How to make kornia HomographyWarper behave like OpenCV warpPerspective?

As the title says, I want to use HomographyWarper from kornia so that it gives the same output as OpenCV warpPerspective.

import cv2
import numpy as np
import torch
from kornia.geometry.transform import HomographyWarper
from kornia.geometry.conversions import normalize_pixel_coordinates

image_cv = cv2.imread("./COCO_train2014_000000000009.jpg")
image_cv = image_cv[0:256, 0:256]
image = torch.tensor(image_cv).permute(2, 0, 1)
image = image.to('cuda:0')
image_reshaped = image.type(torch.float32).view(1, *image.shape)

height, width, _ = image_cv.shape

homography_0_1 = torch.tensor([[ 7.8783e-01,  3.6705e-02,  2.5139e+02],
        [ 1.6186e-02,  1.0893e+00, -2.7614e+01],
        [-4.3304e-04,  7.6681e-04,  1.0000e+00]], device='cuda:0',
       dtype=torch.float64)
homography_0_2 = torch.tensor([[ 7.8938e-01,  3.5727e-02,  1.5221e+02],
        [ 1.8347e-02,  1.0921e+00, -2.8547e+01],
        [-4.3172e-04,  7.7596e-04,  1.0000e+00]], device='cuda:0',
       dtype=torch.float64)
transform_h1_h2 = torch.linalg.inv(homography_0_1).matmul(
                        homography_0_2).type(torch.float32).view(1, 3, 3)

homography_warper_1_2 = HomographyWarper(height, width, padding_mode='zeros', normalized_coordinates=True)
warped_image_torch = homography_warper_1_2(image_reshaped, transform_h1_h2)

warped_image_1_2_cv = cv2.warpPerspective(
                        image_cv,
                        transform_h1_h2.cpu().numpy().squeeze(),
                        dsize=(width, height),
                        borderMode=cv2.BORDER_REFLECT101,
                    )

cv2.namedWindow("original image")
cv2.imshow("original image", image_cv)
cv2.imshow("OpenCV warp", warped_image_1_2_cv)
cv2.imshow("Korni warp", warped_image_torch.squeeze().permute(1, 2, 0).cpu().numpy())

cv2.waitKey(0)
cv2.destroyAllWindows()

With the code above, I get the following output:

With normalized_coordinates=False, I get the following output:

Apparently the homography transformation is applied differently. I would love to know the difference.

Answer

You need to make two changes:

1. Use the same padding mode.

In your example cv2 uses cv2.BORDER_REFLECT101 but the kornia zeros So change zeros to padding_mode='reflection' when calling kornia.

2. You need to specify normalized_homography=False.

So the modified version:

from kornia.geometry.transform.imgwarp import homography_warp
warped_image_torch = homography_warp(image_reshaped, transform_h1_h2, dsize=(height, width), padding_mode="reflection", normalized_homography=False)

Or simply:

from kornia.geometry.transform.imgwarp import warp_perspective
warped_image_torch = warp_perspective(image_reshaped, transform_h1_h2, dsize=(height, width), padding_mode="reflection")

The result (cv2/kornia):

HomographyWarper internally calls homography_warp function https://github.com/kornia/kornia/blob/f696d2fb7313474bbaf5e73d8b5a56077248b508/kornia/geometry/transform/homography_warper.py#L96 but HomographyWarper does not provide normalized_homography argument and homography_warp does.

Complete example:

import cv2
import numpy as np
import torch
from kornia.geometry.transform.imgwarp import warp_perspective, homography_warp


image_cv = cv2.imread("./000000000009.jpg")
image_cv = image_cv[0:256, 0:256]
image = torch.tensor(image_cv).permute(2, 0, 1)
image = image.to("cuda:0")
image_reshaped = image.type(torch.float32).view(1, *image.shape)

height, width, _ = image_cv.shape

homography_0_1 = torch.tensor(
    [
        [7.8783e-01, 3.6705e-02, 2.5139e02],
        [1.6186e-02, 1.0893e00, -2.7614e01],
        [-4.3304e-04, 7.6681e-04, 1.0000e00],
    ],
    device="cuda:0",
    dtype=torch.float64,
)
homography_0_2 = torch.tensor(
    [
        [7.8938e-01, 3.5727e-02, 1.5221e02],
        [1.8347e-02, 1.0921e00, -2.8547e01],
        [-4.3172e-04, 7.7596e-04, 1.0000e00],
    ],
    device="cuda:0",
    dtype=torch.float64,
)
transform_h1_h2 = (
    torch.linalg.inv(homography_0_1)
    .matmul(homography_0_2)
    .type(torch.float32)
    .view(1, 3, 3)
)

# warped_image_torch = homography_warp(image_reshaped, transform_h1_h2, dsize=(height, width), padding_mode="reflection", normalized_homography=False)
warped_image_torch = warp_perspective(image_reshaped, transform_h1_h2, dsize=(height, width), padding_mode="reflection")

warped_image_1_2_cv = cv2.warpPerspective(
    image_cv,
    transform_h1_h2.cpu().numpy().squeeze(),
    dsize=(width, height),
    borderMode=cv2.BORDER_REFLECT101,
)

warped_kornia = warped_image_torch.cpu().numpy().squeeze().transpose(1, 2, 0).astype(np.uint8)
cv2.imwrite("kornia_warp.png", np.hstack((warped_image_1_2_cv, warped_kornia)))