Overlay one image over another

overlay.py

import sys
import cv2
import argparse

def detectObjects(image):
	"""Converts an image to grayscale and returns the locations of any faces found"""
	grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	cascade = cv2.CascadeClassifier(
		'haarcascade_frontalface_alt2.xml')

	scalefactor = 1.09  # How much to increase window size each pass
	minNeighbours = 5  # Smallest face to detect. Up this if you have small falsepositives
	faces = cascade.detectMultiScale(grayscale, scalefactor, minNeighbours)
	return [(x, y, x + w, y + h) for (x,y,w,h) in faces]
def main()
	ap = argparse.ArgumentParser()
	ap.add_argument("-d", "--data", help = "story,face,x,y,w,h")
	args = vars(ap.parse_args())
	data=args["data"].split(",")
	#read the selfie image for processing
	face = cv2.imread(data[1])
	#read the mask. Make sure this is available in the path
	mask = cv2.imread("circlemask.png")
	#detecting faces in the selfie image
	faceboxes = detectObjects(face)
	#get the face box co ordinates
	replacebox=faceboxes[0]
	#crop just the face from the full selfie
	newface = face[replacebox[1]:replacebox[3], replacebox[0]:replacebox[2]]
	#read the story page. We will be replacing face in this image
	story = cv2.imread(data[0])
	#get the box to be replaced based on co ordinates given in parameters
	box1=(int(data[2]),int(data[3]),int(data[2])+int(data[4]),int(data[3])+int(data[5]))
	size = (box1[2] - box1[0], box1[3] - box1[1])
	#scaling selfie and mask to fit the co ordinates given in the new image.
	scalednewface = cv2.resize(newface,size, interpolation = cv2.INTER_CUBIC)
	scaledmask = cv2.resize(mask,size, interpolation = cv2.INTER_CUBIC)
	#seamlessly merge the two images
	op=cv2.seamlessClone(scalednewface,story, scaledmask, (box[0]+(size[0]/2), box[1]+(size[1]/2)),cv2.NORMAL_CLONE)
	cv2.imshow("Output",op)
	cv2.waitKey(0)
if __name__ == "__main__":
	main()