polish infer_rec and add ic15_dict

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 = []