How to replace a contour (rectangle) in an image with a new image using Python?

Question

I'm currently using the opencv (CV2) and Python Pillow image library to try and take an image of arbitrary phones and replace the screen with a new image. I've gotten to the point where I can take an image and identify the screen of the phone and get all the coordinates for the corner, but I'm having a really hard time replacing that area in the image with a new image.

The code I have so far:

import cv2
from PIL import Image

image = cv2.imread('mockup.png')
edged_image = cv2.Canny(image, 30, 200)

(contours, _) = cv2.findContours(edged_image.copy(), cv2.RETR_TREE,     cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)[:10]
screenCnt = None

for contour in contours:
    peri = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.02 * peri, True)

    # if our approximated contour has four points, then
    # we can assume that we have found our screen
    if len(approx) == 4:
        screenCnt = approx
        break

cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)
cv2.imshow("Screen Location", image)
cv2.waitKey(0)

This will give me an image that looks like this:

I can also get the coordinates of the screen corners using this line of code:

screenCoords = [x[0].tolist() for x in screenCnt] 
// [[398, 139], [245, 258], [474, 487], [628, 358]]

However I can't figure out for the life of me how to take a new image and scale it into the shape of the coordinate space I've found and overlay the image ontop.

My guess is that I can do this using an image transform in Pillow using this function that I adapted from this stackoverflow question:

def find_transform_coefficients(pa, pb):
"""Return the coefficients required for a transform from start_points to end_points.

    args:
        start_points -> Tuple of 4 values for start coordinates
        end_points --> Tuple of 4 values for end coordinates
"""
matrix = []
for p1, p2 in zip(pa, pb):
    matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]])
    matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]])

A = numpy.matrix(matrix, dtype=numpy.float)
B = numpy.array(pb).reshape(8)

res = numpy.dot(numpy.linalg.inv(A.T * A) * A.T, B)
return numpy.array(res).reshape(8) 

However I'm in over my head a bit, and I can't get the details right. Could someone give me some help?

EDIT

Ok now that I'm using the getPerspectiveTransform and warpPerspective functions, I've got the following additional code:

screenCoords = numpy.asarray(
    [numpy.asarray(x[0], dtype=numpy.float32) for x in screenCnt],
    dtype=numpy.float32
)

overlay_image = cv2.imread('123.png')
overlay_height, overlay_width = image.shape[:2]

input_coordinates = numpy.asarray(
    [
        numpy.asarray([0, 0], dtype=numpy.float32),
        numpy.asarray([overlay_width, 0], dtype=numpy.float32),
        numpy.asarray([overlay_width, overlay_height],     dtype=numpy.float32),
        numpy.asarray([0, overlay_height], dtype=numpy.float32)
    ],
    dtype=numpy.float32,
)

transformation_matrix = cv2.getPerspectiveTransform(
    numpy.asarray(input_coordinates),
    numpy.asarray(screenCoords),
)

warped_image = cv2.warpPerspective(
    overlay_image,
    transformation_matrix,
    (background_width, background_height),
)
cv2.imshow("Overlay image", warped_image)
cv2.waitKey(0)

The image is getting rotated and skewed properly (I think), but its not the same size as the screen. Its "shorter":

and if I use a different image that is very tall vertically I end up with something that is too "long":

Do I need to apply an additional transformation to scale the image? Not sure whats going on here, I thought the perspective transform would make the image automatically scale out to the provided coordinates.

Answer 1

I downloaded your image data and reproduced the problem in my local machine to find out the solution. Also downloaded lenna.png to fit inside the phone screen.

import cv2
import numpy as np

# Template image of iPhone
img1 = cv2.imread("/Users/anmoluppal/Downloads/46F1U.jpg")
# Sample image to be used for fitting into white cavity
img2 = cv2.imread("/Users/anmoluppal/Downloads/Lenna.png")

rows,cols,ch = img1.shape

# Hard coded the 3 corner points of white cavity labelled with green rect.
pts1 = np.float32([[201, 561], [455, 279], [742, 985]])
# Hard coded the same points on the reference image to be fitted.
pts2 = np.float32([[0, 0], [512, 0], [0, 512]])

# Getting affine transformation form sample image to template.
M = cv2.getAffineTransform(pts2,pts1)

# Applying the transformation, mind the (cols,rows) passed, these define the final dimensions of output after Transformation.
dst = cv2.warpAffine(img2,M,(cols,rows))

# Just for Debugging the output.
final = cv2.addWeighted(dst, 0.5, img1, 0.5, 1)
cv2.imwrite("./garbage.png", final)