Threshold in a superpixel opencv2 - python-3.x

Question

I'm trying to use cv2 module to receive pixel coordinates of relatively dark regions in an image.
First I divide it into super-pixels through the cv2.ximgproc.createSuperpixelSLIC() method.
Then I'd like to consider each super-pixel as a ROI, and threshold it based on its' the intensity, so that the darker regions (i.e., where the intensity is lower than some preconfigured threshold) will be 1, and 0 in regions where the intensity is relatively high (i.e., larger than this threshold).
I tried the following code, but the problem is that is highlights the background (as obviously it also dark).
import cv2
import numpy as np
# Parameters
IMG_FILE_PATH = 'PATH TO THE IMAGE'
REGION_SIZE = 200
RULER = 20
N = 10
SAMPLE_SIZE = 5
INTENSITY_TH = 100
# ---
# 1) Load the image
img = cv2.imread(IMG_FILE_PATH, cv2.IMREAD_GRAYSCALE)
# 2) Compute the superpixels
slic = cv2.ximgproc.createSuperpixelSLIC(img, region_size=REGION_SIZE, ruler=RULER)
# 3) Get the characteristics of the superpixels calculated
slic.iterate(N)
slic.enforceLabelConnectivity()
lbls = slic.getLabels()
num_slic = slic.getNumberOfSuperpixels()
# 4) Sample some of the superpixels
sample_idxs = np.random.choice(np.arange(num_slic), size=SAMPLE_SIZE, replace=False)
for idx in sample_idxs:
img_super_pixel = np.uint8(img * (lbls==idx).astype(np.int16))
ret, mask_fg = cv2.threshold(img_super_pixel, INTENSITY_TH, 255, cv2.THRESH_BINARY)
img_super_pixel_th = cv2.bitwise_and(img_super_pixel, img_super_pixel, mask=mask_fg)
cv2.imshow('Super-pixel', img_super_pixel)
cv2.imshow('Super-pixel - thresholded', img_super_pixel_th)
cv2.waitKey()
cv2.destroyAllWindows()
Here is a sample image:
Current Output Example:
So, as is seen - the background is represented with 1., obviously because it is less than the threshold, but what I need is that that only the black spots in the super-pixel would be white, and the background with the pixels which exceed the threshold in the super-pixel area, would be black.
Is there a way to apply threshold only on the ROI, viz. the super-pixel, and not on the background?
Thanks in advance.

Answer 1

I was able to solve this by manually checking the pixels in the region which are below the threshold, as shown in the following code:
import cv2
import numpy as np
import pandas as pd
from pathlib import Path
# Parameters
IMG_FILE_PATH = 'PATH_TO_IMAGE'
OUTDIR = Path('OUTPUT_FOLDER')
REGION_SIZE = 200
RULER = 20
N = 10
SAMPLE_SIZE = 5
INTENSITY_TH = 100
# ---
# 1) Load the image
img = cv2.imread(IMG_FILE_PATH, cv2.IMREAD_GRAYSCALE)
# 2) Compute the superpixels
slic = cv2.ximgproc.createSuperpixelSLIC(img, region_size=REGION_SIZE, ruler=RULER)
# 3) Get the characteristics of the superpixels calculated
slic.iterate(N)
slic.enforceLabelConnectivity()
mask_slic = slic.getLabelContourMask()
lbls = slic.getLabels()
num_slic = slic.getNumberOfSuperpixels()
# 4) Sample some of the superpixels
sample_idxs = np.random.choice(np.arange(num_slic), size=SAMPLE_SIZE, replace=False)
for idx in sample_idxs:
# 4.1) Create pandas.DataFrame to store the points and their validity based on the threshold
sp_pixels_df = pd.DataFrame(columns=['x', 'y', 'relevant'])
# 4.2) Get the current super-pixel
img_super_pixel = np.uint8(img * (lbls==idx).astype(np.int16))
# 4.3) Find the coordinates of the pixels inside the current super-pixel
img_super_pixel_idx = np.argwhere(lbls==idx)
# 4.4) Separate the x and y coordinates of the points which are located inside the superpixel
xs, ys = np.array([t[0] for t in img_super_pixel_idx]), np.array([t[1] for t in img_super_pixel_idx])
# 4.5) Find the pixels inside the superpixel, which intensity is below the threshold
low_intensity_pixels = img_super_pixel[tuple([xs, ys])] < INTENSITY_TH
# 4.6) Populate the pandas.DataFrame
sp_pixels_df['x'] = xs
sp_pixels_df['y'] = ys
sp_pixels_df['relevant'] = low_intensity_pixels
# 4.7) Get the valid pixel coordinates
relevant_points = sp_pixels_df.loc[sp_pixels_df.relevant, ['x', 'y']].values
# 4.8) Separate the x and y coordinates of the relevant points which are located inside the superpixel
relevant_xs, relevant_ys = np.array([t[0] for t in relevant_points]), np.array([t[1] for t in relevant_points])
# 4.9) Convert the gray-scale image to BGR to be able to mark the relevant pixels in red
img_super_pixel_highlighted = cv2.cvtColor(img_super_pixel, cv2.COLOR_GRAY2BGR)
# 4.10) Highlight the relevant pixels
img_super_pixel_highlighted[tuple([relevant_xs, relevant_ys])] = (0, 0, 255)
cv2.imshow('Original Superpixels', img_super_pixel)
cv2.imshow('Relevant pixels highlighted', img_super_pixel_highlighted)
cv2.waitKey()
cv2.destroyAllWindows()
Output:
Original:
Highlighted:
Cheers!