update for pep8

craft_text_detector/__init__.py

@@ -98,6 +98,5 @@ def detect_text(image_path,
                                  heatmap=prediction_result["heatmap"],
                                  output_dir=output_dir)
 
-
     # return prediction results
     return prediction_result

craft_text_detector/craft_utils.py

@@ -14,7 +14,7 @@
 
 def warpCoord(Minv, pt):
     out = np.matmul(Minv, (pt[0], pt[1], 1))
-    return np.array([out[0]/out[2], out[1]/out[2]])
+    return np.array([out[0] / out[2], out[1] / out[2]])
 
 
 """ end of auxilary functions """
@@ -33,7 +33,7 @@ def getDetBoxes_core(textmap, linkmap,
 
     text_score_comb = np.clip(text_score + link_score, 0, 1)
     nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(
-             text_score_comb.astype(np.uint8), connectivity=4)
+            text_score_comb.astype(np.uint8), connectivity=4)
 
     det = []
     mapper = []
@@ -90,7 +90,7 @@ def getDetBoxes_core(textmap, linkmap,
 
         # make clock-wise order
         startidx = box.sum(axis=1).argmin()
-        box = np.roll(box, 4-startidx, 0)
+        box = np.roll(box, 4 - startidx, 0)
         box = np.array(box)
 
         det.append(box)
@@ -186,8 +186,8 @@ def getPoly_core(boxes, labels, mapper, linkmap):
                 continue  # No polygon area
 
             if prev_h < cur_h:
-                pp[int((seg_num - 1)/2)] = (x, cy)
-                seg_height[int((seg_num - 1)/2)] = cur_h
+                pp[int((seg_num - 1) / 2)] = (x, cy)
+                seg_height[int((seg_num - 1) / 2)] = cur_h
                 prev_h = cur_h
 
         # processing last segment

craft_text_detector/file_utils.py

@@ -100,21 +100,21 @@ def rectify_poly(img, poly):
     width = 0
     height = 0
     for k in range(n):
-        box = np.float32([poly[k], poly[k+1], poly[-k-2], poly[-k-1]])
-        width += int((np.linalg.norm(box[0] - box[1]) + np.linalg.norm(box[2] - box[3]))/2)
+        box = np.float32([poly[k], poly[k + 1], poly[-k - 2], poly[-k - 1]])
+        width += int((np.linalg.norm(box[0] - box[1]) + np.linalg.norm(box[2] - box[3])) / 2)
         height += np.linalg.norm(box[1] - box[2])
     width = int(width)
     height = int(height / n)
 
     output_img = np.zeros((height, width, 3), dtype=np.uint8)
     width_step = 0
     for k in range(n):
-        box = np.float32([poly[k], poly[k+1], poly[-k-2], poly[-k-1]])
-        w = int((np.linalg.norm(box[0] - box[1]) + np.linalg.norm(box[2] - box[3]))/2)
+        box = np.float32([poly[k], poly[k + 1], poly[-k - 2], poly[-k - 1]])
+        w = int((np.linalg.norm(box[0] - box[1]) + np.linalg.norm(box[2] - box[3])) / 2)
 
         # Top triangle
         pts1 = box[:3]
-        pts2 = np.float32([[width_step, 0], [width_step + w - 1, 0], [width_step + w - 1, height-1]])
+        pts2 = np.float32([[width_step, 0], [width_step + w - 1, 0], [width_step + w - 1, height - 1]])
         M = cv2.getAffineTransform(pts1, pts2)
         warped_img = cv2.warpAffine(img, M, (width, height), borderMode=cv2.BORDER_REPLICATE)
         warped_mask = np.zeros((height, width, 3), dtype=np.uint8)
@@ -123,12 +123,12 @@ def rectify_poly(img, poly):
 
         # Bottom triangle
         pts1 = np.vstack((box[0], box[2:]))
-        pts2 = np.float32([[width_step, 0], [width_step + w - 1, height-1], [width_step, height-1]])
+        pts2 = np.float32([[width_step, 0], [width_step + w - 1, height-1], [width_step, height - 1]])
         M = cv2.getAffineTransform(pts1, pts2)
         warped_img = cv2.warpAffine(img, M, (width, height), borderMode=cv2.BORDER_REPLICATE)
         warped_mask = np.zeros((height, width, 3), dtype=np.uint8)
         warped_mask = cv2.fillConvexPoly(warped_mask, np.int32(pts2), (1, 1, 1))
-        cv2.line(warped_mask, (width_step, 0), (width_step + w - 1, height-1), (0, 0, 0), 1)
+        cv2.line(warped_mask, (width_step, 0), (width_step + w - 1, height - 1), (0, 0, 0), 1)
         output_img[warped_mask == 1] = warped_img[warped_mask == 1]
 
         width_step += w

craft_text_detector/predict.py

@@ -193,13 +193,13 @@ def get_prediction(image,
     # calculate box coords as ratios to image size
     boxes_as_ratio = []
     for box in boxes:
-        boxes_as_ratio.append(box/[img_width, img_height])
+        boxes_as_ratio.append(box / [img_width, img_height])
     boxes_as_ratio = np.array(boxes_as_ratio)
 
     # calculate poly coords as ratios to image size
     polys_as_ratio = []
     for poly in polys:
-        polys_as_ratio.append(poly/[img_width, img_height])
+        polys_as_ratio.append(poly / [img_width, img_height])
     polys_as_ratio = np.array(polys_as_ratio)
 
     return {"boxes": boxes,