我使用 opencv python 形式将模拟时钟转换为小时和分钟的数字数据，但我也需要它显示几秒钟

问题描述

我使用 opencv 读取图像，将其转换为灰度，并使用 canny、kernel、thesh、erode 等找到边缘，我使用 HooughLineP() 检测到图像中的所有线条，并且检测到时针和分针，但我还需要找到秒针，这是我使用的代码

import cv2
import math
import numpy as np
from matplotlib import pyplot as plt
from math import sqrt
from math import acos,degrees


kernel = np.ones((5,5),np.uint8)
img1 = cv2.imread('input1.jpg')
img = cv2.imread('input1.jpg',0)
gray = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)

ret,thresh = cv2.threshold(gray,50,255,cv2.THRESH_BINARY)

# Create mask
height,width = img.shape
#height=height-10
#width=width-10
mask = np.zeros((height,width),np.uint8)

edges = cv2.Canny(thresh,100,200)

#cv2.imshow('detected ',gray)
cimg=cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(gray,cv2.HOUGH_GRADIENT,1.2,100)
#circles = cv2.HoughCircles(edges,1000,param1 = 50,param2 = 30,minRadius = 20,maxRadius = 0)
for i in circles[0,:]:
    i[2]=i[2]+4
    # Draw on mask
    cv2.circle(mask,(i[0],i[1]),i[2],(255,255),thickness=-1)

# copy that image using that mask
masked_data = cv2.bitwise_and(img1,img1,mask=mask)

# Apply Threshold
_,thresh = cv2.threshold(mask,1,cv2.THRESH_BINARY)
# Find Contour
contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0])

# Crop masked_data
crop = masked_data[y+30:y+h-30,x+30:x+w-30]
i=crop
height,width,channels = i.shape
print (width,height,channels)
#########################################################################

ret,mask = cv2.threshold(i,10,cv2.THRESH_BINARY)
edges = cv2.Canny(i,200)
kernel = np.ones((11,11),np.uint8)
kernel2 = np.ones((13,13),np.uint8)
edges = cv2.dilate(edges,kernel,iterations = 1)
edges = cv2.erode(edges,kernel2,iterations = 1)
minLineLength = 1000
maxLineGap = 10
lines = cv2.houghlinesp(edges,np.pi/180,15,minLineLength,maxLineGap)
h=[]
xmax1=0
xmax2=0
ymax1=0
ymax2=0
xs1=0
xs2=0
ys1=0
ys2=0

for line in lines:
    x1,y1,x2,y2 = line[0]
    #cv2.line(i,(x1,y1),(x2,y2),(0,0),1)
    dx=x2-x1
    if(dx<0):
        dx=dx*-1
    dy=y2-y1
    if(dy<0):
        dy=dy*-1
        
    hypo=sqrt(dx**2 + dy**2)
    
            
    #print("dx=",dx,"  dy=",dy)
    h.append(hypo)

#print(h)
print(len(h))
a=len(h)
h.sort(reverse=True)
#print(h)
m=0
k=0

for f in range(a):
    for line in lines:
        x1,y2 = line[0]
        #cv2.line(i,3)
        dx=x2-x1
        if(dx<0):
            dx=dx*-1
        dy=y2-y1
        if(dy<0):
            dy=dy*-1

        hypo2=sqrt(dx**2 + dy**2)


        if(hypo2==h[0]):
            m=hypo2
            xmax1=x1
            xmax2=x2
            ymax1=y1
            ymax2=y2
            cv2.line(crop,(xmax1,ymax1),(xmax2,ymax2),3)
            #print("xmax1=",xmax1," ymax1=",ymax1," xmax2=",xmax2," ymax2=",ymax2)

        if(m==h[0]): 
            if(hypo2==h[f]):
                if((sqrt((xmax2-x2)**2 + (ymax2-y2)**2))>20):
                    if((sqrt((xmax1-x1)**2 + (ymax1-y1)**2))>20):
                        xs1=x1
                        xs2=x2
                        ys1=y1
                        ys2=y2
                        cv2.line(crop,(xs1,ys1),(xs2,ys2),3)
                        print("xs1=",xs1," ys1=",ys1," xs2=",xs2," ys2=",ys2)
                        k=1
                        break
    if(k==1):                
        break           

print("xmax1=",ymax2)

我在上面的代码行中将分针和时针分开，但我也需要分开秒针，请任何人帮助我！

Click here for the sample input image

解决方法

基于这篇文章：How to detect lines in OpenCV? 我已经适应了你的图像和你的裁剪方法，它给出了给定图像的有效输出：

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


kernel = np.ones((5,5),np.uint8)
img1 = cv2.imread('clock.jpg')
img = cv2.imread('clock.jpg',0)
gray = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)

ret,thresh = cv2.threshold(gray,50,255,cv2.THRESH_BINARY)

# Create mask
height,width = img.shape
mask = np.zeros((height,width),np.uint8)
edges = cv2.Canny(thresh,100,200)

#cv2.imshow('detected ',gray)
cimg=cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(gray,cv2.HOUGH_GRADIENT,1.2,100)
for i in circles[0,:]:
    i[2]=i[2]+4
    # Draw on mask
    cv2.circle(mask,(i[0],i[1]),i[2],(255,255),thickness=-1)

# Copy that image using that mask
masked_data = cv2.bitwise_and(img1,img1,mask=mask)

# Apply Threshold
_,thresh = cv2.threshold(mask,1,cv2.THRESH_BINARY)
# Find Contour
contours,hierarchy = 
cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0])

# Crop masked_data
crop = masked_data[y+30:y+h-30,x+30:x+w-30]


################################
kernel_size = 5
blur_crop = cv2.GaussianBlur(crop,(kernel_size,kernel_size),0)
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_crop,low_threshold,high_threshold)

rho = 1                     # distance resolution in pixels
theta = np.pi / 180         # angular resolution in radians
threshold = 15              # minimum number of votes 
min_line_length = 100       # minimum number of pixels making up a line
max_line_gap = 10           # maximum gap in pixels between connectable 
line segments
line_image = np.copy(crop) * 0 

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line
lines = cv2.HoughLinesP(edges,rho,theta,threshold,np.array([]),min_line_length,max_line_gap)

for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(line_image,(x1,y1),(x2,y2),0),5)

# Draw the lines on the  image
lines_edges = cv2.addWeighted(crop,0.8,line_image,0)

cv2.imshow('line_image',line_image)
cv2.imshow('crop',crop)

通过对 Hough 检测进行一些参数调整，您应该能够将结果减少到 3 条漂亮的线。