How to plot centroids on image after kmeans clustering?

Question

I have a color image and wanted to do k-means clustering on it using OpenCV.

This is the image on which I wanted to do k-means clustering.

This is my code:

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

image1 = cv2.imread("./triangle.jpg", 0)
Z1 = image1.reshape((-1))

Z1 = np.float32(Z1)

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

K1 = 2

ret, mask, center =cv2.kmeans(Z1,K1,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

center = np.uint8(center)
print(center)
res_image1 = center[mask.flatten()]
clustered_image1 = res_image1.reshape((image1.shape))

for c in center:
        plt.hlines(c, xmin=0, xmax=max(clustered_image1.shape[0], clustered_image1.shape[1]), lw=1.)

plt.imshow(clustered_image1)
plt.show()

This is what I get from the center variable.

[[112]
 [255]]

This is the output image

My problem is that I'm unable to understand the output. I have two lists in the center variable because I wanted two classes. But why do they have only one value?

Shouldn't it be something like this (which makes sense because centroids should be points):

[[x1, y1]
[x2, y2]]

instead of this:

[[x]
[y]]

and if I read the image as a color image like this:

image1 = cv2.imread("./triangle.jpg")
Z1 = image1.reshape((-1, 3))

I get this output:

[[255 255 255]
 [ 89 173   1]]

Color image output

Can someone explain to me how I can get 2d points instead of lines? Also, how do I interpret the output I got from the center variable when using the color image?

Please let me know if I'm unclear anywhere. Thanks!!

Answer 1

Here is an Imagemagick solution, since I am not proficient with OpenCV.

Basically, I convert your actual image (from your link in the comments) to binary, then use image moments to extract the centroid and other statistics.

I suspect you can do something similar in OpenCV, Skimage, or Python Wand, which is based upon Imagemagick. (See for example:

https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga556a180f43cab22649c23ada36a8a139

https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.moments_coords_central

https://en.wikipedia.org/wiki/Image_moment)

Input:

Your image does not have just two colors. Perhaps this image did not have kmeans clustering applied with 2 colors only. So I will do that with an Imagemagick script that I have built.

kmeans -n 2 -m 5 img.png img2.png

final colors:
count,hexcolor
99234,#65345DFF
36926,#27AD0EFF

Then I convert the two colors to black and white by simply thresholding and stretching the dynamic range to full black and white.

convert img2.png -threshold 50% -auto-level img3.png

Then I get all the image moment statistics for the white pixels, which includes the x,y centroid in pixels relative to the top left corner of the image. It also includes the equivalent ellipse major and minor axes, angle of major axis, eccentricity of the ellipse, and equivalent brightness of the ellipse, plus the 8 Hu image moments.

identify -verbose -moments img3.png
  Channel moments:
    Gray:
      --> Centroid: 208.523,196.302 <--
      Ellipse Semi-Major/Minor axis: 170.99,164.34
      Ellipse angle: 140.853
      Ellipse eccentricity: 0.197209
      Ellipse intensity: 106.661 (0.41828)
      I1: 0.00149333 (0.380798)
      I2: 3.50537e-09 (0.000227937)
      I3: 2.10942e-10 (0.00349771)
      I4: 7.75424e-13 (1.28576e-05)
      I5: 9.78445e-24 (2.69016e-09)
      I6: -4.20164e-17 (-1.77656e-07)
      I7: 1.61745e-24 (4.44704e-10)
      I8: 9.25127e-18 (3.91167e-08)

Answer 2

K-Means-clustering finds clusters of similar values. Your input is an array of color values, hence you find the colors that describe the 2 clusters. [255 255 255] is the white color, [ 89 173 1] is the green color. Similar for [112] and [255] in the grayscale version. What you're doing is color quantization

They are correctly the centroids, but their dimension is color, not location. Therefor you cannot plot it anywhere. Well you can, but I looks like this:

See how the 'color location' determines to which class each pixel belongs?

This is not something you can locate in your image. What you can do is find the pixels that belong to the different clusters, and use the locations of the found pixels to determine their centroid or 'average' position.

To get the 'average' position of each color, you have to separate out the pixel coordinates according to the class/color to which they belong. In the code below I used np.where( img <= 240) where 240 is the threshold. I used 240 out of ease, but you could use K-Means to determine where the threshold should be. (inRange() might be useful at some point)) If you sum the coordinates and divide that by the number of pixels found, you'll have what I think you are looking for:

Result:

Code:

import cv2 

# load image as grayscale
img = cv2.imread('D21VU.jpg',0)

# get the positions of all pixels that are not full white (= triangle)
triangle_px = np.where( img <= 240)
# dividing the sum of the values by the number of pixels
# to get the average location
ty = int(sum(triangle_px[0])/len(triangle_px[0]))
tx = int(sum(triangle_px[1])/len(triangle_px[1]))
# print location and draw filled black circle
print("Triangle ({},{})".format(tx,ty))
cv2.circle(img, (tx,ty), 10,(0), -1)


# the same process, but now with only white pixels
white_px = np.where( img > 240)
wy = int(sum(white_px[0])/len(white_px[0]))
wx = int(sum(white_px[1])/len(white_px[1]))
# print location and draw white filled circle
print("White: ({},{})".format(wx,wy))
cv2.circle(img, (wx,wy), 10,(255), -1)

# display result
cv2.imshow('Result',img)
cv2.waitKey(0)
cv2.destroyAllWindows()