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-
- import cv2
- import numpy as np
-
-
-
- watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')
-
-
- def computeSafeRegion(shape,bounding_rect):
- top = bounding_rect[1] # y
- bottom = bounding_rect[1] + bounding_rect[3] # y + h
- left = bounding_rect[0] # x
- right = bounding_rect[0] + bounding_rect[2] # x + w
-
- min_top = 0
- max_bottom = shape[0]
- min_left = 0
- max_right = shape[1]
-
- # print "computeSateRegion input shape",shape
- if top < min_top:
- top = min_top
- # print "tap top 0"
- if left < min_left:
- left = min_left
- # print "tap left 0"
-
- if bottom > max_bottom:
- bottom = max_bottom
- #print "tap max_bottom max"
- if right > max_right:
- right = max_right
- #print "tap max_right max"
-
- # print "corr",left,top,right,bottom
- return [left,top,right-left,bottom-top]
-
-
- def cropped_from_image(image,rect):
- x, y, w, h = computeSafeRegion(image.shape,rect)
- return image[y:y+h,x:x+w]
-
-
- def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
- print(image_gray.shape)
-
- if top_bottom_padding_rate>0.2:
- print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
- exit(1)
-
- height = image_gray.shape[0]
- padding = int(height*top_bottom_padding_rate)
- scale = image_gray.shape[1]/float(image_gray.shape[0])
-
- image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
-
- image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
-
- image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
-
- watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
-
- cropped_images = []
- for (x, y, w, h) in watches:
- cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
- x -= w * 0.14
- w += w * 0.28
- y -= h * 0.6
- h += h * 1.1;
-
- cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
-
-
- cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
- return cropped_images
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