'OpenCV detect towel margins
Solution 1:[1]
Since you have approximately two distinct colors in the image (one each for foreground and background) you could convert your image to HSV color space and visualize each of the individual channels.
Code:
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
Notice that white blob in the center of the towel, it has to be removed. For that purpose I have used morphological opening.
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25, 25))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
cv2.imshow('fin', cv2.bitwise_not(opening))
EDIT
OpenCV provides the functionality to find top, bottom, right-most and left-most corners for a given contour. I obtained the contour of the final resulting image and found the four extreme points.
Code:
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)
I have also marked the extreme points on a copy of the original image:
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
Solution 2:[2]
what you are looking for is edge detection algorithm.
Use the below code from the following Link
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()
Solution 3:[3]
You can use the canny edge detector. For to detect the corner of the image.I have given you the link of 2 sources below.
Code:
import cv2
import matplotlib.pyplot as plt
im = cv2.imread('boat.png')
edges = cv2.Canny(im,25,255,L2gradient=False)
plt.imshow(edges,cmap='gray')
plt.show()
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
Solution | Source |
---|---|
Solution 1 | Community |
Solution 2 | InAFlash |
Solution 3 | Muhammad Usman |