commit for tmp

configs/rec/rec_mv3_none_bilstm_ctc.yml

 Global:
   algorithm: CRNN
-  use_gpu: false
+  use_gpu: true
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 10
@@ -17,6 +17,7 @@ Global:
   pretrain_weights: ./output/rec_CRNN/rec_mv3_none_bilstm_ctc/best_accuracy
   checkpoints:
   save_inference_dir:
+  infer_img:
   
 Architecture:
   function: ppocr.modeling.architectures.rec_model,RecModel

ppocr/data/rec/dataset_traversal.py

@@ -105,7 +105,10 @@ class LMDBReader(object):
                     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)
+                    norm_img = process_image(
+                        img=img,
+                        image_shape=self.image_shape,
+                        char_ops=self.char_ops)
                     yield norm_img
             else:
                 lmdb_sets = self.load_hierarchical_lmdb_dataset()
@@ -182,7 +185,10 @@ class SimpleReader(object):
                     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)
+                    norm_img = process_image(
+                        img=img,
+                        image_shape=self.image_shape,
+                        char_ops=self.char_ops)
                     yield norm_img
             else:
                 with open(self.label_file_path, "rb") as fin:

ppocr/data/rec/img_tools.py

@@ -48,6 +48,32 @@ def resize_norm_img(img, image_shape):
     return padding_im
 
 
+def resize_norm_img_chinese(img, image_shape):
+    imgC, imgH, imgW = image_shape
+    # todo: change to 0 and modified image shape
+    max_wh_ratio = 10
+    h, w = img.shape[0], img.shape[1]
+    ratio = w * 1.0 / h
+    max_wh_ratio = max(max_wh_ratio, ratio)
+    imgW = int(32 * max_wh_ratio)
+    if math.ceil(imgH * ratio) > imgW:
+        resized_w = imgW
+    else:
+        resized_w = int(math.ceil(imgH * ratio))
+    resized_image = cv2.resize(img, (resized_w, imgH))
+    resized_image = resized_image.astype('float32')
+    if image_shape[0] == 1:
+        resized_image = resized_image / 255
+        resized_image = resized_image[np.newaxis, :]
+    else:
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+    padding_im[:, :, 0:resized_w] = resized_image
+    return padding_im
+
+
 def get_img_data(value):
     """get_img_data"""
     if not value:
@@ -67,7 +93,10 @@ def process_image(img,
                   char_ops=None,
                   loss_type=None,
                   max_text_length=None):
+    if char_ops.character_type == "en":
         norm_img = resize_norm_img(img, image_shape)
+    else:
+        norm_img = resize_norm_img_chinese(img, image_shape)
     norm_img = norm_img[np.newaxis, :]
     if label is not None:
         char_num = char_ops.get_char_num()