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In this part of the project, you will use your feature detection and matching
code from part 1 of the project to combine a series of photographs into a
360° panorama. Your software will
automatically align the photographs (determine their overlap and relative
positions) and then blend the resulting photos into a single seamless panorama.
You will then be able to view the resulting panorama inside an interactive Web
viewer.
To start this component, you will be supplied with some test
images and skeleton code that will guide you (the main solution file in
Visual Studio is Features.sln). For Mac users, you can download the
unofficial fixes provided by Johnathan Lyon for both Features and Panorama. In
addition, a solution executable can be found here.
You can download the test images separately here.
Taking
the Pictures
To
Do
Creating
the Panorama
Debugging
Extra
Credit
Panorama
Links
Each group will be checking out a panorama kit (camera, tripod, and Kaidan head). Sign up for a kit here.
Remember to bring extra batteries with you – these cameras drain batteries.
Skip this step for the test dataset. Its camera parameters can be found in the sample commands in stitch4.txt, which is provided along with the skeleton code.
Camera |
resolution |
focal length |
k1 |
k2 |
Canon Powershot A10, tag CS30012716 |
480x640 |
678.21239 pixels |
-0.21001 |
0.26169 |
Canon Powershot A10, tag CS30012717 |
480x640 |
677.50487 pixels |
-0.20406 |
0.23276 |
Canon Powershot A10, tag CS30012718 |
480x640 |
676.48417 pixels |
-0.20845 |
0.25624 |
Canon Powershot A10, tag CS30012927 |
480x640 |
671.16649 pixels |
-0.19270 |
0.14168 |
Canon Powershot A10, tag CS30012928 |
480x640 |
674.82258 pixels |
-0.21528 |
0.30098 |
Canon Powershot A10, tag CS30012929 |
480x640 |
674.79106 pixels |
-0.21483 |
0.32286 |
test images |
384x512 |
595 pixels |
-0.15 |
0.0 |
(Note: If you are using the skeleton software, save your images in (TrueVision) Targa format (.tga), since this is the only format the skeleton software can read. Also make sure the aspect ratio of the image (width vs. height) is either 4:3 or 3:4 (480x640 will do) which is the only aspect ratio supported by the skeleton software.)
Note: The skeleton code includes an image library, ImageLib, that is fairly general and complex. It is NOT necessary for you to peek extensively into this library! We have created some notes for you here.
[TODO] Compute the inverse map to warp the image by filling in the skeleton code in the warpSphericalField routine to:
(Note: You will have to use the focal length f estimates for the half-resolution images provided above (you can either take pictures and save them in small files or save them in large files and reduce them afterwards) . If you use a different image size, do remember to scale f according to the image size.)
To do this, you will have to implement a feature-based translational motion estimation. The skeleton for this code is provided in FeatureAlign.cpp. The main routines that you will be implementing are:
int alignPair(const FeatureSet &f1, const FeatureSet &f2, const vector<FeatureMatch>
&matches, MotionModel m, float
f, int nRANSAC, double RANSACthresh, CTransform3x3& M);
int countInliers(const FeatureSet &f1, const FeatureSet &f2, const vector<FeatureMatch>
&matches, MotionModel m, float
f, CTransform3x3 M, double RANSACthresh, vector<int> &inliers);
int leastSquaresFit(const FeatureSet &f1, const FeatureSet &f2, const vector<FeatureMatch>
&matches, MotionModel m, float
f, const vector<int>
&inliers, CTransform3x3& M);
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 (described below), and estimates and inter-image transform matrix M. For this project, the enum MotionModel only takes on the value eTranslate.
AlignPair uses RANSAC (RAndom SAmpling Consensus) to pull out a minimal set of feature matches (one match for this project), estimates the corresponding motion (alignment) and then invokes countInliers to count how many of the feature matches 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.
CountInliers
computes the number of matches that have a distance below RANSACthresh
is computed. It also returns a list of inlier
match ids.
LeastSquaresFit computes a least squares estimate for the translation using all of the matches previously estimated as inliers. It returns the resulting translation estimate in the last column of M.
[TODO] You will have to fill in the missing code in alignPair to:
[TODO] Then, resample each image to its final location and blend it with its neighbors (AccumulateBlend, NormalizeBlend). Try a simple feathering function as your weighting function (see mosaics lecture slide on "feathering") (this is a simple 1-D 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. In NormalizeBlend, remember to set the alpha channel of the resultant panorama to opaque!
[TODO] Crop the resulting image to make the left and right edges seam perfectly (BlendImages). 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 y-coordinate on both the left and right end. Calculate the value of 'a' needed to perform this transformation.
Note that you can also use SIFT features to do the
alignment, which can be useful for testing this component before the feature
detection and matching component is finished. To do so, add the word
“sift” to the end of the command, as in:
Panorama alignPair warp1.key
warp2.key match1to2.txt 200 1 sift
Sample SIFT features and matches have been provided to you.
You may also refer to the file stitch2.txt provided with the skeleton code for the appropriate command line syntax. This command-line interface allows you to debug each stage of the program independently.
You can use the test results included in the images/ folder to check whether your program is running correctly. Comparing your output to that of the sample solution is also a good way of debugging your program.
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!