CODEWITHSUNDEEP
python-3.xopencvimage-processingopencv3.0
BATspock
BATspock

Reputation: 152

split image on the basis of color

enter image description here

enter image description here

I have obtained an image after applying k-means with clusters = 3. Now I want to obtain 3 separate images on the basis of colours obtained after k-means. For example, consider the attached image. Now I need one image such that it contains only the blue square. One having the letter v and one with just the background Is there any possible way to do that using OpenCV and python.

Upvotes: 3

Views: 7878

Answers (2)

Sagar B Hathwar
Sagar B Hathwar

Reputation: 536

The most general and simplest way to do it is using the three unique gray colors for each region. (Although I could find more than three gray levels in the above image, maybe due to variation as a result of compression of imgur. Though, at the end of the day, k-means should give exactly three BGR values)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
unique = np.unique(gray)
c1, c2, c3 = unique[0], unique[1], unique[2]

mask1 = np.zeros_like(gray)
mask1[gray == c1] = 255

mask2 = np.zeros_like(gray)
mask2[gray == c2] = 255

mask3 = np.zeros_like(gray)
mask3[mask3 == c3] = 255

Upvotes: 1

janu777
janu777

Reputation: 1978

You can solve the problem by calculating the histogram of the image. The below plot shows the peaks of the image.

plot

From this, you can threshold the colors. The code and result:

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

img = cv2.imread("inputs/hist.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hist = cv2.calcHist([gray],[0],None,[256],[0,256])
colors = np.where(hist>5000)
img_number = 0
for color in colors[0]:
    print(color)
    split_image = img.copy()
    split_image[np.where(gray != color)] = 0
    cv2.imwrite(str(img_number)+".jpg",split_image)
    img_number+=1
plt.hist(gray.ravel(),256,[0,256])
plt.savefig('plt')
plt.show()

Results:

image1

image2

image3

Upvotes: 1

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