验证码之滑块验证码

class SlideCrack(object):
    def __init__(self, gap, bg):
        """
        init code
        :param gap: 缺口图片
        :param bg: 背景图片
        :param out: 输出图片
        """
        self.front = gap
        self.bg = bg

    @staticmethod
    def clear_white(img):
        # 清除图片的空白区域，这里主要清除滑块的空白
        img_data = base64.b64decode(img)
        img_array = np.fromstring(img_data, np.uint8)
        img = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
        rows, cols, channel = img.shape
        min_x = 255
        min_y = 255
        max_x = 0
        max_y = 0
        for x in range(1, rows):
            for y in range(1, cols):
                t = set(img[x, y])
                if len(t) >= 2:
                    if x <= min_x:
                        min_x = x
                    elif x >= max_x:
                        max_x = x

                    if y <= min_y:
                        min_y = y
                    elif y >= max_y:
                        max_y = y
        img1 = img[min_x:max_x, min_y: max_y]
        return img1

    def template_match(self, tpl, target):
        th, tw = tpl.shape[:2]
        result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
        # 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
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        tl = max_loc
        br = (tl[0] + tw, tl[1] + th)
        # 绘制矩形边框，将匹配区域标注出来
        # target：目标图像
        # tl：矩形定点
        # br：矩形的宽高
        # (0,0,255)：矩形边框颜色
        # 1：矩形边框大小
        cv2.rectangle(target, tl, br, (0, 0, 255), 2)
        return tl

    @staticmethod
    def image_edge_detection(img):
        edges = cv2.Canny(img, 100, 200)
        return edges

    def discern(self):
        img1 = self.clear_white(self.front)
        img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
        slide = self.image_edge_detection(img1)
        img_data = base64.b64decode(self.bg)
        img_array = np.fromstring(img_data, np.uint8)
        back = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
        back = self.image_edge_detection(back)

        slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
        back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
        pos = self.template_match(slide_pic, back_pic)
        # 输出横坐标, 即 滑块在图片上的位置
        return pos

标签：滑块,img,max,self,cv2,验证码,back,tl
来源： https://www.cnblogs.com/Eliphaz/p/16138877.html