scatter plot of pixel values at the same locations in the image

Question

Suppose we extract pixels with a specific value from a grayscale image and then highlight these pixels by using a scatter plot on the same image.

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

image = cv2.imread('images/wa_state_highway.jpg')
copy_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(copy_image, cv2.COLOR_RGB2GRAY)

plt.matshow(gray_image, cmap='gray')

# Get the index of elements with value 12
result = np.where(gray_image == 12)

print('Tuple of arrays returned : ', result, sep='\n')

print('List of coordinates where element with value 0 exists in red channel : ')
# zip the 2 arrays to get the exact coordinates
listOfCoordinates = list(zip(result[0], result[1]))
# iterate over the list of coordinates
# for cord in listOfCoordinates:
#     print(cord)
    
x_val = [x[0] for x in listOfCoordinates]
y_val = [x[1] for x in listOfCoordinates]

plt.scatter(x_val, y_val)
plt.show()

I need to match the coordinates of scatter points with the corresponding coordinate on the image.

---

Solved after @warped comment:

Answer 1

Digital images are accessed by a pair of coordinates (x,y) with the positive x axis pointing to the right and the positive y axis pointing down, so that x specifies the column and y specifies the row , and (0,0) indicates the top-left pixel. Then a pixle pair would be in the form (col, row) .

On the other hand, an entry of a matrix is written using two indices, say (x,y), where x is the row number and y is the column number. Then a matrix index would be in the form (row, col) .

So suppose we have a list of tuples returned by np.where , we need one more step to convert matrix indices to pixle coordinates simply by reversing the elemnets in each tuple.

# Create a 5x5 image using just grayscale, numerical values
tiny_image = np.array([[0, 20, 30, 150, 120],
                      [200, 200, 250, 70, 3],
                      [50, 180, 85, 40, 90],
                      [240, 100, 50, 255, 10],
                      [30, 0, 75, 190, 220]])

# To show the pixel grid, use matshow
# plt.matshow(tiny_image, cmap='gray')

# Get the index of elements with value zero (black pixles)
result = np.where(tiny_image == 250);

listOfCoordinates = list(zip(result[0], result[1]))
print('Matrix index: ', listOfCoordinates)
print(tiny_image[1,2])

x_val= [x[0] for x in listOfCoordinates]
y_val = [x[1] for x in listOfCoordinates]

# Reverse each tuple in a list of tuples: https://www.geeksforgeeks.org/python-reverse-each-tuple-in-a-list-of-tuples
print('Pixle Coordinates: ', [tup[::-1] for tup in listOfCoordinates])

plt.matshow(tiny_image, cmap='gray')
plt.scatter(y_val, x_val)
plt.show()


# print(len(result))
# print(result[0].shape)
# print(result[1].shape)
# print(tiny_image.shape)
# print(tiny_image.size)

# print(result[0][0:100])
# print(result[1][0:100])

 Matrix index: [(1, 2)] 250 Pixle Coordinates: [(2, 1)]