Image Registration

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

Learn more: https://paperswithcode.com/task/image-registration

We provide the ImageRegistrator API, which you can use to automatically align two images by direct optimization, leveraging PyTorch autograd.

import torch
from kornia.geometry import ImageRegistrator

img_src = torch.rand(1, 1, 32, 32)
img_dst = torch.rand(1, 1, 32, 32)
registrator = ImageRegistrator("similarity")
homo = registrator.register(img_src, img_dst)  # (1, 3, 3) transform that warps img_src onto img_dst

Then, if you want to perform a more sophisticated process:

# LICENSE HEADER MANAGED BY add-license-header
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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import os

import cv2
import imageio
import torch

import kornia as K
import kornia.geometry as KG


def load_timg(file_name):
    """Loads the image with OpenCV and converts to torch.Tensor."""
    assert os.path.isfile(file_name), f"Invalid file {file_name}"  # nosec
    # load image with OpenCV
    img = cv2.imread(file_name, cv2.IMREAD_COLOR)
    # convert image to torch tensor
    tensor = K.image_to_tensor(img, None).float() / 255.0
    return K.color.bgr_to_rgb(tensor)


registrator = KG.ImageRegistrator("similarity")

img1 = K.resize(load_timg("/Users/oldufo/datasets/stewart/MR-CT/CT.png"), (400, 600))
img2 = K.resize(load_timg("/Users/oldufo/datasets/stewart/MR-CT/MR.png"), (400, 600))
model, intermediate = registrator.register(img1, img2, output_intermediate_models=True)

video_writer = imageio.get_writer("medical_registration.gif", fps=2)

timg_dst_first = img1.clone()
timg_dst_first[0, 0, :, :] = img2[0, 0, :, :]
video_writer.append_data(K.tensor_to_image((timg_dst_first * 255.0).byte()))

with torch.no_grad():
    for m in intermediate:
        timg_dst = KG.homography_warp(img1, m, img2.shape[-2:])
        timg_dst[0, 0, :, :] = img2[0, 0, :, :]
        video_writer.append_data(K.tensor_to_image((timg_dst_first * 255.0).byte()))
video_writer.close()

To reproduce the same results as in the video shown above, you can go through our full tutorial using Colab, found here.