滑块缺口识别_c# 图片滑块识别

带缺口的图片长这样：

缺口图片：

 识别过程：

# -*- coding: utf-8 -*-
 
import cv2
from matplotlib import pyplot as plt
 
# 带有缺口的图片
path1 = r"jietu_1.png"
# 模板图片
path2 = r"jietu_2.png"
 
 
def pic2gray(pic_path, new_path=''):
    # 转灰度图
    pic_path_rgb = cv2.imread(pic_path)
    pic_path_gray = cv2.cvtColor(pic_path_rgb, cv2.COLOR_BGR2GRAY)
    if new_path:
        cv2.imwrite(new_path, pic_path_gray)
    return pic_path_gray
 
 
def canny_edge(image_array, new_path=''):
    # 灰度图锐化
    can = cv2.Canny(image_array, threshold1=200, threshold2=300)
    if new_path:
        cv2.imwrite(new_path, can)
    return can
 
 
# 获取灰度图
gray_path = r"jietu_1_gray.png"
image_gray1 = pic2gray(path1, gray_path)
gray_path = r"jietu_2_gray.png"
image_gray2 = pic2gray(path2, gray_path)
 
# 获取锐化后的图片，当实际图片内容复杂时，背景图片和缺口图片的锐化参数应该是不同的
can_path1 = r"jietu_1_can.png"
canny_edge(image_gray1, can_path1)
can_path2 = r"jietu_2_can.png"
canny_edge(image_gray2, can_path2)
 
img = cv2.imread(can_path1)
template = cv2.imread(can_path2)
# 实行缺口匹配算法
res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print(min_val, max_val, min_loc, max_loc)
 
h, w = template.shape[:2]
# h1, w1 = img.shape[:2]
# print(h1, w1)
 
left_top = max_loc   # 左上角
right_bottom = (left_top[0] + w, left_top[1] + h)   # 右下角
cv2.rectangle(img, left_top, right_bottom, 255, 2)  # 画出矩形位置
 
plt.subplot(121), plt.imshow(res, cmap='gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
 
plt.subplot(122), plt.imshow(img, cmap='gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.show()

 运行结果：

可以知道确实找到缺口位置了，done。

参考：

https://zhuanlan.zhihu.com/p/115317146
https://www.cnblogs.com/gezhuangzhuang/p/10724769.html