See the Tips document for a suggestion on how to deal with this. Some of the pixels in frame 270 will correspond to negative coordinates in your canvas. Then, for each pixel in the canvas, you apply your homographies to find the corresponding coordinate in source image and retrieve the color (or interpolated color). To blend your images, you need to create a canvas (a blank image) that is large enough to display the warped pixels of each original image. Img_warped = cv2.warpPerspective(img, H, (output_width, output_height)) That cv2.warpPerspective() takes output sizes, not coordinates, You may also want to experiment with the threshold used for RANSAC and/or recompute the homography based on all inliers after RANSAC.Ĭheck that your homography is correct by plotting four points that form a square in frame 270 and their You need to provide the parameters, the score function, and the homography estimation function. The starter notebook includes most of auto_homography that performs the steps of extracting SIFT features, matching, and setting up RANSAC. Once you have recovered the homography, you can use it to project all frames onto the same coordinate space, and stitch them together to generate the video output. To do that, you need to identify keypoints in both images, match between them to find point correspondences, and compute a projective transformation, called a homography that maps from one set of points to the other. To stitch two overlapping video frames together, you first need to map one image plane to the other. Homography H between frame 450 and each other frame (2) projecting each frame onto the same surface Other frames onto the "plane" of this frame using a homography transformation. We use frame number 450 as the reference frame. Both the video and the decomposed jpg framesĪre included in the starter kit. We use the firstģ0 seconds that has 900 frames. Your main project is on a video from 10 years ago in Jaipur, Northern India. The starter package includes a input video, extracted frames, and utils for extracting frames and creating videos from saved frames or numpy arrays. Photo stitching and homography as bells and whistles. You canĪlso investigate cylindrical and spherical projection and other extensions of Techniques to videos by projecting and manipulating individual frames. Robust matching with RANSAC, homography, and background subtraction. In doing so, you will explore correspondence using interest points, You will manipulate videos by applying several transformations frame byįrame. In this project, you will experiment with interest points, image projection, and Programming Project #5: Video Stitching and ProcessingĬS445: Computational Photography Due Date: 11:59pm on Wednesday Dec 1, 2021
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