Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleOCR i…

…nto fixocr

configs/rec/rec_chinese_reader.yml → configs/rec/rec_icdar15_reader.yml

@@ -1,13 +1,13 @@
 TrainReader:
  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
  num_workers: 8
- img_set_dir: .
- label_file_path: ./train_data/hard_label.txt
+ img_set_dir: ./train_data
+ label_file_path: ./train_data/rec_gt_train.txt
 
 EvalReader:
  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader
- img_set_dir: .
- label_file_path: ./train_data/label_val_all.txt
+ img_set_dir: ./train_data
+ label_file_path: ./train_data/rec_gt_test.txt
 
 TestReader:
  reader_function: ppocr.data.rec.dataset_traversal,SimpleReader

configs/rec/rec_chinese_lite_train.yml → configs/rec/rec_icdar15_train.yml

@@ -4,19 +4,20 @@ Global:
  epoch_num: 300
  log_smooth_window: 20
  print_batch_step: 10
- save_model_dir: output
+ save_model_dir: output_ic15
  save_epoch_step: 3
  eval_batch_step: 2000
  train_batch_size_per_card: 256
  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_dict_path: ./ppocr/utils/ic15_dict.txt
  loss_type: ctc
- reader_yml: ./configs/rec/rec_chinese_reader.yml
- pretrain_weights:
-
+ reader_yml: ./configs/rec/rec_icdar15_reader.yml
+ pretrain_weights: ./pretrain_models/CRNN/best_accuracy 
+ checkpoints:
+ save_inference_dir:
 Architecture:
  function: ppocr.modeling.architectures.rec_model,RecModel
 

ppocr/data/rec/dataset_traversal.py

@@ -22,6 +22,7 @@
 import lmdb
 
 from ppocr.utils.utility import initial_logger
+from ppocr.utils.utility import get_image_file_list
 logger = initial_logger()
 
 from .img_tools import process_image, get_img_data
@@ -143,8 +144,9 @@ def __init__(self, params):
  self.num_workers = 1
  else:
  self.num_workers = params['num_workers']
- self.img_set_dir = params['img_set_dir']
- self.label_file_path = params['label_file_path']
+ if params['mode'] != 'test':
+ self.img_set_dir = params['img_set_dir']
+ self.label_file_path = params['label_file_path']
  self.char_ops = params['char_ops']
  self.image_shape = params['image_shape']
  self.loss_type = params['loss_type']
@@ -164,29 +166,34 @@ def __call__(self, process_id):
 
  def sample_iter_reader():
  if self.mode == 'test':
- print("infer_img:", self.infer_img)
- img = cv2.imread(self.infer_img)
- norm_img = process_image(img, self.image_shape)
- yield norm_img
- with open(self.label_file_path, "rb") as fin:
- label_infor_list = fin.readlines()
- img_num = len(label_infor_list)
- img_id_list = list(range(img_num))
- random.shuffle(img_id_list)
- for img_id in range(process_id, img_num, self.num_workers):
- label_infor = label_infor_list[img_id_list[img_id]]
- substr = label_infor.decode('utf-8').strip("\n").split("\t")
- img_path = self.img_set_dir + "/" + substr[0]
- img = cv2.imread(img_path)
- if img is None:
- continue
- label = substr[1]
- outs = process_image(img, self.image_shape, label,
- self.char_ops, self.loss_type,
- self.max_text_length)
- if outs is None:
- continue
- yield outs
+ image_file_list = get_image_file_list(self.infer_img)
+ for single_img in image_file_list:
+ img = cv2.imread(single_img)
+ if img.shape[-1]==1 or len(list(img.shape))==2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+ norm_img = process_image(img, self.image_shape)
+ yield norm_img
+ else:
+ with open(self.label_file_path, "rb") as fin:
+ label_infor_list = fin.readlines()
+ img_num = len(label_infor_list)
+ img_id_list = list(range(img_num))
+ random.shuffle(img_id_list)
+ for img_id in range(process_id, img_num, self.num_workers):
+ label_infor = label_infor_list[img_id_list[img_id]]
+ substr = label_infor.decode('utf-8').strip("\n").split("\t")
+ img_path = self.img_set_dir + "/" + substr[0]
+ img = cv2.imread(img_path)
+ if img is None:
+ logger.info("{} does not exist!".format(img_path))
+ continue
+ label = substr[1]
+ outs = process_image(img, self.image_shape, label,
+ self.char_ops, self.loss_type,
+ self.max_text_length)
+ if outs is None:
+ continue
+ yield outs
 
  def batch_iter_reader():
  batch_outs = []
@@ -198,4 +205,6 @@ def batch_iter_reader():
  if len(batch_outs) != 0:
  yield batch_outs
 
- return batch_iter_reader
+ if self.mode != 'test':
+ return batch_iter_reader
+ return sample_iter_reader

ppocr/utils/ic15_dict.txt

@@ -0,0 +1,36 @@
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9

tools/eval.py

@@ -80,7 +80,7 @@ def main():
  metrics = eval_det_run(exe, config, eval_info_dict, "test")
  else:
  reader_type = config['Global']['reader_yml']
- if "chinese" in reader_type:
+ if "benchmark" not in reader_type:
  eval_reader = reader_main(config=config, mode="eval")
  eval_info_dict = {'program': eval_program, \
  'reader': eval_reader, \

tools/infer_rec.py

@@ -21,7 +21,6 @@
 import multiprocessing
 import numpy as np
 
-
 def set_paddle_flags(**kwargs):
  for key, value in kwargs.items():
  if os.environ.get(key, None) is None:
@@ -47,7 +46,7 @@ def set_paddle_flags(**kwargs):
 from ppocr.utils.save_load import init_model
 from ppocr.utils.character import CharacterOps
 from ppocr.utils.utility import create_module
-
+from ppocr.utils.utility import get_image_file_list
 logger = initial_logger()
 
 
@@ -79,9 +78,15 @@ def main():
 
  init_model(config, eval_prog, exe)
 
- blobs = reader_main(config, 'test')
- imgs = next(blobs())
- for img in imgs:
+ blobs = reader_main(config, 'test')()
+ infer_img = config['TestReader']['infer_img']
+ infer_list = get_image_file_list(infer_img)
+ max_img_num = len(infer_list)
+ if len(infer_list) == 0:
+ logger.info("Can not find img in infer_img dir.")
+ for i in range(max_img_num):
+ print("infer_img:",infer_list[i])
+ img = next(blobs)
  predict = exe.run(program=eval_prog,
  feed={"image": img},
  fetch_list=fetch_varname_list,
@@ -101,8 +106,8 @@ def main():
  preds_text = preds_text.reshape(-1)
  preds_text = char_ops.decode(preds_text)
 
- print(preds)
- print(preds_text)
+ print("\t index:",preds)
+ print("\t word :",preds_text)
 
  # save for inference model
  target_var = []