fix bug in results visualization

ppocr/data/det/db_process.py

@@ -25,6 +25,10 @@ from .make_border_map import MakeBorderMap
 
 
 class DBProcessTrain(object):
+    """
+    DB pre-process for Train mode
+    """
+
     def __init__(self, params):
         self.img_set_dir = params['img_set_dir']
         self.image_shape = params['image_shape']
@@ -109,6 +113,10 @@ class DBProcessTrain(object):
 
 
 class DBProcessTest(object):
+    """
+    DB pre-process for Test mode
+    """
+
     def __init__(self, params):
         super(DBProcessTest, self).__init__()
         self.resize_type = 0
@@ -124,6 +132,10 @@ class DBProcessTest(object):
     def resize_image_type0(self, im):
         """
         resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
         """
         max_side_len = self.max_side_len
         h, w, _ = im.shape

tools/infer/utility.py

@@ -107,7 +107,7 @@ def create_predictor(args, mode):
     return predictor, input_tensor, output_tensors
 
 
-def draw_text_det_res(dt_boxes, img_path, return_img=True):
+def draw_text_det_res(dt_boxes, img_path):
     src_im = cv2.imread(img_path)
     for box in dt_boxes:
         box = np.array(box).astype(np.int32).reshape(-1, 2)
@@ -117,10 +117,10 @@ def draw_text_det_res(dt_boxes, img_path, return_img=True):
 
 def resize_img(img, input_size=600):
     """
+    resize img and limit the longest side of the image to input_size
     """
     img = np.array(img)
     im_shape = img.shape
-    im_size_min = np.min(im_shape[0:2])
     im_size_max = np.max(im_shape[0:2])
     im_scale = float(input_size) / float(im_size_max)
     im = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
@@ -131,7 +131,7 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
     """
     Visualize the results of OCR detection and recognition
     args:
-        image(Image): image from Image.open
+        image(Image|array): RGB image
         boxes(list): boxes with shape(N, 4, 2)
         txts(list): the texts
         scores(list): txxs corresponding scores
@@ -140,31 +140,14 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
     return(array):
         the visualized img
     """
-    from PIL import Image, ImageDraw, ImageFont
-
-    img = image.copy()
-    draw = ImageDraw.Draw(img)
+    img = image
     if scores is None:
         scores = [1] * len(boxes)
     for (box, score) in zip(boxes, scores):
-        if score < drop_score:
+        if score < drop_score or math.isnan(score):
             continue
-        draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
-        draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
-        draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
-        draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
-        draw.line(
-            [(box[0][0] - 1, box[0][1] + 1), (box[1][0] - 1, box[1][1] + 1)],
-            fill='red')
-        draw.line(
-            [(box[1][0] - 1, box[1][1] + 1), (box[2][0] - 1, box[2][1] + 1)],
-            fill='red')
-        draw.line(
-            [(box[2][0] - 1, box[2][1] + 1), (box[3][0] - 1, box[3][1] + 1)],
-            fill='red')
-        draw.line(
-            [(box[3][0] - 1, box[3][1] + 1), (box[0][0] - 1, box[0][1] + 1)],
-            fill='red')
+        box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
+        img = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 3)
 
     if draw_txt:
         img = np.array(resize_img(img, input_size=600))
@@ -233,7 +216,7 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
     count, index = 0, 0
     for idx, txt in enumerate(texts):
         index += 1
-        if scores[idx] < threshold:
+        if scores[idx] < threshold or math.isnan(scores[idx]):
             index -= 1
             continue
         first_line = True
@@ -260,7 +243,7 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
         draw_txt.text((0, gap * (count + 1)), new_txt, txt_color, font=font)
         count += 1
         # whether add new blank img or not
-        if count >= img_h // gap - 1 and idx + 1 < len(texts):
+        if count > img_h // gap - 1 and idx + 1 < len(texts):
             txt_img_list.append(np.array(blank_img))
             blank_img, draw_txt = create_blank_img()
             count = 0
@@ -270,7 +253,6 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
         blank_img = np.array(txt_img_list[0])
     else:
         blank_img = np.concatenate(txt_img_list, axis=1)
-    # cv2.imwrite("./draw_txt.jpg", np.array(blank_img))
     return np.array(blank_img)