Perform skin segmentation by combining masks obtained by thresholding an image using multipe colour models

segmentation.py

# Perform skin segmentation by combining masks obtained by thresholding an image using multipe colour models
# Modified from source: https://medium.com/swlh/human-skin-color-classification-using-the-threshold-classifier-rgb-ycbcr-hsv-python-code-d34d51febdf8


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

def generate_RGB_mask(img):
    B_Frame, G_Frame, R_Frame =  [img[..., BGR] for BGR in range(3)]

    BRG_Max = np.maximum.reduce([B_Frame, G_Frame, R_Frame])
    BRG_Min = np.minimum.reduce([B_Frame, G_Frame, R_Frame])

    # Uniform daylight
    Rule_1 = np.logical_and.reduce([R_Frame > 95, G_Frame > 40, B_Frame > 20 , BRG_Max - BRG_Min > 15,abs(R_Frame - G_Frame) > 15, R_Frame > G_Frame, R_Frame > B_Frame])
    
    # Flashlight or daylight lateral illumination
    Rule_2 = np.logical_and.reduce([R_Frame > 220, G_Frame > 210, B_Frame > 170, abs(R_Frame - G_Frame) <= 15, R_Frame > B_Frame, G_Frame > B_Frame])
    
    RGB_Rule = np.logical_or(Rule_1, Rule_2)

    return RGB_Rule

def generate_YCbCr_mask(img):
    YCrCb_Frame = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)

    Y_Frame,Cr_Frame, Cb_Frame = [YCrCb_Frame[...,YCrCb] for YCrCb in range(3)]
    
    line1 = 1.5862  * Cb_Frame + 20
    line2 = 0.3448  * Cb_Frame + 76.2069
    line3 = -1.005 * Cb_Frame + 234.5652
    line4 = -1.15   * Cb_Frame + 301.75

    YCrCb_Rule = np.logical_and.reduce([line1 - Cr_Frame >= 0,
                                    line2 - Cr_Frame <= 0,
                                    line3 - Cr_Frame <= 0,
                                    line4 - Cr_Frame >= 0])
    return YCrCb_Rule

def generate_HSV_mask(img):
    HSV_Frame = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    Hue, Sat, Val = [HSV_Frame[..., i] for i in range(3)]

    HSV_ = np.logical_or(Hue < 50, Hue > 150)

    return HSV_

def show_mask(mask, title="Mask"):
    mask = np.where(mask == False, 0, 255)
    mask = mask.astype(np.uint8)

    cv2.imshow(title, mask)

    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == "__main__":
    import glob

    for img_file in glob.glob("test/*"):
        print(img_file)
        img = cv2.imread(img_file)

        mask_RGB = generate_RGB_mask(img)
        # show_mask(mask_RGB, "RGB Mask")

        mask_YCbCr = generate_YCbCr_mask(img)
        # show_mask(mask_RGB, "YCbCr Mask")

        mask_HSV = generate_HSV_mask(img)
        # show_mask(mask_RGB, "HSV Mask")

        combined_mask = np.logical_and(mask_RGB, mask_YCbCr, mask_HSV)
        show_mask(combined_mask)