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90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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import cv2
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class SlideCrack(object):
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def __init__(self, gap, bg, out):
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"""
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init code
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:param gap: 缺口图片
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:param bg: 背景图片
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:param out: 输出图片
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"""
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self.gap = gap
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self.bg = bg
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self.out = out
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@staticmethod
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def clear_white(img):
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# 清除图片的空白区域，这里主要清除滑块的空白
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img = cv2.imread(img)
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rows, cols, channel = img.shape
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min_x = 255
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min_y = 255
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max_x = 0
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max_y = 0
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for x in range(1, rows):
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for y in range(1, cols):
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t = set(img[x, y])
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if len(t) >= 2:
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if x <= min_x:
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min_x = x
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elif x >= max_x:
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max_x = x
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if y <= min_y:
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min_y = y
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elif y >= max_y:
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max_y = y
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img1 = img[min_x:max_x, min_y: max_y]
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return img1
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def template_match(self, tpl, target):
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th, tw = tpl.shape[:2]
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result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
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# 寻找矩阵(一维数组当作向量,用Mat定义) 中最小值和最大值的位置
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
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tl = max_loc
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br = (tl[0] + tw, tl[1] + th)
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# 绘制矩形边框，将匹配区域标注出来
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# target：目标图像
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# tl：矩形定点
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# br：矩形的宽高
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# (0,0,255)：矩形边框颜色
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# 1：矩形边框大小
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cv2.rectangle(target, tl, br, (0, 0, 255), 2)
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cv2.imwrite(self.out, target)
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return tl[0]
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@staticmethod
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def image_edge_detection(img):
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edges = cv2.Canny(img, 100, 200)
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return edges
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def discern(self):
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img1 = self.clear_white(self.gap)
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img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
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slide = self.image_edge_detection(img1)
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back = cv2.imread(self.bg, 0)
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back = self.image_edge_detection(back)
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slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
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back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
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x = self.template_match(slide_pic, back_pic)
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# 输出横坐标, 即 滑块在图片上的位置
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return x
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def get_gap():
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# 滑块图片
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image1 = "img/slide.png"
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# 背景图片
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image2 = "img/bg.png"
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# 处理结果图片,用红线标注
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image3 = "img/show_image.png"
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sc = SlideCrack(image1, image2, image3)
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return sc.discern()
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