opencv+python计算仪表盘读数

我使用的是最基础，精确度最低的方法，我可这真是太菜了

代码很简单，那些函数百度一下就可以搜到了，我就不详细讲了。写这篇文章的目的是，我之前做过很多程序中其实都用过这些函数，但是哪些程序在我改了一边又一边之后有的函数就没用，现在我要再用就要重新搜这些函数怎么用的，所以我决定写一个程序就发一个，方便我找。唉，我真菜（嚎啕大哭）

import cv2
import numpy as np
import matplotlib.pyplot as plt

img1 = cv2.imread('demo/1.jpg')
target = cv2.imread('demo/11.jpg')
th, tw = target.shape[:2]
# 进行模板匹配
result = cv2.matchTemplate(target, img1, cv2.TM_SQDIFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
tl = min_loc
br = (tl[0] + tw, tl[1] + th)
# 绘制矩形
cv2.rectangle(img1, tl, br, (0, 0, 255), 2)
l_img = img1[tl[1] + 2:tl[1] + th-2, tl[0]+2:tl[0] + tw-2]
grey = cv2.cvtColor(l_img, cv2.COLOR_BGR2GRAY)
gaussian = cv2.GaussianBlur(grey, (3, 3), 0)
# HoughCircles方法检测图中的圆形
circles1 = cv2.HoughCircles(gaussian, cv2.HOUGH_GRADIENT, 1, 100, param1=100, param2=30, minRadius=15, maxRadius=80)
circles = circles1[0, :, :]
circles = np.uint16(np.around(circles))
for i in circles[:]:
    cv2.circle(grey, (i[0], i[1]), i[2], (0, 255, 0), 3)  # 画圆
edges = cv2.Canny(grey, 50, 120)
minLineLength = 100
maxLineGap = 40
# HoughLinesP检测图中的直线
lines = cv2.HoughLinesP(edges, 1.0, np.pi / 180, 20, minLineLength, maxLineGap)
for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(l_img, (x1, y1), (x2, y2), (0, 255, 0), 1)
        x1 = float(x1)
        x2 = float(x2)
        y1 = float(y1)
        y2 = float(y2)
        # 计算斜率
        if x2 - x1 == 0:
            print("直线是竖直的")
            result = 90
        elif y2 - y1 == 0:
            print("直线是水平的")
            result = 0
        else:
            k = -(y2 - y1) / (x2 - x1)
            # 求反正切，再将得到的弧度转换为度
            result = np.arctan(k) * 57.29577
            print("直线倾斜角度为：" + str(result) + "度")

cv2.namedWindow("img", 0)
cv2.namedWindow("img_3", 0)
cv2.imshow("img", img1)
cv2.imshow("img_3", l_img)

cv2.waitKey(0)
cv2.destroyAllWindows()