提交 9023a5c5 编写于 作者: T tink2123

update infer doc and fix yml

上级 80c18878
Global:
algorithm: CRNN
use_gpu: false
use_gpu: true
epoch_num: 3000
log_smooth_window: 20
print_batch_step: 10
......@@ -16,7 +16,7 @@ Global:
character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
loss_type: ctc
reader_yml: ./configs/rec/rec_chinese_reader.yml
pretrain_weights: output/rec_CRNN/rec_mv3_crnn/best_accuracy
pretrain_weights:
checkpoints:
save_inference_dir:
infer_img:
......
......@@ -15,7 +15,7 @@ Global:
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_icdar15_reader.yml
pretrain_weights:
pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
checkpoints:
save_inference_dir:
infer_img:
......
Global:
algorithm: CRNN
use_gpu: false
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
......
Global:
algorithm: RARE
use_gpu: false
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
......@@ -12,8 +12,7 @@ Global:
test_batch_size_per_card: 256
image_shape: [3, 32, 100]
max_text_length: 25
character_type: ch
character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
character_type: en
loss_type: attention
tps: true
reader_yml: ./configs/rec/rec_benchmark_reader.yml
......
......@@ -165,6 +165,12 @@ STAR-Net文本识别模型推理,可以执行如下命令:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```
RARE 文本识别模型推理,可以执行如下命令:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/sare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
```
![](imgs_words_en/word_336.png)
执行命令后,上面图像的识别结果如下:
......
......@@ -32,10 +32,14 @@ class TextRecognizer(object):
self.rec_image_shape = image_shape
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
char_ops_params = {}
char_ops_params["character_type"] = args.rec_char_type
char_ops_params["character_dict_path"] = args.rec_char_dict_path
if self.rec_algorithm != "RARE":
char_ops_params['loss_type'] = 'ctc'
else:
char_ops_params['loss_type'] = 'attention'
self.char_ops = CharacterOps(char_ops_params)
def resize_norm_img(self, img, max_wh_ratio):
......@@ -81,7 +85,7 @@ class TextRecognizer(object):
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.zero_copy_run()
if args.rec_algorithm != "RARE":
if self.rec_algorithm != "RARE":
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
rec_idx_lod = self.output_tensors[0].lod()[0]
predict_batch = self.output_tensors[1].copy_to_cpu()
......@@ -104,6 +108,8 @@ class TextRecognizer(object):
else:
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
predict_batch = self.output_tensors[1].copy_to_cpu()
elapse = time.time() - starttime
predict_time += elapse
for rno in range(len(rec_idx_batch)):
end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
if len(end_pos) <= 1:
......@@ -112,8 +118,6 @@ class TextRecognizer(object):
else:
preds = rec_idx_batch[rno, 1:end_pos[1]]
score = np.mean(predict_batch[rno, 1:end_pos[1]])
#attenton index has 2 offset: beg and end
preds = preds - 2
preds_text = self.char_ops.decode(preds)
rec_res.append([preds_text, score])
......
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