Skin Color Detection Using OpenCV in Python

The article introduces a skin‑color detection feature implemented with the OpenCV library, an open‑source, cross‑platform computer‑vision toolkit that provides C functions, C++ classes, and bindings for Python, Ruby, MATLAB, and other languages.

Overview : The detection pipeline uses color‑space conversion, channel extraction, Gaussian smoothing, and Otsu thresholding to isolate skin regions.

References : The author cites OpenCV tutorials on skin detection and related learning resources.

Preparation :

Install the required Python packages using the following commands (mirrored from the University of Science and Technology of China for faster download):

pip install opencv-python -i https://mirrors.ustc.edu.cn/pypi/web/simple

pip install numpy -i https://mirrors.ustc.edu.cn/pypi/web/simple

Basic Image Operations :

import numpy as np
import cv2
imname = "6358772.jpg"
# Read the image
img = cv2.imread(imname, cv2.IMREAD_COLOR)
cv2.imshow("image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite(imname + "01.jpg", img)

Algorithm 1 – YCrCb Cr Component + Otsu Thresholding :

img = cv2.imread(imname, cv2.IMREAD_COLOR)
ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
(y, cr, cb) = cv2.split(ycrcb)
cr1 = cv2.GaussianBlur(cr, (5, 5), 0)
_, skin1 = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow("image CR", cr1)
cv2.imshow("Skin Cr+OSTU", skin1)

Algorithm 2 – YCrCb Cr and Cb Range Filtering :

img = cv2.imread(imname, cv2.IMREAD_COLOR)
ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
(y, cr, cb) = cv2.split(ycrcb)
skin2 = np.zeros(cr.shape, dtype=np.uint8)
(x, y) = cr.shape
for i in range(0, x):
    for j in range(0, y):
        if (cr[i][j] > 140) and (cr[i][j] < 175) and (cb[i][j] > 100) and (cb[i][j] < 120):
            skin2[i][j] = 255
        else:
            skin2[i][j] = 0
cv2.imshow(imname, img)
cv2.imshow(imname + " Skin2 Cr+Cb", skin2)

Algorithm 3 – HSV H, S, V Range Filtering :

img = cv2.imread(imname, cv2.IMREAD_COLOR)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
(_h, _s, _v) = cv2.split(hsv)
skin3 = np.zeros(_h.shape, dtype=np.uint8)
(x, y) = _h.shape
for i in range(0, x):
    for j in range(0, y):
        if (_h[i][j] > 7) and (_h[i][j] < 20) and (_s[i][j] > 28) and (_s[i][j] < 255) and (_v[i][j] > 50) and (_v[i][j] < 255):
            skin3[i][j] = 255
        else:
            skin3[i][j] = 0
cv2.imshow(imname, img)
cv2.imshow(imname + " Skin3 HSV", skin3)

The article presents screenshots of the detection results for each method and a final comparison image that visualizes the differences among the three algorithms.

Overall, the guide provides a practical, step‑by‑step tutorial for building skin‑color detection pipelines in Python using OpenCV, suitable for beginners and intermediate developers interested in computer‑vision applications.