OpenCV 錶盤指標自動讀數的示例程式碼
阿新 • • 發佈：2020-04-12
前段時間參加了一個錶盤指標讀數的比賽，今天來總結一下
資料集一共有一千張圖片：
方法一：徑向灰度求和
基本原理：
將影象以錶盤圓心轉換成極座標，然後通過矩陣按行求和找到二值圖最大值即為指標尖端
匯入需要用到的包
import cv2 as cv import numpy as np import math from matplotlib import pyplot as plt import os
影象預處理
去除背景：利用提取紅色實現
def extract_red(image): """ 通過紅色過濾提取出指標 """ red_lower1 = np.array([0,43,46]) red_upper1 = np.array([10,255,255]) red_lower2 = np.array([156,46]) red_upper2 = np.array([180,255]) dst = cv.cvtColor(image,cv.COLOR_BGR2HSV) mask1 = cv.inRange(dst,lowerb=red_lower1,upperb=red_upper1) mask2 = cv.inRange(dst,lowerb=red_lower2,upperb=red_upper2) mask = cv.add(mask1,mask2) return mask
獲得鐘錶中心：輪廓查詢，取出輪廓的外接矩形，根據矩形面積找出圓心
def get_center(image):
"""
獲取鐘錶中心
"""
edg_output = cv.Canny(image,100,150,2) # canny運算元提取邊緣
cv.imshow('dsd',edg_output)
# 獲取圖片輪廓
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 # 面積
if area < 100 or area > 4000:
continue
cv.rectangle(image,(x,y),(x + w,y + h),(255,0),1)
cx = w / 2
cy = h / 2
cv.circle(image,(np.int(x + cx),np.int(y + cy)),1,0)) ## 在圖上標出圓心
center = [np.int(x + cx),np.int(y + cy)]
break
return center[::-1]
由上面的影象可以看出，圓心定位還是非常準確的
圖片裁剪
def ChangeImage(image): """ 影象裁剪 """ # 指標提取 mask = extract_red(image) mask = cv.medianBlur(mask,ksize=5)#去噪 # 獲取中心 center = get_center(mask) # 去除多餘黑色邊框 [y,x] = center cut = mask[y-300:y+300,x-300:x+300] # 因為mask處理後已經是二值影象，故不用轉化為灰度影象 return cut
剪裁後的影象如下圖所示：
極座標轉換
注意：需要將圖片裁剪成正方形
def polar(image):
"""
轉換成極座標
"""
x,y = 300,300
maxRadius = 300*math.sqrt(2)
linear_polar = cv.linearPolar(image,(y,x),maxRadius,cv.WARP_FILL_OUTLIERS + cv.INTER_LINEAR)
mypolar = linear_polar.copy()
#將圖片調整為從0度開始
mypolar[:150,:] = linear_polar[450:,:]
mypolar[150:,:] = linear_polar[:450,:]
cv.imshow("linear_polar",linear_polar)
cv.imshow("mypolar",mypolar)
return mypolar
由影象就可以很容易發現指標的頂點
計算角度
def Get_Reading(sumdata):
"""
讀數並輸出
"""
peak = []
# s記錄遍歷時波是否在上升
s = sumdata[0] < sumdata[1]
for i in range(599):
# 上升階段
if s==True and sumdata[i] > sumdata[i+1] and sumdata[i] > 70000:
peak.append(sumdata[i])
s=False
# 下降階段
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
# 先初始化和長的同一刻度
#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)
# 在短指標可能範圍內尋找插值符合條件的值
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法測試
"""
image = cv.imread("./BONC/1_{0:0>4d}".format(400) + ".jpg")
newimg = ChangeImage(image)
polarimg = polar(newimg)
psum = polarimg.sum(axis=1,dtype = 'int32')
result = Get_Reading(list(psum))
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]
方法二：Hough直線檢測
原理：利用Hough變換檢測出指標的兩條邊，從而兩條邊的中線角度即為指標刻度
資料預處理與上面的方法類似
可以看到分別檢測出了兩個指標的左右兩條邊，然後可以由這四個角度算出兩個指標中線的角度，具體計算過程寫的有點複雜
class Apparatus:
def __init__(self,name):
self.name = name
self.angle = []
self.src = cv.imread(name)
def line_detect_possible_demo(self,image,center,tg):
'''
:param image: 二值圖
:param center: 圓心
:param tg: 直線檢測maxLineGap
'''
res = {} # 存放線段的斜率和資訊
edges = cv.Canny(image,50,apertureSize=7)
cv.imshow("abcdefg",edges)
lines = cv.HoughLinesP(edges,np.pi/360,13,minLineLength=20,maxLineGap=tg)
for line in lines:
x_1,y_1,x_2,y_2 = line[0]
# 將座標原點移動到圓心
x1 = x_1 - center[0]
y1 = center[1] - y_1
x2 = x_2 - center[0]
y2 = center[1] - y_2
# 計算斜率
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) # 線段長度
d2 = np.sqrt(min(abs(x2),abs(x1)) ** 2 + (min(abs(y2),abs(y1))) ** 2)
# 將長指標與短指標做標記
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) # 將14象限與23象限分離
cv.line(self.src,(x1 + center[0],center[1] - y1),(x2 + center[0],center[1] - y2),1)
cv.imshow("line_detect-posssible_demo",self.src)
# 計算線段中點的梯度來判斷是指標的左側線段還是右側線段
middle_x = int((x_1 + x_2) / 2)
middle_y = int((y_1 + y_2) / 2)
grad_mat = image[middle_y-5:middle_y+6,middle_x-5:middle_x+6]
cv.imshow("grad_mat",grad_mat)
grad_x = cv.Sobel(grad_mat,cv.CV_32F,0)
grad_y = cv.Sobel(grad_mat,1)
gradx = np.max(grad_x) if np.max(grad_x) != 0 else np.min(grad_x)
grady = np.max(grad_y) if np.max(grad_y) != 0 else np.min(grad_y)
if ((gradx >=0 and grady >= 0) or (gradx <= 0 and grady >= 0)) and res[k][1] == 1:
res[k].append(1) # 右測
elif ((gradx <= 0 and grady <= 0) or (gradx >= 0 and grady <= 0)) and res[k][1] == 0:
res[k].append(1)
else:
res[k].append(0) # 左側
# 計算角度
angle1 = [i for i in res if res[i][0] == 1]
angle2 = [i for i in res if res[i][0] == 2]
# 長指標
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('長指標讀數：%f' % self.angle[0])
# 短指標
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('短指標讀數：%f' % self.angle[1])
def get_center(self,mask):
edg_output = cv.Canny(mask,66,2)
cv.imshow('edg',edg_output)
# 外接矩形
contours,cv.CHAIN_APPROX_SIMPLE)
center = []
for i,contour in enumerate(contours):
x,h = cv.boundingRect(contour) # 外接矩形
area = w * h # 面積
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,1)
cx = w / 2
cy = h / 2
cv.circle(self.src,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([0,46])
red_lower2 = np.array([156,46])
red_upper1 = np.array([10,255])
red_upper2 = np.array([180,255])
frame = cv.cvtColor(image,cv.COLOR_BGR2HSV)
mask1 = cv.inRange(frame,upperb=red_upper1)
mask2 = cv.inRange(frame,upperb=red_upper2)
mask = cv.add(mask1,mask2)
mask = cv.bitwise_not(mask)
cv.imshow('mask',mask)
return mask
def test(self):
self.src = cv.resize(self.src,dsize=None,fx=0.5,fy=0.5) # 此處可以修改插值方式interpolation
mask = self.extract(self.src)
mask = cv.medianBlur(mask,ksize=5) # 去噪
# 獲取中心
center = self.get_center(mask)
# 去除多餘黑色邊框
[y,x] = center
mask = mask[x - 155:x + 155,y - 155:y + 155]
cv.imshow('mask',mask)
#self.find_short(center,mask)
try:
self.line_detect_possible_demo(mask,20)
except IndexError:
try:
self.src = cv.imread(self.name)
self.src = cv.resize(self.src,fy=0.5) # 此處可以修改插值方式interpolation
self.src = cv.convertScaleAbs(self.src,alpha=1.4,beta=0)
blur = cv.pyrMeanShiftFiltering(self.src,10,17)
mask = self.extract(blur)
self.line_detect_possible_demo(mask,20)
except IndexError:
self.src = cv.imread(self.name)
self.src = cv.resize(self.src,fy=0.5) # 此處可以修改插值方式interpolation
self.src = cv.normalize(self.src,dst=None,alpha=200,beta=10,norm_type=cv.NORM_MINMAX)
blur = cv.pyrMeanShiftFiltering(self.src,20)
if __name__ == '__main__':
apparatus = Apparatus('./BONC/1_0555.jpg')
# 讀取圖片
apparatus.test()
k = cv.waitKey(0)
if k == 27:
cv.destroyAllWindows()
elif k == ord('s'):
cv.imwrite('new.jpg',apparatus.src)
cv.destroyAllWindows()
長指標讀數：77.070291
短指標讀數：218.896747
由結果可以看出精確度還是挺高的，但是這種方法有三個缺點：
- 當兩個指標重合時候不太好處理
- 有時候hough直線檢測只能檢測出箭頭的一條邊，這時候就會報錯，可以利用影象增強、角點檢測和影象梯度來輔助解決，但是效果都不太好
- 計算角度很複雜！！（也可能是我想複雜了，不過這段程式碼確實花了大量時間）
程式碼裡可能還有很多問題，希望大家多多指出
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