I am using k means clustering on an image. I am finding 2 or 3 regions via k-means. Now I want to create a mask for each region so that I can separate them in my original image. Following is the code I am using currently.
import numpy as np
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
# Read in the image
image = cv2.imread('/content/istockphoto-1201224719-612x612.jpg')
# Change color to RGB (from BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
pixel_vals = image.reshape((-1,3))
# Convert to float type
pixel_vals = np.float32(pixel_vals)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.85)
# then perform k-means clustering wit h number of clusters defined as 3
#also random centres are initially choosed for k-means clustering
k = 2
retval, labels, centers = cv2.kmeans(pixel_vals, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
# convert data into 8-bit values
centers = np.uint8(centers)
segmented_data = centers[labels.flatten()]
# reshape data into the original image dimensions
segmented_image = segmented_data.reshape((image.shape))
plt.imshow(segmented_image)
Following is the image
Following is the output of the above image using k-means clustering (k=2)
How can I create a mask for that yellow part to segment it from original?
