#!/usr/bin/python
import numpy as np
import cv2

def blend_transparent(face_img, overlay_t_img):
    # Split out the transparency mask from the colour info
    overlay_img = overlay_t_img[:,:,:3] # Grab the BRG planes
    overlay_mask = overlay_t_img[:,:,3:]  # And the alpha plane

    # Again calculate the inverse mask
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0))

glasses = cv2.imread('fake_glasses.png', -1)
face    = cv2.imread('cage.png', -1)

pts1 = np.float32([[  0,  0], [599,  0],
                   [  0,208], [599,208]])
pts2 = np.float32([[ 94,231], [354,181],
                   [115,316], [375,281]])

M = cv2.getPerspectiveTransform(pts1,pts2)

rotated = cv2.warpPerspective(glasses, M, (face.shape[1], face.shape[0]))
cv2.imwrite("rotated_glasses.png", rotated)

result_2 = blend_transparent(face, rotated)
cv2.imwrite("merged_transparent.png", result_2)