In this project, you will implement a system to combine a series of horizontally overlapping photographs into a single panoramic image. We give the ORB feature detector and descriptor. You will use ORB to first detect discriminating features in the images and find the best matching features in the other images. Then, using RANSAC, you will automatically align the photographs (determine their overlap and relative positions) and then blend the resulting images into a single seamless panorama. We have provided you with a graphical interface that lets you view the results of the various intermediate steps of the process. We have also provided you with some test images and skeleton code to get you started with the project.
The project will consist of a pipeline of tabs visualized through
AutostichUI that will operate on images or intermediate
results to produce the final panorama output.
The steps required to create a panorama are listed below. You will be creating two ways to stitch a panorama: using translations (where you'll need to presphericallywarp the input images) and homographies, where you align the input images directly. The steps in square brackets are only used with the spherical warping route:

Step 

1. 
Take pictures on a tripod (or handheld) 

2. 
[Warp to spherical coordinates] 

3. 
Extract features 

4. 
Match features 

5. 
Align neighboring pairs using RANSAC 

6. 
Write out list of neighboring translations 

7. 
Correct for drift 

8. 
Read in [warped] images and blend them 

9. 
Crop the result and import into a viewer 

We also post slides to better visualize some steps of the algorithm. These can be found here.
Python+NumPy+SciPy is a very powerful scientific computing environment, and makes computer vision tasks much easier. A crashcourse on Python and NumPy can be found here.
[TODO 1] Compute the inverse map to warp the image by filling in the skeleton code in the computeSphericalWarpMappings routine to:
(Note: You will have to use the focal length f estimates for the images provided below. If you use a different image size, do remember to scale f according to the image size.)
(Note 2: This step is not used when estimating homographies between images, only translations.)
[TODO 2, 3] computeHomography takes two feature sets from image 1 and image 2, f1 and f2 and a list of feature matches matches and estimates a homography from image 1 to image 2.
(Note 3: In computeHomography, you will compute the bestfit homography using the Singular Value Decomposition. Let us denote transpose of A as A'. From lecture 11: "For equation Ah = 0, the solution h is the eigenvector of A'A with the smallest eigenvalue." Recall that the SVD decomposes a matrix by A = USV' where U and V are unitary matrices and their column vectors are the left and right singular vectors, and S is a diagonal matrix of singular values, conventionally ordered from largest to smallest. Furthermore, there is a very strong connection between singular vectors and eigenvectors. Consider: A'A = (VSU')(USV') = V(S^2)V', since U is unitary. That is, right singular vectors of A are eigenvectors of A'A, and eigenvalues of A'A are the squares of the singular values of A. Returning to the problem, this means that the solution h is the right singular vector corresponding to the smallest singular value of A. For more details, the wikipedia article on the SVD is very good.)
[TODO 4] AlignPair takes two feature sets, f1 and f2, the list of feature matches obtained from the feature detecting and matching component (described in the first part of the project), a motion model, m (described below) as parameters. Then it estimates and returns the interimage transform matrix M. For this project, the enum MotionModel may have two possible values: eTranslate and eHomography. AlignPair uses RANSAC (RAndom SAmpling Consensus) to estimate M. First, it randomly pulls out a minimal set of feature matches (one match for the case of translations, four for homographies), estimates the corresponding motion (alignment) and then invokes getInliers to get the indices of feature matches (indexing into matches) that agree with the current motion estimate. After repeated trials, the motion estimate with the largest number of inliers is used to compute a least squares estimate for the motion, which is then returned in the motion estimate M.
[TODO 5]
getInliers computes the indices of the matches that
have a Euclidean distance below RANSACthresh given
features f1 and f2 from
image 1 and image 2 and an interimage transformation matrix from image 1 to image 2.
[TODO 6, 7] LeastSquaresFit computes a least squares estimate for the translation or homography using all of the matches previously estimated as inliers. It returns the resulting translation or homography output transform M.
[TODO 8] Given an image and a homography, figure out the box bounding the image after applying the homography.(imageBoundingBox.)
[TODO 9] Given the warped images and their relative displacements, figure out how large the final stitched image will be and their absolute displacements in the panorama.(blendImages.)
[TODO 10] Then, resample each image to its final location (you will need to use inverse warping here) and blend it with its neighbors. Try a simple feathering function as your weighting function (see mosaics lecture slide on "feathering") (this is a simple 1D version of the distance map described in [Szeliski & Shum]). For extra credit, you can try other blending functions or figure out some way to compensate for exposure differences. (accumulateBlend.)
[Additional hints] 1) When working with homogeneous coordinates, don't forget to normalize when converting them back to Cartesian coordinates. 2) Watch out for black pixels in the source image when inverse warping. You don't want to include them in the accumulation. 3) When doing inverse warping, use linear interpolation for the source image pixels. 4) First try to work out the code by looping over each pixel. Later you can optimize your code using array instructions and numpy tricks (numpy.meshgrid, cv2.remap). You are not required to do this optimization.
[TODO 11] Normalize the image with the accumulated weight channel. Pay attention not to divide by zero. Remember to set the alpha channel of the resulting panorama to opaque! (normalizeBlend.)
[TODO 12] In case of 360 degree panoramas, make the left and right edges have perfect seams. The horizontal extent can be computed in the previous blending routine since the first image occurs at both the left and right end of the stitched sequence (draw the "cut" line halfway through this image). Use a linear warp to the mosaic to remove any vertical "drift" between the first and last image. This warp, of the form y' = y + ax, should transform the y coordinates of the mosaic such that the first image has the same ycoordinate on both the left and right end. Calculate the value of 'a' needed to perform this transformation. (blendImages)
Summary of potentially useful functions (you do not have to use any of these):The skeleton code that we provide comes with a graphical interface, with the module gui.py, which makes it easy for you to do the following:
You can use the GUI visualizations to check whether your program is running correctly.
First, your source code should be zipped up into an archive called 'code.zip', and uploaded to CMS. In addition, turn in a panorama as JPG as your artifact. In particular, turn in a panorama from a handheld sequence. This panorama can be either translationaligned (360 or not), or aligned with homographies (your choice).
Take a series of images with a digital camera mounted on a tripod or a handheld camera. For best results, overlap each image by 50% with the previous one, and keep the camera level. You can use your own camera for this or get one from Mann Library. Some cameras have a "stitch assist" mode you can use to overlap your images correctly, which only works in regular landscape mode. In order to use your camera, you have to estimate the focal length. The simplest way to do this is through the EXIF tags of the images, as described here. Alternatively, you can use a camera calibration toolkit to get more precise focal length and radial distortion coefficients. Finally, Brett Allen describes one creative way to measure rough focal length using just a book and a box.
Skeleton code: Available through CMS.
Test sets: Look inside the resources subdirectory. You will find three datasets: yosemite, campus and melbourne.
Virtual machine: The class virtual machine has the necessary packages installed to run the project code. If you are not using the class VM then you may need the following packages:
Here is a list of suggestions for extending the program for extra credit. You are encouraged to come up with your own extensions. We're always interested in seeing new, unanticipated ways to use this program! Please use the extracredit flag in gui.py. You will need to use args in line 539 and modify the code as necessary. If we run your program without the flag, it must perform the basic implementation.
Last modified on March 21st, 2016