making a user defined threshold in opencv

Question

I am trying to detect skin using this proposed algorithm.
In order to so, I need to take only the pixels that fall under this category: conditions

I can do so by "manualy" iterating over each of the image's pixel and using an if statement.
Is there a way i can use (preferably) opencv or numpy to do it more efficiently?

Answer 1

You can do it in the following way:

import cv2
import numpy as np
from pathlib import PosixPath

RED_MIN, RED_MAX = 20, 200
BLUE_MIN, BLUE_MAX = 20, 200
GREEN_MIN, GREEN_MAX = 20, 200

if __name__ == '__main__':
    # 1) Load the image
    p = PosixPath('../../Pictures/Spring.jpeg')
    img = cv2.imread(str(p))
    cv2.imshow('Spring', img)

    # 2) Split the image into Blue, Green and Red channels
    # (remember, in OpenCV the channels' order is BGR and not RGB)
    blue_img = img[:, :, 0]
    green_img = img[:, :, 1]
    red_img = img[:, :, 2]

    # 3) Create masks for each color range
    blue_mask = cv2.inRange(blue_img, BLUE_MIN, BLUE_MAX)
    blue_mask[np.where(blue_mask == 255)] = 1

    green_mask = cv2.inRange(green_img, GREEN_MIN, GREEN_MAX)
    green_mask[np.where(green_mask == 255)] = 1

    red_mask = cv2.inRange(red_img, RED_MIN, RED_MAX)
    red_mask[np.where(red_mask == 255)] = 1

    # 4) Combine the relevant image pixels into one final image
    filtered_img = np.zeros_like(img)
    filtered_img[:, :, 0] = blue_img * blue_mask
    filtered_img[:, :, 1] = green_img * green_mask
    filtered_img[:, :, 2] = red_img * red_mask

    cv2.imshow('Filtered image', filtered_img)

    cv2.waitKey()
    cv2.destroyAllWindows()

Cheers.