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