Calculate And Plot Segmentation Mask Pixels

I have the following image: Below is a segmentation mask within this image: From the image above, I attempted to calculate the non-zero pixel coordinates. That is, I tried to get

Solution 1:

You may be looking for matplotlib.pyplot.scatter()

Seems to match the thresholded image

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

image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 110, 255, cv2.THRESH_BINARY)[1]

x, y = np.where(thresh > 0)
plt.scatter(y[::-1], x)
plt.title('Plot segmentation mask pixels')
plt.show()

Solution 2:

To add upon @nathancy's answer and explicitly answer the question: the pixels calculated from the segmentation mask are indeed accurate. The reason for the jagged shape in the last plot above is because every coordinate from the mask is plotted as a single (closed) polygon, and all of these points are connected. That is the definition of a polygon:

• "A number of coplanar line segments, each connected end to end to form a closed shape." source

So the final plot represents a closed polygon containing every pixel/coordinate from the original segmentation mask.

To produce an accurate polygonal shape, you would need to find the vertices of (any possible) contours from the mask. Example:

# set up canvas for mask
mask = np.zeros((img_h, img_w), dtype=np.uint8)

# assign specific region to mask
mask[ymin:ymax,xmin:xmax] = 1# read in image
im_color = cv2.imread(path, cv2.IMREAD_COLOR)

# convert image to grayscale
im_gray = cv2.cvtColor(im_color, cv2.COLOR_BGR2GRAY)

# create threshold of image
_, thresh = cv2.threshold(im_gray, thresh=100, maxval=255, type=cv2.THRESH_BINARY)

# create segmentation mask of thresholded image
masked_gray = cv2.bitwise_and(thresh, thresh, mask = mask)

# calculate contours of segmentation mask
contours, hierarchy = cv2.findContours(masked_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# get largest contour
tc = max(contours, key=len)

# plot contours
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.set_title('Segmentation mask')
plt.imshow(masked_gray, interpolation='none')
poly = patches.Polygon(tc[:,0])
ax.add_patch(poly)
plt.show()

Result: