如何讓korniaHomographyWarper表現得像OpenCVwarpPerspective？

正如標題所說，我想使用來自 kornia 的 HomographyWarper，以便它提供與 OpenCV warpPerspective 相同的輸出。

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()

使用上面的代碼，我得到以下輸出：

使用normalized_coordinates=False，我得到以下輸出：

顯然，單應變換的應用方式不同。我很想知道其中的區別。

uj5u.com熱心網友回復：

您需要進行兩項更改：

1. 使用相同的填充模式。

在您的示例中，cv2 使用cv2.BORDER_REFLECT101但 korniazeros 所以在呼叫 kornia 時更改zeros為。padding_mode='reflection'

2. 您需要指定normalized_homography=False.

所以修改后的版本：

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)

或者簡單地說：

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

結果（cv2/kornia）：

HomographyWarper 內部呼叫homography_warp函式https://github.com/kornia/kornia/blob/f696d2fb7313474bbaf5e73d8b5a56077248b508/kornia/geometry/transform/homography_warper.py#L96但 HomographyWarper 不提供normalized_homography引數并且提供homography_warp。

完整示例：

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)))

標籤：Pythonopencv图像处理科尼亚

上一篇：將cv2.waitKey()分配給python中的變數？

下一篇：OpenCV/Python-獲取特定的幀范圍，CAP_PROP_POS_FRAMES不起作用