Merge pull request #4 from tink2123/develop

polish infer_rec and add ic15_dict

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

 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

@@ -4,7 +4,7 @@ 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
@@ -12,11 +12,12 @@ Global:
   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 string
 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
@@ -143,6 +144,7 @@ class SimpleReader(object):
             self.num_workers = 1
         else:
             self.num_workers = params['num_workers']
+        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']
@@ -164,10 +166,14 @@ class SimpleReader(object):
 
         def sample_iter_reader():
             if self.mode == 'test':
-                print("infer_img:", self.infer_img)
-                img = cv2.imread(self.infer_img)
+                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)
@@ -179,6 +185,7 @@ class SimpleReader(object):
                     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,
@@ -198,4 +205,6 @@ class SimpleReader(object):
             if len(batch_outs) != 0:
                 yield batch_outs
 
+        if self.mode != 'test':
             return batch_iter_reader
+        return sample_iter_reader

ppocr/utils/ic15_dict.txt

+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

set_env.sh

-#. /paddle/set_env.sh↩
-export CUDA_VISIBLE_DEVICES="0,1,2,3"↩
-export PYTHONPATH=$PYTHONPATH:.↩
-export FLAGS_fraction_of_gpu_memory_to_use=1.0↩
-↩
-python_bin_dir="/opt/_internal/cpython-3.7.0/bin/"↩
-alias python=$python_bin_dir"python3.7"↩
-alias pip=$python_bin_dir"pip3.7"↩
-alias ipython=$python_bin_dir"ipython3"↩
-export  LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib:$LD_LIBRARY_PATH↩
-export PYTHONPATH=$PYTHONPATH:.↩
-ldconfig↩

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 time
 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 @@ from ppocr.data.reader_main import reader_main
 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 = []