由于您的图像大约有两种不同的颜色(前景和背景各一种),因此您可以将图像转换为HSV颜色空间,并可视化每个单独的通道。
代码:
path = r'C:\Users\Desktop'
filename = 'towel.jpg'
img = cv2.imread(os.path.join(path, filename))
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) #--- convert to HSV
cv2.imshow('hsv.jpg', hsv)
h = hsv[:,:,0]
cv2.imshow('h.jpg', h) #--- visualize the hue channel
ret, thresh = cv2.threshold(h, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)
cv2.imshow('thresh1', thresh) #--- apply Otsu threshold on hue channel
请注意毛巾中心的白色斑点,它必须被去除。为此,我使用了形态学开运算。
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25, 25))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
cv2.imshow('fin', cv2.bitwise_not(opening))
OpenCV提供了查找给定轮廓的顶部、底部、最右侧和最左侧角点的功能。我获取了最终结果图像的轮廓并找到了四个极点。
代码:
im2, contours, hierarchy = cv2.findContours(cv2.bitwise_not(opening), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #--- finding contours
cnt = contours[0] #--- since there is only one contour present
leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])
rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])
topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])
print('The extreme points are leftmost: {}, rightmost: {}, topmost: {} and bottommost: {}'.format(leftmost, rightmost, topmost, bottommost))
The extreme points are leftmost: (32, 336), rightmost: (807, 439), topmost: (459, 12) and bottommost: (699, 743)
我还在原始图像的副本上标记了极端点:
img2 = img.copy()
cv2.circle(img2, leftmost, 5, (0, 255, 255), -1) #-- leftmost
cv2.circle(img2, rightmost, 5, (0, 255, 255), -1) #-- rightmost
cv2.circle(img2, topmost, 5, (0, 255, 255), -1) #-- topmost
cv2.circle(img2, bottommost, 5, (0, 255, 255), -1) #-- bottommost
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('Your towel image.jpg',0)
edges = cv.Canny(img,100,200)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()