CODEWITHSUNDEEP
pythonopencvimage-resizingsuperpixels
killtask
killtask

Reputation: 1

Cut all except region of interest python

i'm totally new to this kind of things, i used SLIC to get superpixels from an image, now i have extracted the single superpixel detected but it's like the whole start img dimension except that there is the superpixel and the rest of the image is black, i'm sorry for my bad english, i'll try to explain below.

import cv2
import numpy as np
from skimage.segmentation import slic

myimg = cv2.imread('4.5.jpg')
segments = slic(myimg, n_segments=200, compactness=10, sigma=1)



for i, segVal in enumerate(np.unique(segments)):


    mask = np.zeros(myimg.shape[:2], dtype = "uint8")
    mask[segments == segVal] = 255
    cv2.imwrite('output.png', cv2.bitwise_and(myimg, myimg, mask = mask))

    #show the masked region
    #cv2.imshow("Mask", mask)
    cv2.imshow("Applied", cv2.bitwise_and(myimg, myimg, mask = mask))
    cv2.waitKey(1)

that's actually my code to get superpixels, but when i store the single superpixel what i get is in that link (i'm not allowed yet to embed images): superpixel

now as u can see there is a big black region with the H and W of the original image and the superpixel, i wish to crop only a "rectangle or square" with the superpixel region, how can i do that? thank you and sorry for my english

Upvotes: 0

Views: 165

Answers (1)

Vardan Agarwal
Vardan Agarwal

Reputation: 2181

For this task you can use cv2.findContours. Refer to its documentation to know how to use it. After finding out the contours which will just be one in your case you can use 

x,y,w,h = cv2.boundingRect(cnt)

where x, y are the coordinates of top left corner and w, h are the width and height of the rectangle. Now we can know all the points of the required recatngle and you can crop it using numpy indexing.

Upvotes: 0

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