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提交 7b53596c 编写于 作者: W WenmuZhou
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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph_rc

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README_ch.md
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PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
**近期更新**
- 2020.12.07 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
- 2020.9.19 更新超轻量压缩ppocr_mobile_slim系列模型，整体模型3.5M(详见[PP-OCR Pipeline](#PP-OCR))，适合在移动端部署使用。[模型下载](#模型下载)
- 2020.9.17 更新超轻量ppocr_mobile系列和通用ppocr_server系列中英文ocr模型，媲美商业效果。[模型下载](#模型下载)
- 2020.9.17 更新[英文识别模型](./doc/doc_ch/models_list.md#英文识别模型)和[多语言识别模型](doc/doc_ch/models_list.md#多语言识别模型)，已支持`德语、法语、日语、韩语`，更多语种识别模型将持续更新。
- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
- [More](./doc/doc_ch/update.md)
## 特性
- PPOCR系列高质量预训练模型，准确的识别效果
......@@ -48,15 +50,14 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- 代码体验：从[快速安装](./doc/doc_ch/installation.md) 开始
<a name="模型下载"></a>
## PP-OCR 1.1系列模型列表（9月17日更新）
## PP-OCR 2.0系列模型列表（更新中）
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_pre.tar) |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/det/ch_ppocr_server_v1.1_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/20-09-22/server/rec/ch_ppocr_server_v1.1_rec_pre.tar) |
| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_det_prune_opt.nb) |[推理模型](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_cls_quant_opt.nb)| [推理模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar) / [slim模型](https://paddleocr.bj.bcebos.com/20-09-22/mobile/lite/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| 中英文通用OCR模型（143M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](./doc/doc_ch/models_list.md)
更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
## 文档教程
- [快速安装](./doc/doc_ch/installation.md)
......@@ -76,9 +77,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./deploy/hubserving/readme.md)
- [端侧部署](./deploy/lite/readme.md)
- [模型量化](./deploy/slim/quantization/README.md)
- [模型裁剪](./deploy/slim/prune/README.md)
- [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
- [模型量化](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README.md)
- [模型裁剪](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/README.md)
- [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
......@@ -96,6 +97,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [许可证书](#许可证书)
- [贡献代码](#贡献代码)
***注意：动态图端侧部署仍在开发中，目前仅支持动态图训练、python端预测，C++预测，
如果您有需要移动端部署案例或者量化裁剪，请切换到静态图分支；***
<a name="PP-OCR"></a>
## PP-OCR Pipline
<div align="center">
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## 贡献代码
我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
......@@ -148,3 +153,6 @@ PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框
- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用。
- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料。
- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了PPOCRLabel的完整代码。
README_en.md
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configs/cls/cls_mv3.yml
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# 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:
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Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/det_r50_vd/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 8
# 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: True
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
regularizer:
name: 'L2'
factor: 0
Architecture:
type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 50
Neck:
name: FPN
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
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: ./detection/
file_list:
- ./detection/train_icdar2015_label.txt # dataset1
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: [ 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'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size: 16
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: ./detection/
file_list:
- ./detection/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: 1 # must be 1
num_workers: 8
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configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml
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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
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
......
configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml
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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
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
......
configs/det/det_mv3_db.yml
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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
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
......
configs/det/det_r50_vd_db.yml
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......@@ -3,7 +3,7 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/det_rc/det_r50_vd/
save_model_dir: ./output/det_r50_vd/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [5000,4000]
......
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Global:
use_gpu: false
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
# 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:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: False
infer_mode: False
use_tps: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
type: rec
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride: [ 1, 2, 2, 2 ]
Neck:
name: SequenceEncoder
encoder_type: fc
hidden_size: 96
Head:
name: CTC
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
TRAIN:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 8
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Global:
use_gpu: false
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/res34_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
# 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:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: False
infer_mode: False
use_tps: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
type: rec
algorithm: CRNN
Transform:
Backbone:
name: ResNet
layers: 34
Neck:
name: SequenceEncoder
encoder_type: fc
hidden_size: 96
Head:
name: CTC
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
TRAIN:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 8
configs/rec/bak/rec_r34_vd_none_none_ctc.yml → configs/rec/rec_icdar15_train.yml
浏览文件 @ 7b53596c
Global:
use_gpu: false
epoch_num: 500
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/res34_none_none_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
save_model_dir: ./output/rec/ic15/
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
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
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
max_text_length: 80
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: False
character_dict_path: ppocr/utils/ic15_dict.txt
character_type: ch
max_text_length: 25
infer_mode: False
use_tps: False
use_space_char: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
lr:
learning_rate: 0.0005
regularizer:
name: 'L2'
factor: 0.00001
factor: 0
Architecture:
type: rec
model_type: rec
algorithm: CRNN
Transform:
Backbone:
......@@ -43,10 +40,11 @@ Architecture:
layers: 34
Neck:
name: SequenceEncoder
encoder_type: reshape
encoder_type: rnn
hidden_size: 256
Head:
name: CTC
fc_decay: 0.00001
name: CTCHead
fc_decay: 0
Loss:
name: CTCLoss
......@@ -58,46 +56,42 @@ Metric:
name: RecMetric
main_indicator: acc
TRAIN:
Train:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 8
EVAL:
Eval:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/val.txt
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
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: 256
num_workers: 8
batch_size_per_card: 256
num_workers: 4
deploy/android_demo/.gitignore 已删除 100644 → 0
浏览文件 @ 0458f0cc
*.iml
.gradle
/local.properties
/.idea/*
.DS_Store
/build
/captures
.externalNativeBuild
deploy/android_demo/README.md 已删除 100644 → 0
浏览文件 @ 0458f0cc
# 如何快速测试
### 1. 安装最新版本的Android Studio
可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
### 2. 按照NDK 20 以上版本
Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
如果您是初学者，可以用以下方式安装和测试NDK编译环境。
点击 File -> New ->New Project， 新建 "Native C++" project
### 3. 导入项目
点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
# 获得更多支持
前往[端计算模型生成平台EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite)，获得更多开发支持：
- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发
deploy/android_demo/app/build.gradle 已删除 100644 → 0
浏览文件 @ 0458f0cc
import java.security.MessageDigest
apply plugin: 'com.android.application'
android {
compileSdkVersion 29
defaultConfig {
applicationId "com.baidu.paddle.lite.demo.ocr"
minSdkVersion 23
targetSdkVersion 29
versionCode 1
versionName "1.0"
testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
externalNativeBuild {
cmake {
cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
}
}
ndk {
// abiFilters "arm64-v8a", "armeabi-v7a"
abiFilters "arm64-v8a", "armeabi-v7a"
ldLibs "jnigraphics"
}
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
}
}
externalNativeBuild {
cmake {
path "src/main/cpp/CMakeLists.txt"
version "3.10.2"
}
}
}
dependencies {
implementation fileTree(include: ['*.jar'], dir: 'libs')
implementation 'androidx.appcompat:appcompat:1.1.0'
implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
testImplementation 'junit:junit:4.12'
androidTestImplementation 'com.android.support.test:runner:1.0.2'
androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
}
def archives = [
[
'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/paddle_lite_libs_v2_6_1.tar.gz',
'dest': 'PaddleLite'
],
[
'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
'dest': 'OpenCV'
],
[
'src' : 'https://paddleocr.bj.bcebos.com/deploy/lite/ocr_v1_for_cpu.tar.gz',
'dest' : 'src/main/assets/models/ocr_v1_for_cpu'
]
]
task downloadAndExtractArchives(type: DefaultTask) {
doFirst {
println "Downloading and extracting archives including libs and models"
}
doLast {
// Prepare cache folder for archives
String cachePath = "cache"
if (!file("${cachePath}").exists()) {
mkdir "${cachePath}"
}
archives.eachWithIndex { archive, index ->
MessageDigest messageDigest = MessageDigest.getInstance('MD5')
messageDigest.update(archive.src.bytes)
String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
// Download the target archive if not exists
boolean copyFiles = !file("${archive.dest}").exists()
if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
copyFiles = true; // force to copy files from the latest archive files
}
// Extract the target archive if its dest path does not exists
if (copyFiles) {
copy {
from tarTree("${cachePath}/${cacheName}.tar.gz")
into "${archive.dest}"
}
}
}
}
}
preBuild.dependsOn downloadAndExtractArchives
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deploy/android_demo/app/proguard-rules.pro 已删除 100644 → 0
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# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile
deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.content.Context;
import android.support.test.InstrumentationRegistry;
import android.support.test.runner.AndroidJUnit4;
import org.junit.Test;
import org.junit.runner.RunWith;
import static org.junit.Assert.*;
/**
* Instrumented test, which will execute on an Android device.
*
* @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
*/
@RunWith(AndroidJUnit4.class)
public class ExampleInstrumentedTest {
@Test
public void useAppContext() {
// Context of the app under test.
Context appContext = InstrumentationRegistry.getTargetContext();
assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
}
}
deploy/android_demo/app/src/main/AndroidManifest.xml 已删除 100644 → 0
浏览文件 @ 0458f0cc
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
package="com.baidu.paddle.lite.demo.ocr">
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
<uses-permission android:name="android.permission.CAMERA"/>
<application
android:allowBackup="true"
android:icon="@mipmap/ic_launcher"
android:label="@string/app_name"
android:roundIcon="@mipmap/ic_launcher_round"
android:supportsRtl="true"
android:theme="@style/AppTheme">
<!-- to test MiniActivity, change this to com.baidu.paddle.lite.demo.ocr.MiniActivity -->
<activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
<intent-filter>
<action android:name="android.intent.action.MAIN"/>
<category android:name="android.intent.category.LAUNCHER"/>
</intent-filter>
</activity>
<activity
android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
android:label="Settings">
</activity>
<provider
android:name="androidx.core.content.FileProvider"
android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
android:exported="false"
android:grantUriPermissions="true">
<meta-data
android:name="android.support.FILE_PROVIDER_PATHS"
android:resource="@xml/file_paths"></meta-data>
</provider>
</application>
</manifest>
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 0458f0cc
# For more information about using CMake with Android Studio, read the
# documentation: https://d.android.com/studio/projects/add-native-code.html
# Sets the minimum version of CMake required to build the native library.
cmake_minimum_required(VERSION 3.4.1)
# Creates and names a library, sets it as either STATIC or SHARED, and provides
# the relative paths to its source code. You can define multiple libraries, and
# CMake builds them for you. Gradle automatically packages shared libraries with
# your APK.
set(PaddleLite_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../PaddleLite")
include_directories(${PaddleLite_DIR}/cxx/include)
set(OpenCV_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../OpenCV/sdk/native/jni")
message(STATUS "opencv dir: ${OpenCV_DIR}")
find_package(OpenCV REQUIRED)
message(STATUS "OpenCV libraries: ${OpenCV_LIBS}")
include_directories(${OpenCV_INCLUDE_DIRS})
aux_source_directory(. SOURCES)
set(CMAKE_CXX_FLAGS
"${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os"
)
set(CMAKE_CXX_FLAGS
"${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -fdata-sections -ffunction-sections"
)
set(CMAKE_SHARED_LINKER_FLAGS
"${CMAKE_SHARED_LINKER_FLAGS} -Wl,--gc-sections -Wl,-z,nocopyreloc")
add_library(
# Sets the name of the library.
Native
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
${SOURCES})
find_library(
# Sets the name of the path variable.
log-lib
# Specifies the name of the NDK library that you want CMake to locate.
log)
add_library(
# Sets the name of the library.
paddle_light_api_shared
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
IMPORTED)
set_target_properties(
# Specifies the target library.
paddle_light_api_shared
# Specifies the parameter you want to define.
PROPERTIES
IMPORTED_LOCATION
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
# Provides the path to the library you want to import.
)
# Specifies libraries CMake should link to your target library. You can link
# multiple libraries, such as libraries you define in this build script,
# prebuilt third-party libraries, or system libraries.
target_link_libraries(
# Specifies the target library.
Native
paddle_light_api_shared
${OpenCV_LIBS}
GLESv2
EGL
jnigraphics
${log-lib}
)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libc++_shared.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libc++_shared.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libpaddle_light_api_shared.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir_build.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir_build.so)
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/common.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fu on 4/25/18.
//
#pragma once
#import <vector>
#import <numeric>
#ifdef __ANDROID__
#include <android/log.h>
#define LOG_TAG "OCR_NDK"
#define LOGI(...) \
__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
#define LOGW(...) \
__android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__)
#define LOGE(...) \
__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)
#else
#include <stdio.h>
#define LOGI(format, ...) \
fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
#define LOGW(format, ...) \
fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
#define LOGE(format, ...) \
fprintf(stderr, "[" LOG_TAG "]Error: " format "\n", ##__VA_ARGS__)
#endif
enum RETURN_CODE {
RETURN_OK = 0
};
enum NET_TYPE{
NET_OCR = 900100,
NET_OCR_INTERNAL = 991008
};
template <typename T>
inline T product(const std::vector<T> &vec) {
if (vec.empty()){
return 0;
}
return std::accumulate(vec.begin(), vec.end(), 1, std::multiplies<T>());
}
deploy/android_demo/app/src/main/cpp/native.cpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fujiayi on 2020/7/5.
//
#include "native.h"
#include "ocr_ppredictor.h"
#include <string>
#include <algorithm>
#include <paddle_api.h>
static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
extern "C"
JNIEXPORT jlong JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(JNIEnv *env, jobject thiz,
jstring j_det_model_path,
jstring j_rec_model_path,
jint j_thread_num,
jstring j_cpu_mode) {
std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
int thread_num = j_thread_num;
std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
ppredictor::OCR_Config conf;
conf.thread_num = thread_num;
conf.mode = str_to_cpu_mode(cpu_mode);
ppredictor::OCR_PPredictor *orc_predictor = new ppredictor::OCR_PPredictor{conf};
orc_predictor->init_from_file(det_model_path, rec_model_path);
return reinterpret_cast<jlong>(orc_predictor);
}
/**
* "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
* @param cpu_mode
* @return
*/
static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode) {
static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
{"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
{"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
{"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
{"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
{"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
{"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}
};
std::string upper_key;
std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(), ::toupper);
auto index = cpu_mode_map.find(upper_key);
if (index == cpu_mode_map.end()) {
LOGE("cpu_mode not found %s", upper_key.c_str());
return paddle::lite_api::LITE_POWER_HIGH;
} else {
return index->second;
}
}
extern "C"
JNIEXPORT jfloatArray JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(JNIEnv *env, jobject thiz,
jlong java_pointer, jfloatArray buf,
jfloatArray ddims,
jobject original_image) {
LOGI("begin to run native forward");
if (java_pointer == 0) {
LOGE("JAVA pointer is NULL");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
cv::Mat origin = bitmap_to_cv_mat(env, original_image);
if (origin.size == 0) {
LOGE("origin bitmap cannot convert to CV Mat");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
std::vector<int64_t> dims_arr;
dims_arr.resize(dims_float_arr.size());
std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
// 这里值有点大，就不调用jfloatarray_to_float_vector了
int64_t buf_len = (int64_t) env->GetArrayLength(buf);
jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
float *data = (jfloat *) buf_data;
std::vector<ppredictor::OCRPredictResult> results = ppredictor->infer_ocr(dims_arr, data,
buf_len,
NET_OCR, origin);
LOGI("infer_ocr finished with boxes %ld", results.size());
// 这里将std::vector<ppredictor::OCRPredictResult> 序列化成 float数组，传输到java层再反序列化
std::vector<float> float_arr;
for (const ppredictor::OCRPredictResult &r :results) {
float_arr.push_back(r.points.size());
float_arr.push_back(r.word_index.size());
float_arr.push_back(r.score);
for (const std::vector<int> &point : r.points) {
float_arr.push_back(point.at(0));
float_arr.push_back(point.at(1));
}
for (int index: r.word_index) {
float_arr.push_back(index);
}
}
return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
}
extern "C"
JNIEXPORT void JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(JNIEnv *env, jobject thiz,
jlong java_pointer){
if (java_pointer == 0) {
LOGE("JAVA pointer is NULL");
return;
}
ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
delete ppredictor;
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/native.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fujiayi on 2020/7/5.
//
#pragma once
#include <jni.h>
#include <string>
#include <vector>
#include <android/bitmap.h>
#include <opencv2/opencv.hpp>
#include "common.h"
inline std::string jstring_to_cpp_string(JNIEnv *env, jstring jstr) {
// In java, a unicode char will be encoded using 2 bytes (utf16).
// so jstring will contain characters utf16. std::string in c++ is
// essentially a string of bytes, not characters, so if we want to
// pass jstring from JNI to c++, we have convert utf16 to bytes.
if (!jstr) {
return "";
}
const jclass stringClass = env->GetObjectClass(jstr);
const jmethodID getBytes =
env->GetMethodID(stringClass, "getBytes", "(Ljava/lang/String;)[B");
const jbyteArray stringJbytes = (jbyteArray) env->CallObjectMethod(
jstr, getBytes, env->NewStringUTF("UTF-8"));
size_t length = (size_t) env->GetArrayLength(stringJbytes);
jbyte *pBytes = env->GetByteArrayElements(stringJbytes, NULL);
std::string ret = std::string(reinterpret_cast<char *>(pBytes), length);
env->ReleaseByteArrayElements(stringJbytes, pBytes, JNI_ABORT);
env->DeleteLocalRef(stringJbytes);
env->DeleteLocalRef(stringClass);
return ret;
}
inline jstring cpp_string_to_jstring(JNIEnv *env, std::string str) {
auto *data = str.c_str();
jclass strClass = env->FindClass("java/lang/String");
jmethodID strClassInitMethodID =
env->GetMethodID(strClass, "<init>", "([BLjava/lang/String;)V");
jbyteArray bytes = env->NewByteArray(strlen(data));
env->SetByteArrayRegion(bytes, 0, strlen(data),
reinterpret_cast<const jbyte *>(data));
jstring encoding = env->NewStringUTF("UTF-8");
jstring res = (jstring) (
env->NewObject(strClass, strClassInitMethodID, bytes, encoding));
env->DeleteLocalRef(strClass);
env->DeleteLocalRef(encoding);
env->DeleteLocalRef(bytes);
return res;
}
inline jfloatArray cpp_array_to_jfloatarray(JNIEnv *env, const float *buf,
int64_t len) {
if (len == 0) {
return env->NewFloatArray(0);
}
jfloatArray result = env->NewFloatArray(len);
env->SetFloatArrayRegion(result, 0, len, buf);
return result;
}
inline jintArray cpp_array_to_jintarray(JNIEnv *env, const int *buf,
int64_t len) {
jintArray result = env->NewIntArray(len);
env->SetIntArrayRegion(result, 0, len, buf);
return result;
}
inline jbyteArray cpp_array_to_jbytearray(JNIEnv *env, const int8_t *buf,
int64_t len) {
jbyteArray result = env->NewByteArray(len);
env->SetByteArrayRegion(result, 0, len, buf);
return result;
}
inline jlongArray int64_vector_to_jlongarray(JNIEnv *env,
const std::vector<int64_t> &vec) {
jlongArray result = env->NewLongArray(vec.size());
jlong *buf = new jlong[vec.size()];
for (size_t i = 0; i < vec.size(); ++i) {
buf[i] = (jlong) vec[i];
}
env->SetLongArrayRegion(result, 0, vec.size(), buf);
delete[] buf;
return result;
}
inline std::vector<int64_t> jlongarray_to_int64_vector(JNIEnv *env,
jlongArray data) {
int data_size = env->GetArrayLength(data);
jlong *data_ptr = env->GetLongArrayElements(data, nullptr);
std::vector<int64_t> data_vec(data_ptr, data_ptr + data_size);
env->ReleaseLongArrayElements(data, data_ptr, 0);
return data_vec;
}
inline std::vector<float> jfloatarray_to_float_vector(JNIEnv *env,
jfloatArray data) {
int data_size = env->GetArrayLength(data);
jfloat *data_ptr = env->GetFloatArrayElements(data, nullptr);
std::vector<float> data_vec(data_ptr, data_ptr + data_size);
env->ReleaseFloatArrayElements(data, data_ptr, 0);
return data_vec;
}
inline cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
AndroidBitmapInfo info;
int result = AndroidBitmap_getInfo(env, bitmap, &info);
if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
LOGE("AndroidBitmap_getInfo failed, result: %d", result);
return cv::Mat{};
}
if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
LOGE("Bitmap format is not RGBA_8888 !");
return cv::Mat{};
}
unsigned char *srcData = NULL;
AndroidBitmap_lockPixels(env, bitmap, (void **) &srcData);
cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
memcpy(mat.data, srcData, info.height * info.width * 4);
AndroidBitmap_unlockPixels(env, bitmap);
cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
/**
if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
LOGE("Write image failed " );
}
*/
return mat;
}
deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
/*******************************************************************************
* *
* Author : Angus Johnson *
* Version : 6.4.2 *
* Date : 27 February 2017 *
* Website : http://www.angusj.com *
* Copyright : Angus Johnson 2010-2017 *
* *
* License: *
* Use, modification & distribution is subject to Boost Software License Ver 1. *
* http://www.boost.org/LICENSE_1_0.txt *
* *
* Attributions: *
* The code in this library is an extension of Bala Vatti's clipping algorithm: *
* "A generic solution to polygon clipping" *
* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63. *
* http://portal.acm.org/citation.cfm?id=129906 *
* *
* Computer graphics and geometric modeling: implementation and algorithms *
* By Max K. Agoston *
* Springer; 1 edition (January 4, 2005) *
* http://books.google.com/books?q=vatti+clipping+agoston *
* *
* See also: *
* "Polygon Offsetting by Computing Winding Numbers" *
* Paper no. DETC2005-85513 pp. 565-575 *
* ASME 2005 International Design Engineering Technical Conferences *
* and Computers and Information in Engineering Conference (IDETC/CIE2005) *
* September 24-28, 2005 , Long Beach, California, USA *
* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf *
* *
*******************************************************************************/
#ifndef clipper_hpp
#define clipper_hpp
#define CLIPPER_VERSION "6.4.2"
//use_int32: When enabled 32bit ints are used instead of 64bit ints. This
//improve performance but coordinate values are limited to the range +/- 46340
//#define use_int32
//use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
//#define use_xyz
//use_lines: Enables line clipping. Adds a very minor cost to performance.
#define use_lines
//use_deprecated: Enables temporary support for the obsolete functions
//#define use_deprecated
#include <vector>
#include <list>
#include <set>
#include <stdexcept>
#include <cstring>
#include <cstdlib>
#include <ostream>
#include <functional>
#include <queue>
namespace ClipperLib {
enum ClipType {
ctIntersection, ctUnion, ctDifference, ctXor
};
enum PolyType {
ptSubject, ptClip
};
//By far the most widely used winding rules for polygon filling are
//EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
//Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
//see http://glprogramming.com/red/chapter11.html
enum PolyFillType {
pftEvenOdd, pftNonZero, pftPositive, pftNegative
};
#ifdef use_int32
typedef int cInt;
static cInt const loRange = 0x7FFF;
static cInt const hiRange = 0x7FFF;
#else
typedef signed long long cInt;
static cInt const loRange = 0x3FFFFFFF;
static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
typedef signed long long long64; //used by Int128 class
typedef unsigned long long ulong64;
#endif
struct IntPoint {
cInt X;
cInt Y;
#ifdef use_xyz
cInt Z;
IntPoint(cInt x = 0, cInt y = 0, cInt z = 0): X(x), Y(y), Z(z) {};
#else
IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y) {};
#endif
friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
return a.X == b.X && a.Y == b.Y;
}
friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
return a.X != b.X || a.Y != b.Y;
}
};
//------------------------------------------------------------------------------
typedef std::vector <IntPoint> Path;
typedef std::vector <Path> Paths;
inline Path &operator<<(Path &poly, const IntPoint &p) {
poly.push_back(p);
return poly;
}
inline Paths &operator<<(Paths &polys, const Path &p) {
polys.push_back(p);
return polys;
}
std::ostream &operator<<(std::ostream &s, const IntPoint &p);
std::ostream &operator<<(std::ostream &s, const Path &p);
std::ostream &operator<<(std::ostream &s, const Paths &p);
struct DoublePoint {
double X;
double Y;
DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
DoublePoint(IntPoint ip) : X((double) ip.X), Y((double) ip.Y) {}
};
//------------------------------------------------------------------------------
#ifdef use_xyz
typedef void (*ZFillCallback)(IntPoint& e1bot, IntPoint& e1top, IntPoint& e2bot, IntPoint& e2top, IntPoint& pt);
#endif
enum InitOptions {
ioReverseSolution = 1, ioStrictlySimple = 2, ioPreserveCollinear = 4
};
enum JoinType {
jtSquare, jtRound, jtMiter
};
enum EndType {
etClosedPolygon, etClosedLine, etOpenButt, etOpenSquare, etOpenRound
};
class PolyNode;
typedef std::vector<PolyNode *> PolyNodes;
class PolyNode {
public:
PolyNode();
virtual ~PolyNode() {};
Path Contour;
PolyNodes Childs;
PolyNode *Parent;
PolyNode *GetNext() const;
bool IsHole() const;
bool IsOpen() const;
int ChildCount() const;
private:
//PolyNode& operator =(PolyNode& other);
unsigned Index; //node index in Parent.Childs
bool m_IsOpen;
JoinType m_jointype;
EndType m_endtype;
PolyNode *GetNextSiblingUp() const;
void AddChild(PolyNode &child);
friend class Clipper; //to access Index
friend class ClipperOffset;
};
class PolyTree : public PolyNode {
public:
~PolyTree() { Clear(); };
PolyNode *GetFirst() const;
void Clear();
int Total() const;
private:
//PolyTree& operator =(PolyTree& other);
PolyNodes AllNodes;
friend class Clipper; //to access AllNodes
};
bool Orientation(const Path &poly);
double Area(const Path &poly);
int PointInPolygon(const IntPoint &pt, const Path &path);
void SimplifyPolygon(const Path &in_poly, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
void SimplifyPolygons(const Paths &in_polys, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
void CleanPolygon(Path &poly, double distance = 1.415);
void CleanPolygons(const Paths &in_polys, Paths &out_polys, double distance = 1.415);
void CleanPolygons(Paths &polys, double distance = 1.415);
void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution, bool pathIsClosed);
void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution, bool pathIsClosed);
void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
void ReversePath(Path &p);
void ReversePaths(Paths &p);
struct IntRect {
cInt left;
cInt top;
cInt right;
cInt bottom;
};
//enums that are used internally ...
enum EdgeSide {
esLeft = 1, esRight = 2
};
//forward declarations (for stuff used internally) ...
struct TEdge;
struct IntersectNode;
struct LocalMinimum;
struct OutPt;
struct OutRec;
struct Join;
typedef std::vector<OutRec *> PolyOutList;
typedef std::vector<TEdge *> EdgeList;
typedef std::vector<Join *> JoinList;
typedef std::vector<IntersectNode *> IntersectList;
//------------------------------------------------------------------------------
//ClipperBase is the ancestor to the Clipper class. It should not be
//instantiated directly. This class simply abstracts the conversion of sets of
//polygon coordinates into edge objects that are stored in a LocalMinima list.
class ClipperBase {
public:
ClipperBase();
virtual ~ClipperBase();
virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
virtual void Clear();
IntRect GetBounds();
bool PreserveCollinear() { return m_PreserveCollinear; };
void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
protected:
void DisposeLocalMinimaList();
TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
virtual void Reset();
TEdge *ProcessBound(TEdge *E, bool IsClockwise);
void InsertScanbeam(const cInt Y);
bool PopScanbeam(cInt &Y);
bool LocalMinimaPending();
bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
OutRec *CreateOutRec();
void DisposeAllOutRecs();
void DisposeOutRec(PolyOutList::size_type index);
void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
void DeleteFromAEL(TEdge *e);
void UpdateEdgeIntoAEL(TEdge *&e);
typedef std::vector <LocalMinimum> MinimaList;
MinimaList::iterator m_CurrentLM;
MinimaList m_MinimaList;
bool m_UseFullRange;
EdgeList m_edges;
bool m_PreserveCollinear;
bool m_HasOpenPaths;
PolyOutList m_PolyOuts;
TEdge *m_ActiveEdges;
typedef std::priority_queue <cInt> ScanbeamList;
ScanbeamList m_Scanbeam;
};
//------------------------------------------------------------------------------
class Clipper : public virtual ClipperBase {
public:
Clipper(int initOptions = 0);
bool Execute(ClipType clipType,
Paths &solution,
PolyFillType fillType = pftEvenOdd);
bool Execute(ClipType clipType,
Paths &solution,
PolyFillType subjFillType,
PolyFillType clipFillType);
bool Execute(ClipType clipType,
PolyTree &polytree,
PolyFillType fillType = pftEvenOdd);
bool Execute(ClipType clipType,
PolyTree &polytree,
PolyFillType subjFillType,
PolyFillType clipFillType);
bool ReverseSolution() { return m_ReverseOutput; };
void ReverseSolution(bool value) { m_ReverseOutput = value; };
bool StrictlySimple() { return m_StrictSimple; };
void StrictlySimple(bool value) { m_StrictSimple = value; };
//set the callback function for z value filling on intersections (otherwise Z is 0)
#ifdef use_xyz
void ZFillFunction(ZFillCallback zFillFunc);
#endif
protected:
virtual bool ExecuteInternal();
private:
JoinList m_Joins;
JoinList m_GhostJoins;
IntersectList m_IntersectList;
ClipType m_ClipType;
typedef std::list <cInt> MaximaList;
MaximaList m_Maxima;
TEdge *m_SortedEdges;
bool m_ExecuteLocked;
PolyFillType m_ClipFillType;
PolyFillType m_SubjFillType;
bool m_ReverseOutput;
bool m_UsingPolyTree;
bool m_StrictSimple;
#ifdef use_xyz
ZFillCallback m_ZFill; //custom callback
#endif
void SetWindingCount(TEdge &edge);
bool IsEvenOddFillType(const TEdge &edge) const;
bool IsEvenOddAltFillType(const TEdge &edge) const;
void InsertLocalMinimaIntoAEL(const cInt botY);
void InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge);
void AddEdgeToSEL(TEdge *edge);
bool PopEdgeFromSEL(TEdge *&edge);
void CopyAELToSEL();
void DeleteFromSEL(TEdge *e);
void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
bool IsContributing(const TEdge &edge) const;
bool IsTopHorz(const cInt XPos);
void DoMaxima(TEdge *e);
void ProcessHorizontals();
void ProcessHorizontal(TEdge *horzEdge);
void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
OutPt *AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
OutRec *GetOutRec(int idx);
void AppendPolygon(TEdge *e1, TEdge *e2);
void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
OutPt *AddOutPt(TEdge *e, const IntPoint &pt);
OutPt *GetLastOutPt(TEdge *e);
bool ProcessIntersections(const cInt topY);
void BuildIntersectList(const cInt topY);
void ProcessIntersectList();
void ProcessEdgesAtTopOfScanbeam(const cInt topY);
void BuildResult(Paths &polys);
void BuildResult2(PolyTree &polytree);
void SetHoleState(TEdge *e, OutRec *outrec);
void DisposeIntersectNodes();
bool FixupIntersectionOrder();
void FixupOutPolygon(OutRec &outrec);
void FixupOutPolyline(OutRec &outrec);
bool IsHole(TEdge *e);
bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
void FixHoleLinkage(OutRec &outrec);
void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
void ClearJoins();
void ClearGhostJoins();
void AddGhostJoin(OutPt *op, const IntPoint offPt);
bool JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2);
void JoinCommonEdges();
void DoSimplePolygons();
void FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec);
void FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec);
void FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec);
#ifdef use_xyz
void SetZ(IntPoint& pt, TEdge& e1, TEdge& e2);
#endif
};
//------------------------------------------------------------------------------
class ClipperOffset {
public:
ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
~ClipperOffset();
void AddPath(const Path &path, JoinType joinType, EndType endType);
void AddPaths(const Paths &paths, JoinType joinType, EndType endType);
void Execute(Paths &solution, double delta);
void Execute(PolyTree &solution, double delta);
void Clear();
double MiterLimit;
double ArcTolerance;
private:
Paths m_destPolys;
Path m_srcPoly;
Path m_destPoly;
std::vector <DoublePoint> m_normals;
double m_delta, m_sinA, m_sin, m_cos;
double m_miterLim, m_StepsPerRad;
IntPoint m_lowest;
PolyNode m_polyNodes;
void FixOrientations();
void DoOffset(double delta);
void OffsetPoint(int j, int &k, JoinType jointype);
void DoSquare(int j, int k);
void DoMiter(int j, int k, double r);
void DoRound(int j, int k);
};
//------------------------------------------------------------------------------
class clipperException : public std::exception {
public:
clipperException(const char *description) : m_descr(description) {}
virtual ~clipperException() throw() {}
virtual const char *what() const throw() { return m_descr.c_str(); }
private:
std::string m_descr;
};
//------------------------------------------------------------------------------
} //ClipperLib namespace
#endif //clipper_hpp
deploy/android_demo/app/src/main/cpp/ocr_crnn_process.cpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
// 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.
#include "ocr_crnn_process.h"
#include <iostream>
#include <vector>
#include <iostream>
#include <cstring>
#include <fstream>
#include <cmath>
const std::string CHARACTER_TYPE = "ch";
const int MAX_DICT_LENGTH = 6624;
const std::vector<int> REC_IMAGE_SHAPE = {3, 32, 320};
static cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int imgH = REC_IMAGE_SHAPE[1];
if (CHARACTER_TYPE == "ch")
imgW = int(32 * wh_ratio);
float ratio = float(img.cols) / float(img.rows);
int resize_w = 0;
if (ceilf(imgH * ratio) > imgW)
resize_w = imgW;
else
resize_w = int(ceilf(imgH * ratio));
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_CUBIC);
resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
for (int h = 0; h < resize_img.rows; h++) {
for (int w = 0; w < resize_img.cols; w++) {
resize_img.at<cv::Vec3f>(h, w)[0] = (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) * 2;
resize_img.at<cv::Vec3f>(h, w)[1] = (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) * 2;
resize_img.at<cv::Vec3f>(h, w)[2] = (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) * 2;
}
}
cv::Mat dist;
cv::copyMakeBorder(resize_img, dist, 0, 0, 0, int(imgW - resize_w), cv::BORDER_CONSTANT,
{0, 0, 0});
return dist;
}
cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int imgH = REC_IMAGE_SHAPE[1];
if (CHARACTER_TYPE == "ch") {
imgW = int(32 * wh_ratio);
}
float ratio = float(img.cols) / float(img.rows);
int resize_w = 0;
if (ceilf(imgH * ratio) > imgW)
resize_w = imgW;
else
resize_w = int(ceilf(imgH * ratio));
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(resize_w, imgH));
return resize_img;
}
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box) {
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
int right = int(*std::max_element(x_collect, x_collect + 4));
int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
cv::Mat img_crop;
srcimage(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M, cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
/*
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
*/
cv::transpose(dst_img, dst_img);
cv::flip(dst_img, dst_img, 0);
return dst_img;
} else {
return dst_img;
}
}
deploy/android_demo/app/src/main/cpp/ocr_crnn_process.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fujiayi on 2020/7/3.
//
#pragma once
#include <vector>
#include <opencv2/opencv.hpp>
#include "common.h"
extern const std::vector<int> REC_IMAGE_SHAPE;
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box);
cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio);
template<class ForwardIterator>
inline size_t argmax(ForwardIterator first, ForwardIterator last) {
return std::distance(first, std::max_element(first, last));
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_db_post_process.cpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
// 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.
#include <iostream>
#include <vector>
#include <math.h>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "ocr_clipper.hpp"
static void getcontourarea(float **box, float unclip_ratio, float &distance) {
int pts_num = 4;
float area = 0.0f;
float dist = 0.0f;
for (int i = 0; i < pts_num; i++) {
area += box[i][0] * box[(i + 1) % pts_num][1] - box[i][1] * box[(i + 1) % pts_num][0];
dist += sqrtf(
(box[i][0] - box[(i + 1) % pts_num][0]) * (box[i][0] - box[(i + 1) % pts_num][0]) +
(box[i][1] - box[(i + 1) % pts_num][1]) * (box[i][1] - box[(i + 1) % pts_num][1]));
}
area = fabs(float(area / 2.0));
distance = area * unclip_ratio / dist;
}
static cv::RotatedRect unclip(float **box) {
float unclip_ratio = 2.0;
float distance = 1.0;
getcontourarea(box, unclip_ratio, distance);
ClipperLib::ClipperOffset offset;
ClipperLib::Path p;
p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
<< ClipperLib::IntPoint(int(box[1][0]), int(box[1][1])) <<
ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
<< ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
ClipperLib::Paths soln;
offset.Execute(soln, distance);
std::vector<cv::Point2f> points;
for (int j = 0; j < soln.size(); j++) {
for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
points.emplace_back(soln[j][i].X, soln[j][i].Y);
}
}
cv::RotatedRect res = cv::minAreaRect(points);
return res;
}
static float **Mat2Vec(cv::Mat mat) {
auto **array = new float *[mat.rows];
for (int i = 0; i < mat.rows; ++i){
array[i] = new float[mat.cols];
}
for (int i = 0; i < mat.rows; ++i) {
for (int j = 0; j < mat.cols; ++j) {
array[i][j] = mat.at<float>(i, j);
}
}
return array;
}
static void quickSort(float **s, int l, int r) {
if (l < r) {
int i = l, j = r;
float x = s[l][0];
float *xp = s[l];
while (i < j) {
while (i < j && s[j][0] >= x){
j--;
}
if (i < j){
std::swap(s[i++], s[j]);
}
while (i < j && s[i][0] < x){
i++;
}
if (i < j){
std::swap(s[j--], s[i]);
}
}
s[i] = xp;
quickSort(s, l, i - 1);
quickSort(s, i + 1, r);
}
}
static void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r, int axis) {
if (l < r) {
int i = l, j = r;
int x = box[l][axis];
std::vector<int> xp(box[l]);
while (i < j) {
while (i < j && box[j][axis] >= x){
j--;
}
if (i < j){
std::swap(box[i++], box[j]);
}
while (i < j && box[i][axis] < x){
i++;
}
if (i < j){
std::swap(box[j--], box[i]);
}
}
box[i] = xp;
quickSort_vector(box, l, i - 1, axis);
quickSort_vector(box, i + 1, r, axis);
}
}
static std::vector<std::vector<int>> order_points_clockwise(std::vector<std::vector<int>> pts) {
std::vector<std::vector<int>> box = pts;
quickSort_vector(box, 0, int(box.size() - 1), 0);
std::vector<std::vector<int>> leftmost = {box[0], box[1]};
std::vector<std::vector<int>> rightmost = {box[2], box[3]};
if (leftmost[0][1] > leftmost[1][1]){
std::swap(leftmost[0], leftmost[1]);
}
if (rightmost[0][1] > rightmost[1][1]){
std::swap(rightmost[0], rightmost[1]);
}
std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1], leftmost[1]};
return rect;
}
static float **get_mini_boxes(cv::RotatedRect box, float &ssid) {
ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
cv::Mat points;
cv::boxPoints(box, points);
// sorted box points
auto array = Mat2Vec(points);
quickSort(array, 0, 3);
float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
if (array[3][1] <= array[2][1]) {
idx2 = array[3];
idx3 = array[2];
} else {
idx2 = array[2];
idx3 = array[3];
}
if (array[1][1] <= array[0][1]) {
idx1 = array[1];
idx4 = array[0];
} else {
idx1 = array[0];
idx4 = array[1];
}
array[0] = idx1;
array[1] = idx2;
array[2] = idx3;
array[3] = idx4;
return array;
}
template <class T> T clamp(T x, T min, T max) {
if (x > max){
return max;
}
if (x < min){
return min;
}
return x;
}
static float clampf(float x, float min, float max) {
if (x > max)
return max;
if (x < min)
return min;
return x;
}
float box_score_fast(float **box_array, cv::Mat pred) {
auto array = box_array;
int width = pred.cols;
int height = pred.rows;
float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0, width - 1);
int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0, width - 1);
int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0, height - 1);
int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0, height - 1);
cv::Mat mask;
mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
cv::Point root_point[4];
root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
const cv::Point *ppt[1] = {root_point};
int npt[] = {4};
cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
cv::Mat croppedImg;
pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
auto score = cv::mean(croppedImg, mask)[0];
return score;
}
std::vector<std::vector<std::vector<int>>>
boxes_from_bitmap(const cv::Mat& pred, const cv::Mat& bitmap) {
const int min_size = 3;
const int max_candidates = 1000;
const float box_thresh = 0.5;
int width = bitmap.cols;
int height = bitmap.rows;
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
int num_contours = contours.size() >= max_candidates ? max_candidates : contours.size();
std::vector<std::vector<std::vector<int>>> boxes;
for (int _i = 0; _i < num_contours; _i++) {
float ssid;
cv::RotatedRect box = cv::minAreaRect(contours[_i]);
auto array = get_mini_boxes(box, ssid);
auto box_for_unclip = array;
//end get_mini_box
if (ssid < min_size) {
continue;
}
float score;
score = box_score_fast(array, pred);
//end box_score_fast
if (score < box_thresh){
continue;
}
// start for unclip
cv::RotatedRect points = unclip(box_for_unclip);
// end for unclip
cv::RotatedRect clipbox = points;
auto cliparray = get_mini_boxes(clipbox, ssid);
if (ssid < min_size + 2) continue;
int dest_width = pred.cols;
int dest_height = pred.rows;
std::vector<std::vector<int>> intcliparray;
for (int num_pt = 0; num_pt < 4; num_pt++) {
std::vector<int> a{
int(clampf(roundf(cliparray[num_pt][0] / float(width) * float(dest_width)), 0,
float(dest_width))),
int(clampf(roundf(cliparray[num_pt][1] / float(height) * float(dest_height)), 0,
float(dest_height)))};
intcliparray.emplace_back(std::move(a));
}
boxes.emplace_back(std::move(intcliparray));
}//end for
return boxes;
}
int _max(int a, int b) {
return a >= b ? a : b;
}
int _min(int a, int b) {
return a >= b ? b : a;
}
std::vector<std::vector<std::vector<int>>>
filter_tag_det_res(const std::vector<std::vector<std::vector<int>>>& o_boxes,
float ratio_h, float ratio_w,const cv::Mat& srcimg) {
int oriimg_h = srcimg.rows;
int oriimg_w = srcimg.cols;
std::vector<std::vector<std::vector<int>>> boxes{o_boxes};
std::vector<std::vector<std::vector<int>>> root_points;
for (int n = 0; n < boxes.size(); n++) {
boxes[n] = order_points_clockwise(boxes[n]);
for (int m = 0; m < boxes[0].size(); m++) {
boxes[n][m][0] /= ratio_w;
boxes[n][m][1] /= ratio_h;
boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
}
}
for (int n = 0; n < boxes.size(); n++) {
int rect_width, rect_height;
rect_width = int(sqrt(
pow(boxes[n][0][0] - boxes[n][1][0], 2) + pow(boxes[n][0][1] - boxes[n][1][1], 2)));
rect_height = int(sqrt(
pow(boxes[n][0][0] - boxes[n][3][0], 2) + pow(boxes[n][0][1] - boxes[n][3][1], 2)));
if (rect_width <= 10 || rect_height <= 10)
continue;
root_points.push_back(boxes[n]);
}
return root_points;
}
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deploy/android_demo/app/src/main/cpp/ocr_db_post_process.h 已删除 100644 → 0
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//
// Created by fujiayi on 2020/7/2.
//
#pragma once
#include <vector>
#include <opencv2/opencv.hpp>
std::vector<std::vector<std::vector<int>>>
boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap);
std::vector<std::vector<std::vector<int>>>
filter_tag_det_res(
const std::vector<std::vector<std::vector<int>>> &o_boxes,
float ratio_h,
float ratio_w,
const cv::Mat &srcimg
);
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fujiayi on 2020/7/1.
//
#include "ocr_ppredictor.h"
#include "preprocess.h"
#include "common.h"
#include "ocr_db_post_process.h"
#include "ocr_crnn_process.h"
namespace ppredictor {
OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {
}
int
OCR_PPredictor::init(const std::string &det_model_content, const std::string &rec_model_content) {
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_nb(det_model_content);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_nb(rec_model_content);
return RETURN_OK;
}
int OCR_PPredictor::init_from_file(const std::string &det_model_path, const std::string &rec_model_path){
_det_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR, _config.mode});
_det_predictor->init_from_file(det_model_path);
_rec_predictor = std::unique_ptr<PPredictor>(
new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
_rec_predictor->init_from_file(rec_model_path);
return RETURN_OK;
}
/**
* for debug use, show result of First Step
* @param filter_boxes
* @param boxes
* @param srcimg
*/
static void visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &srcimg) {
// visualization
cv::Point rook_points[filter_boxes.size()][4];
for (int n = 0; n < filter_boxes.size(); n++) {
for (int m = 0; m < filter_boxes[0].size(); m++) {
rook_points[n][m] = cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
}
}
cv::Mat img_vis;
srcimg.copyTo(img_vis);
for (int n = 0; n < boxes.size(); n++) {
const cv::Point *ppt[1] = {rook_points[n]};
int npt[] = {4};
cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
}
// 调试用，自行替换需要修改的路径
cv::imwrite("/sdcard/1/vis.png", img_vis);
}
std::vector<OCRPredictResult>
OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
int net_flag, cv::Mat &origin) {
PredictorInput input = _det_predictor->get_first_input();
input.set_dims(dims);
input.set_data(input_data, input_len);
std::vector<PredictorOutput> results = _det_predictor->infer();
PredictorOutput &res = results.at(0);
std::vector<std::vector<std::vector<int>>> filtered_box
= calc_filtered_boxes(res.get_float_data(), res.get_size(), (int) dims[2], (int) dims[3],
origin);
LOGI("Filter_box size %ld", filtered_box.size());
return infer_rec(filtered_box, origin);
}
std::vector<OCRPredictResult>
OCR_PPredictor::infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
const cv::Mat &origin_img) {
std::vector<float> mean = {0.5f, 0.5f, 0.5f};
std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
std::vector<int64_t> dims = {1, 3, 0, 0};
std::vector<OCRPredictResult> ocr_results;
PredictorInput input = _rec_predictor->get_first_input();
for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
const std::vector<std::vector<int>> &box = *bp;
cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
const float *dimg = reinterpret_cast<const float *>(input_image.data);
int input_size = input_image.rows * input_image.cols;
dims[2] = input_image.rows;
dims[3] = input_image.cols;
input.set_dims(dims);
neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean, scale);
std::vector<PredictorOutput> results = _rec_predictor->infer();
OCRPredictResult res;
res.word_index = postprocess_rec_word_index(results.at(0));
if (res.word_index.empty()) {
continue;
}
res.score = postprocess_rec_score(results.at(1));
res.points = box;
ocr_results.emplace_back(std::move(res));
}
LOGI("ocr_results finished %lu", ocr_results.size());
return ocr_results;
}
std::vector<std::vector<std::vector<int>>>
OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size, int output_height,
int output_width, const cv::Mat &origin) {
const double threshold = 0.3;
const double maxvalue = 1;
cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
memcpy(pred_map.data, pred, pred_size * sizeof(float));
cv::Mat cbuf_map;
pred_map.convertTo(cbuf_map, CV_8UC1);
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
std::vector<std::vector<std::vector<int>>> boxes = boxes_from_bitmap(pred_map, bit_map);
float ratio_h = output_height * 1.0f / origin.rows;
float ratio_w = output_width * 1.0f / origin.cols;
std::vector<std::vector<std::vector<int>>> filter_boxes = filter_tag_det_res(boxes, ratio_h,
ratio_w, origin);
return filter_boxes;
}
std::vector<int> OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
const int *rec_idx = res.get_int_data();
const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
std::vector<int> pred_idx;
for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
pred_idx.emplace_back(rec_idx[n]);
}
return pred_idx;
}
float OCR_PPredictor::postprocess_rec_score(const PredictorOutput &res) {
const float *predict_batch = res.get_float_data();
const std::vector<int64_t> predict_shape = res.get_shape();
const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
int blank = predict_shape[1];
float score = 0.f;
int count = 0;
for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
int argmax_idx = argmax(predict_batch + n * predict_shape[1],
predict_batch + (n + 1) * predict_shape[1]);
float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
if (blank - 1 - argmax_idx > 1e-5) {
score += max_value;
count += 1;
}
}
if (count == 0) {
LOGE("calc score count 0");
} else {
score /= count;
}
LOGI("calc score: %f", score);
return score;
}
NET_TYPE OCR_PPredictor::get_net_flag() const {
return NET_OCR;
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
//
// Created by fujiayi on 2020/7/1.
//
#pragma once
#include <string>
#include <opencv2/opencv.hpp>
#include <paddle_api.h>
#include "ppredictor.h"
namespace ppredictor {
/**
* Config
*/
struct OCR_Config {
int thread_num = 4; // Thread num
paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
};
/**
* PolyGone Result
*/
struct OCRPredictResult {
std::vector<int> word_index;
std::vector<std::vector<int>> points;
float score;
};
/**
* OCR there are 2 models
* 1. First model（det），select polygones to show where are the texts
* 2. crop from the origin images, use these polygones to infer
*/
class OCR_PPredictor : public PPredictor_Interface {
public:
OCR_PPredictor(const OCR_Config &config);
virtual ~OCR_PPredictor() {
}
/**
* 初始化二个模型的Predictor
* @param det_model_content
* @param rec_model_content
* @return
*/
int init(const std::string &det_model_content, const std::string &rec_model_content);
int init_from_file(const std::string &det_model_path, const std::string &rec_model_path);
/**
* Return OCR result
* @param dims
* @param input_data
* @param input_len
* @param net_flag
* @param origin
* @return
*/
virtual std::vector<OCRPredictResult>
infer_ocr(const std::vector<int64_t> &dims, const float *input_data, int input_len,
int net_flag, cv::Mat &origin);
virtual NET_TYPE get_net_flag() const;
private:
/**
* calcul Polygone from the result image of first model
* @param pred
* @param output_height
* @param output_width
* @param origin
* @return
*/
std::vector<std::vector<std::vector<int>>>
calc_filtered_boxes(const float *pred, int pred_size, int output_height, int output_width,
const cv::Mat &origin);
/**
* infer for second model
*
* @param boxes
* @param origin
* @return
*/
std::vector<OCRPredictResult>
infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes, const cv::Mat &origin);
/**
* Postprocess or sencod model to extract text
* @param res
* @return
*/
std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
/**
* calculate confidence of second model text result
* @param res
* @return
*/
float postprocess_rec_score(const PredictorOutput &res);
std::unique_ptr<PPredictor> _det_predictor;
std::unique_ptr<PPredictor> _rec_predictor;
OCR_Config _config;
};
}
deploy/android_demo/app/src/main/cpp/ppredictor.cpp 已删除 100644 → 0
浏览文件 @ 0458f0cc
#include "ppredictor.h"
#include "common.h"
namespace ppredictor {
PPredictor::PPredictor(int thread_num, int net_flag, paddle::lite_api::PowerMode mode) :
_thread_num(thread_num), _net_flag(net_flag), _mode(mode) {
}
int PPredictor::init_nb(const std::string &model_content) {
paddle::lite_api::MobileConfig config;
config.set_model_from_buffer(model_content);
return _init(config);
}
int PPredictor::init_from_file(const std::string &model_content){
paddle::lite_api::MobileConfig config;
config.set_model_from_file(model_content);
return _init(config);
}
template<typename ConfigT>
int PPredictor::_init(ConfigT &config) {
config.set_threads(_thread_num);
config.set_power_mode(_mode);
_predictor = paddle::lite_api::CreatePaddlePredictor(config);
LOGI("paddle instance created");
return RETURN_OK;
}
PredictorInput PPredictor::get_input(int index) {
PredictorInput input{_predictor->GetInput(index), index, _net_flag};
_is_input_get = true;
return input;
}
std::vector<PredictorInput> PPredictor::get_inputs(int num) {
std::vector<PredictorInput> results;
for (int i = 0; i < num; i++) {
results.emplace_back(get_input(i));
}
return results;
}
PredictorInput PPredictor::get_first_input() {
return get_input(0);
}
std::vector<PredictorOutput> PPredictor::infer() {
LOGI("infer Run start %d", _net_flag);
std::vector<PredictorOutput> results;
if (!_is_input_get) {
return results;
}
_predictor->Run();
LOGI("infer Run end");
for (int i = 0; i < _predictor->GetOutputNames().size(); i++) {
std::unique_ptr<const paddle::lite_api::Tensor> output_tensor = _predictor->GetOutput(i);
LOGI("output tensor[%d] size %ld", i, product(output_tensor->shape()));
PredictorOutput result{std::move(output_tensor), i, _net_flag};
results.emplace_back(std::move(result));
}
return results;
}
NET_TYPE PPredictor::get_net_flag() const {
return (NET_TYPE) _net_flag;
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/ppredictor.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
#pragma once
#include "paddle_api.h"
#include "predictor_input.h"
#include "predictor_output.h"
namespace ppredictor {
/**
* PaddleLite Preditor Common Interface
*/
class PPredictor_Interface {
public:
virtual ~PPredictor_Interface() {
}
virtual NET_TYPE get_net_flag() const = 0;
};
/**
* Common Predictor
*/
class PPredictor : public PPredictor_Interface {
public:
PPredictor(int thread_num, int net_flag = 0,
paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
virtual ~PPredictor() {
}
/**
* init paddlitelite opt model，nb format ，or use ini_paddle
* @param model_content
* @return 0
*/
virtual int init_nb(const std::string &model_content);
virtual int init_from_file(const std::string &model_content);
std::vector<PredictorOutput> infer();
std::shared_ptr<paddle::lite_api::PaddlePredictor> get_predictor() {
return _predictor;
}
virtual std::vector<PredictorInput> get_inputs(int num);
virtual PredictorInput get_input(int index);
virtual PredictorInput get_first_input();
virtual NET_TYPE get_net_flag() const;
protected:
template<typename ConfigT>
int _init(ConfigT &config);
private:
int _thread_num;
paddle::lite_api::PowerMode _mode;
std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;
bool _is_input_get = false;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/predictor_input.cpp 已删除 100644 → 0
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#include "predictor_input.h"
namespace ppredictor {
void PredictorInput::set_dims(std::vector<int64_t> dims) {
// yolov3
if (_net_flag == 101 && _index == 1) {
_tensor->Resize({1, 2});
_tensor->mutable_data<int>()[0] = (int) dims.at(2);
_tensor->mutable_data<int>()[1] = (int) dims.at(3);
} else {
_tensor->Resize(dims);
}
_is_dims_set = true;
}
float *PredictorInput::get_mutable_float_data() {
if (!_is_dims_set) {
LOGE("PredictorInput::set_dims is not called");
}
return _tensor->mutable_data<float>();
}
void PredictorInput::set_data(const float *input_data, int input_float_len) {
float *input_raw_data = get_mutable_float_data();
memcpy(input_raw_data, input_data, input_float_len * sizeof(float));
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/predictor_input.h 已删除 100644 → 0
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#pragma once
#include <paddle_api.h>
#include <vector>
#include "common.h"
namespace ppredictor {
class PredictorInput {
public:
PredictorInput(std::unique_ptr<paddle::lite_api::Tensor> &&tensor, int index, int net_flag) :
_tensor(std::move(tensor)), _index(index),_net_flag(net_flag) {
}
void set_dims(std::vector<int64_t> dims);
float *get_mutable_float_data();
void set_data(const float *input_data, int input_float_len);
private:
std::unique_ptr<paddle::lite_api::Tensor> _tensor;
bool _is_dims_set = false;
int _index;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/predictor_output.cpp 已删除 100644 → 0
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#include "predictor_output.h"
namespace ppredictor {
const float* PredictorOutput::get_float_data() const{
return _tensor->data<float>();
}
const int* PredictorOutput::get_int_data() const{
return _tensor->data<int>();
}
const std::vector<std::vector<uint64_t>> PredictorOutput::get_lod() const{
return _tensor->lod();
}
int64_t PredictorOutput::get_size() const{
if (_net_flag == NET_OCR) {
return _tensor->shape().at(2) * _tensor->shape().at(3);
} else {
return product(_tensor->shape());
}
}
const std::vector<int64_t> PredictorOutput::get_shape() const{
return _tensor->shape();
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/predictor_output.h 已删除 100644 → 0
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#pragma once
#include <paddle_api.h>
#include <vector>
#include "common.h"
namespace ppredictor {
class PredictorOutput {
public:
PredictorOutput(){
}
PredictorOutput(std::unique_ptr<const paddle::lite_api::Tensor> &&tensor, int index, int net_flag) :
_tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {
}
const float* get_float_data() const;
const int* get_int_data() const;
int64_t get_size() const;
const std::vector<std::vector<uint64_t>> get_lod() const;
const std::vector<int64_t> get_shape() const;
std::vector<float> data; // return float, or use data_int
std::vector<int> data_int; // several layers return int ，or use data
std::vector<int64_t> shape; // PaddleLite output shape
std::vector<std::vector<uint64_t>> lod; // PaddleLite output lod
private:
std::unique_ptr<const paddle::lite_api::Tensor> _tensor;
int _index;
int _net_flag;
};
}
deploy/android_demo/app/src/main/cpp/preprocess.cpp 已删除 100644 → 0
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#include "preprocess.h"
#include <android/bitmap.h>
cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
AndroidBitmapInfo info;
int result = AndroidBitmap_getInfo(env, bitmap, &info);
if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
LOGE("AndroidBitmap_getInfo failed, result: %d", result);
return cv::Mat{};
}
if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
LOGE("Bitmap format is not RGBA_8888 !");
return cv::Mat{};
}
unsigned char *srcData = NULL;
AndroidBitmap_lockPixels(env, bitmap, (void **) &srcData);
cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
memcpy(mat.data, srcData, info.height * info.width * 4);
AndroidBitmap_unlockPixels(env, bitmap);
cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
/**
if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
LOGE("Write image failed " );
}
*/
return mat;
}
cv::Mat resize_img(const cv::Mat& img, int height, int width){
if (img.rows == height && img.cols == width){
return img;
}
cv::Mat new_img;
cv::resize(img, new_img, cv::Size(height, width));
return new_img;
}
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float>& mean,
const std::vector<float>& scale) {
if (mean.size() != 3 || scale.size() != 3) {
LOGE("[ERROR] mean or scale size must equal to 3");
return;
}
float32x4_t vmean0 = vdupq_n_f32(mean[0]);
float32x4_t vmean1 = vdupq_n_f32(mean[1]);
float32x4_t vmean2 = vdupq_n_f32(mean[2]);
float32x4_t vscale0 = vdupq_n_f32(scale[0]);
float32x4_t vscale1 = vdupq_n_f32(scale[1]);
float32x4_t vscale2 = vdupq_n_f32(scale[2]);
float* dout_c0 = dout;
float* dout_c1 = dout + size;
float* dout_c2 = dout + size * 2;
int i = 0;
for (; i < size - 3; i += 4) {
float32x4x3_t vin3 = vld3q_f32(din);
float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
vst1q_f32(dout_c0, vs0);
vst1q_f32(dout_c1, vs1);
vst1q_f32(dout_c2, vs2);
din += 12;
dout_c0 += 4;
dout_c1 += 4;
dout_c2 += 4;
}
for (; i < size; i++) {
*(dout_c0++) = (*(din++) - mean[0]) * scale[0];
*(dout_c1++) = (*(din++) - mean[1]) * scale[1];
*(dout_c2++) = (*(din++) - mean[2]) * scale[2];
}
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/preprocess.h 已删除 100644 → 0
浏览文件 @ 0458f0cc
#pragma once
#include <jni.h>
#include <opencv2/opencv.hpp>
#include "common.h"
cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap);
cv::Mat resize_img(const cv::Mat& img, int height, int width);
void neon_mean_scale(const float* din,
float* dout,
int size,
const std::vector<float>& mean,
const std::vector<float>& scale);
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/AppCompatPreferenceActivity.java 已删除 100644 → 0
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/*
* Copyright (C) 2014 The Android Open Source Project
*
* 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.
*/
package com.baidu.paddle.lite.demo.ocr;
import android.content.res.Configuration;
import android.os.Bundle;
import android.preference.PreferenceActivity;
import android.view.MenuInflater;
import android.view.View;
import android.view.ViewGroup;
import androidx.annotation.LayoutRes;
import androidx.annotation.Nullable;
import androidx.appcompat.app.ActionBar;
import androidx.appcompat.app.AppCompatDelegate;
import androidx.appcompat.widget.Toolbar;
/**
* A {@link PreferenceActivity} which implements and proxies the necessary calls
* to be used with AppCompat.
* <p>
* This technique can be used with an {@link android.app.Activity} class, not just
* {@link PreferenceActivity}.
*/
public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
private AppCompatDelegate mDelegate;
@Override
protected void onCreate(Bundle savedInstanceState) {
getDelegate().installViewFactory();
getDelegate().onCreate(savedInstanceState);
super.onCreate(savedInstanceState);
}
@Override
protected void onPostCreate(Bundle savedInstanceState) {
super.onPostCreate(savedInstanceState);
getDelegate().onPostCreate(savedInstanceState);
}
public ActionBar getSupportActionBar() {
return getDelegate().getSupportActionBar();
}
public void setSupportActionBar(@Nullable Toolbar toolbar) {
getDelegate().setSupportActionBar(toolbar);
}
@Override
public MenuInflater getMenuInflater() {
return getDelegate().getMenuInflater();
}
@Override
public void setContentView(@LayoutRes int layoutResID) {
getDelegate().setContentView(layoutResID);
}
@Override
public void setContentView(View view) {
getDelegate().setContentView(view);
}
@Override
public void setContentView(View view, ViewGroup.LayoutParams params) {
getDelegate().setContentView(view, params);
}
@Override
public void addContentView(View view, ViewGroup.LayoutParams params) {
getDelegate().addContentView(view, params);
}
@Override
protected void onPostResume() {
super.onPostResume();
getDelegate().onPostResume();
}
@Override
protected void onTitleChanged(CharSequence title, int color) {
super.onTitleChanged(title, color);
getDelegate().setTitle(title);
}
@Override
public void onConfigurationChanged(Configuration newConfig) {
super.onConfigurationChanged(newConfig);
getDelegate().onConfigurationChanged(newConfig);
}
@Override
protected void onStop() {
super.onStop();
getDelegate().onStop();
}
@Override
protected void onDestroy() {
super.onDestroy();
getDelegate().onDestroy();
}
public void invalidateOptionsMenu() {
getDelegate().invalidateOptionsMenu();
}
private AppCompatDelegate getDelegate() {
if (mDelegate == null) {
mDelegate = AppCompatDelegate.create(this, null);
}
return mDelegate;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.Manifest;
import android.app.ProgressDialog;
import android.content.ContentResolver;
import android.content.Context;
import android.content.Intent;
import android.content.SharedPreferences;
import android.content.pm.PackageManager;
import android.database.Cursor;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.media.ExifInterface;
import android.net.Uri;
import android.os.Bundle;
import android.os.Environment;
import android.os.Handler;
import android.os.HandlerThread;
import android.os.Message;
import android.preference.PreferenceManager;
import android.provider.MediaStore;
import android.text.method.ScrollingMovementMethod;
import android.util.Log;
import android.view.Menu;
import android.view.MenuInflater;
import android.view.MenuItem;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import androidx.annotation.NonNull;
import androidx.appcompat.app.AppCompatActivity;
import androidx.core.app.ActivityCompat;
import androidx.core.content.ContextCompat;
import androidx.core.content.FileProvider;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.text.SimpleDateFormat;
import java.util.Date;
public class MainActivity extends AppCompatActivity {
private static final String TAG = MainActivity.class.getSimpleName();
public static final int OPEN_GALLERY_REQUEST_CODE = 0;
public static final int TAKE_PHOTO_REQUEST_CODE = 1;
public static final int REQUEST_LOAD_MODEL = 0;
public static final int REQUEST_RUN_MODEL = 1;
public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
public static final int RESPONSE_RUN_MODEL_FAILED = 3;
protected ProgressDialog pbLoadModel = null;
protected ProgressDialog pbRunModel = null;
protected Handler receiver = null; // Receive messages from worker thread
protected Handler sender = null; // Send command to worker thread
protected HandlerThread worker = null; // Worker thread to load&run model
// UI components of object detection
protected TextView tvInputSetting;
protected ImageView ivInputImage;
protected TextView tvOutputResult;
protected TextView tvInferenceTime;
// Model settings of object detection
protected String modelPath = "";
protected String labelPath = "";
protected String imagePath = "";
protected int cpuThreadNum = 1;
protected String cpuPowerMode = "";
protected String inputColorFormat = "";
protected long[] inputShape = new long[]{};
protected float[] inputMean = new float[]{};
protected float[] inputStd = new float[]{};
protected float scoreThreshold = 0.1f;
private String currentPhotoPath;
protected Predictor predictor = new Predictor();
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
// Clear all setting items to avoid app crashing due to the incorrect settings
SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.clear();
editor.commit();
// Prepare the worker thread for mode loading and inference
receiver = new Handler() {
@Override
public void handleMessage(Message msg) {
switch (msg.what) {
case RESPONSE_LOAD_MODEL_SUCCESSED:
pbLoadModel.dismiss();
onLoadModelSuccessed();
break;
case RESPONSE_LOAD_MODEL_FAILED:
pbLoadModel.dismiss();
Toast.makeText(MainActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
onLoadModelFailed();
break;
case RESPONSE_RUN_MODEL_SUCCESSED:
pbRunModel.dismiss();
onRunModelSuccessed();
break;
case RESPONSE_RUN_MODEL_FAILED:
pbRunModel.dismiss();
Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
onRunModelFailed();
break;
default:
break;
}
}
};
worker = new HandlerThread("Predictor Worker");
worker.start();
sender = new Handler(worker.getLooper()) {
public void handleMessage(Message msg) {
switch (msg.what) {
case REQUEST_LOAD_MODEL:
// Load model and reload test image
if (onLoadModel()) {
receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_SUCCESSED);
} else {
receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_FAILED);
}
break;
case REQUEST_RUN_MODEL:
// Run model if model is loaded
if (onRunModel()) {
receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_SUCCESSED);
} else {
receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_FAILED);
}
break;
default:
break;
}
}
};
// Setup the UI components
tvInputSetting = findViewById(R.id.tv_input_setting);
ivInputImage = findViewById(R.id.iv_input_image);
tvInferenceTime = findViewById(R.id.tv_inference_time);
tvOutputResult = findViewById(R.id.tv_output_result);
tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
}
@Override
protected void onResume() {
super.onResume();
SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
boolean settingsChanged = false;
String model_path = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
getString(R.string.LABEL_PATH_DEFAULT));
String image_path = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
getString(R.string.IMAGE_PATH_DEFAULT));
settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
settingsChanged |= !label_path.equalsIgnoreCase(labelPath);
settingsChanged |= !image_path.equalsIgnoreCase(imagePath);
int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
getString(R.string.CPU_THREAD_NUM_DEFAULT)));
settingsChanged |= cpu_thread_num != cpuThreadNum;
String cpu_power_mode =
sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
getString(R.string.CPU_POWER_MODE_DEFAULT));
settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
String input_color_format =
sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
settingsChanged |= !input_color_format.equalsIgnoreCase(inputColorFormat);
long[] input_shape =
Utils.parseLongsFromString(sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
getString(R.string.INPUT_SHAPE_DEFAULT)), ",");
float[] input_mean =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
getString(R.string.INPUT_MEAN_DEFAULT)), ",");
float[] input_std =
Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_STD_KEY)
, getString(R.string.INPUT_STD_DEFAULT)), ",");
settingsChanged |= input_shape.length != inputShape.length;
settingsChanged |= input_mean.length != inputMean.length;
settingsChanged |= input_std.length != inputStd.length;
if (!settingsChanged) {
for (int i = 0; i < input_shape.length; i++) {
settingsChanged |= input_shape[i] != inputShape[i];
}
for (int i = 0; i < input_mean.length; i++) {
settingsChanged |= input_mean[i] != inputMean[i];
}
for (int i = 0; i < input_std.length; i++) {
settingsChanged |= input_std[i] != inputStd[i];
}
}
float score_threshold =
Float.parseFloat(sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
getString(R.string.SCORE_THRESHOLD_DEFAULT)));
settingsChanged |= scoreThreshold != score_threshold;
if (settingsChanged) {
modelPath = model_path;
labelPath = label_path;
imagePath = image_path;
cpuThreadNum = cpu_thread_num;
cpuPowerMode = cpu_power_mode;
inputColorFormat = input_color_format;
inputShape = input_shape;
inputMean = input_mean;
inputStd = input_std;
scoreThreshold = score_threshold;
// Update UI
tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\n" + "CPU" +
" Thread Num: " + Integer.toString(cpuThreadNum) + "\n" + "CPU Power Mode: " + cpuPowerMode);
tvInputSetting.scrollTo(0, 0);
// Reload model if configure has been changed
loadModel();
}
}
public void loadModel() {
pbLoadModel = ProgressDialog.show(this, "", "Loading model...", false, false);
sender.sendEmptyMessage(REQUEST_LOAD_MODEL);
}
public void runModel() {
pbRunModel = ProgressDialog.show(this, "", "Running model...", false, false);
sender.sendEmptyMessage(REQUEST_RUN_MODEL);
}
public boolean onLoadModel() {
return predictor.init(MainActivity.this, modelPath, labelPath, cpuThreadNum,
cpuPowerMode,
inputColorFormat,
inputShape, inputMean,
inputStd, scoreThreshold);
}
public boolean onRunModel() {
return predictor.isLoaded() && predictor.runModel();
}
public void onLoadModelSuccessed() {
// Load test image from path and run model
try {
if (imagePath.isEmpty()) {
return;
}
Bitmap image = null;
// Read test image file from custom path if the first character of mode path is '/', otherwise read test
// image file from assets
if (!imagePath.substring(0, 1).equals("/")) {
InputStream imageStream = getAssets().open(imagePath);
image = BitmapFactory.decodeStream(imageStream);
} else {
if (!new File(imagePath).exists()) {
return;
}
image = BitmapFactory.decodeFile(imagePath);
}
if (image != null && predictor.isLoaded()) {
predictor.setInputImage(image);
runModel();
}
} catch (IOException e) {
Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
e.printStackTrace();
}
}
public void onLoadModelFailed() {
}
public void onRunModelSuccessed() {
// Obtain results and update UI
tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
Bitmap outputImage = predictor.outputImage();
if (outputImage != null) {
ivInputImage.setImageBitmap(outputImage);
}
tvOutputResult.setText(predictor.outputResult());
tvOutputResult.scrollTo(0, 0);
}
public void onRunModelFailed() {
}
public void onImageChanged(Bitmap image) {
// Rerun model if users pick test image from gallery or camera
if (image != null && predictor.isLoaded()) {
predictor.setInputImage(image);
runModel();
}
}
public void onSettingsClicked() {
startActivity(new Intent(MainActivity.this, SettingsActivity.class));
}
@Override
public boolean onCreateOptionsMenu(Menu menu) {
MenuInflater inflater = getMenuInflater();
inflater.inflate(R.menu.menu_action_options, menu);
return true;
}
public boolean onPrepareOptionsMenu(Menu menu) {
boolean isLoaded = predictor.isLoaded();
menu.findItem(R.id.open_gallery).setEnabled(isLoaded);
menu.findItem(R.id.take_photo).setEnabled(isLoaded);
return super.onPrepareOptionsMenu(menu);
}
@Override
public boolean onOptionsItemSelected(MenuItem item) {
switch (item.getItemId()) {
case android.R.id.home:
finish();
break;
case R.id.open_gallery:
if (requestAllPermissions()) {
openGallery();
}
break;
case R.id.take_photo:
if (requestAllPermissions()) {
takePhoto();
}
break;
case R.id.settings:
if (requestAllPermissions()) {
// Make sure we have SDCard r&w permissions to load model from SDCard
onSettingsClicked();
}
break;
}
return super.onOptionsItemSelected(item);
}
@Override
public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
@NonNull int[] grantResults) {
super.onRequestPermissionsResult(requestCode, permissions, grantResults);
if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
Toast.makeText(this, "Permission Denied", Toast.LENGTH_SHORT).show();
}
}
private boolean requestAllPermissions() {
if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE)
!= PackageManager.PERMISSION_GRANTED || ContextCompat.checkSelfPermission(this,
Manifest.permission.CAMERA)
!= PackageManager.PERMISSION_GRANTED) {
ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
Manifest.permission.CAMERA},
0);
return false;
}
return true;
}
private void openGallery() {
Intent intent = new Intent(Intent.ACTION_PICK, null);
intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");
startActivityForResult(intent, OPEN_GALLERY_REQUEST_CODE);
}
private void takePhoto() {
Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
// Ensure that there's a camera activity to handle the intent
if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
// Create the File where the photo should go
File photoFile = null;
try {
photoFile = createImageFile();
} catch (IOException ex) {
Log.e("MainActitity", ex.getMessage(), ex);
Toast.makeText(MainActivity.this,
"Create Camera temp file failed: " + ex.getMessage(), Toast.LENGTH_SHORT).show();
}
// Continue only if the File was successfully created
if (photoFile != null) {
Log.i(TAG, "FILEPATH " + getExternalFilesDir("Pictures").getAbsolutePath());
Uri photoURI = FileProvider.getUriForFile(this,
"com.baidu.paddle.lite.demo.ocr.fileprovider",
photoFile);
currentPhotoPath = photoFile.getAbsolutePath();
takePictureIntent.putExtra(MediaStore.EXTRA_OUTPUT, photoURI);
startActivityForResult(takePictureIntent, TAKE_PHOTO_REQUEST_CODE);
Log.i(TAG, "startActivityForResult finished");
}
}
}
private File createImageFile() throws IOException {
// Create an image file name
String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
String imageFileName = "JPEG_" + timeStamp + "_";
File storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES);
File image = File.createTempFile(
imageFileName, /* prefix */
".bmp", /* suffix */
storageDir /* directory */
);
return image;
}
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (resultCode == RESULT_OK) {
switch (requestCode) {
case OPEN_GALLERY_REQUEST_CODE:
if (data == null) {
break;
}
try {
ContentResolver resolver = getContentResolver();
Uri uri = data.getData();
Bitmap image = MediaStore.Images.Media.getBitmap(resolver, uri);
String[] proj = {MediaStore.Images.Media.DATA};
Cursor cursor = managedQuery(uri, proj, null, null, null);
cursor.moveToFirst();
onImageChanged(image);
} catch (IOException e) {
Log.e(TAG, e.toString());
}
break;
case TAKE_PHOTO_REQUEST_CODE:
if (currentPhotoPath != null) {
ExifInterface exif = null;
try {
exif = new ExifInterface(currentPhotoPath);
} catch (IOException e) {
e.printStackTrace();
}
int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION,
ExifInterface.ORIENTATION_UNDEFINED);
Log.i(TAG, "rotation " + orientation);
Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
image = Utils.rotateBitmap(image, orientation);
onImageChanged(image);
} else {
Log.e(TAG, "currentPhotoPath is null");
}
break;
default:
break;
}
}
}
@Override
protected void onDestroy() {
if (predictor != null) {
predictor.releaseModel();
}
worker.quit();
super.onDestroy();
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MiniActivity.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Build;
import android.os.Bundle;
import android.os.Handler;
import android.os.HandlerThread;
import android.os.Message;
import android.util.Log;
import android.view.View;
import android.widget.Button;
import android.widget.ImageView;
import android.widget.TextView;
import android.widget.Toast;
import androidx.appcompat.app.AppCompatActivity;
import java.io.IOException;
import java.io.InputStream;
public class MiniActivity extends AppCompatActivity {
public static final int REQUEST_LOAD_MODEL = 0;
public static final int REQUEST_RUN_MODEL = 1;
public static final int REQUEST_UNLOAD_MODEL = 2;
public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
public static final int RESPONSE_RUN_MODEL_FAILED = 3;
private static final String TAG = "MiniActivity";
protected Handler receiver = null; // Receive messages from worker thread
protected Handler sender = null; // Send command to worker thread
protected HandlerThread worker = null; // Worker thread to load&run model
protected volatile Predictor predictor = null;
private String assetModelDirPath = "models/ocr_v1_for_cpu";
private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
private Button button;
private ImageView imageView; // image result
private TextView textView; // text result
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_mini);
Log.i(TAG, "SHOW in Logcat");
// Prepare the worker thread for mode loading and inference
worker = new HandlerThread("Predictor Worker");
worker.start();
sender = new Handler(worker.getLooper()) {
public void handleMessage(Message msg) {
switch (msg.what) {
case REQUEST_LOAD_MODEL:
// Load model and reload test image
if (!onLoadModel()) {
runOnUiThread(new Runnable() {
@Override
public void run() {
Toast.makeText(MiniActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
}
});
}
break;
case REQUEST_RUN_MODEL:
// Run model if model is loaded
final boolean isSuccessed = onRunModel();
runOnUiThread(new Runnable() {
@Override
public void run() {
if (isSuccessed){
onRunModelSuccessed();
}else{
Toast.makeText(MiniActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
}
}
});
break;
}
}
};
sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
imageView = findViewById(R.id.imageView);
textView = findViewById(R.id.sample_text);
button = findViewById(R.id.button);
button.setOnClickListener(new View.OnClickListener() {
@Override
public void onClick(View v) {
sender.sendEmptyMessage(REQUEST_RUN_MODEL);
}
});
}
@Override
protected void onDestroy() {
onUnloadModel();
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN_MR2) {
worker.quitSafely();
} else {
worker.quit();
}
super.onDestroy();
}
/**
* call in onCreate, model init
*
* @return
*/
private boolean onLoadModel() {
if (predictor == null) {
predictor = new Predictor();
}
return predictor.init(this, assetModelDirPath, assetlabelFilePath);
}
/**
* init engine
* call in onCreate
*
* @return
*/
private boolean onRunModel() {
try {
String assetImagePath = "images/5.jpg";
InputStream imageStream = getAssets().open(assetImagePath);
Bitmap image = BitmapFactory.decodeStream(imageStream);
// Input is Bitmap
predictor.setInputImage(image);
return predictor.isLoaded() && predictor.runModel();
} catch (IOException e) {
e.printStackTrace();
return false;
}
}
private void onRunModelSuccessed() {
Log.i(TAG, "onRunModelSuccessed");
textView.setText(predictor.outputResult);
imageView.setImageBitmap(predictor.outputImage);
}
private void onUnloadModel() {
if (predictor != null) {
predictor.releaseModel();
}
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Bitmap;
import android.util.Log;
import java.util.ArrayList;
import java.util.concurrent.atomic.AtomicBoolean;
public class OCRPredictorNative {
private static final AtomicBoolean isSOLoaded = new AtomicBoolean();
public static void loadLibrary() throws RuntimeException {
if (!isSOLoaded.get() && isSOLoaded.compareAndSet(false, true)) {
try {
System.loadLibrary("Native");
} catch (Throwable e) {
RuntimeException exception = new RuntimeException(
"Load libNative.so failed, please check it exists in apk file.", e);
throw exception;
}
}
}
private Config config;
private long nativePointer = 0;
public OCRPredictorNative(Config config) {
this.config = config;
loadLibrary();
nativePointer = init(config.detModelFilename, config.recModelFilename,
config.cpuThreadNum, config.cpuPower);
Log.i("OCRPredictorNative", "load success " + nativePointer);
}
public void release() {
if (nativePointer != 0) {
nativePointer = 0;
destory(nativePointer);
}
}
public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
float[] dims = new float[]{1, channels, height, width};
float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
ArrayList<OcrResultModel> results = postprocess(rawResults);
return results;
}
public static class Config {
public int cpuThreadNum;
public String cpuPower;
public String detModelFilename;
public String recModelFilename;
}
protected native long init(String detModelPath, String recModelPath, int threadNum, String cpuMode);
protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
protected native void destory(long pointer);
private ArrayList<OcrResultModel> postprocess(float[] raw) {
ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();
int begin = 0;
while (begin < raw.length) {
int point_num = Math.round(raw[begin]);
int word_num = Math.round(raw[begin + 1]);
OcrResultModel model = parse(raw, begin + 2, point_num, word_num);
begin += 2 + 1 + point_num * 2 + word_num;
results.add(model);
}
return results;
}
private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
int current = begin;
OcrResultModel model = new OcrResultModel();
model.setConfidence(raw[current]);
current++;
for (int i = 0; i < pointNum; i++) {
model.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
}
current += (pointNum * 2);
for (int i = 0; i < wordNum; i++) {
int index = Math.round(raw[current + i]);
model.addWordIndex(index);
}
Log.i("OCRPredictorNative", "word finished " + wordNum);
return model;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OcrResultModel.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.graphics.Point;
import java.util.ArrayList;
import java.util.List;
public class OcrResultModel {
private List<Point> points;
private List<Integer> wordIndex;
private String label;
private float confidence;
public OcrResultModel() {
super();
points = new ArrayList<>();
wordIndex = new ArrayList<>();
}
public void addPoints(int x, int y) {
Point point = new Point(x, y);
points.add(point);
}
public void addWordIndex(int index) {
wordIndex.add(index);
}
public List<Point> getPoints() {
return points;
}
public List<Integer> getWordIndex() {
return wordIndex;
}
public String getLabel() {
return label;
}
public void setLabel(String label) {
this.label = label;
}
public float getConfidence() {
return confidence;
}
public void setConfidence(float confidence) {
this.confidence = confidence;
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.content.Context;
import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Color;
import android.graphics.Paint;
import android.graphics.Path;
import android.graphics.Point;
import android.util.Log;
import java.io.File;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.Vector;
import static android.graphics.Color.*;
public class Predictor {
private static final String TAG = Predictor.class.getSimpleName();
public boolean isLoaded = false;
public int warmupIterNum = 1;
public int inferIterNum = 1;
public int cpuThreadNum = 4;
public String cpuPowerMode = "LITE_POWER_HIGH";
public String modelPath = "";
public String modelName = "";
protected OCRPredictorNative paddlePredictor = null;
protected float inferenceTime = 0;
// Only for object detection
protected Vector<String> wordLabels = new Vector<String>();
protected String inputColorFormat = "BGR";
protected long[] inputShape = new long[]{1, 3, 960};
protected float[] inputMean = new float[]{0.485f, 0.456f, 0.406f};
protected float[] inputStd = new float[]{1.0f / 0.229f, 1.0f / 0.224f, 1.0f / 0.225f};
protected float scoreThreshold = 0.1f;
protected Bitmap inputImage = null;
protected Bitmap outputImage = null;
protected volatile String outputResult = "";
protected float preprocessTime = 0;
protected float postprocessTime = 0;
public Predictor() {
}
public boolean init(Context appCtx, String modelPath, String labelPath) {
isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
if (!isLoaded) {
return false;
}
isLoaded = loadLabel(appCtx, labelPath);
return isLoaded;
}
public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
String inputColorFormat,
long[] inputShape, float[] inputMean,
float[] inputStd, float scoreThreshold) {
if (inputShape.length != 3) {
Log.e(TAG, "Size of input shape should be: 3");
return false;
}
if (inputMean.length != inputShape[1]) {
Log.e(TAG, "Size of input mean should be: " + Long.toString(inputShape[1]));
return false;
}
if (inputStd.length != inputShape[1]) {
Log.e(TAG, "Size of input std should be: " + Long.toString(inputShape[1]));
return false;
}
if (inputShape[0] != 1) {
Log.e(TAG, "Only one batch is supported in the image classification demo, you can use any batch size in " +
"your Apps!");
return false;
}
if (inputShape[1] != 1 && inputShape[1] != 3) {
Log.e(TAG, "Only one/three channels are supported in the image classification demo, you can use any " +
"channel size in your Apps!");
return false;
}
if (!inputColorFormat.equalsIgnoreCase("BGR")) {
Log.e(TAG, "Only BGR color format is supported.");
return false;
}
boolean isLoaded = init(appCtx, modelPath, labelPath);
if (!isLoaded) {
return false;
}
this.inputColorFormat = inputColorFormat;
this.inputShape = inputShape;
this.inputMean = inputMean;
this.inputStd = inputStd;
this.scoreThreshold = scoreThreshold;
return true;
}
protected boolean loadModel(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
// Release model if exists
releaseModel();
// Load model
if (modelPath.isEmpty()) {
return false;
}
String realPath = modelPath;
if (!modelPath.substring(0, 1).equals("/")) {
// Read model files from custom path if the first character of mode path is '/'
// otherwise copy model to cache from assets
realPath = appCtx.getCacheDir() + "/" + modelPath;
Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
}
if (realPath.isEmpty()) {
return false;
}
OCRPredictorNative.Config config = new OCRPredictorNative.Config();
config.cpuThreadNum = cpuThreadNum;
config.detModelFilename = realPath + File.separator + "ch_det_mv3_db_opt.nb";
config.recModelFilename = realPath + File.separator + "ch_rec_mv3_crnn_opt.nb";
Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename);
config.cpuPower = cpuPowerMode;
paddlePredictor = new OCRPredictorNative(config);
this.cpuThreadNum = cpuThreadNum;
this.cpuPowerMode = cpuPowerMode;
this.modelPath = realPath;
this.modelName = realPath.substring(realPath.lastIndexOf("/") + 1);
return true;
}
public void releaseModel() {
if (paddlePredictor != null) {
paddlePredictor.release();
paddlePredictor = null;
}
isLoaded = false;
cpuThreadNum = 1;
cpuPowerMode = "LITE_POWER_HIGH";
modelPath = "";
modelName = "";
}
protected boolean loadLabel(Context appCtx, String labelPath) {
wordLabels.clear();
// Load word labels from file
try {
InputStream assetsInputStream = appCtx.getAssets().open(labelPath);
int available = assetsInputStream.available();
byte[] lines = new byte[available];
assetsInputStream.read(lines);
assetsInputStream.close();
String words = new String(lines);
String[] contents = words.split("\n");
for (String content : contents) {
wordLabels.add(content);
}
Log.i(TAG, "Word label size: " + wordLabels.size());
} catch (Exception e) {
Log.e(TAG, e.getMessage());
return false;
}
return true;
}
public boolean runModel() {
if (inputImage == null || !isLoaded()) {
return false;
}
// Pre-process image, and feed input tensor with pre-processed data
Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
Date start = new Date();
int channels = (int) inputShape[1];
int width = scaleImage.getWidth();
int height = scaleImage.getHeight();
float[] inputData = new float[channels * width * height];
if (channels == 3) {
int[] channelIdx = null;
if (inputColorFormat.equalsIgnoreCase("RGB")) {
channelIdx = new int[]{0, 1, 2};
} else if (inputColorFormat.equalsIgnoreCase("BGR")) {
channelIdx = new int[]{2, 1, 0};
} else {
Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
"supported!");
return false;
}
int[] channelStride = new int[]{width * height, width * height * 2};
int p = scaleImage.getPixel(scaleImage.getWidth() - 1, scaleImage.getHeight() - 1);
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
int color = scaleImage.getPixel(x, y);
float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
(float) blue(color) / 255.0f};
inputData[y * width + x] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
inputData[y * width + x + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
inputData[y * width + x + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
}
}
} else if (channels == 1) {
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
int color = inputImage.getPixel(x, y);
float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
inputData[y * width + x] = (gray - inputMean[0]) / inputStd[0];
}
}
} else {
Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ", only channel 1 and 3 is " +
"supported!");
return false;
}
float[] pixels = inputData;
Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
+ " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
Date end = new Date();
preprocessTime = (float) (end.getTime() - start.getTime());
// Warm up
for (int i = 0; i < warmupIterNum; i++) {
paddlePredictor.runImage(inputData, width, height, channels, inputImage);
}
warmupIterNum = 0; // do not need warm
// Run inference
start = new Date();
ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
end = new Date();
inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
results = postprocess(results);
Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
+ " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
drawResults(results);
return true;
}
public boolean isLoaded() {
return paddlePredictor != null && isLoaded;
}
public String modelPath() {
return modelPath;
}
public String modelName() {
return modelName;
}
public int cpuThreadNum() {
return cpuThreadNum;
}
public String cpuPowerMode() {
return cpuPowerMode;
}
public float inferenceTime() {
return inferenceTime;
}
public Bitmap inputImage() {
return inputImage;
}
public Bitmap outputImage() {
return outputImage;
}
public String outputResult() {
return outputResult;
}
public float preprocessTime() {
return preprocessTime;
}
public float postprocessTime() {
return postprocessTime;
}
public void setInputImage(Bitmap image) {
if (image == null) {
return;
}
this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
}
private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
for (OcrResultModel r : results) {
StringBuffer word = new StringBuffer();
for (int index : r.getWordIndex()) {
if (index >= 0 && index < wordLabels.size()) {
word.append(wordLabels.get(index));
} else {
Log.e(TAG, "Word index is not in label list:" + index);
word.append("×");
}
}
r.setLabel(word.toString());
}
return results;
}
private void drawResults(ArrayList<OcrResultModel> results) {
StringBuffer outputResultSb = new StringBuffer("");
for (int i = 0; i < results.size(); i++) {
OcrResultModel result = results.get(i);
StringBuilder sb = new StringBuilder("");
sb.append(result.getLabel());
sb.append(" ").append(result.getConfidence());
sb.append("; Points: ");
for (Point p : result.getPoints()) {
sb.append("(").append(p.x).append(",").append(p.y).append(") ");
}
Log.i(TAG, sb.toString()); // show LOG in Logcat panel
outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
}
outputResult = outputResultSb.toString();
outputImage = inputImage;
Canvas canvas = new Canvas(outputImage);
Paint paintFillAlpha = new Paint();
paintFillAlpha.setStyle(Paint.Style.FILL);
paintFillAlpha.setColor(Color.parseColor("#3B85F5"));
paintFillAlpha.setAlpha(50);
Paint paint = new Paint();
paint.setColor(Color.parseColor("#3B85F5"));
paint.setStrokeWidth(5);
paint.setStyle(Paint.Style.STROKE);
for (OcrResultModel result : results) {
Path path = new Path();
List<Point> points = result.getPoints();
path.moveTo(points.get(0).x, points.get(0).y);
for (int i = points.size() - 1; i >= 0; i--) {
Point p = points.get(i);
path.lineTo(p.x, p.y);
}
canvas.drawPath(path, paint);
canvas.drawPath(path, paintFillAlpha);
}
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/SettingsActivity.java 已删除 100644 → 0
浏览文件 @ 0458f0cc
package com.baidu.paddle.lite.demo.ocr;
import android.content.SharedPreferences;
import android.os.Bundle;
import android.preference.CheckBoxPreference;
import android.preference.EditTextPreference;
import android.preference.ListPreference;
import androidx.appcompat.app.ActionBar;
import java.util.ArrayList;
import java.util.List;
public class SettingsActivity extends AppCompatPreferenceActivity implements SharedPreferences.OnSharedPreferenceChangeListener {
ListPreference lpChoosePreInstalledModel = null;
CheckBoxPreference cbEnableCustomSettings = null;
EditTextPreference etModelPath = null;
EditTextPreference etLabelPath = null;
EditTextPreference etImagePath = null;
ListPreference lpCPUThreadNum = null;
ListPreference lpCPUPowerMode = null;
ListPreference lpInputColorFormat = null;
EditTextPreference etInputShape = null;
EditTextPreference etInputMean = null;
EditTextPreference etInputStd = null;
EditTextPreference etScoreThreshold = null;
List<String> preInstalledModelPaths = null;
List<String> preInstalledLabelPaths = null;
List<String> preInstalledImagePaths = null;
List<String> preInstalledInputShapes = null;
List<String> preInstalledCPUThreadNums = null;
List<String> preInstalledCPUPowerModes = null;
List<String> preInstalledInputColorFormats = null;
List<String> preInstalledInputMeans = null;
List<String> preInstalledInputStds = null;
List<String> preInstalledScoreThresholds = null;
@Override
public void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
addPreferencesFromResource(R.xml.settings);
ActionBar supportActionBar = getSupportActionBar();
if (supportActionBar != null) {
supportActionBar.setDisplayHomeAsUpEnabled(true);
}
// Initialized pre-installed models
preInstalledModelPaths = new ArrayList<String>();
preInstalledLabelPaths = new ArrayList<String>();
preInstalledImagePaths = new ArrayList<String>();
preInstalledInputShapes = new ArrayList<String>();
preInstalledCPUThreadNums = new ArrayList<String>();
preInstalledCPUPowerModes = new ArrayList<String>();
preInstalledInputColorFormats = new ArrayList<String>();
preInstalledInputMeans = new ArrayList<String>();
preInstalledInputStds = new ArrayList<String>();
preInstalledScoreThresholds = new ArrayList<String>();
// Add ssd_mobilenet_v1_pascalvoc_for_cpu
preInstalledModelPaths.add(getString(R.string.MODEL_PATH_DEFAULT));
preInstalledLabelPaths.add(getString(R.string.LABEL_PATH_DEFAULT));
preInstalledImagePaths.add(getString(R.string.IMAGE_PATH_DEFAULT));
preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
preInstalledInputColorFormats.add(getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
preInstalledInputShapes.add(getString(R.string.INPUT_SHAPE_DEFAULT));
preInstalledInputMeans.add(getString(R.string.INPUT_MEAN_DEFAULT));
preInstalledInputStds.add(getString(R.string.INPUT_STD_DEFAULT));
preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
// Setup UI components
lpChoosePreInstalledModel =
(ListPreference) findPreference(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY));
String[] preInstalledModelNames = new String[preInstalledModelPaths.size()];
for (int i = 0; i < preInstalledModelPaths.size(); i++) {
preInstalledModelNames[i] =
preInstalledModelPaths.get(i).substring(preInstalledModelPaths.get(i).lastIndexOf("/") + 1);
}
lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
lpChoosePreInstalledModel.setEntryValues(preInstalledModelPaths.toArray(new String[preInstalledModelPaths.size()]));
cbEnableCustomSettings =
(CheckBoxPreference) findPreference(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY));
etModelPath = (EditTextPreference) findPreference(getString(R.string.MODEL_PATH_KEY));
etModelPath.setTitle("Model Path (SDCard: " + Utils.getSDCardDirectory() + ")");
etLabelPath = (EditTextPreference) findPreference(getString(R.string.LABEL_PATH_KEY));
etImagePath = (EditTextPreference) findPreference(getString(R.string.IMAGE_PATH_KEY));
lpCPUThreadNum =
(ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
lpCPUPowerMode =
(ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
lpInputColorFormat =
(ListPreference) findPreference(getString(R.string.INPUT_COLOR_FORMAT_KEY));
etInputShape = (EditTextPreference) findPreference(getString(R.string.INPUT_SHAPE_KEY));
etInputMean = (EditTextPreference) findPreference(getString(R.string.INPUT_MEAN_KEY));
etInputStd = (EditTextPreference) findPreference(getString(R.string.INPUT_STD_KEY));
etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
}
private void reloadPreferenceAndUpdateUI() {
SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
boolean enableCustomSettings =
sharedPreferences.getBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
String modelPath = sharedPreferences.getString(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
int modelIdx = lpChoosePreInstalledModel.findIndexOfValue(modelPath);
if (modelIdx >= 0 && modelIdx < preInstalledModelPaths.size()) {
if (!enableCustomSettings) {
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.putString(getString(R.string.MODEL_PATH_KEY), preInstalledModelPaths.get(modelIdx));
editor.putString(getString(R.string.LABEL_PATH_KEY), preInstalledLabelPaths.get(modelIdx));
editor.putString(getString(R.string.IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
editor.putString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
preInstalledInputColorFormats.get(modelIdx));
editor.putString(getString(R.string.INPUT_SHAPE_KEY), preInstalledInputShapes.get(modelIdx));
editor.putString(getString(R.string.INPUT_MEAN_KEY), preInstalledInputMeans.get(modelIdx));
editor.putString(getString(R.string.INPUT_STD_KEY), preInstalledInputStds.get(modelIdx));
editor.putString(getString(R.string.SCORE_THRESHOLD_KEY),
preInstalledScoreThresholds.get(modelIdx));
editor.commit();
}
lpChoosePreInstalledModel.setSummary(modelPath);
}
cbEnableCustomSettings.setChecked(enableCustomSettings);
etModelPath.setEnabled(enableCustomSettings);
etLabelPath.setEnabled(enableCustomSettings);
etImagePath.setEnabled(enableCustomSettings);
lpCPUThreadNum.setEnabled(enableCustomSettings);
lpCPUPowerMode.setEnabled(enableCustomSettings);
lpInputColorFormat.setEnabled(enableCustomSettings);
etInputShape.setEnabled(enableCustomSettings);
etInputMean.setEnabled(enableCustomSettings);
etInputStd.setEnabled(enableCustomSettings);
etScoreThreshold.setEnabled(enableCustomSettings);
modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
getString(R.string.MODEL_PATH_DEFAULT));
String labelPath = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
getString(R.string.LABEL_PATH_DEFAULT));
String imagePath = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
getString(R.string.IMAGE_PATH_DEFAULT));
String cpuThreadNum = sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
getString(R.string.CPU_THREAD_NUM_DEFAULT));
String cpuPowerMode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
getString(R.string.CPU_POWER_MODE_DEFAULT));
String inputColorFormat = sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
String inputShape = sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
getString(R.string.INPUT_SHAPE_DEFAULT));
String inputMean = sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
getString(R.string.INPUT_MEAN_DEFAULT));
String inputStd = sharedPreferences.getString(getString(R.string.INPUT_STD_KEY),
getString(R.string.INPUT_STD_DEFAULT));
String scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
getString(R.string.SCORE_THRESHOLD_DEFAULT));
etModelPath.setSummary(modelPath);
etModelPath.setText(modelPath);
etLabelPath.setSummary(labelPath);
etLabelPath.setText(labelPath);
etImagePath.setSummary(imagePath);
etImagePath.setText(imagePath);
lpCPUThreadNum.setValue(cpuThreadNum);
lpCPUThreadNum.setSummary(cpuThreadNum);
lpCPUPowerMode.setValue(cpuPowerMode);
lpCPUPowerMode.setSummary(cpuPowerMode);
lpInputColorFormat.setValue(inputColorFormat);
lpInputColorFormat.setSummary(inputColorFormat);
etInputShape.setSummary(inputShape);
etInputShape.setText(inputShape);
etInputMean.setSummary(inputMean);
etInputMean.setText(inputMean);
etInputStd.setSummary(inputStd);
etInputStd.setText(inputStd);
etScoreThreshold.setText(scoreThreshold);
etScoreThreshold.setSummary(scoreThreshold);
}
@Override
protected void onResume() {
super.onResume();
getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
reloadPreferenceAndUpdateUI();
}
@Override
protected void onPause() {
super.onPause();
getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
}
@Override
public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
if (key.equals(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY))) {
SharedPreferences.Editor editor = sharedPreferences.edit();
editor.putBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
editor.commit();
}
reloadPreferenceAndUpdateUI();
}
}
deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Utils.java 已删除 100644 → 0
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package com.baidu.paddle.lite.demo.ocr;
import android.content.Context;
import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.media.ExifInterface;
import android.os.Environment;
import java.io.*;
public class Utils {
private static final String TAG = Utils.class.getSimpleName();
public static void copyFileFromAssets(Context appCtx, String srcPath, String dstPath) {
if (srcPath.isEmpty() || dstPath.isEmpty()) {
return;
}
InputStream is = null;
OutputStream os = null;
try {
is = new BufferedInputStream(appCtx.getAssets().open(srcPath));
os = new BufferedOutputStream(new FileOutputStream(new File(dstPath)));
byte[] buffer = new byte[1024];
int length = 0;
while ((length = is.read(buffer)) != -1) {
os.write(buffer, 0, length);
}
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
} finally {
try {
os.close();
is.close();
} catch (IOException e) {
e.printStackTrace();
}
}
}
public static void copyDirectoryFromAssets(Context appCtx, String srcDir, String dstDir) {
if (srcDir.isEmpty() || dstDir.isEmpty()) {
return;
}
try {
if (!new File(dstDir).exists()) {
new File(dstDir).mkdirs();
}
for (String fileName : appCtx.getAssets().list(srcDir)) {
String srcSubPath = srcDir + File.separator + fileName;
String dstSubPath = dstDir + File.separator + fileName;
if (new File(srcSubPath).isDirectory()) {
copyDirectoryFromAssets(appCtx, srcSubPath, dstSubPath);
} else {
copyFileFromAssets(appCtx, srcSubPath, dstSubPath);
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
public static float[] parseFloatsFromString(String string, String delimiter) {
String[] pieces = string.trim().toLowerCase().split(delimiter);
float[] floats = new float[pieces.length];
for (int i = 0; i < pieces.length; i++) {
floats[i] = Float.parseFloat(pieces[i].trim());
}
return floats;
}
public static long[] parseLongsFromString(String string, String delimiter) {
String[] pieces = string.trim().toLowerCase().split(delimiter);
long[] longs = new long[pieces.length];
for (int i = 0; i < pieces.length; i++) {
longs[i] = Long.parseLong(pieces[i].trim());
}
return longs;
}
public static String getSDCardDirectory() {
return Environment.getExternalStorageDirectory().getAbsolutePath();
}
public static boolean isSupportedNPU() {
return false;
// String hardware = android.os.Build.HARDWARE;
// return hardware.equalsIgnoreCase("kirin810") || hardware.equalsIgnoreCase("kirin990");
}
public static Bitmap resizeWithStep(Bitmap bitmap, int maxLength, int step) {
int width = bitmap.getWidth();
int height = bitmap.getHeight();
int maxWH = Math.max(width, height);
float ratio = 1;
int newWidth = width;
int newHeight = height;
if (maxWH > maxLength) {
ratio = maxLength * 1.0f / maxWH;
newWidth = (int) Math.floor(ratio * width);
newHeight = (int) Math.floor(ratio * height);
}
newWidth = newWidth - newWidth % step;
if (newWidth == 0) {
newWidth = step;
}
newHeight = newHeight - newHeight % step;
if (newHeight == 0) {
newHeight = step;
}
return Bitmap.createScaledBitmap(bitmap, newWidth, newHeight, true);
}
public static Bitmap rotateBitmap(Bitmap bitmap, int orientation) {
Matrix matrix = new Matrix();
switch (orientation) {
case ExifInterface.ORIENTATION_NORMAL:
return bitmap;
case ExifInterface.ORIENTATION_FLIP_HORIZONTAL:
matrix.setScale(-1, 1);
break;
case ExifInterface.ORIENTATION_ROTATE_180:
matrix.setRotate(180);
break;
case ExifInterface.ORIENTATION_FLIP_VERTICAL:
matrix.setRotate(180);
matrix.postScale(-1, 1);
break;
case ExifInterface.ORIENTATION_TRANSPOSE:
matrix.setRotate(90);
matrix.postScale(-1, 1);
break;
case ExifInterface.ORIENTATION_ROTATE_90:
matrix.setRotate(90);
break;
case ExifInterface.ORIENTATION_TRANSVERSE:
matrix.setRotate(-90);
matrix.postScale(-1, 1);
break;
case ExifInterface.ORIENTATION_ROTATE_270:
matrix.setRotate(-90);
break;
default:
return bitmap;
}
try {
Bitmap bmRotated = Bitmap.createBitmap(bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), matrix, true);
bitmap.recycle();
return bmRotated;
}
catch (OutOfMemoryError e) {
e.printStackTrace();
return null;
}
}
}
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<vector xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:aapt="http://schemas.android.com/aapt"
android:width="108dp"
android:height="108dp"
android:viewportWidth="108"
android:viewportHeight="108">
<path
android:fillType="evenOdd"
android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
android:strokeWidth="1"
android:strokeColor="#00000000">
<aapt:attr name="android:fillColor">
<gradient
android:endX="78.5885"
android:endY="90.9159"
android:startX="48.7653"
android:startY="61.0927"
android:type="linear">
<item
android:color="#44000000"
android:offset="0.0" />
<item
android:color="#00000000"
android:offset="1.0" />
</gradient>
</aapt:attr>
</path>
<path
android:fillColor="#FFFFFF"
android:fillType="nonZero"
android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
android:strokeWidth="1"
android:strokeColor="#00000000" />
</vector>
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<?xml version="1.0" encoding="utf-8"?>
<!-- for MiniActivity Use Only -->
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:layout_width="match_parent"
android:layout_height="match_parent"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintLeft_toRightOf="parent"
tools:context=".MainActivity">
<TextView
android:id="@+id/sample_text"
android:layout_width="0dp"
android:layout_height="wrap_content"
android:text="Hello World!"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
app:layout_constraintTop_toBottomOf="@id/imageView"
android:scrollbars="vertical"
/>
<ImageView
android:id="@+id/imageView"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:paddingTop="20dp"
android:paddingBottom="20dp"
app:layout_constraintBottom_toTopOf="@id/imageView"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
app:layout_constraintTop_toTopOf="parent"
tools:srcCompat="@tools:sample/avatars" />
<Button
android:id="@+id/button"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_marginBottom="4dp"
android:text="Button"
app:layout_constraintBottom_toBottomOf="parent"
app:layout_constraintLeft_toLeftOf="parent"
app:layout_constraintRight_toRightOf="parent"
tools:layout_editor_absoluteX="161dp" />
</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
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<menu xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto">
<group android:id="@+id/pick_image">
<item
android:id="@+id/open_gallery"
android:title="Open Gallery"
app:showAsAction="withText"/>
<item
android:id="@+id/take_photo"
android:title="Take Photo"
app:showAsAction="withText"/>
</group>
<group>
<item
android:id="@+id/settings"
android:title="Settings..."
app:showAsAction="withText"/>
</group>
</menu>
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<?xml version="1.0" encoding="utf-8"?>
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
<background android:drawable="@drawable/ic_launcher_background" />
<foreground android:drawable="@drawable/ic_launcher_foreground" />
</adaptive-icon>
\ No newline at end of file
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<?xml version="1.0" encoding="utf-8"?>
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
<background android:drawable="@drawable/ic_launcher_background" />
<foreground android:drawable="@drawable/ic_launcher_foreground" />
</adaptive-icon>
\ No newline at end of file
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ppocr/postprocess/db_postprocess.py 100644 → 100755
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ppocr/utils/gen_label.py 0 → 100644
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ppocr/utils/ic15_dict.txt
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tools/export_model.py
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tools/infer/predict_det.py
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tools/infer/utility.py
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tools/program.py
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