辅导data程序设计、讲解Python/Matlab/C++编程

日期：2021-04-11 10:38

Assignment 2
Computer Vision and Image Processing

Given stereo image pairs (three stereo pairs are provided in https://drive.google.com/drive/folders/1pdDkFpa59m4A02pLkUqI1H2Zb_GerLkK?usp=sharing ), you are required to write a program with C++ or Python or Matlab to compute the the Disparity Maps of each left (view1) image.
For each stereo pair, the two images (named view1.png and view5.png) are already rectified so that all image motion is purely horizontal. Ground-truth disparity maps of the left view (view1) are also provided for reference. It should be noted that disparities are represented "as is", i.e., intensity 60 means the disparity is 60. The exception is intensity 0, which means unknown disparity. More details about the stereo pairs can be found in https://vision.middlebury.edu/stereo/data/scenes2005/.
The reconstructed results will be evaluated by Peak_Signal-to-Noise_Ratio (PSNR): https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio . You can use the source code implemented by Matlab (https://www.mathworks.com/help/vision/ref/psnr.html) or Python ( https://cvnote.ddlee.cc/2019/09/12/psnr-ssim-python ) for PSNR calculation. The PSNR is calculated against the ground-truth disparity map. The higher PSNR you can achieve, the better performance you will get.
Hints:
To complete the task, if you select OpenCV, the following OpenCV functions you can use:
oxfeatures2d.SIFT_create() // SIFT
oBFMatcher() // feature matching
ofindFundamentalMat() // fundamental matrix
ocomputeCorrespondEpilines() // epipolar line
ostereoRectifyUncalibrated() // image rectification
owarpPerspective() // perspective warping

Submission
Please zip the followings and submit to Canvas:
?Computer program: Python/Matlab/C++ recommended. If you want to use another program language, please send an email to our Teaching Assistant.
?Submission Files: The following files should be provided: a. three disparity maps estimated by your program, as well as the PSNR results of the reconstructed disparity maps (20%); b. source code (40%); c. report (40%).
?Report: Report should be brief and no more than 6 pages. The report should briefly describe and analyze your methods.

Additional note from professor :
Dear students,
In assignment2, to fairly evaluate the estimated disparity maps, we have provided the official codes (both Matlab and Python) for the PSNR calculation.
It is worth noting that: 1) the pixels in the ground-truth disparity map (disp1.png) with ‘0’ values are neglected in PSNR calculation. 2) We discard the pixels in the left part region (1-250 columns) of the left image (view1.png), as the corresponding pixels do not exist in the right image (view5.png).