Hi JCD,
Hi JCD,
Now that you have provided the full code, let me break down your requirements and provide a structured solution.You have calibration images from four cameras, and you're focusing on processing one camera for simplicity. The goal is to rectify the raw image (calImg) from image coordinates (pixels) to real-world coordinates (meters) using a quadratic transformation. You have `I` (points in image coordinates) and `W` (same points in real-world coordinates) for calibration. The function `calibrate_camera` computes the transformation function (`rectify`) based on the specified order (`trans_order = 2` for quadratic transformation). After obtaining `rectify` using calibration data, you apply it to the raw image `calImg` to convert the entire image from image coordinates to real-world coordinates. You've successfully plotted detected points in both image coordinates (`subplot(121)`) and real-world coordinates (`subplot(122)` using scatter plots). Now, you want to visualize the rectified image (`calImg` transformed to real-world coordinates) similar to how `subplot(121)` displays the raw image.Here's how you can proceed with the rectification and visualization:
% Load calibration image and data load('calImg.mat'); % Assuming calImg is loaded from your data file
% Define calibration points (I: image coordinates, W: real-world coordinates) % This is just an example, you need to provide actual values or load them I = [ ... ]; % Image coordinates of calibration points (n x 2) W = [ ... ]; % Real-world coordinates corresponding to I (n x 2)
% Perform calibration to obtain rectification function trans_order = 2; rectify_quad = calibrate_camera(I, W, trans_order);
% Apply rectification function to the entire image % Assuming calImg is the raw image in image coordinates [rows, cols] = size(calImg); [X, Y] = meshgrid(1:cols, 1:rows); % Create meshgrid of image coordinates imagePoints2 = rectify_quad([X(:), Y(:)]); % Apply rectification to all pixels
% Reshape rectified image back to original dimensions rectified_image = reshape(imagePoints2, size(calImg, 1), size(calImg, 2), []);
% Visualize results figure; subplot(121); imagesc(calImg); colormap(gray); hold on; scatter(I(:,1), I(:,2), 'r', 'LineWidth', 1); axis equal; xlabel('pixel'); ylabel('pixel'); title('Image and detected points (Image coord)');
subplot(122); imagesc(rectified_image); colormap(gray); scatter(W(:,1), W(:,2), 'r', 'LineWidth', 1); axis equal; xlabel('meters'); ylabel('meters'); title('Rectified image in real world coordinates');
% Additional adjustments for inverted image if necessary set(gca, 'YDir', 'normal'); % Ensure correct orientation if the image appears inverted
% Display or save the figure as needed
Explanation:
Calibration and Transformation: The function `calibrate_camera` calculates coefficients (`B`) based on the calibration points (`I` and `W`) and the chosen transformation order (2 for quadratic).
Rectification:The rectification function (`rectify`) is applied to each pixel of `calImg` using meshgrid to map image coordinates to real-world coordinates.
Visualization:Subplot (121) shows the original image with detected calibration points, while subplot (122) displays the rectified image in real-world coordinates, ensuring correct orientation if the image is upside down.
By visualizing both the original and rectified images, you can verify the effectiveness of your rectification process. Also, adjustments such as handling inverted images are also addressed to ensure clarity and correctness in your results. Please let me know if you have further questions.
