_quad[3])) + + # generate tcl map and text, 128 * 128 + tcl_poly = self.poly2tcl(poly, tcl_ratio) + + # generate poly_tv_xy_map + for idx in range(4): + cv2.fillPoly(poly_tv_xy_map[2 * idx], + np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), + float(min(max(min_area_quad[idx, 0], 0), w))) + cv2.fillPoly(poly_tv_xy_map[2 * idx + 1], + np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), + float(min(max(min_area_quad[idx, 1], 0), h))) + + # generate poly_tc_xy_map + for idx in range(2): + cv2.fillPoly(poly_tc_xy_map[idx], + np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), float(center_point[idx])) + + # generate poly_short_edge_map + cv2.fillPoly(poly_short_edge_map, + np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), + float(max(min(min_area_quad_h, min_area_quad_w), 1.0))) + + # generate poly_mask and training_mask + cv2.fillPoly(poly_mask, np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), 1) + + tvo_map *= poly_mask + tvo_map[:8] -= poly_tv_xy_map + tvo_map[-1] /= poly_short_edge_map + tvo_map = tvo_map.transpose((1, 2, 0)) + + tco_map *= poly_mask + tco_map[:2] -= poly_tc_xy_map + tco_map[-1] /= poly_short_edge_map + tco_map = tco_map.transpose((1, 2, 0)) + + return tvo_map, tco_map + + def adjust_point(self, poly): + """ + adjust point order. + """ + point_num = poly.shape[0] + if point_num == 4: + len_1 = np.linalg.norm(poly[0] - poly[1]) + len_2 = np.linalg.norm(poly[1] - poly[2]) + len_3 = np.linalg.norm(poly[2] - poly[3]) + len_4 = np.linalg.norm(poly[3] - poly[0]) + + if (len_1 + len_3) * 1.5 < (len_2 + len_4): + poly = poly[[1, 2, 3, 0], :] + + elif point_num > 4: + vector_1 = poly[0] - poly[1] + vector_2 = poly[1] - poly[2] + cos_theta = np.dot(vector_1, vector_2) / (np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6) + theta = np.arccos(np.round(cos_theta, decimals=4)) + + if abs(theta) > (70 / 180 * math.pi): + index = list(range(1, point_num)) + [0] + poly = poly[np.array(index), :] + return poly + + def gen_min_area_quad_from_poly(self, poly): + """ + Generate min area quad from poly. + """ + point_num = poly.shape[0] + min_area_quad = np.zeros((4, 2), dtype=np.float32) + if point_num == 4: + min_area_quad = poly + center_point = np.sum(poly, axis=0) / 4 + else: + rect = cv2.minAreaRect(poly.astype(np.int32)) # (center (x,y), (width, height), angle of rotation) + center_point = rect[0] + box = np.array(cv2.boxPoints(rect)) + + first_point_idx = 0 + min_dist = 1e4 + for i in range(4): + dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ + np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ + np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ + np.linalg.norm(box[(i + 3) % 4] - poly[-1]) + if dist < min_dist: + min_dist = dist + first_point_idx = i + + for i in range(4): + min_area_quad[i] = box[(first_point_idx + i) % 4] + + return min_area_quad, center_point + + def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.): + """ + Generate shrink_quad_along_width. + """ + ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32) + p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair + p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair + return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]]) + + def shrink_poly_along_width(self, quads, shrink_ratio_of_width, expand_height_ratio=1.0): + """ + shrink poly with given length. + """ + upper_edge_list = [] + + def get_cut_info(edge_len_list, cut_len): + for idx, edge_len in enumerate(edge_len_list): + cut_len -= edge_len + if cut_len <= 0.000001: + ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx] + return idx, ratio + + for quad in quads: + upper_edge_len = np.linalg.norm(quad[0] - quad[1]) + upper_edge_list.append(upper_edge_len) + + # length of left edge and right edge. + left_length = np.linalg.norm(quads[0][0] - quads[0][3]) * expand_height_ratio + right_length = np.linalg.norm(quads[-1][1] - quads[-1][2]) * expand_height_ratio + + shrink_length = min(left_length, right_length, sum(upper_edge_list)) * shrink_ratio_of_width + # shrinking length + upper_len_left = shrink_length + upper_len_right = sum(upper_edge_list) - shrink_length + + left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left) + left_quad = self.shrink_quad_along_width(quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1) + right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right) + right_quad = self.shrink_quad_along_width(quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio) + + out_quad_list = [] + if left_idx == right_idx: + out_quad_list.append([left_quad[0], right_quad[1], right_quad[2], left_quad[3]]) + else: + out_quad_list.append(left_quad) + for idx in range(left_idx + 1, right_idx): + out_quad_list.append(quads[idx]) + out_quad_list.append(right_quad) + + return np.array(out_quad_list), list(range(left_idx, right_idx + 1)) + + def vector_angle(self, A, B): + """ + Calculate the angle between vector AB and x-axis positive direction. + """ + AB = np.array([B[1] - A[1], B[0] - A[0]]) + return np.arctan2(*AB) + + def theta_line_cross_point(self, theta, point): + """ + Calculate the line through given point and angle in ax + by + c =0 form. + """ + x, y = point + cos = np.cos(theta) + sin = np.sin(theta) + return [sin, -cos, cos * y - sin * x] + + def line_cross_two_point(self, A, B): + """ + Calculate the line through given point A and B in ax + by + c =0 form. + """ + angle = self.vector_angle(A, B) + return self.theta_line_cross_point(angle, A) + + def average_angle(self, poly): + """ + Calculate the average angle between left and right edge in given poly. + """ + p0, p1, p2, p3 = poly + angle30 = self.vector_angle(p3, p0) + angle21 = self.vector_angle(p2, p1) + return (angle30 + angle21) / 2 + + def line_cross_point(self, line1, line2): + """ + line1 and line2 in 0=ax+by+c form, compute the cross point of line1 and line2 + """ + a1, b1, c1 = line1 + a2, b2, c2 = line2 + d = a1 * b2 - a2 * b1 + + if d == 0: + #print("line1", line1) + #print("line2", line2) + print('Cross point does not exist') + return np.array([0, 0], dtype=np.float32) + else: + x = (b1 * c2 - b2 * c1) / d + y = (a2 * c1 - a1 * c2) / d + + return np.array([x, y], dtype=np.float32) + + def quad2tcl(self, poly, ratio): + """ + Generate center line by poly clock-wise point. (4, 2) + """ + ratio_pair = np.array([[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32) + p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair + p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair + return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]]) + + def poly2tcl(self, poly, ratio): + """ + Generate center line by poly clock-wise point. + """ + ratio_pair = np.array([[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32) + tcl_poly = np.zeros_like(poly) + point_num = poly.shape[0] + + for idx in range(point_num // 2): + point_pair = poly[idx] + (poly[point_num - 1 - idx] - poly[idx]) * ratio_pair + tcl_poly[idx] = point_pair[0] + tcl_poly[point_num - 1 - idx] = point_pair[1] + return tcl_poly + + def gen_quad_tbo(self, quad, tcl_mask, tbo_map): + """ + Generate tbo_map for give quad. + """ + # upper and lower line function: ax + by + c = 0; + up_line = self.line_cross_two_point(quad[0], quad[1]) + lower_line = self.line_cross_two_point(quad[3], quad[2]) + + quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) + quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) + + # average angle of left and right line. + angle = self.average_angle(quad) + + xy_in_poly = np.argwhere(tcl_mask == 1) + for y, x in xy_in_poly: + point = (x, y) + line = self.theta_line_cross_point(angle, point) + cross_point_upper = self.line_cross_point(up_line, line) + cross_point_lower = self.line_cross_point(lower_line, line) + ##FIX, offset reverse + upper_offset_x, upper_offset_y = cross_point_upper - point + lower_offset_x, lower_offset_y = cross_point_lower - point + tbo_map[y, x, 0] = upper_offset_y + tbo_map[y, x, 1] = upper_offset_x + tbo_map[y, x, 2] = lower_offset_y + tbo_map[y, x, 3] = lower_offset_x + tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2 + return tbo_map + + def poly2quads(self, poly): + """ + Split poly into quads. + """ + quad_list = [] + point_num = poly.shape[0] + + # point pair + point_pair_list = [] + for idx in range(point_num // 2): + point_pair = [poly[idx], poly[point_num - 1 - idx]] + point_pair_list.append(point_pair) + + quad_num = point_num // 2 - 1 + for idx in range(quad_num): + # reshape and adjust to clock-wise + quad_list.append((np.array(point_pair_list)[[idx, idx + 1]]).reshape(4, 2)[[0, 2, 3, 1]]) + + return np.array(quad_list) + + def __call__(self, data): + im = data['image'] + text_polys = data['polys'] + text_tags = data['ignore_tags'] + if im is None: + return None + if text_polys.shape[0] == 0: + return None + + h, w, _ = im.shape + text_polys, text_tags, hv_tags = self.check_and_validate_polys(text_polys, text_tags, (h, w)) + + if text_polys.shape[0] == 0: + return None + + #set aspect ratio and keep area fix + asp_scales = np.arange(1.0, 1.55, 0.1) + asp_scale = np.random.choice(asp_scales) + + if np.random.rand() < 0.5: + asp_scale = 1.0 / asp_scale + asp_scale = math.sqrt(asp_scale) + + asp_wx = asp_scale + asp_hy = 1.0 / asp_scale + im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy) + text_polys[:, :, 0] *= asp_wx + text_polys[:, :, 1] *= asp_hy + + h, w, _ = im.shape + if max(h, w) > 2048: + rd_scale = 2048.0 / max(h, w) + im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale) + text_polys *= rd_scale + h, w, _ = im.shape + if min(h, w) < 16: + return None + + #no background + im, text_polys, text_tags, hv_tags = self.crop_area(im, \ + text_polys, text_tags, hv_tags, crop_background=False) + + if text_polys.shape[0] == 0: + return None + #continue for all ignore case + if np.sum((text_tags * 1.0)) >= text_tags.size: + return None + new_h, new_w, _ = im.shape + if (new_h is None) or (new_w is None): + return None + #resize image + std_ratio = float(self.input_size) / max(new_w, new_h) + rand_scales = np.array([0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0]) + rz_scale = std_ratio * np.random.choice(rand_scales) + im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale) + text_polys[:, :, 0] *= rz_scale + text_polys[:, :, 1] *= rz_scale + + #add gaussian blur + if np.random.rand() < 0.1 * 0.5: + ks = np.random.permutation(5)[0] + 1 + ks = int(ks/2)*2 + 1 + im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0) + #add brighter + if np.random.rand() < 0.1 * 0.5: + im = im * (1.0 + np.random.rand() * 0.5) + im = np.clip(im, 0.0, 255.0) + #add darker + if np.random.rand() < 0.1 * 0.5: + im = im * (1.0 - np.random.rand() * 0.5) + im = np.clip(im, 0.0, 255.0) + + # Padding the im to [input_size, input_size] + new_h, new_w, _ = im.shape + if min(new_w, new_h) < self.input_size * 0.5: + return None + + im_padded = np.ones((self.input_size, self.input_size, 3), dtype=np.float32) + im_padded[:, :, 2] = 0.485 * 255 + im_padded[:, :, 1] = 0.456 * 255 + im_padded[:, :, 0] = 0.406 * 255 + + # Random the start position + del_h = self.input_size - new_h + del_w = self.input_size - new_w + sh, sw = 0, 0 + if del_h > 1: + sh = int(np.random.rand() * del_h) + if del_w > 1: + sw = int(np.random.rand() * del_w) + + # Padding + im_padded[sh: sh + new_h, sw: sw + new_w, :] = im.copy() + text_polys[:, :, 0] += sw + text_polys[:, :, 1] += sh + + score_map, border_map, training_mask = self.generate_tcl_label((self.input_size, self.input_size), + text_polys, text_tags, 0.25) + + # SAST head + tvo_map, tco_map = self.generate_tvo_and_tco((self.input_size, self.input_size), text_polys, text_tags, tcl_ratio=0.3, ds_ratio=0.25) + # print("test--------tvo_map shape:", tvo_map.shape) + + im_padded[:, :, 2] -= 0.485 * 255 + im_padded[:, :, 1] -= 0.456 * 255 + im_padded[:, :, 0] -= 0.406 * 255 + im_padded[:, :, 2] /= (255.0 * 0.229) + im_padded[:, :, 1] /= (255.0 * 0.224) + im_padded[:, :, 0] /= (255.0 * 0.225) + im_padded = im_padded.transpose((2, 0, 1)) + + data['image'] = im_padded[::-1, :, :] + data['score_map'] = score_map[np.newaxis, :, :] + data['border_map'] = border_map.transpose((2, 0, 1)) + data['training_mask'] = training_mask[np.newaxis, :, :] + data['tvo_map'] = tvo_map.transpose((2, 0, 1)) + data['tco_map'] = tco_map.transpose((2, 0, 1)) + return data \ No newline at end of file diff --git a/ppocr/data/simple_dataset.py b/ppocr/data/simple_dataset.py index 097da768aa4da2eb023c4a346fc30f0e704ab953..ab17dd1a3816a86f92707e2812881c892ac59ae6 100644 --- a/ppocr/data/simple_dataset.py +++ b/ppocr/data/simple_dataset.py @@ -27,14 +27,13 @@ class SimpleDataSet(Dataset): global_config = config['Global'] dataset_config = config[mode]['dataset'] loader_config = config[mode]['loader'] - batch_size = loader_config['batch_size_per_card'] self.delimiter = dataset_config.get('delimiter', '\t') label_file_list = dataset_config.pop('label_file_list') data_source_num = len(label_file_list) ratio_list = dataset_config.get("ratio_list", [1.0]) if isinstance(ratio_list, (float, int)): - ratio_list = [float(ratio_list)] * len(data_source_num) + ratio_list = [float(ratio_list)] * int(data_source_num) assert len( ratio_list @@ -76,6 +75,8 @@ class SimpleDataSet(Dataset): label = substr[1] img_path = os.path.join(self.data_dir, file_name) data = {'img_path': img_path, 'label': label} + if not os.path.exists(img_path): + raise Exception("{} does not exist!".format(img_path)) with open(data['img_path'], 'rb') as f: img = f.read() data['image'] = img diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index 564956e000a1a6eb1567a884f806801059687994..4673d35cece0af221d41132a34c2932246567383 100755 --- a/ppocr/losses/__init__.py +++ b/ppocr/losses/__init__.py @@ -18,6 +18,8 @@ import copy def build_loss(config): # det loss from .det_db_loss import DBLoss + from .det_east_loss import EASTLoss + from .det_sast_loss import SASTLoss # rec loss from .rec_ctc_loss import CTCLoss @@ -25,7 +27,7 @@ def build_loss(config): # cls loss from .cls_loss import ClsLoss - support_dict = ['DBLoss', 'CTCLoss', 'ClsLoss'] + support_dict = ['DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss'] config = copy.deepcopy(config) module_name = config.pop('name') diff --git a/ppocr/losses/det_east_loss.py b/ppocr/losses/det_east_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..bcf5372b72d49ebeb34c0ce127e7221d563a6c35 --- /dev/null +++ b/ppocr/losses/det_east_loss.py @@ -0,0 +1,63 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +from .det_basic_loss import DiceLoss + + +class EASTLoss(nn.Layer): + """ + """ + + def __init__(self, + eps=1e-6, + **kwargs): + super(EASTLoss, self).__init__() + self.dice_loss = DiceLoss(eps=eps) + + def forward(self, predicts, labels): + l_score, l_geo, l_mask = labels[1:] + f_score = predicts['f_score'] + f_geo = predicts['f_geo'] + + dice_loss = self.dice_loss(f_score, l_score, l_mask) + + #smoooth_l1_loss + channels = 8 + l_geo_split = paddle.split( + l_geo, num_or_sections=channels + 1, axis=1) + f_geo_split = paddle.split(f_geo, num_or_sections=channels, axis=1) + smooth_l1 = 0 + for i in range(0, channels): + geo_diff = l_geo_split[i] - f_geo_split[i] + abs_geo_diff = paddle.abs(geo_diff) + smooth_l1_sign = paddle.less_than(abs_geo_diff, l_score) + smooth_l1_sign = paddle.cast(smooth_l1_sign, dtype='float32') + in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + \ + (abs_geo_diff - 0.5) * (1.0 - smooth_l1_sign) + out_loss = l_geo_split[-1] / channels * in_loss * l_score + smooth_l1 += out_loss + smooth_l1_loss = paddle.mean(smooth_l1 * l_score) + + dice_loss = dice_loss * 0.01 + total_loss = dice_loss + smooth_l1_loss + losses = {"loss":total_loss, \ + "dice_loss":dice_loss,\ + "smooth_l1_loss":smooth_l1_loss} + return losses diff --git a/ppocr/losses/det_sast_loss.py b/ppocr/losses/det_sast_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..a07af6a4598ffb97167094b40a7851ec1089e798 --- /dev/null +++ b/ppocr/losses/det_sast_loss.py @@ -0,0 +1,121 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +from .det_basic_loss import DiceLoss +import paddle.fluid as fluid +import numpy as np + + +class SASTLoss(nn.Layer): + """ + """ + + def __init__(self, + eps=1e-6, + **kwargs): + super(SASTLoss, self).__init__() + self.dice_loss = DiceLoss(eps=eps) + + def forward(self, predicts, labels): + """ + tcl_pos: N x 128 x 3 + tcl_mask: N x 128 x 1 + tcl_label: N x X list or LoDTensor + """ + + f_score = predicts['f_score'] + f_border = predicts['f_border'] + f_tvo = predicts['f_tvo'] + f_tco = predicts['f_tco'] + + l_score, l_border, l_mask, l_tvo, l_tco = labels[1:] + + #score_loss + intersection = paddle.sum(f_score * l_score * l_mask) + union = paddle.sum(f_score * l_mask) + paddle.sum(l_score * l_mask) + score_loss = 1.0 - 2 * intersection / (union + 1e-5) + + #border loss + l_border_split, l_border_norm = paddle.split(l_border, num_or_sections=[4, 1], axis=1) + f_border_split = f_border + border_ex_shape = l_border_norm.shape * np.array([1, 4, 1, 1]) + l_border_norm_split = paddle.expand(x=l_border_norm, shape=border_ex_shape) + l_border_score = paddle.expand(x=l_score, shape=border_ex_shape) + l_border_mask = paddle.expand(x=l_mask, shape=border_ex_shape) + + border_diff = l_border_split - f_border_split + abs_border_diff = paddle.abs(border_diff) + border_sign = abs_border_diff < 1.0 + border_sign = paddle.cast(border_sign, dtype='float32') + border_sign.stop_gradient = True + border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \ + (abs_border_diff - 0.5) * (1.0 - border_sign) + border_out_loss = l_border_norm_split * border_in_loss + border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \ + (paddle.sum(l_border_score * l_border_mask) + 1e-5) + + #tvo_loss + l_tvo_split, l_tvo_norm = paddle.split(l_tvo, num_or_sections=[8, 1], axis=1) + f_tvo_split = f_tvo + tvo_ex_shape = l_tvo_norm.shape * np.array([1, 8, 1, 1]) + l_tvo_norm_split = paddle.expand(x=l_tvo_norm, shape=tvo_ex_shape) + l_tvo_score = paddle.expand(x=l_score, shape=tvo_ex_shape) + l_tvo_mask = paddle.expand(x=l_mask, shape=tvo_ex_shape) + # + tvo_geo_diff = l_tvo_split - f_tvo_split + abs_tvo_geo_diff = paddle.abs(tvo_geo_diff) + tvo_sign = abs_tvo_geo_diff < 1.0 + tvo_sign = paddle.cast(tvo_sign, dtype='float32') + tvo_sign.stop_gradient = True + tvo_in_loss = 0.5 * abs_tvo_geo_diff * abs_tvo_geo_diff * tvo_sign + \ + (abs_tvo_geo_diff - 0.5) * (1.0 - tvo_sign) + tvo_out_loss = l_tvo_norm_split * tvo_in_loss + tvo_loss = paddle.sum(tvo_out_loss * l_tvo_score * l_tvo_mask) / \ + (paddle.sum(l_tvo_score * l_tvo_mask) + 1e-5) + + #tco_loss + l_tco_split, l_tco_norm = paddle.split(l_tco, num_or_sections=[2, 1], axis=1) + f_tco_split = f_tco + tco_ex_shape = l_tco_norm.shape * np.array([1, 2, 1, 1]) + l_tco_norm_split = paddle.expand(x=l_tco_norm, shape=tco_ex_shape) + l_tco_score = paddle.expand(x=l_score, shape=tco_ex_shape) + l_tco_mask = paddle.expand(x=l_mask, shape=tco_ex_shape) + + tco_geo_diff = l_tco_split - f_tco_split + abs_tco_geo_diff = paddle.abs(tco_geo_diff) + tco_sign = abs_tco_geo_diff < 1.0 + tco_sign = paddle.cast(tco_sign, dtype='float32') + tco_sign.stop_gradient = True + tco_in_loss = 0.5 * abs_tco_geo_diff * abs_tco_geo_diff * tco_sign + \ + (abs_tco_geo_diff - 0.5) * (1.0 - tco_sign) + tco_out_loss = l_tco_norm_split * tco_in_loss + tco_loss = paddle.sum(tco_out_loss * l_tco_score * l_tco_mask) / \ + (paddle.sum(l_tco_score * l_tco_mask) + 1e-5) + + + # total loss + tvo_lw, tco_lw = 1.5, 1.5 + score_lw, border_lw = 1.0, 1.0 + total_loss = score_loss * score_lw + border_loss * border_lw + \ + tvo_loss * tvo_lw + tco_loss * tco_lw + + losses = {'loss':total_loss, "score_loss":score_loss,\ + "border_loss":border_loss, 'tvo_loss':tvo_loss, 'tco_loss':tco_loss} + return losses \ No newline at end of file diff --git a/ppocr/modeling/architectures/base_model.py b/ppocr/modeling/architectures/base_model.py index 0c4fe6503f6e6401581bf92dc9b3421f40722c7c..ab44b53a2bf8214b63954aa867b67a3ed1e05fab 100644 --- a/ppocr/modeling/architectures/base_model.py +++ b/ppocr/modeling/architectures/base_model.py @@ -16,7 +16,7 @@ from __future__ import division from __future__ import print_function from paddle import nn -from ppocr.modeling.transform import build_transform +from ppocr.modeling.transforms import build_transform from ppocr.modeling.backbones import build_backbone from ppocr.modeling.necks import build_neck from ppocr.modeling.heads import build_head diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index 7085d3af317be5664d8d8a10a5fd9bb8deb9267b..43103e53d2413eb63c2831cfd54e91f357b3b496 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/ppocr/modeling/backbones/__init__.py @@ -19,6 +19,7 @@ def build_backbone(config, model_type): if model_type == 'det': from .det_mobilenet_v3 import MobileNetV3 from .det_resnet_vd import ResNet + from .det_resnet_vd_sast import ResNet_SAST support_dict = ['MobileNetV3', 'ResNet', 'ResNet_SAST'] elif model_type == 'rec' or model_type == 'cls': from .rec_mobilenet_v3 import MobileNetV3 diff --git a/ppocr/modeling/backbones/det_mobilenet_v3.py b/ppocr/modeling/backbones/det_mobilenet_v3.py index 017dce2f4ce624c83529492ea9050703814569d4..f97bcfca472310cfd681025595b7bce57d1ccf85 100755 --- a/ppocr/modeling/backbones/det_mobilenet_v3.py +++ b/ppocr/modeling/backbones/det_mobilenet_v3.py @@ -34,13 +34,21 @@ def make_divisible(v, divisor=8, min_value=None): class MobileNetV3(nn.Layer): - def __init__(self, in_channels=3, model_name='large', scale=0.5, **kwargs): + def __init__(self, + in_channels=3, + model_name='large', + scale=0.5, + disable_se=False, + **kwargs): """ the MobilenetV3 backbone network for detection module. Args: params(dict): the super parameters for build network """ super(MobileNetV3, self).__init__() + + self.disable_se = disable_se + if model_name == "large": cfg = [ # k, exp, c, se, nl, s, @@ -103,6 +111,7 @@ class MobileNetV3(nn.Layer): i = 0 inplanes = make_divisible(inplanes * scale) for (k, exp, c, se, nl, s) in cfg: + se = se and not self.disable_se if s == 2 and i > 2: self.out_channels.append(inplanes) self.stages.append(nn.Sequential(*block_list)) @@ -273,4 +282,4 @@ class SEModule(nn.Layer): outputs = F.relu(outputs) outputs = self.conv2(outputs) outputs = F.activation.hard_sigmoid(outputs) - return inputs * outputs \ No newline at end of file + return inputs * outputs diff --git a/ppocr/modeling/backbones/det_resnet_vd_sast.py b/ppocr/modeling/backbones/det_resnet_vd_sast.py new file mode 100644 index 0000000000000000000000000000000000000000..c9376a8d56e9fa8eff5d2980f63b0b8b955efe88 --- /dev/null +++ b/ppocr/modeling/backbones/det_resnet_vd_sast.py @@ -0,0 +1,285 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import ParamAttr +import paddle.nn as nn +import paddle.nn.functional as F + +__all__ = ["ResNet_SAST"] + + +class ConvBNLayer(nn.Layer): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + groups=1, + is_vd_mode=False, + act=None, + name=None, ): + super(ConvBNLayer, self).__init__() + + self.is_vd_mode = is_vd_mode + self._pool2d_avg = nn.AvgPool2D( + kernel_size=2, stride=2, padding=0, ceil_mode=True) + self._conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + if name == "conv1": + bn_name = "bn_" + name + else: + bn_name = "bn" + name[3:] + self._batch_norm = nn.BatchNorm( + out_channels, + act=act, + param_attr=ParamAttr(name=bn_name + '_scale'), + bias_attr=ParamAttr(bn_name + '_offset'), + moving_mean_name=bn_name + '_mean', + moving_variance_name=bn_name + '_variance') + + def forward(self, inputs): + if self.is_vd_mode: + inputs = self._pool2d_avg(inputs) + y = self._conv(inputs) + y = self._batch_norm(y) + return y + + +class BottleneckBlock(nn.Layer): + def __init__(self, + in_channels, + out_channels, + stride, + shortcut=True, + if_first=False, + name=None): + super(BottleneckBlock, self).__init__() + + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + act='relu', + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + act='relu', + name=name + "_branch2b") + self.conv2 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels * 4, + kernel_size=1, + act=None, + name=name + "_branch2c") + + if not shortcut: + self.short = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels * 4, + kernel_size=1, + stride=1, + is_vd_mode=False if if_first else True, + name=name + "_branch1") + + self.shortcut = shortcut + + def forward(self, inputs): + y = self.conv0(inputs) + conv1 = self.conv1(y) + conv2 = self.conv2(conv1) + + if self.shortcut: + short = inputs + else: + short = self.short(inputs) + y = paddle.add(x=short, y=conv2) + y = F.relu(y) + return y + + +class BasicBlock(nn.Layer): + def __init__(self, + in_channels, + out_channels, + stride, + shortcut=True, + if_first=False, + name=None): + super(BasicBlock, self).__init__() + self.stride = stride + self.conv0 = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + act='relu', + name=name + "_branch2a") + self.conv1 = ConvBNLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=3, + act=None, + name=name + "_branch2b") + + if not shortcut: + self.short = ConvBNLayer( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + is_vd_mode=False if if_first else True, + name=name + "_branch1") + + self.shortcut = shortcut + + def forward(self, inputs): + y = self.conv0(inputs) + conv1 = self.conv1(y) + + if self.shortcut: + short = inputs + else: + short = self.short(inputs) + y = paddle.add(x=short, y=conv1) + y = F.relu(y) + return y + + +class ResNet_SAST(nn.Layer): + def __init__(self, in_channels=3, layers=50, **kwargs): + super(ResNet_SAST, self).__init__() + + self.layers = layers + supported_layers = [18, 34, 50, 101, 152, 200] + assert layers in supported_layers, \ + "supported layers are {} but input layer is {}".format( + supported_layers, layers) + + if layers == 18: + depth = [2, 2, 2, 2] + elif layers == 34 or layers == 50: + # depth = [3, 4, 6, 3] + depth = [3, 4, 6, 3, 3] + elif layers == 101: + depth = [3, 4, 23, 3] + elif layers == 152: + depth = [3, 8, 36, 3] + elif layers == 200: + depth = [3, 12, 48, 3] + # num_channels = [64, 256, 512, + # 1024] if layers >= 50 else [64, 64, 128, 256] + # num_filters = [64, 128, 256, 512] + num_channels = [64, 256, 512, + 1024, 2048] if layers >= 50 else [64, 64, 128, 256] + num_filters = [64, 128, 256, 512, 512] + + self.conv1_1 = ConvBNLayer( + in_channels=in_channels, + out_channels=32, + kernel_size=3, + stride=2, + act='relu', + name="conv1_1") + self.conv1_2 = ConvBNLayer( + in_channels=32, + out_channels=32, + kernel_size=3, + stride=1, + act='relu', + name="conv1_2") + self.conv1_3 = ConvBNLayer( + in_channels=32, + out_channels=64, + kernel_size=3, + stride=1, + act='relu', + name="conv1_3") + self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) + + self.stages = [] + self.out_channels = [3, 64] + if layers >= 50: + for block in range(len(depth)): + block_list = [] + shortcut = False + for i in range(depth[block]): + if layers in [101, 152] and block == 2: + if i == 0: + conv_name = "res" + str(block + 2) + "a" + else: + conv_name = "res" + str(block + 2) + "b" + str(i) + else: + conv_name = "res" + str(block + 2) + chr(97 + i) + bottleneck_block = self.add_sublayer( + 'bb_%d_%d' % (block, i), + BottleneckBlock( + in_channels=num_channels[block] + if i == 0 else num_filters[block] * 4, + out_channels=num_filters[block], + stride=2 if i == 0 and block != 0 else 1, + shortcut=shortcut, + if_first=block == i == 0, + name=conv_name)) + shortcut = True + block_list.append(bottleneck_block) + self.out_channels.append(num_filters[block] * 4) + self.stages.append(nn.Sequential(*block_list)) + else: + for block in range(len(depth)): + block_list = [] + shortcut = False + for i in range(depth[block]): + conv_name = "res" + str(block + 2) + chr(97 + i) + basic_block = self.add_sublayer( + 'bb_%d_%d' % (block, i), + BasicBlock( + in_channels=num_channels[block] + if i == 0 else num_filters[block], + out_channels=num_filters[block], + stride=2 if i == 0 and block != 0 else 1, + shortcut=shortcut, + if_first=block == i == 0, + name=conv_name)) + shortcut = True + block_list.append(basic_block) + self.out_channels.append(num_filters[block]) + self.stages.append(nn.Sequential(*block_list)) + + def forward(self, inputs): + out = [inputs] + y = self.conv1_1(inputs) + y = self.conv1_2(y) + y = self.conv1_3(y) + out.append(y) + y = self.pool2d_max(y) + for block in self.stages: + y = block(y) + out.append(y) + return out \ No newline at end of file diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 673f6fb4b6ffa72abc9fe1571869169f51604b5c..78074709056f4865098600a28e63b54e68eecf80 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/ppocr/modeling/heads/__init__.py @@ -18,13 +18,15 @@ __all__ = ['build_head'] def build_head(config): # det head from .det_db_head import DBHead + from .det_east_head import EASTHead + from .det_sast_head import SASTHead # rec head from .rec_ctc_head import CTCHead # cls head from .cls_head import ClsHead - support_dict = ['DBHead', 'CTCHead', 'ClsHead'] + support_dict = ['DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead'] module_name = config.pop('name') assert module_name in support_dict, Exception('head only support {}'.format( diff --git a/ppocr/modeling/heads/det_east_head.py b/ppocr/modeling/heads/det_east_head.py new file mode 100644 index 0000000000000000000000000000000000000000..9d0c3c4cf83adb018fcc368374cbe305658e07a9 --- /dev/null +++ b/ppocr/modeling/heads/det_east_head.py @@ -0,0 +1,121 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import nn +import paddle.nn.functional as F +from paddle import ParamAttr + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups=1, + if_act=True, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class EASTHead(nn.Layer): + """ + """ + def __init__(self, in_channels, model_name, **kwargs): + super(EASTHead, self).__init__() + self.model_name = model_name + if self.model_name == "large": + num_outputs = [128, 64, 1, 8] + else: + num_outputs = [64, 32, 1, 8] + + self.det_conv1 = ConvBNLayer( + in_channels=in_channels, + out_channels=num_outputs[0], + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="det_head1") + self.det_conv2 = ConvBNLayer( + in_channels=num_outputs[0], + out_channels=num_outputs[1], + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="det_head2") + self.score_conv = ConvBNLayer( + in_channels=num_outputs[1], + out_channels=num_outputs[2], + kernel_size=1, + stride=1, + padding=0, + if_act=False, + act=None, + name="f_score") + self.geo_conv = ConvBNLayer( + in_channels=num_outputs[1], + out_channels=num_outputs[3], + kernel_size=1, + stride=1, + padding=0, + if_act=False, + act=None, + name="f_geo") + + def forward(self, x): + f_det = self.det_conv1(x) + f_det = self.det_conv2(f_det) + f_score = self.score_conv(f_det) + f_score = F.sigmoid(f_score) + f_geo = self.geo_conv(f_det) + f_geo = (F.sigmoid(f_geo) - 0.5) * 2 * 800 + + pred = {'f_score': f_score, 'f_geo': f_geo} + return pred diff --git a/ppocr/modeling/heads/det_sast_head.py b/ppocr/modeling/heads/det_sast_head.py new file mode 100644 index 0000000000000000000000000000000000000000..263b28672299e733369938fa03952dca7685fabe --- /dev/null +++ b/ppocr/modeling/heads/det_sast_head.py @@ -0,0 +1,128 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +from paddle import nn +import paddle.nn.functional as F +from paddle import ParamAttr + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + groups=1, + if_act=True, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class SAST_Header1(nn.Layer): + def __init__(self, in_channels, **kwargs): + super(SAST_Header1, self).__init__() + out_channels = [64, 64, 128] + self.score_conv = nn.Sequential( + ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_score1'), + ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_score2'), + ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_score3'), + ConvBNLayer(out_channels[2], 1, 3, 1, act=None, name='f_score4') + ) + self.border_conv = nn.Sequential( + ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_border1'), + ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_border2'), + ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_border3'), + ConvBNLayer(out_channels[2], 4, 3, 1, act=None, name='f_border4') + ) + + def forward(self, x): + f_score = self.score_conv(x) + f_score = F.sigmoid(f_score) + f_border = self.border_conv(x) + return f_score, f_border + + +class SAST_Header2(nn.Layer): + def __init__(self, in_channels, **kwargs): + super(SAST_Header2, self).__init__() + out_channels = [64, 64, 128] + self.tvo_conv = nn.Sequential( + ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tvo1'), + ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tvo2'), + ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tvo3'), + ConvBNLayer(out_channels[2], 8, 3, 1, act=None, name='f_tvo4') + ) + self.tco_conv = nn.Sequential( + ConvBNLayer(in_channels, out_channels[0], 1, 1, act='relu', name='f_tco1'), + ConvBNLayer(out_channels[0], out_channels[1], 3, 1, act='relu', name='f_tco2'), + ConvBNLayer(out_channels[1], out_channels[2], 1, 1, act='relu', name='f_tco3'), + ConvBNLayer(out_channels[2], 2, 3, 1, act=None, name='f_tco4') + ) + + def forward(self, x): + f_tvo = self.tvo_conv(x) + f_tco = self.tco_conv(x) + return f_tvo, f_tco + + +class SASTHead(nn.Layer): + """ + """ + def __init__(self, in_channels, **kwargs): + super(SASTHead, self).__init__() + + self.head1 = SAST_Header1(in_channels) + self.head2 = SAST_Header2(in_channels) + + def forward(self, x): + f_score, f_border = self.head1(x) + f_tvo, f_tco = self.head2(x) + + predicts = {} + predicts['f_score'] = f_score + predicts['f_border'] = f_border + predicts['f_tvo'] = f_tvo + predicts['f_tco'] = f_tco + return predicts \ No newline at end of file diff --git a/ppocr/modeling/necks/__init__.py b/ppocr/modeling/necks/__init__.py index a9bf414b71489985f3d63d5aabea0805dc312c27..405e062b352da759b743d5997fd6e4c8b89c038b 100644 --- a/ppocr/modeling/necks/__init__.py +++ b/ppocr/modeling/necks/__init__.py @@ -16,8 +16,10 @@ __all__ = ['build_neck'] def build_neck(config): from .db_fpn import DBFPN + from .east_fpn import EASTFPN + from .sast_fpn import SASTFPN from .rnn import SequenceEncoder - support_dict = ['DBFPN', 'SequenceEncoder'] + support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder'] module_name = config.pop('name') assert module_name in support_dict, Exception('neck only support {}'.format( diff --git a/ppocr/modeling/necks/east_fpn.py b/ppocr/modeling/necks/east_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..120ff156cba2f7f8f8a562e76c82f1fac8584d4a --- /dev/null +++ b/ppocr/modeling/necks/east_fpn.py @@ -0,0 +1,188 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +import paddle.nn.functional as F +from paddle import ParamAttr + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups=1, + if_act=True, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class DeConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + groups=1, + if_act=True, + act=None, + name=None): + super(DeConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.deconv = nn.Conv2DTranspose( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.deconv(x) + x = self.bn(x) + return x + + +class EASTFPN(nn.Layer): + def __init__(self, in_channels, model_name, **kwargs): + super(EASTFPN, self).__init__() + self.model_name = model_name + if self.model_name == "large": + self.out_channels = 128 + else: + self.out_channels = 64 + self.in_channels = in_channels[::-1] + self.h1_conv = ConvBNLayer( + in_channels=self.out_channels+self.in_channels[1], + out_channels=self.out_channels, + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="unet_h_1") + self.h2_conv = ConvBNLayer( + in_channels=self.out_channels+self.in_channels[2], + out_channels=self.out_channels, + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="unet_h_2") + self.h3_conv = ConvBNLayer( + in_channels=self.out_channels+self.in_channels[3], + out_channels=self.out_channels, + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="unet_h_3") + self.g0_deconv = DeConvBNLayer( + in_channels=self.in_channels[0], + out_channels=self.out_channels, + kernel_size=4, + stride=2, + padding=1, + if_act=True, + act='relu', + name="unet_g_0") + self.g1_deconv = DeConvBNLayer( + in_channels=self.out_channels, + out_channels=self.out_channels, + kernel_size=4, + stride=2, + padding=1, + if_act=True, + act='relu', + name="unet_g_1") + self.g2_deconv = DeConvBNLayer( + in_channels=self.out_channels, + out_channels=self.out_channels, + kernel_size=4, + stride=2, + padding=1, + if_act=True, + act='relu', + name="unet_g_2") + self.g3_conv = ConvBNLayer( + in_channels=self.out_channels, + out_channels=self.out_channels, + kernel_size=3, + stride=1, + padding=1, + if_act=True, + act='relu', + name="unet_g_3") + + def forward(self, x): + f = x[::-1] + + h = f[0] + g = self.g0_deconv(h) + h = paddle.concat([g, f[1]], axis=1) + h = self.h1_conv(h) + g = self.g1_deconv(h) + h = paddle.concat([g, f[2]], axis=1) + h = self.h2_conv(h) + g = self.g2_deconv(h) + h = paddle.concat([g, f[3]], axis=1) + h = self.h3_conv(h) + g = self.g3_conv(h) + + return g \ No newline at end of file diff --git a/ppocr/modeling/necks/sast_fpn.py b/ppocr/modeling/necks/sast_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..9b6024590ac574f725fc6c519dd96ee4462276d6 --- /dev/null +++ b/ppocr/modeling/necks/sast_fpn.py @@ -0,0 +1,284 @@ +# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +from paddle import nn +import paddle.nn.functional as F +from paddle import ParamAttr + + +class ConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + groups=1, + if_act=True, + act=None, + name=None): + super(ConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.conv = nn.Conv2D( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class DeConvBNLayer(nn.Layer): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + groups=1, + if_act=True, + act=None, + name=None): + super(DeConvBNLayer, self).__init__() + self.if_act = if_act + self.act = act + self.deconv = nn.Conv2DTranspose( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - 1) // 2, + groups=groups, + weight_attr=ParamAttr(name=name + '_weights'), + bias_attr=False) + self.bn = nn.BatchNorm( + num_channels=out_channels, + act=act, + param_attr=ParamAttr(name="bn_" + name + "_scale"), + bias_attr=ParamAttr(name="bn_" + name + "_offset"), + moving_mean_name="bn_" + name + "_mean", + moving_variance_name="bn_" + name + "_variance") + + def forward(self, x): + x = self.deconv(x) + x = self.bn(x) + return x + + +class FPN_Up_Fusion(nn.Layer): + def __init__(self, in_channels): + super(FPN_Up_Fusion, self).__init__() + in_channels = in_channels[::-1] + out_channels = [256, 256, 192, 192, 128] + + self.h0_conv = ConvBNLayer(in_channels[0], out_channels[0], 1, 1, act=None, name='fpn_up_h0') + self.h1_conv = ConvBNLayer(in_channels[1], out_channels[1], 1, 1, act=None, name='fpn_up_h1') + self.h2_conv = ConvBNLayer(in_channels[2], out_channels[2], 1, 1, act=None, name='fpn_up_h2') + self.h3_conv = ConvBNLayer(in_channels[3], out_channels[3], 1, 1, act=None, name='fpn_up_h3') + self.h4_conv = ConvBNLayer(in_channels[4], out_channels[4], 1, 1, act=None, name='fpn_up_h4') + + self.g0_conv = DeConvBNLayer(out_channels[0], out_channels[1], 4, 2, act=None, name='fpn_up_g0') + + self.g1_conv = nn.Sequential( + ConvBNLayer(out_channels[1], out_channels[1], 3, 1, act='relu', name='fpn_up_g1_1'), + DeConvBNLayer(out_channels[1], out_channels[2], 4, 2, act=None, name='fpn_up_g1_2') + ) + self.g2_conv = nn.Sequential( + ConvBNLayer(out_channels[2], out_channels[2], 3, 1, act='relu', name='fpn_up_g2_1'), + DeConvBNLayer(out_channels[2], out_channels[3], 4, 2, act=None, name='fpn_up_g2_2') + ) + self.g3_conv = nn.Sequential( + ConvBNLayer(out_channels[3], out_channels[3], 3, 1, act='relu', name='fpn_up_g3_1'), + DeConvBNLayer(out_channels[3], out_channels[4], 4, 2, act=None, name='fpn_up_g3_2') + ) + + self.g4_conv = nn.Sequential( + ConvBNLayer(out_channels[4], out_channels[4], 3, 1, act='relu', name='fpn_up_fusion_1'), + ConvBNLayer(out_channels[4], out_channels[4], 1, 1, act=None, name='fpn_up_fusion_2') + ) + + def _add_relu(self, x1, x2): + x = paddle.add(x=x1, y=x2) + x = F.relu(x) + return x + + def forward(self, x): + f = x[2:][::-1] + h0 = self.h0_conv(f[0]) + h1 = self.h1_conv(f[1]) + h2 = self.h2_conv(f[2]) + h3 = self.h3_conv(f[3]) + h4 = self.h4_conv(f[4]) + + g0 = self.g0_conv(h0) + g1 = self._add_relu(g0, h1) + g1 = self.g1_conv(g1) + g2 = self.g2_conv(self._add_relu(g1, h2)) + g3 = self.g3_conv(self._add_relu(g2, h3)) + g4 = self.g4_conv(self._add_relu(g3, h4)) + + return g4 + + +class FPN_Down_Fusion(nn.Layer): + def __init__(self, in_channels): + super(FPN_Down_Fusion, self).__init__() + out_channels = [32, 64, 128] + + self.h0_conv = ConvBNLayer(in_channels[0], out_channels[0], 3, 1, act=None, name='fpn_down_h0') + self.h1_conv = ConvBNLayer(in_channels[1], out_channels[1], 3, 1, act=None, name='fpn_down_h1') + self.h2_conv = ConvBNLayer(in_channels[2], out_channels[2], 3, 1, act=None, name='fpn_down_h2') + + self.g0_conv = ConvBNLayer(out_channels[0], out_channels[1], 3, 2, act=None, name='fpn_down_g0') + + self.g1_conv = nn.Sequential( + ConvBNLayer(out_channels[1], out_channels[1], 3, 1, act='relu', name='fpn_down_g1_1'), + ConvBNLayer(out_channels[1], out_channels[2], 3, 2, act=None, name='fpn_down_g1_2') + ) + + self.g2_conv = nn.Sequential( + ConvBNLayer(out_channels[2], out_channels[2], 3, 1, act='relu', name='fpn_down_fusion_1'), + ConvBNLayer(out_channels[2], out_channels[2], 1, 1, act=None, name='fpn_down_fusion_2') + ) + + def forward(self, x): + f = x[:3] + h0 = self.h0_conv(f[0]) + h1 = self.h1_conv(f[1]) + h2 = self.h2_conv(f[2]) + g0 = self.g0_conv(h0) + g1 = paddle.add(x=g0, y=h1) + g1 = F.relu(g1) + g1 = self.g1_conv(g1) + g2 = paddle.add(x=g1, y=h2) + g2 = F.relu(g2) + g2 = self.g2_conv(g2) + return g2 + + +class Cross_Attention(nn.Layer): + def __init__(self, in_channels): + super(Cross_Attention, self).__init__() + self.theta_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_theta') + self.phi_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_phi') + self.g_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act='relu', name='f_g') + + self.fh_weight_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fh_weight') + self.fh_sc_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fh_sc') + + self.fv_weight_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fv_weight') + self.fv_sc_conv = ConvBNLayer(in_channels, in_channels, 1, 1, act=None, name='fv_sc') + + self.f_attn_conv = ConvBNLayer(in_channels * 2, in_channels, 1, 1, act='relu', name='f_attn') + + def _cal_fweight(self, f, shape): + f_theta, f_phi, f_g = f + #flatten + f_theta = paddle.transpose(f_theta, [0, 2, 3, 1]) + f_theta = paddle.reshape(f_theta, [shape[0] * shape[1], shape[2], 128]) + f_phi = paddle.transpose(f_phi, [0, 2, 3, 1]) + f_phi = paddle.reshape(f_phi, [shape[0] * shape[1], shape[2], 128]) + f_g = paddle.transpose(f_g, [0, 2, 3, 1]) + f_g = paddle.reshape(f_g, [shape[0] * shape[1], shape[2], 128]) + #correlation + f_attn = paddle.matmul(f_theta, paddle.transpose(f_phi, [0, 2, 1])) + #scale + f_attn = f_attn / (128**0.5) + f_attn = F.softmax(f_attn) + #weighted sum + f_weight = paddle.matmul(f_attn, f_g) + f_weight = paddle.reshape( + f_weight, [shape[0], shape[1], shape[2], 128]) + return f_weight + + def forward(self, f_common): + f_shape = paddle.shape(f_common) + # print('f_shape: ', f_shape) + + f_theta = self.theta_conv(f_common) + f_phi = self.phi_conv(f_common) + f_g = self.g_conv(f_common) + + ######## horizon ######## + fh_weight = self._cal_fweight([f_theta, f_phi, f_g], + [f_shape[0], f_shape[2], f_shape[3]]) + fh_weight = paddle.transpose(fh_weight, [0, 3, 1, 2]) + fh_weight = self.fh_weight_conv(fh_weight) + #short cut + fh_sc = self.fh_sc_conv(f_common) + f_h = F.relu(fh_weight + fh_sc) + + ######## vertical ######## + fv_theta = paddle.transpose(f_theta, [0, 1, 3, 2]) + fv_phi = paddle.transpose(f_phi, [0, 1, 3, 2]) + fv_g = paddle.transpose(f_g, [0, 1, 3, 2]) + fv_weight = self._cal_fweight([fv_theta, fv_phi, fv_g], + [f_shape[0], f_shape[3], f_shape[2]]) + fv_weight = paddle.transpose(fv_weight, [0, 3, 2, 1]) + fv_weight = self.fv_weight_conv(fv_weight) + #short cut + fv_sc = self.fv_sc_conv(f_common) + f_v = F.relu(fv_weight + fv_sc) + + ######## merge ######## + f_attn = paddle.concat([f_h, f_v], axis=1) + f_attn = self.f_attn_conv(f_attn) + return f_attn + + +class SASTFPN(nn.Layer): + def __init__(self, in_channels, with_cab=False, **kwargs): + super(SASTFPN, self).__init__() + self.in_channels = in_channels + self.with_cab = with_cab + self.FPN_Down_Fusion = FPN_Down_Fusion(self.in_channels) + self.FPN_Up_Fusion = FPN_Up_Fusion(self.in_channels) + self.out_channels = 128 + self.cross_attention = Cross_Attention(self.out_channels) + + def forward(self, x): + #down fpn + f_down = self.FPN_Down_Fusion(x) + + #up fpn + f_up = self.FPN_Up_Fusion(x) + + #fusion + f_common = paddle.add(x=f_down, y=f_up) + f_common = F.relu(f_common) + + if self.with_cab: + # print('enhence f_common with CAB.') + f_common = self.cross_attention(f_common) + + return f_common diff --git a/ppocr/modeling/transform/__init__.py b/ppocr/modeling/transforms/__init__.py similarity index 100% rename from ppocr/modeling/transform/__init__.py rename to ppocr/modeling/transforms/__init__.py diff --git a/ppocr/modeling/transform/tps.py b/ppocr/modeling/transforms/tps.py similarity index 100% rename from ppocr/modeling/transform/tps.py rename to ppocr/modeling/transforms/tps.py diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index e08a217da867020e31183cc45893ed5cdd2aee1e..c9b42e08391d0405a58a071460eb6f57bbb62c43 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -24,11 +24,13 @@ __all__ = ['build_post_process'] def build_post_process(config, global_config=None): from .db_postprocess import DBPostProcess + from .east_postprocess import EASTPostProcess + from .sast_postprocess import SASTPostProcess from .rec_postprocess import CTCLabelDecode, AttnLabelDecode from .cls_postprocess import ClsPostProcess support_dict = [ - 'DBPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess' + 'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess' ] config = copy.deepcopy(config) diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index 316f7fc202c73dbb9a40dbd806f72e4506b991c5..0be2c12ad4bb9e70708c45d3e3f60dd526dc4e83 100644 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -33,12 +33,14 @@ class DBPostProcess(object): box_thresh=0.7, max_candidates=1000, unclip_ratio=2.0, + use_dilation=False, **kwargs): self.thresh = thresh self.box_thresh = box_thresh self.max_candidates = max_candidates self.unclip_ratio = unclip_ratio self.min_size = 3 + self.dilation_kernel = None if not use_dilation else [[1, 1], [1, 1]] def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): ''' @@ -138,9 +140,15 @@ class DBPostProcess(object): boxes_batch = [] for batch_index in range(pred.shape[0]): - height, width = shape_list[batch_index] - boxes, scores = self.boxes_from_bitmap( - pred[batch_index], segmentation[batch_index], width, height) + src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] + if self.dilation_kernel is not None: + mask = cv2.dilate( + np.array(segmentation[batch_index]).astype(np.uint8), + self.dilation_kernel) + else: + mask = segmentation[batch_index] + boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, + src_w, src_h) boxes_batch.append({'points': boxes}) - return boxes_batch \ No newline at end of file + return boxes_batch diff --git a/ppocr/postprocess/east_postprocess.py b/ppocr/postprocess/east_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..0b669405562aef9812b9771977bf82f362beb75e --- /dev/null +++ b/ppocr/postprocess/east_postprocess.py @@ -0,0 +1,141 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from .locality_aware_nms import nms_locality +import cv2 + +import os +import sys +# __dir__ = os.path.dirname(os.path.abspath(__file__)) +# sys.path.append(__dir__) +# sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) + + +class EASTPostProcess(object): + """ + The post process for EAST. + """ + def __init__(self, + score_thresh=0.8, + cover_thresh=0.1, + nms_thresh=0.2, + **kwargs): + + self.score_thresh = score_thresh + self.cover_thresh = cover_thresh + self.nms_thresh = nms_thresh + + # c++ la-nms is faster, but only support python 3.5 + self.is_python35 = False + if sys.version_info.major == 3 and sys.version_info.minor == 5: + self.is_python35 = True + + def restore_rectangle_quad(self, origin, geometry): + """ + Restore rectangle from quadrangle. + """ + # quad + origin_concat = np.concatenate( + (origin, origin, origin, origin), axis=1) # (n, 8) + pred_quads = origin_concat - geometry + pred_quads = pred_quads.reshape((-1, 4, 2)) # (n, 4, 2) + return pred_quads + + def detect(self, + score_map, + geo_map, + score_thresh=0.8, + cover_thresh=0.1, + nms_thresh=0.2): + """ + restore text boxes from score map and geo map + """ + score_map = score_map[0] + geo_map = np.swapaxes(geo_map, 1, 0) + geo_map = np.swapaxes(geo_map, 1, 2) + # filter the score map + xy_text = np.argwhere(score_map > score_thresh) + if len(xy_text) == 0: + return [] + # sort the text boxes via the y axis + xy_text = xy_text[np.argsort(xy_text[:, 0])] + #restore quad proposals + text_box_restored = self.restore_rectangle_quad( + xy_text[:, ::-1] * 4, geo_map[xy_text[:, 0], xy_text[:, 1], :]) + boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32) + boxes[:, :8] = text_box_restored.reshape((-1, 8)) + boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]] + if self.is_python35: + import lanms + boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh) + else: + boxes = nms_locality(boxes.astype(np.float64), nms_thresh) + if boxes.shape[0] == 0: + return [] + # Here we filter some low score boxes by the average score map, + # this is different from the orginal paper. + for i, box in enumerate(boxes): + mask = np.zeros_like(score_map, dtype=np.uint8) + cv2.fillPoly(mask, box[:8].reshape( + (-1, 4, 2)).astype(np.int32) // 4, 1) + boxes[i, 8] = cv2.mean(score_map, mask)[0] + boxes = boxes[boxes[:, 8] > cover_thresh] + return boxes + + def sort_poly(self, p): + """ + Sort polygons. + """ + min_axis = np.argmin(np.sum(p, axis=1)) + p = p[[min_axis, (min_axis + 1) % 4,\ + (min_axis + 2) % 4, (min_axis + 3) % 4]] + if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]): + return p + else: + return p[[0, 3, 2, 1]] + + def __call__(self, outs_dict, shape_list): + score_list = outs_dict['f_score'] + geo_list = outs_dict['f_geo'] + img_num = len(shape_list) + dt_boxes_list = [] + for ino in range(img_num): + score = score_list[ino].numpy() + geo = geo_list[ino].numpy() + boxes = self.detect( + score_map=score, + geo_map=geo, + score_thresh=self.score_thresh, + cover_thresh=self.cover_thresh, + nms_thresh=self.nms_thresh) + boxes_norm = [] + if len(boxes) > 0: + h, w = score.shape[1:] + src_h, src_w, ratio_h, ratio_w = shape_list[ino] + boxes = boxes[:, :8].reshape((-1, 4, 2)) + boxes[:, :, 0] /= ratio_w + boxes[:, :, 1] /= ratio_h + for i_box, box in enumerate(boxes): + box = self.sort_poly(box.astype(np.int32)) + if np.linalg.norm(box[0] - box[1]) < 5 \ + or np.linalg.norm(box[3] - box[0]) < 5: + continue + boxes_norm.append(box) + dt_boxes_list.append({'points': np.array(boxes_norm)}) + return dt_boxes_list \ No newline at end of file diff --git a/ppocr/postprocess/locality_aware_nms.py b/ppocr/postprocess/locality_aware_nms.py new file mode 100644 index 0000000000000000000000000000000000000000..53280cc13ed7e41859e23e2517938d4f6eb07076 --- /dev/null +++ b/ppocr/postprocess/locality_aware_nms.py @@ -0,0 +1,199 @@ +""" +Locality aware nms. +""" + +import numpy as np +from shapely.geometry import Polygon + + +def intersection(g, p): + """ + Intersection. + """ + g = Polygon(g[:8].reshape((4, 2))) + p = Polygon(p[:8].reshape((4, 2))) + g = g.buffer(0) + p = p.buffer(0) + if not g.is_valid or not p.is_valid: + return 0 + inter = Polygon(g).intersection(Polygon(p)).area + union = g.area + p.area - inter + if union == 0: + return 0 + else: + return inter / union + + +def intersection_iog(g, p): + """ + Intersection_iog. + """ + g = Polygon(g[:8].reshape((4, 2))) + p = Polygon(p[:8].reshape((4, 2))) + if not g.is_valid or not p.is_valid: + return 0 + inter = Polygon(g).intersection(Polygon(p)).area + #union = g.area + p.area - inter + union = p.area + if union == 0: + print("p_area is very small") + return 0 + else: + return inter / union + + +def weighted_merge(g, p): + """ + Weighted merge. + """ + g[:8] = (g[8] * g[:8] + p[8] * p[:8]) / (g[8] + p[8]) + g[8] = (g[8] + p[8]) + return g + + +def standard_nms(S, thres): + """ + Standard nms. + """ + order = np.argsort(S[:, 8])[::-1] + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + ovr = np.array([intersection(S[i], S[t]) for t in order[1:]]) + + inds = np.where(ovr <= thres)[0] + order = order[inds + 1] + + return S[keep] + + +def standard_nms_inds(S, thres): + """ + Standard nms, retun inds. + """ + order = np.argsort(S[:, 8])[::-1] + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + ovr = np.array([intersection(S[i], S[t]) for t in order[1:]]) + + inds = np.where(ovr <= thres)[0] + order = order[inds + 1] + + return keep + + +def nms(S, thres): + """ + nms. + """ + order = np.argsort(S[:, 8])[::-1] + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + ovr = np.array([intersection(S[i], S[t]) for t in order[1:]]) + + inds = np.where(ovr <= thres)[0] + order = order[inds + 1] + + return keep + + +def soft_nms(boxes_in, Nt_thres=0.3, threshold=0.8, sigma=0.5, method=2): + """ + soft_nms + :para boxes_in, N x 9 (coords + score) + :para threshould, eliminate cases min score(0.001) + :para Nt_thres, iou_threshi + :para sigma, gaussian weght + :method, linear or gaussian + """ + boxes = boxes_in.copy() + N = boxes.shape[0] + if N is None or N < 1: + return np.array([]) + pos, maxpos = 0, 0 + weight = 0.0 + inds = np.arange(N) + tbox, sbox = boxes[0].copy(), boxes[0].copy() + for i in range(N): + maxscore = boxes[i, 8] + maxpos = i + tbox = boxes[i].copy() + ti = inds[i] + pos = i + 1 + #get max box + while pos < N: + if maxscore < boxes[pos, 8]: + maxscore = boxes[pos, 8] + maxpos = pos + pos = pos + 1 + #add max box as a detection + boxes[i, :] = boxes[maxpos, :] + inds[i] = inds[maxpos] + #swap + boxes[maxpos, :] = tbox + inds[maxpos] = ti + tbox = boxes[i].copy() + pos = i + 1 + #NMS iteration + while pos < N: + sbox = boxes[pos].copy() + ts_iou_val = intersection(tbox, sbox) + if ts_iou_val > 0: + if method == 1: + if ts_iou_val > Nt_thres: + weight = 1 - ts_iou_val + else: + weight = 1 + elif method == 2: + weight = np.exp(-1.0 * ts_iou_val**2 / sigma) + else: + if ts_iou_val > Nt_thres: + weight = 0 + else: + weight = 1 + boxes[pos, 8] = weight * boxes[pos, 8] + #if box score falls below thresold, discard the box by + #swaping last box update N + if boxes[pos, 8] < threshold: + boxes[pos, :] = boxes[N - 1, :] + inds[pos] = inds[N - 1] + N = N - 1 + pos = pos - 1 + pos = pos + 1 + + return boxes[:N] + + +def nms_locality(polys, thres=0.3): + """ + locality aware nms of EAST + :param polys: a N*9 numpy array. first 8 coordinates, then prob + :return: boxes after nms + """ + S = [] + p = None + for g in polys: + if p is not None and intersection(g, p) > thres: + p = weighted_merge(g, p) + else: + if p is not None: + S.append(p) + p = g + if p is not None: + S.append(p) + + if len(S) == 0: + return np.array([]) + return standard_nms(np.array(S), thres) + + +if __name__ == '__main__': + # 343,350,448,135,474,143,369,359 + print( + Polygon(np.array([[343, 350], [448, 135], [474, 143], [369, 359]])) + .area) \ No newline at end of file diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 6943f84f87913b5e4093c4ced2beebc8de98da73..a18e101bf428930246d04b1553113184cdf2753d 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -27,7 +27,7 @@ class BaseRecLabelDecode(object): 'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean' ] assert character_type in support_character_type, "Only {} are supported now but get {}".format( - support_character_type, self.character_str) + support_character_type, character_type) if character_type == "en": self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" diff --git a/ppocr/postprocess/sast_postprocess.py b/ppocr/postprocess/sast_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..03b0e8f17d60a8863a2d1d5900e01dcd4874d5a9 --- /dev/null +++ b/ppocr/postprocess/sast_postprocess.py @@ -0,0 +1,295 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys +__dir__ = os.path.dirname(__file__) +sys.path.append(__dir__) +sys.path.append(os.path.join(__dir__, '..')) + +import numpy as np +from .locality_aware_nms import nms_locality +# import lanms +import cv2 +import time + + +class SASTPostProcess(object): + """ + The post process for SAST. + """ + + def __init__(self, + score_thresh=0.5, + nms_thresh=0.2, + sample_pts_num=2, + shrink_ratio_of_width=0.3, + expand_scale=1.0, + tcl_map_thresh=0.5, + **kwargs): + + self.score_thresh = score_thresh + self.nms_thresh = nms_thresh + self.sample_pts_num = sample_pts_num + self.shrink_ratio_of_width = shrink_ratio_of_width + self.expand_scale = expand_scale + self.tcl_map_thresh = tcl_map_thresh + + # c++ la-nms is faster, but only support python 3.5 + self.is_python35 = False + if sys.version_info.major == 3 and sys.version_info.minor == 5: + self.is_python35 = True + + def point_pair2poly(self, point_pair_list): + """ + Transfer vertical point_pairs into poly point in clockwise. + """ + # constract poly + point_num = len(point_pair_list) * 2 + point_list = [0] * point_num + for idx, point_pair in enumerate(point_pair_list): + point_list[idx] = point_pair[0] + point_list[point_num - 1 - idx] = point_pair[1] + return np.array(point_list).reshape(-1, 2) + + def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.): + """ + Generate shrink_quad_along_width. + """ + ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32) + p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair + p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair + return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]]) + + def expand_poly_along_width(self, poly, shrink_ratio_of_width=0.3): + """ + expand poly along width. + """ + point_num = poly.shape[0] + left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32) + left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \ + (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6) + left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0) + right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1], + poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32) + right_ratio = 1.0 + \ + shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \ + (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6) + right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio) + poly[0] = left_quad_expand[0] + poly[-1] = left_quad_expand[-1] + poly[point_num // 2 - 1] = right_quad_expand[1] + poly[point_num // 2] = right_quad_expand[2] + return poly + + def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map): + """Restore quad.""" + xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh) + xy_text = xy_text[:, ::-1] # (n, 2) + + # Sort the text boxes via the y axis + xy_text = xy_text[np.argsort(xy_text[:, 1])] + + scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] + scores = scores[:, np.newaxis] + + # Restore + point_num = int(tvo_map.shape[-1] / 2) + assert point_num == 4 + tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :] + xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2) + quads = xy_text_tile - tvo_map + + return scores, quads, xy_text + + def quad_area(self, quad): + """ + compute area of a quad. + """ + edge = [ + (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]), + (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]), + (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]), + (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1]) + ] + return np.sum(edge) / 2. + + def nms(self, dets): + if self.is_python35: + import lanms + dets = lanms.merge_quadrangle_n9(dets, self.nms_thresh) + else: + dets = nms_locality(dets, self.nms_thresh) + return dets + + def cluster_by_quads_tco(self, tcl_map, tcl_map_thresh, quads, tco_map): + """ + Cluster pixels in tcl_map based on quads. + """ + instance_count = quads.shape[0] + 1 # contain background + instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32) + if instance_count == 1: + return instance_count, instance_label_map + + # predict text center + xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh) + n = xy_text.shape[0] + xy_text = xy_text[:, ::-1] # (n, 2) + tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2) + pred_tc = xy_text - tco + + # get gt text center + m = quads.shape[0] + gt_tc = np.mean(quads, axis=1) # (m, 2) + + pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2) + gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2) + dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m) + xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,) + + instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign + return instance_count, instance_label_map + + def estimate_sample_pts_num(self, quad, xy_text): + """ + Estimate sample points number. + """ + eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0 + ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0 + + dense_sample_pts_num = max(2, int(ew)) + dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num, + endpoint=True, dtype=np.float32).astype(np.int32)] + + dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1] + estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1)) + + sample_pts_num = max(2, int(estimate_arc_len / eh)) + return sample_pts_num + + def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, + shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0): + """ + first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys + """ + # restore quad + scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map) + dets = np.hstack((quads, scores)).astype(np.float32, copy=False) + dets = self.nms(dets) + if dets.shape[0] == 0: + return [] + quads = dets[:, :-1].reshape(-1, 4, 2) + + # Compute quad area + quad_areas = [] + for quad in quads: + quad_areas.append(-self.quad_area(quad)) + + # instance segmentation + # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8) + instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map) + + # restore single poly with tcl instance. + poly_list = [] + for instance_idx in range(1, instance_count): + xy_text = np.argwhere(instance_label_map == instance_idx)[:, ::-1] + quad = quads[instance_idx - 1] + q_area = quad_areas[instance_idx - 1] + if q_area < 5: + continue + + # + len1 = float(np.linalg.norm(quad[0] -quad[1])) + len2 = float(np.linalg.norm(quad[1] -quad[2])) + min_len = min(len1, len2) + if min_len < 3: + continue + + # filter small CC + if xy_text.shape[0] <= 0: + continue + + # filter low confidence instance + xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] + if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1: + # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05: + continue + + # sort xy_text + left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0, + (quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2) + right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0, + (quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2) + proj_unit_vec = (right_center_pt - left_center_pt) / \ + (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6) + proj_value = np.sum(xy_text * proj_unit_vec, axis=1) + xy_text = xy_text[np.argsort(proj_value)] + + # Sample pts in tcl map + if self.sample_pts_num == 0: + sample_pts_num = self.estimate_sample_pts_num(quad, xy_text) + else: + sample_pts_num = self.sample_pts_num + xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num, + endpoint=True, dtype=np.float32).astype(np.int32)] + + point_pair_list = [] + for x, y in xy_center_line: + # get corresponding offset + offset = tbo_map[y, x, :].reshape(2, 2) + if offset_expand != 1.0: + offset_length = np.linalg.norm(offset, axis=1, keepdims=True) + expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0) + offset_detal = offset / offset_length * expand_length + offset = offset + offset_detal + # original point + ori_yx = np.array([y, x], dtype=np.float32) + point_pair = (ori_yx + offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) + point_pair_list.append(point_pair) + + # ndarry: (x, 2), expand poly along width + detected_poly = self.point_pair2poly(point_pair_list) + detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width) + detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w) + detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h) + poly_list.append(detected_poly) + + return poly_list + + def __call__(self, outs_dict, shape_list): + score_list = outs_dict['f_score'] + border_list = outs_dict['f_border'] + tvo_list = outs_dict['f_tvo'] + tco_list = outs_dict['f_tco'] + + img_num = len(shape_list) + poly_lists = [] + for ino in range(img_num): + p_score = score_list[ino].transpose((1,2,0)).numpy() + p_border = border_list[ino].transpose((1,2,0)).numpy() + p_tvo = tvo_list[ino].transpose((1,2,0)).numpy() + p_tco = tco_list[ino].transpose((1,2,0)).numpy() + src_h, src_w, ratio_h, ratio_w = shape_list[ino] + + poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, + shrink_ratio_of_width=self.shrink_ratio_of_width, + tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale) + poly_lists.append({'points': np.array(poly_list)}) + + return poly_lists + diff --git a/ppocr/utils/character.py b/ppocr/utils/character.py deleted file mode 100755 index b4b2021e02c9905623fd9fad5c9673543569c1c2..0000000000000000000000000000000000000000 --- a/ppocr/utils/character.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import string -import re -from .check import check_config_params -import sys - - -class CharacterOps(object): - """ Convert between text-label and text-index """ - - def __init__(self, config): - self.character_type = config['character_type'] - self.loss_type = config['loss_type'] - self.max_text_len = config['max_text_length'] - if self.character_type == "en": - self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" - dict_character = list(self.character_str) - elif self.character_type == "ch": - character_dict_path = config['character_dict_path'] - add_space = False - if 'use_space_char' in config: - add_space = config['use_space_char'] - self.character_str = "" - with open(character_dict_path, "rb") as fin: - lines = fin.readlines() - for line in lines: - line = line.decode('utf-8').strip("\n").strip("\r\n") - self.character_str += line - if add_space: - self.character_str += " " - dict_character = list(self.character_str) - elif self.character_type == "en_sensitive": - # same with ASTER setting (use 94 char). - self.character_str = string.printable[:-6] - dict_character = list(self.character_str) - else: - self.character_str = None - assert self.character_str is not None, \ - "Nonsupport type of the character: {}".format(self.character_str) - self.beg_str = "sos" - self.end_str = "eos" - if self.loss_type == "attention": - dict_character = [self.beg_str, self.end_str] + dict_character - elif self.loss_type == "srn": - dict_character = dict_character + [self.beg_str, self.end_str] - self.dict = {} - for i, char in enumerate(dict_character): - self.dict[char] = i - self.character = dict_character - - def encode(self, text): - """convert text-label into text-index. - input: - text: text labels of each image. [batch_size] - - output: - text: concatenated text index for CTCLoss. - [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)] - length: length of each text. [batch_size] - """ - if self.character_type == "en": - text = text.lower() - - text_list = [] - for char in text: - if char not in self.dict: - continue - text_list.append(self.dict[char]) - text = np.array(text_list) - return text - - def decode(self, text_index, is_remove_duplicate=False): - """ convert text-index into text-label. """ - char_list = [] - char_num = self.get_char_num() - - if self.loss_type == "attention": - beg_idx = self.get_beg_end_flag_idx("beg") - end_idx = self.get_beg_end_flag_idx("end") - ignored_tokens = [beg_idx, end_idx] - else: - ignored_tokens = [char_num] - - for idx in range(len(text_index)): - if text_index[idx] in ignored_tokens: - continue - if is_remove_duplicate: - if idx > 0 and text_index[idx - 1] == text_index[idx]: - continue - char_list.append(self.character[int(text_index[idx])]) - text = ''.join(char_list) - return text - - def get_char_num(self): - return len(self.character) - - def get_beg_end_flag_idx(self, beg_or_end): - if self.loss_type == "attention": - if beg_or_end == "beg": - idx = np.array(self.dict[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx"\ - % beg_or_end - return idx - else: - err = "error in get_beg_end_flag_idx when using the loss %s"\ - % (self.loss_type) - assert False, err - - -def cal_predicts_accuracy(char_ops, - preds, - preds_lod, - labels, - labels_lod, - is_remove_duplicate=False): - acc_num = 0 - img_num = 0 - for ino in range(len(labels_lod) - 1): - beg_no = preds_lod[ino] - end_no = preds_lod[ino + 1] - preds_text = preds[beg_no:end_no].reshape(-1) - preds_text = char_ops.decode(preds_text, is_remove_duplicate) - - beg_no = labels_lod[ino] - end_no = labels_lod[ino + 1] - labels_text = labels[beg_no:end_no].reshape(-1) - labels_text = char_ops.decode(labels_text, is_remove_duplicate) - img_num += 1 - - if preds_text == labels_text: - acc_num += 1 - acc = acc_num * 1.0 / img_num - return acc, acc_num, img_num - - -def cal_predicts_accuracy_srn(char_ops, - preds, - labels, - max_text_len, - is_debug=False): - acc_num = 0 - img_num = 0 - - char_num = char_ops.get_char_num() - - total_len = preds.shape[0] - img_num = int(total_len / max_text_len) - for i in range(img_num): - cur_label = [] - cur_pred = [] - for j in range(max_text_len): - if labels[j + i * max_text_len] != int(char_num-1): #0 - cur_label.append(labels[j + i * max_text_len][0]) - else: - break - - for j in range(max_text_len + 1): - if j < len(cur_label) and preds[j + i * max_text_len][ - 0] != cur_label[j]: - break - elif j == len(cur_label) and j == max_text_len: - acc_num += 1 - break - elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1): - acc_num += 1 - break - acc = acc_num * 1.0 / img_num - return acc, acc_num, img_num - - -def convert_rec_attention_infer_res(preds): - img_num = preds.shape[0] - target_lod = [0] - convert_ids = [] - for ino in range(img_num): - end_pos = np.where(preds[ino, :] == 1)[0] - if len(end_pos) <= 1: - text_list = preds[ino, 1:] - else: - text_list = preds[ino, 1:end_pos[1]] - target_lod.append(target_lod[ino] + len(text_list)) - convert_ids = convert_ids + list(text_list) - convert_ids = np.array(convert_ids) - convert_ids = convert_ids.reshape((-1, 1)) - return convert_ids, target_lod - - -def convert_rec_label_to_lod(ori_labels): - img_num = len(ori_labels) - target_lod = [0] - convert_ids = [] - for ino in range(img_num): - target_lod.append(target_lod[ino] + len(ori_labels[ino])) - convert_ids = convert_ids + list(ori_labels[ino]) - convert_ids = np.array(convert_ids) - convert_ids = convert_ids.reshape((-1, 1)) - return convert_ids, target_lod diff --git a/ppocr/utils/check.py b/ppocr/utils/check.py deleted file mode 100755 index 3a0b14061e2058464c4942e6cf4f891a2a77ba69..0000000000000000000000000000000000000000 --- a/ppocr/utils/check.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import sys - -import logging -logger = logging.getLogger(__name__) - - -def check_config_params(config, config_name, params): - for param in params: - if param not in config: - err = "param %s didn't find in %s!" % (param, config_name) - assert False, err - return diff --git a/ppocr/utils/dict/en_dict.txt b/ppocr/utils/dict/en_dict.txt new file mode 100644 index 0000000000000000000000000000000000000000..6fbd99f46acca8391a5e86ae546c637399204506 --- /dev/null +++ b/ppocr/utils/dict/en_dict.txt @@ -0,0 +1,63 @@ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +A +B +C +D +E +F +G +H +I +J +K +L +M +N +O +P +Q +R +S +T +U +V +W +X +Y +Z + diff --git a/ppocr/utils/dict/french_dict.txt b/ppocr/utils/dict/french_dict.txt index a74c60ad39a8382da4658b4af3adc2d9b1df317c..e8f657db35bf0b74f779f38a9e3b9b47b007e3c4 100644 --- a/ppocr/utils/dict/french_dict.txt +++ b/ppocr/utils/dict/french_dict.txt @@ -132,4 +132,5 @@ j ³ Å $ -# \ No newline at end of file +# + diff --git a/ppocr/utils/dict/german_dict.txt b/ppocr/utils/dict/german_dict.txt index ba9d472adc5a1c15b860a3cbb934cec97db4f268..af0b01ebc9c5f588b621e318a9d85760cd8f42d9 100644 --- a/ppocr/utils/dict/german_dict.txt +++ b/ppocr/utils/dict/german_dict.txt @@ -123,4 +123,5 @@ z â å æ -é \ No newline at end of file +é + diff --git a/ppocr/utils/dict/japan_dict.txt b/ppocr/utils/dict/japan_dict.txt index 926979bc6b901e915931480010bee4aa720a8cbb..339d4b89e5159a346636641a0814874faa59754a 100644 --- a/ppocr/utils/dict/japan_dict.txt +++ b/ppocr/utils/dict/japan_dict.txt @@ -4395,4 +4395,5 @@ z ｙ ｚ ～ -･ \ No newline at end of file +･ + diff --git a/ppocr/utils/dict/korean_dict.txt b/ppocr/utils/dict/korean_dict.txt index 77ae5c301a1173622b1537d01da34009f2c40a4c..a13899f14dfe3bfc25b34904390c7b1e4ed8674b 100644 --- a/ppocr/utils/dict/korean_dict.txt +++ b/ppocr/utils/dict/korean_dict.txt @@ -179,7 +179,7 @@ z с т я - +​ ’ “ ” @@ -3684,4 +3684,5 @@ z 立 茶 切 -宅 \ No newline at end of file +宅 + diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py index 004322c832195fdbe5cbdaf20f4186ba7e9f8a26..af2de054de3656638fee8d4328765c21b4deaea4 100644 --- a/ppocr/utils/save_load.py +++ b/ppocr/utils/save_load.py @@ -55,8 +55,8 @@ def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False): weight_name = weight_name.replace('binarize', '').replace( 'thresh', '') # for DB if weight_name in pre_state_dict.keys(): - logger.info('Load weight: {}, shape: {}'.format( - weight_name, pre_state_dict[weight_name].shape)) + # logger.info('Load weight: {}, shape: {}'.format( + # weight_name, pre_state_dict[weight_name].shape)) if 'encoder_rnn' in key: # delete axis which is 1 pre_state_dict[weight_name] = pre_state_dict[ diff --git a/setup.py b/setup.py index 6b503ce33e2917196ab352bc10c765a543772ec1..bef6dbbfc41f6f0ced3a6dc0cf8c6a8c7270dedf 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ setup( package_dir={'paddleocr': ''}, include_package_data=True, entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]}, - version='0.0.3', + version='2.0', install_requires=requirements, license='Apache License 2.0', description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices', diff --git a/tools/export_model.py b/tools/export_model.py index cf568884f695f647c50d375d50d168d5ca1ea86e..51c061788e65575a8a8c69ba60d42a6334b4ad5e 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -28,37 +28,17 @@ from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import init_model from ppocr.utils.logging import get_logger -from tools.program import load_config - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument("-c", "--config", help="configuration file to use") - parser.add_argument( - "-o", "--output_path", type=str, default='./output/infer/') - return parser.parse_args() - - -class Model(paddle.nn.Layer): - def __init__(self, model): - super(Model, self).__init__() - self.pre_model = model - - # Please modify the 'shape' according to actual needs - @to_static(input_spec=[ - paddle.static.InputSpec( - shape=[None, 3, 640, 640], dtype='float32') - ]) - def forward(self, inputs): - x = self.pre_model(inputs) - return x +from tools.program import load_config, merge_config,ArgsParser def main(): - FLAGS = parse_args() + FLAGS = ArgsParser().parse_args() config = load_config(FLAGS.config) + merge_config(FLAGS.opt) logger = get_logger() + print(config) # build post process + post_process_class = build_post_process(config['PostProcess'], config['Global']) @@ -71,9 +51,15 @@ def main(): init_model(config, model, logger) model.eval() - model = Model(model) - save_path = '{}/{}'.format(FLAGS.output_path, - config['Architecture']['model_type']) + save_path = '{}/inference'.format(config['Global']['save_inference_dir']) + infer_shape = [3, 32, 100] if config['Architecture'][ + 'model_type'] != "det" else [3, 640, 640] + model = to_static( + model, + input_spec=[ + paddle.static.InputSpec( + shape=[None] + infer_shape, dtype='float32') + ]) paddle.jit.save(model, save_path) logger.info('inference model is saved to {}'.format(save_path)) diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py index ae660fdedad9580f098420119946a0291d3aa1f8..07dfc216a2aaa509a6625df2a29a3429a6b05086 100755 --- a/tools/infer/predict_system.py +++ b/tools/infer/predict_system.py @@ -13,6 +13,7 @@ # limitations under the License. import os import sys + __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) @@ -30,12 +31,15 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif from ppocr.utils.logging import get_logger from tools.infer.utility import draw_ocr_box_txt +logger = get_logger() + class TextSystem(object): def __init__(self, args): self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) self.use_angle_cls = args.use_angle_cls + self.drop_score = args.drop_score if self.use_angle_cls: self.text_classifier = predict_cls.TextClassifier(args) @@ -81,7 +85,8 @@ class TextSystem(object): def __call__(self, img): ori_im = img.copy() dt_boxes, elapse = self.text_detector(img) - logger.info("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse)) + logger.info("dt_boxes num : {}, elapse : {}".format( + len(dt_boxes), elapse)) if dt_boxes is None: return None, None img_crop_list = [] @@ -99,9 +104,16 @@ class TextSystem(object): len(img_crop_list), elapse)) rec_res, elapse = self.text_recognizer(img_crop_list) - logger.info("rec_res num : {}, elapse : {}".format(len(rec_res), elapse)) + logger.info("rec_res num : {}, elapse : {}".format( + len(rec_res), elapse)) # self.print_draw_crop_rec_res(img_crop_list, rec_res) - return dt_boxes, rec_res + filter_boxes, filter_rec_res = [], [] + for box, rec_reuslt in zip(dt_boxes, rec_res): + text, score = rec_reuslt + if score >= self.drop_score: + filter_boxes.append(box) + filter_rec_res.append(rec_reuslt) + return filter_boxes, filter_rec_res def sorted_boxes(dt_boxes): @@ -117,8 +129,8 @@ def sorted_boxes(dt_boxes): _boxes = list(sorted_boxes) for i in range(num_boxes - 1): - if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \ - (_boxes[i + 1][0][0] < _boxes[i][0][0]): + if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \ + (_boxes[i + 1][0][0] < _boxes[i][0][0]): tmp = _boxes[i] _boxes[i] = _boxes[i + 1] _boxes[i + 1] = tmp @@ -143,12 +155,8 @@ def main(args): elapse = time.time() - starttime logger.info("Predict time of %s: %.3fs" % (image_file, elapse)) - dt_num = len(dt_boxes) - for dno in range(dt_num): - text, score = rec_res[dno] - if score >= drop_score: - text_str = "%s, %.3f" % (text, score) - logger.info(text_str) + for text, score in rec_res: + logger.info("{}, {:.3f}".format(text, score)) if is_visualize: image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) @@ -174,5 +182,4 @@ def main(args): if __name__ == "__main__": - logger = get_logger() - main(utility.parse_args()) + main(utility.parse_args()) \ No newline at end of file diff --git a/tools/infer/utility.py b/tools/infer/utility.py index ee1f954dcc4b6518cfe454a86650b397b9db449e..4b06b60b9e25954be7375882b5fb67343312b222 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -100,8 +100,8 @@ def create_predictor(args, mode, logger): if model_dir is None: logger.info("not find {} model file path {}".format(mode, model_dir)) sys.exit(0) - model_file_path = model_dir + "/model" - params_file_path = model_dir + "/params" + model_file_path = model_dir + "/inference.pdmodel" + params_file_path = model_dir + "/inference.pdiparams" if not os.path.exists(model_file_path): logger.info("not find model file path {}".format(model_file_path)) sys.exit(0) @@ -230,10 +230,10 @@ def draw_ocr_box_txt(image, box[2][1], box[3][0], box[3][1] ], outline=color) - box_height = math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][ - 1]) ** 2) - box_width = math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][ - 1]) ** 2) + box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][ + 1])**2) + box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][ + 1])**2) if box_height > 2 * box_width: font_size = max(int(box_width * 0.9), 10) font = ImageFont.truetype(font_path, font_size, encoding="utf-8") @@ -260,7 +260,6 @@ def str_count(s): Count the number of Chinese characters, a single English character and a single number equal to half the length of Chinese characters. - args: s(string): the input of string return(int): @@ -295,7 +294,6 @@ def text_visual(texts, img_w(int): the width of blank img font_path: the path of font which is used to draw text return(array): - """ if scores is not None: assert len(texts) == len(