% Read two images %image1 = imread('1.png'); %image2 = imread('2.png'); image1 = imread('40.bmp'); image2 = imread('乙醇.bmp'); % Down-sample the image to half its original resolution downsampled_image1 = imresize(image1, 0.1); downsampled_image2 = imresize(image2, 0.1); % Convert images to grayscale image1 = rgb2gray(downsampled_image1); image2 = rgb2gray(downsampled_image2); % Convert images to double precision for computations image1 = double(image1); image2 = double(image2); % Determine size of images [n, m] = size(image1); % Initialize matrices for displacement fields u = zeros(n, m); v = zeros(n, m); % Set window size for correlation (odd number to have a central pixel) window_size = 15; half_window_size = (window_size-1)/2; % You need to initialize these variables before the loop uTemp = zeros(n, m); vTemp = zeros(n, m); for i = 1+half_window_size : n-half_window_size fprintf('The value of i is: %d\n', i); parfor j = 1+half_window_size : m-half_window_size fprintf('The value of j is: %d\n', j); % Extract sub-window from image1 sub_window1 = image1(i-half_window_size : i+half_window_size, j-half_window_size : j+half_window_size); % Skip this sub-window if all its values are the same if numel(unique(sub_window1)) == 1 continue; end % Correlate this with image2 within a search area (here, the whole image) correlation = normxcorr2(sub_window1, image2); % Find the peak correlation [ypeak, xpeak] = find(correlation == max(correlation(:))); % If there are multiple, just take the first one ypeak = ypeak(1); xpeak = xpeak(1); % Compute displacements (be careful about off-by-one due to zero-based and one-based indexing) uTemp(i,j) = ypeak - i; vTemp(i,j) = xpeak - j; end end % Copy the temporary variables back to the original ones after the loop u = uTemp; v = vTemp; % Flatten the images into 1D arrays image1_1D = image1(:); image2_1D = image2(:); % Compute the correlation coefficient correlationCoefficient = corrcoef(image1_1D, image2_1D); % The correlation coefficient is the value at position (1,2) or (2,1) in the output matrix correlationCoefficient = correlationCoefficient(1,2); fprintf('The value of correlationCoefficient is: %d\n', correlationCoefficient); % Display original images and displacement field figure, subplot(1,3,1), imshow(image1, []), title('Image 1'); subplot(1,3,2), imshow(image2, []), title('Image 2'); subplot(1,3,3), quiver(u, v), title('Displacement Field');
时间: 2024-04-27 17:24:45 浏览: 18
这段代码是一个基于互相关的图像位移估计算法的实现。这个算法的基本思路是在一个图像中选取一个子窗口,在另一个图像中搜索与之最相似的子窗口,通过计算它们之间的位移来估计图像的位移。具体实现中,首先读取两张图像,并对它们进行降采样和灰度化处理。然后,通过计算互相关来找到最相似的子窗口,并计算它们之间的位移。最后,将位移场可视化并计算两张图像的相关系数。
代码中的parfor循环是一个并行循环,用于加速计算。它会自动地将循环中的任务分配到多个处理器上并行执行。在这个例子中,它会将每个像素的处理任务分配到多个处理器上并行执行,以提高计算速度。
相关问题
image = cv2.imread 读取png
使用cv2.imread函数读取图片时,可以指定要读取的图片的路径和读取的方式。在示例中,使用cv2.imread('1.jpg',0)来读取图片文件'1.jpg',并且指定读取的方式为灰度图形式。然后使用cv2.imwrite函数将读取到的图片保存为'1.png'文件。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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imageLabeler labels = imread('Label_1.png');
As an AI language model, I do not have access to any image files, but I can provide information on the code snippet you have shared.
The code snippet reads an image file called 'Label_1.png' using the imread function in MATLAB's image processing toolbox. The image is then stored in a variable called 'labels', which can be used for further image processing operations.