Extraction get_rotate_crop_image

ppocr/data/imaug/copy_paste.py

@@ -20,40 +20,7 @@ from shapely.geometry import Polygon
 
 from ppocr.data.imaug.iaa_augment import IaaAugment
 from ppocr.data.imaug.random_crop_data import is_poly_outside_rect
-
+from tools.infer.utility import get_rotate_crop_image
-
-def get_rotate_crop_image(img, points):
-    '''
-    img_height, img_width = img.shape[0:2]
-    left = int(np.min(points[:, 0]))
-    right = int(np.max(points[:, 0]))
-    top = int(np.min(points[:, 1]))
-    bottom = int(np.max(points[:, 1]))
-    img_crop = img[top:bottom, left:right, :].copy()
-    points[:, 0] = points[:, 0] - left
-    points[:, 1] = points[:, 1] - top
-    '''
-    img_crop_width = int(
-        max(
-            np.linalg.norm(points[0] - points[1]),
-            np.linalg.norm(points[2] - points[3])))
-    img_crop_height = int(
-        max(
-            np.linalg.norm(points[0] - points[3]),
-            np.linalg.norm(points[1] - points[2])))
-    pts_std = np.float32([[0, 0], [img_crop_width, 0],
-                          [img_crop_width, img_crop_height],
-                          [0, img_crop_height]])
-    M = cv2.getPerspectiveTransform(points, pts_std)
-    dst_img = cv2.warpPerspective(
-        img,
-        M, (img_crop_width, img_crop_height),
-        borderMode=cv2.BORDER_REPLICATE,
-        flags=cv2.INTER_CUBIC)
-    dst_img_height, dst_img_width = dst_img.shape[0:2]
-    if dst_img_height * 1.0 / dst_img_width >= 1.5:
-        dst_img = np.rot90(dst_img)
-    return dst_img
 
 
 class CopyPaste(object):
@@ -164,6 +131,17 @@ def get_intersection(pD, pG):
 
 
 def rotate_bbox(img, text_polys, angle, scale=1):
+    """
+    from https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/augment.py
+    Args:
+        img: np.ndarray
+        text_polys: np.ndarray N*4*2
+        angle: int
+        scale: int
+
+    Returns:
+
+    """
     w = img.shape[1]
     h = img.shape[0]
 

tools/infer/predict_system.py

@@ -31,7 +31,7 @@ import tools.infer.predict_det as predict_det
 import tools.infer.predict_cls as predict_cls
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
-from tools.infer.utility import draw_ocr_box_txt, get_current_memory_mb
+from tools.infer.utility import draw_ocr_box_txt, get_current_memory_mb, get_rotate_crop_image
 import tools.infer.benchmark_utils as benchmark_utils
 logger = get_logger()
 
@@ -45,39 +45,6 @@ class TextSystem(object):
         if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
-    def get_rotate_crop_image(self, img, points):
-        '''
-        img_height, img_width = img.shape[0:2]
-        left = int(np.min(points[:, 0]))
-        right = int(np.max(points[:, 0]))
-        top = int(np.min(points[:, 1]))
-        bottom = int(np.max(points[:, 1]))
-        img_crop = img[top:bottom, left:right, :].copy()
-        points[:, 0] = points[:, 0] - left
-        points[:, 1] = points[:, 1] - top
-        '''
-        img_crop_width = int(
-            max(
-                np.linalg.norm(points[0] - points[1]),
-                np.linalg.norm(points[2] - points[3])))
-        img_crop_height = int(
-            max(
-                np.linalg.norm(points[0] - points[3]),
-                np.linalg.norm(points[1] - points[2])))
-        pts_std = np.float32([[0, 0], [img_crop_width, 0],
-                              [img_crop_width, img_crop_height],
-                              [0, img_crop_height]])
-        M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(
-            img,
-            M, (img_crop_width, img_crop_height),
-            borderMode=cv2.BORDER_REPLICATE,
-            flags=cv2.INTER_CUBIC)
-        dst_img_height, dst_img_width = dst_img.shape[0:2]
-        if dst_img_height * 1.0 / dst_img_width >= 1.5:
-            dst_img = np.rot90(dst_img)
-        return dst_img
-
     def print_draw_crop_rec_res(self, img_crop_list, rec_res):
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
@@ -89,7 +56,6 @@ class TextSystem(object):
         dt_boxes, elapse = self.text_detector(img)
 
         logger.info("dt_boxes num : {}, elapse : {}".format(
-
             len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
@@ -99,7 +65,7 @@ class TextSystem(object):
 
         for bno in range(len(dt_boxes)):
             tmp_box = copy.deepcopy(dt_boxes[bno])
-            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
+            img_crop = get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
         if self.use_angle_cls and cls:
             img_crop_list, angle_list, elapse = self.text_classifier(

tools/infer/utility.py

@@ -113,7 +113,6 @@ def init_args():
     parser.add_argument("--benchmark", type=bool, default=False)
     parser.add_argument("--save_log_path", type=str, default="./log_output/")
 
-
     return parser
 
 
@@ -615,5 +614,39 @@ def get_current_memory_mb(gpu_id=None):
     return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)
 
 
+def get_rotate_crop_image(img, points):
+    '''
+    img_height, img_width = img.shape[0:2]
+    left = int(np.min(points[:, 0]))
+    right = int(np.max(points[:, 0]))
+    top = int(np.min(points[:, 1]))
+    bottom = int(np.max(points[:, 1]))
+    img_crop = img[top:bottom, left:right, :].copy()
+    points[:, 0] = points[:, 0] - left
+    points[:, 1] = points[:, 1] - top
+    '''
+    img_crop_width = int(
+        max(
+            np.linalg.norm(points[0] - points[1]),
+            np.linalg.norm(points[2] - points[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(points[0] - points[3]),
+            np.linalg.norm(points[1] - points[2])))
+    pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    M = cv2.getPerspectiveTransform(points, pts_std)
+    dst_img = cv2.warpPerspective(
+        img,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+    dst_img_height, dst_img_width = dst_img.shape[0:2]
+    if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        dst_img = np.rot90(dst_img)
+    return dst_img
+
+
 if __name__ == '__main__':
     pass