Question

Write a function, call it myWarp, that takes img2 and the estimated flow, u and v, as input and outputs the (back)warped image. If the images are identical except for a translation and the estimated flow is correct then the warped img2 will be identical to img1 (ignoring discretization artifacts).

Answer 1

To create the function myWarp, we can use image warping techniques. One commonly used method is inverse mapping, where for each pixel in the output image, we find the corresponding location in the input image using the estimated flow (u, v). We then use bilinear interpolation to determine the color of the output pixel based on the colors of the neighboring pixels in the input image.

Step-by-step explanation:

To create the function myWarp, we can use image warping techniques.

One commonly used method is inverse mapping, where for each pixel in the output image, we find the corresponding location in the input image using the estimated flow (u, v). We then use bilinear interpolation to determine the color of the output pixel based on the colors of the neighboring pixels in the input image.

Here's an example of a Python implementation for the function:

import numpy as np
from scipy.ndimage import map_coordinates

def myWarp(img2, u, v):
h, w = img2.shape
x, y = np.meshgrid(np.arange(w), np.arange(h))
x += u
y += v
warped_img = map_coordinates(img2, [y, x], order=1, mode='reflect')
warped_img = warped_img.reshape(h, w)

return warped_img