In [1]:

    

# http://www.pyimagesearch.com/2014/08/18/skin-detection-step-step-example-using-python-opencv/?__s=sgiodc9fdbcs7csewpiy
from pyimagesearch import imutils
import numpy as np
import cv2


    

In [2]:

    

# define the upper and lower boundaries of the HSV pixel
# intensities to be considered 'skin'
lower = np.array([0, 48, 80], dtype = "uint8")
upper = np.array([20, 255, 255], dtype = "uint8")


    

In [3]:

    

camera = cv2.VideoCapture(0)


    

In [4]:

    

# keep looping over the frames in the video
while True:
	# grab the current frame
	(grabbed, frame) = camera.read()

	# resize the frame, convert it to the HSV color space,
	# and determine the HSV pixel intensities that fall into
	# the speicifed upper and lower boundaries
	frame = imutils.resize(frame, width = 400)
	converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
	skinMask = cv2.inRange(converted, lower, upper)
 
	# apply a series of erosions and dilations to the mask
	# using an elliptical kernel
	kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
	skinMask = cv2.erode(skinMask, kernel, iterations = 2)
	skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
 
	# blur the mask to help remove noise, then apply the
	# mask to the frame
	skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
	skin = cv2.bitwise_and(frame, frame, mask = skinMask)
 
	# show the skin in the image along with the mask
	cv2.imshow("images", np.hstack([frame, skin]))
 
	# if the 'q' key is pressed, stop the loop
	if cv2.waitKey(1) & 0xFF == ord("q"):
		break
 
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()