Assigned: Monday April 21, 2003
Due: Sunday May 4th, 2003, artifacts due Tuesday May 6 (both by 11:59pm)
In this project, you will implement a system 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 your project, you will be supplied with some test images and skeleton code you can use as the basis of your project, a sample solution executable you can use to compare with your program, and instructions on how to use the viewer.
Due to the large number of students in this class, we ask students to form groups of two to take pictures that will form panoramas. This will involve signing out camera equipment as groups of two. You MUST WORK INDIVIDUALLY when writing the code; but you will turn in one artifact per team of two.
To see examples of previous students panoramas, click here.
Here are the suggested steps you should follow:
Skip this step for the test data. Its camera parameters can be found in the sample commands in stitch2.txt, which is provided along with the skeleton code.
|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 currently 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. Finally, ensure that the image is saved in 24bit tga, not 32bit. This is very important for Lucas-Kanade to work.)
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
(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 hierarchical (coarse-to-fine) Lucas-Kanade style translational motion estimation. The skeleton for this code is provided in LucasKanade.cpp.
PyramidalLucasKanade constructs an image pyramid of specified number of levels for two images img1 and img2, and walks down the hierarchy (from coarse to fine), at each level updating the displacement of img2 from img1 by iteratively calling LucasKanadeStep.
LucasKanadeStep takes two images img1, img2, and the initial translation vector (u,v) as input, and computes an updated translation (u',v') = (u+du,v+dv) which minimizes |img2(x+u',y+v')-img1(x,y)| over all x, y. Note that, instead of evaluating the image derivatives (Ix, Iy and It) between img2(x+u,y+v) and img1(x,y), it first creates img2t, a warp of img2 using the translation (u,v), then computes the image derivatives between img2t(x,y) and img1(x,y).
[TODO] First, write a loop in PyramidalLucasKanade which
walks through the image pyramid from coarse to fine and calls the LucasKanadeStep
routine to update the displacement at each level of the pyramid (motion
slide 26). More specifically:
[TODO] Then, you will have to fill in the missing
code in LucasKanadeStep to:
[TODO] Then, resample each image to its final location and blend it with its neighbors (AccumulateBlend, NormalizeBlend). Try a simple horizontal “hat” function as your weighting function, similar to the one described in lecture (sampling slide 12) (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.
You may also refer to the file stitch2.txt
provided along with the skeleton code for the appropriate command line
syntax. This command-line interface allows you to debug each stage of the
You can use the test results included in the images/ folder to make sure whether your program is running correctly. Comparing your output to that of the sample solution is also a good way of debugging your program.
Turn in the executable (v4gP1.exe).
Turn in the code that you wrote (just the .cpp files you modified and any new files you needed).
In the artifact directory, turn in a web page (here are some tips) containing the following:
At least three panoramas: (1) the test sequence, (2), one from the Kaidan head, and (3) one from a hand-held sequence. Each panorama should be shown as (1) a low-res inlined image on the web page, (2) a link that you can click on to show the full-resolution .jpg file, AND (3) embedded in a viewer as described above.
a short description of what worked well and what didn’t. If you tried several variants or did something non-standard, please describe this as well.
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!
Although Lukas-Kanade gives sub-pixel motion estimation, the motion vectors are rounded to integers when blending the images into the mosaic in BlendImages.cpp. Try to blend images with sub-pixel localization.
Sometimes, there exists exposure difference between images, which results in brightness fluctuation in the final mosaic. Try to get rid of this artifact.
Make edits to one of your panoramas using your scissoring tool and photoshop. Import it to a viewer and include as a fourth panorama on the artifact page.
Try shooting a sequence with some objects moving. What did you do to remove “ghosted” versions of the objects?
Try a sequence in which the same person appears multiple times, as in this example.
Implement pyramid blending, as shown in class and described in Burt & Adelson's paper.
Implement and create full-view panoramas, such as spherical or cubic. This is discussed more in the assigned reading.
Last modified on April 17, 2003