# OpenCV 表盘指针自动读数的示例代码

```import cv2 as cv
import numpy as np
import math
from matplotlib import pyplot as plt
import os```

```def extract_red(image):
"""
通过红色过滤提取出指针
"""
red_lower1 = np.array([0, 43, 46])
red_upper1 = np.array([10, 255, 255])
red_lower2 = np.array([156, 43, 46])
red_upper2 = np.array([180, 255, 255])
dst = cv.cvtColor(image, cv.COLOR_BGR2HSV)

```def get_center(image):
"""
获取钟表中心
"""
edg_output = cv.Canny(image, 100, 150, 2) # canny算子提取边缘
cv.imshow('dsd', edg_output)
# 获取图片轮廓
contours, hireachy = cv.findContours(edg_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
center = []
cut=[0, 0]
for i, contour in enumerate(contours):
x, y, w, h = cv.boundingRect(contour) # 外接矩形
area = w * h # 面积
if area < 100 or area > 4000:
continue
cv.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 1)
cx = w / 2
cy = h / 2
cv.circle(image, (np.int(x + cx), np.int(y + cy)), 1, (255, 0, 0)) ## 在图上标出圆心
center = [np.int(x + cx), np.int(y + cy)]
break
return center[::-1]```

```def ChangeImage(image):
"""
图像裁剪
"""
# 指针提取
# 获取中心
# 去除多余黑色边框
[y, x] = center
return cut```

```def polar(image):
"""
转换成极坐标
"""
x, y = 300, 300
linear_polar = cv.linearPolar(image, (y, x), maxRadius, cv.WARP_FILL_OUTLIERS + cv.INTER_LINEAR)
mypolar = linear_polar.copy()
#将图片调整为从0度开始
mypolar[:150, :] = linear_polar[450:, :]
mypolar[150:, :] = linear_polar[:450, :]
cv.imshow("linear_polar", linear_polar)
cv.imshow("mypolar", mypolar)
return mypolar```

```def Get_Reading(sumdata):
"""
读数并输出
"""
peak = []
# s记录遍历时波是否在上升
s = sumdata[0] < sumdata[1]
for i in range(599):
# 上升阶段
if s==True and sumdata[i] > sumdata[i+1] and sumdata[i] > 70000:
peak.append(sumdata[i])
s=False
# 下降阶段
if s==False and sumdata[i] < sumdata[i+1]:
s=True
peak.sort()
a = sumdata[0]
b = sumdata[-1]
if not peak or max(a,b) > peak[-1]:
peak.append(max(a,b))
longindex = (sumdata.index(peak[-1]))%599
longnum = (longindex + 1)//25*50
# 先初始化和长的同一刻度
#shortindex = longindex
shortnum = round(longindex / 6)
try:
shortindex = sumdata.index(peak[-2])
shortnum = round(shortindex / 6)
except IndexError:
i=0
while i<300:
i += 1
l = sumdata[(longindex-i)%600]
r = sumdata[(longindex+i)%600]
possibleshort = max(l,r)
# 在短指针可能范围内寻找插值符合条件的值
if possibleshort > 80000:
continue
elif possibleshort < 60000:
break
else:
if abs(l-r) > 17800:
shortindex = sumdata.index(possibleshort) - 1
shortnum = round(shortindex / 6)
break
return [longnum,shortnum%100]```
```def test():
"""
RGS法测试
"""
newimg = ChangeImage(image)
polarimg = polar(newimg)
psum = polarimg.sum(axis=1, dtype = 'int32')
print(result)```
```if __name__ == "__main__":
test()
k = cv.waitKey(0)
if k == 27:
cv.destroyAllWindows()
elif k == ord('s'):
cv.imwrite('new.jpg', src)
cv.destroyAllWindows()```

[1050, 44]

```class Apparatus:
def __init__(self, name):
self.name = name
self.angle = []

def line_detect_possible_demo(self, image, center, tg):
'''
:param image: 二值图
:param center: 圆心
:param tg: 直线检测maxLineGap
'''
res = {} # 存放线段的斜率和信息
edges = cv.Canny(image, 50, 150, apertureSize=7)
cv.imshow("abcdefg", edges)
lines = cv.HoughLinesP(edges, 1, np.pi/360, 13, minLineLength=20, maxLineGap=tg)
for line in lines:
x_1, y_1, x_2, y_2 = line[0]
# 将坐标原点移动到圆心
x1 = x_1 - center[0]
y1 = center[1] - y_1
x2 = x_2 - center[0]
y2 = center[1] - y_2

# 计算斜率
if x2 - x1 == 0:
k = float('inf')
else:
k = (y2-y1)/(x2-x1)
d1 = np.sqrt(max(abs(x2), abs(x1)) ** 2 + (max(abs(y2), abs(y1))) ** 2) # 线段长度
d2 = np.sqrt(min(abs(x2), abs(x1)) ** 2 + (min(abs(y2), abs(y1))) ** 2)
# 将长指针与短指针做标记
if d1 < 155 and d1 > 148 and d2 > 115:
res[k] = [1]
elif d1 < 110 and d1 > 100 and d2 > 75:
res[k] = [2]
else:
continue
res[k].append(1) if (x2 + x1) /2 > 0 else res[k].append(0) # 将14象限与23象限分离
cv.line(self.src, (x1 + center[0], center[1] - y1), (x2 + center[0], center[1] - y2), (255, 0, 0), 1)
cv.imshow("line_detect-posssible_demo", self.src)

# 计算线段中点的梯度来判断是指针的左侧线段还是右侧线段
middle_x = int((x_1 + x_2) / 2)
middle_y = int((y_1 + y_2) / 2)
res[k].append(1) # 右测
res[k].append(1)
else:
res[k].append(0) # 左侧
# 计算角度
angle1 = [i for i in res if res[i][0] == 1]
angle2 = [i for i in res if res[i][0] == 2]
# 长指针
a = np.arctan(angle1[0])
b = np.arctan(angle1[1])
if a * b < 0 and max(abs(a), abs(b)) > np.pi / 4:
if a + b < 0:
self.angle.append(math.degrees(-(a + b) / 2)) if res[angle1[1]][1] == 1 else self.angle.append(
math.degrees(-(a + b) / 2) + 180)
else:
self.angle.append(math.degrees(np.pi - (a + b) / 2)) if res[angle1[1]][1] == 1 else self.angle.append(
math.degrees(np.pi - (a + b) / 2) + 180)
else:
self.angle.append(math.degrees(np.pi / 2 - (a + b) / 2)) if res[angle1[1]][1] == 1 else self.angle.append(math.degrees(np.pi / 2 - (a + b) / 2) + 180)
print('长指针读数：%f' % self.angle[0])

# 短指针
a = np.arctan(angle2[0])
b = np.arctan(angle2[1])
if a * b < 0 and max(abs(a), abs(b)) > np.pi / 4:
if a + b < 0:
self.angle.append(math.degrees(-(a + b) / 2)) if res[angle2[1]][1] == 1 else self.angle.append(
math.degrees(-(a + b) / 2) + 180)
else:
self.angle.append(math.degrees(np.pi - (a + b) / 2)) if res[angle2[1]][1] == 1 else self.angle.append(
math.degrees(np.pi - (a + b) / 2) + 180)
else:
self.angle.append(math.degrees(np.pi / 2 - (a + b) / 2)) if res[angle2[1]][1] == 1 else self.angle.append(math.degrees(np.pi / 2 - (a + b) / 2) + 180)
print('短指针读数：%f' % self.angle[1])

edg_output = cv.Canny(mask, 66, 150, 2)
cv.imshow('edg', edg_output)
# 外接矩形
contours, hireachy = cv.findContours(edg_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
center = []
for i, contour in enumerate(contours):
x, y, w, h = cv.boundingRect(contour) # 外接矩形
area = w * h # 面积
if area > 1000 or area < 40:
continue
#print(area)
# cv.circle(src, (np.int(cx), np.int(cy)), 3, (255), -1)
cv.rectangle(self.src, (x, y), (x + w, y + h), (255, 0, 0), 1)
cx = w / 2
cy = h / 2
cv.circle(self.src, (np.int(x + cx), np.int(y + cy)), 1, (255, 0, 0))
center.extend([np.int(x + cx), np.int(y + cy)])
break

cv.imshow('center', self.src)
return center

def extract(self, image):
red_lower1 = np.array([0, 43, 46])
red_lower2 = np.array([156, 43, 46])
red_upper1 = np.array([10, 255, 255])
red_upper2 = np.array([180, 255, 255])
frame = cv.cvtColor(image, cv.COLOR_BGR2HSV)

def test(self):
self.src = cv.resize(self.src, dsize=None, fx=0.5, fy=0.5) # 此处可以修改插值方式interpolation
# 获取中心
# 去除多余黑色边框
[y, x] = center
mask = mask[x - 155:x + 155, y - 155:y + 155]
try:
except IndexError:
try:
self.src = cv.resize(self.src, dsize=None, fx=0.5, fy=0.5) # 此处可以修改插值方式interpolation
self.src = cv.convertScaleAbs(self.src, alpha=1.4, beta=0)
blur = cv.pyrMeanShiftFiltering(self.src, 10, 17)
except IndexError:
self.src = cv.resize(self.src, dsize=None, fx=0.5, fy=0.5) # 此处可以修改插值方式interpolation
self.src = cv.normalize(self.src, dst=None, alpha=200, beta=10, norm_type=cv.NORM_MINMAX)

blur = cv.pyrMeanShiftFiltering(self.src, 10, 17)

if __name__ == '__main__':
apparatus = Apparatus('./BONC/1_0555.jpg')
# 读取图片
apparatus.test()
k = cv.waitKey(0)
if k == 27:
cv.destroyAllWindows()
elif k == ord('s'):
cv.imwrite('new.jpg', apparatus.src)
cv.destroyAllWindows()```

• 当两个指针重合时候不太好处理
• 有时候hough直线检测只能检测出箭头的一条边，这时候就会报错，可以利用图像增强、角点检测和图像梯度来辅助解决，但是效果都不太好
• 计算角度很复杂！！（也可能是我想复杂了，不过这段代码确实花了大量时间）