face_blurring.py

# import the necessary packages
import numpy as np
import cv2

def anonymize_face_simple(image, factor=3.0):
	# automatically determine the size of the blurring kernel based
	# on the spatial dimensions of the input image
	(h, w) = image.shape[:2]
	kW = int(w / factor)
	kH = int(h / factor)

	# ensure the width of the kernel is odd
	if kW % 2 == 0:
		kW -= 1

	# ensure the height of the kernel is odd
	if kH % 2 == 0:
		kH -= 1

	# apply a Gaussian blur to the input image using our computed
	# kernel size
	return cv2.GaussianBlur(image, (kW, kH), 0)

def anonymize_face_pixelate(image, blocks=3):
	# divide the input image into NxN blocks
	(h, w) = image.shape[:2]
	xSteps = np.linspace(0, w, blocks + 1, dtype="int")
	ySteps = np.linspace(0, h, blocks + 1, dtype="int")

	# loop over the blocks in both the x and y direction
	for i in range(1, len(ySteps)):
		for j in range(1, len(xSteps)):
			# compute the starting and ending (x, y)-coordinates
			# for the current block
			startX = xSteps[j - 1]
			startY = ySteps[i - 1]
			endX = xSteps[j]
			endY = ySteps[i]

			# extract the ROI using NumPy array slicing, compute the
			# mean of the ROI, and then draw a rectangle with the
			# mean RGB values over the ROI in the original image
			roi = image[startY:endY, startX:endX]
			(B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
			cv2.rectangle(image, (startX, startY), (endX, endY),
				(B, G, R), -1)

	# return the pixelated blurred image
	return image
	
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

img = cv2.imread('images/messi.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
    img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    
    face = anonymize_face_simple(roi_color)
    img[y:y+h, x:x+w] = face
    
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()