use mmcv instead (#237)

* use mmcv instead

* update

mmocr/datasets/pipelines/dbnet_transforms.py

@@ -1,6 +1,6 @@
-import cv2
 import imgaug
 import imgaug.augmenters as iaa
+import mmcv
 import numpy as np
 
 from mmdet.core.mask import PolygonMasks
@@ -145,7 +145,7 @@ class EastRandomCrop:
         padded_img = np.zeros(
             (self.target_size[1], self.target_size[0], img.shape[2]),
             img.dtype)
-        padded_img[:h, :w] = cv2.resize(
+        padded_img[:h, :w] = mmcv.imresize(
             img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
 
         # for bboxes

mmocr/datasets/pipelines/ocr_transforms.py

@@ -1,6 +1,5 @@
 import math
 
-import cv2
 import mmcv
 import numpy as np
 import torch
@@ -91,8 +90,8 @@ class ResizeOCR:
             if dst_max_width is not None:
                 valid_ratio = min(1.0, 1.0 * new_width / dst_max_width)
                 resize_width = min(dst_max_width, new_width)
-                img_resize = cv2.resize(results['img'],
+                img_resize = mmcv.imresize(results['img'],
-                                        (resize_width, dst_height))
+                                           (resize_width, dst_height))
                 resize_shape = img_resize.shape
                 pad_shape = img_resize.shape
                 if new_width < dst_max_width:
@@ -102,13 +101,13 @@ class ResizeOCR:
                         pad_val=self.img_pad_value)
                     pad_shape = img_resize.shape
             else:
-                img_resize = cv2.resize(results['img'],
+                img_resize = mmcv.imresize(results['img'],
-                                        (new_width, dst_height))
+                                           (new_width, dst_height))
                 resize_shape = img_resize.shape
                 pad_shape = img_resize.shape
         else:
-            img_resize = cv2.resize(results['img'],
+            img_resize = mmcv.imresize(results['img'],
-                                    (dst_max_width, dst_height))
+                                       (dst_max_width, dst_height))
             resize_shape = img_resize.shape
             pad_shape = img_resize.shape
 
@@ -286,10 +285,10 @@ class RandomPaddingOCR:
         random_padding_bottom = round(
             np.random.uniform(0, self.max_ratio[3]) * ori_height)
 
-        img = np.copy(results['img'])
+        padding = (random_padding_left, random_padding_top,
-        img = cv2.copyMakeBorder(img, random_padding_top,
+                   random_padding_right, random_padding_bottom)
-                                 random_padding_bottom, random_padding_left,
+        img = mmcv.impad(results['img'], padding=padding, padding_mode='edge')
-                                 random_padding_right, cv2.BORDER_REPLICATE)
+
         results['img'] = img
         results['img_shape'] = img.shape
 

mmocr/datasets/pipelines/transforms.py

@@ -1,6 +1,7 @@
 import math
 
 import cv2
+import mmcv
 import numpy as np
 import Polygon as plg
 import torchvision.transforms as transforms
@@ -587,7 +588,7 @@ class RandomRotatePolyInstances:
             (h_ind, w_ind) = (np.random.randint(0, h * 7 // 8),
                               np.random.randint(0, w * 7 // 8))
             img_cut = img[h_ind:(h_ind + h // 9), w_ind:(w_ind + w // 9)]
-            img_cut = cv2.resize(img_cut, (canvas_size[1], canvas_size[0]))
+            img_cut = mmcv.imresize(img_cut, (canvas_size[1], canvas_size[0]))
             mask = cv2.warpAffine(
                 mask,
                 rotation_matrix, (canvas_size[1], canvas_size[0]),
@@ -670,7 +671,7 @@ class SquareResizePad:
             t_w = self.target_size if h <= w else int(w * self.target_size / h)
         else:
             t_h = t_w = self.target_size
-        img = cv2.resize(img, (t_w, t_h))
+        img = mmcv.imresize(img, (t_w, t_h))
         return img, (t_h, t_w)
 
     def square_pad(self, img):
@@ -685,7 +686,7 @@ class SquareResizePad:
             (h_ind, w_ind) = (np.random.randint(0, h * 7 // 8),
                               np.random.randint(0, w * 7 // 8))
             img_cut = img[h_ind:(h_ind + h // 9), w_ind:(w_ind + w // 9)]
-            expand_img = cv2.resize(img_cut, (pad_size, pad_size))
+            expand_img = mmcv.imresize(img_cut, (pad_size, pad_size))
         if h > w:
             y0, x0 = 0, (h - w) // 2
         else:
@@ -758,7 +759,7 @@ class RandomScaling:
         scales = self.size * 1.0 / max(h, w) * aspect_ratio
         scales = np.array([scales, scales])
         out_size = (int(h * scales[1]), int(w * scales[0]))
-        image = cv2.resize(image, out_size[::-1])
+        image = mmcv.imresize(image, out_size[::-1])
 
         results['img'] = image
         results['img_shape'] = image.shape

mmocr/models/ner/convertors/ner_convertor.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 from mmocr.models.builder import CONVERTORS
+from mmocr.utils import list_from_file
 
 
 @CONVERTORS.register_module()
@@ -36,10 +37,10 @@ class NerConvertor:
         assert self.max_len > 2
         assert self.annotation_type in ['bio', 'bioes']
 
-        lines = open(vocab_file, encoding='utf-8').readlines()
+        vocabs = list_from_file(vocab_file)
-        self.vocab_size = len(lines)
+        self.vocab_size = len(vocabs)
-        for i in range(len(lines)):
+        for idx, vocab in enumerate(vocabs):
-            self.word2ids.update({lines[i].rstrip(): i})
+            self.word2ids.update({vocab: idx})
 
         if self.annotation_type == 'bio':
             self.label2id_dict, self.id2label, self.ignore_id = \