This part of their course is also using the Computer Vision: Algorithms and Applications book as source. What exactly is Epipolar Geometry? The “Computer Vision” course of the Carnegie Mellon University has excellent slides that explain the principles and the mathematics. It can be used to map points of one image to lines in another. A major step to get there: the fundamental matrix describes the relationship between two images in the same scene. However, to put the images in a 3D relationship, we should use epipolar geometry. The matching we have performed so far is a pure 2D keypoint matching. View this gist on GitHub 2c) Stereo Rectification Its patent expired in March 2020, and the algorithm got included in the main OpenCV implementation. Here, we’ll use the traditional SIFT algorithm. In the Simultaneous Localization and Mapping (SLAM) article series, I provided some background on different algorithms and showed how they work. OpenCV has a tutorial that lists multiple ways of matching features between frames. But it’s using well-known and established open-source algorithms instead.
Zed camera get depth map value python code#
The code is not exactly what Google built. But it also works satisfactory without prior calibration. In the ideal case, you’d have a calibrated camera to start with. 2a) Detecting Keypointsīut how does the complete stereo rectification process work? Let’s build our own code to better understand what’s happening. Google additionally addresses issues that might happen, especially in mobile scenarios. Google’s research improves upon the research performed by Pollefeys et al. This enables efficient pixel / block comparison to calculate the disparity map (= how much offset the same block has between both images) for all regions of the image (not just the keypoints!). Matching keypoints are on the same horizontal epipolar line in both images. Using these, we can rectify the images to a common image plane.We then need the best keypoints where we are sure they are matched in both images to calculate reprojection matrices.To perform stereo rectification, we need to perform two important tasks: In more technical terms, this means that after stereo rectification, all epipolar lines are parallel to the horizontal axis of the image. As such, the stereo rectification needs to be very intelligent in matching & wrapping the images! Stereo Rectification: reprojecting images to make calculating depth maps easier. The depth map algorithm only has the freedom to choose two distinct keyframes from the live camera stream. With smartphone-based AR like in ARCore, the user can freely move the camera in the real world. This simplifies calculating the disparities of each pixel! The result is that they appear as if they have been taken only with a horizontal displacement. When the camera rotates or moves forward / backward, the pixels don’t just move left or right they could also be found further up or down in the image. A process called stereo rectification is crucial to easily compare pixels in both images to triangulate the scene’s depth!įor triangulation, we need to match each pixel from one image with the same pixel in another image.
However, the images don’t line up perfectly fine. We have captured a scene from two distinct positions and loaded them with Python and OpenCV. In part 1 of the article series, we’ve identified the key steps to create a depth map.
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