import cv2
import sys

def blit(l_img, s_img, x_offset, y_offset):
    for c in range(0, 3):
        l_img[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1], c] = s_img[:,:,c] * (s_img[:,:,3]/255.0) + l_img[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1], c] * (1.0 - s_img[:,:,3]/255.0)

def downscale(img, width, height):
    factor = min(1.0*height/img.shape[0], 1.0*width/img.shape[1])
    return cv2.resize(img, None, None, factor, factor)

def popeize(faceCascade, image, papaj, faceScale=1.5):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = faceCascade.detectMultiScale(gray, faceScale, 5)

    print "Found {0} faces!".format(len(faces))

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        factor = min(1.0*h/papaj.shape[0], 1.0*w/papaj.shape[1]) * 1.3
        scaledpapaj = cv2.resize(papaj, None, None, factor, factor)
        dx = x + (w/2) - scaledpapaj.shape[1]/2
        dy = y + (h/2) - scaledpapaj.shape[0]/2

        print dx, dy, factor

        try:
            blit(image, scaledpapaj, dx, dy)
        except Exception as e:
            print e, dx, dy, factor
        #cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

if __name__ == '__main__':
    imagePath = sys.argv[1]
    cascPath = sys.argv[2]

    # Create the haar cascade
    faceCascade = cv2.CascadeClassifier(cascPath)

    # Read the image
    image = cv2.imread(imagePath)
    papaj = cv2.imread("zoltamorda.png", -1)

    cv2.namedWindow("image")

    def trackbarChanged(value):
        image = cv2.imread(imagePath)
        popeize(faceCascade, image, papaj, 1.1 + value/100.0)
        cv2.imshow("image", downscale(image, 1024, 768))
        print 'showed'

    cv2.createTrackbar("scale", "image", 0, 300, trackbarChanged)

    popeize(faceCascade, image, papaj, 1.1)
    cv2.imwrite("output.jpg", image)
    cv2.imshow("image", downscale(image, 1024, 768))

    while cv2.waitKey(0) & 0xff != ord('q'):
        pass