General and description
Cpfind is a control point detector for hugin. It expects a project file as input and write a project file with control points on success. The general usage is
cpfind -o output.pto input.pto
Internal the control point detector algorithm is divided into 2 parts:
- The first step is the feature description: In this step the images of the project file are loaded and so called keypoints are searched. They describe distinctive features in the image. Cpfind is using a gradient based descriptor for the feature description of the keypoints.
- In a second step, the feature matching, all keypoints of two images are matched against each other to find features which are on both images. If this matching was successful two keypoints in the two images become one control point.
Rectilinear and fisheye images
Cpfind can find control points in rectilinear and fisheye images. To achieve good control points images with a high horizontal field of view (e.g. ultra wide rectilinear or fisheye) are therefore remapped into a conformal space (cpfind is using the stereographic projection) and the feature matching occurs in this space. Before writing the control points the coordinates are remapped back to the image space. This happens automatic depending on the information about the lens in the input project file. So check that your input project file contains reasonable information about the used lens.
WARNING: cpfind in hugin 2010.4 has some bugs and won't work properly with fisheye images. Use a 2010.5 development snapshot (rev c137328f1418, from 2011-01-20) or wait for the next release.
Outdoor panorama often contains clouds. Clouds are bad areas for setting control points because they are moving object. Cpfind can use the same algorithm as celeste_standalone to masked out areas which contains clouds. (This is only done internal for the keypoint finding step and does not change the alpha channel of your image. If you want to generate a mask image use celeste_standalone). To run cpfind with celeste use
cpfind --celeste -o output.pto input.pto
Using cpfind with integrated celeste should be superior against using cpfind and celeste_standalone sequential. When running cpfind with celeste areas of clouds, which often contains keypoints with a high quality measure, are disregarded and areas without clouds are used instead. When running cpfind without celeste also keypoints on clouds are found. When afterwards running celeste_standalone these control points are removed. In the worst case all control points of a certain image pair are removed.
So running cpfind with celeste leads to a better "control point quality" for outdoor panorama (e.g. panorama with clouds). Running cpfind with celeste takes longer than cpfind alone. So for indoor panorama this option does not need to specified (because of longer computation time).
The celeste step can be fine tuned by the parameters --celesteRadius and --celesteThreshold.
This is the default matching strategy. Here all image pairs are matched against each other. E.g. if your project contains 5 images then cpfind matches the image pairs: 0-1, 0-2, 0-3, 0-4, 1-2, 1-3, 1-4, 2-3, 2-4 and 3-4
This strategy works for all shooting strategy (single-row, multi-row, unordered). It finds (nearly) all connected image pairs. But it is computational expensive for projects with many images, because it test many image pairs which are not connected.
This matching strategy works best for single row panoramas:
cpfind --linearmatch -o output.pto input.pto
This will only detect matches between adjacent images, e.g. for the 5 image example it will matches images pairs 0-1, 1-2, 2-3 and 3-4. The matching distance can be increased with the switch --linearmatchlen. E.g. with --linearmatchlen 2 cpfind will match a image with the next image and the image after next, in our example it would be 0-1, 0-2, 1-2, 1-3, 2-3, 2-4 and 3-4.
This is an optimized matching strategy for single and multi-row panorama:
cpfind --multirow -o output.pto input.pto
The algorithm is the same as described in multi-row panorama. By integrating this algorithm into cpfind it is faster by using several cores of modern CPUs and don't caching the keypoints to disc (which is time consuming). If you want to use this multi-row matching inside hugin set the control point detector type to All images at once.
Keypoints caching to disc
The calculation of keypoints takes some time. So cpfind offers the possibility to save the keypoints to a file and reuse them later again. With --kall the keypoints for all images in the project are saved to disc. If you only want the keypoints of particular image use the parameter -k with the image number:
cpfind --kall input.pto cpfind -k 0 -k 1 input.pto
The keypoint files are saved by default into the same directory as the images with the extension .key. In this case no matching of images occurs and therefore no output project file needs to specified. If cpfind finds keyfiles for an image in the project it will use them automatically and not run the feature descriptor again on this image. If you want to save them to another directory use the --keypath switch.
This procedure can also be automate with the switch --cache:
cpfind --cache -o output.pto input.pto
In this case it tries to load existing keypoint files. For images, which don't have a keypoint file, the keypoints are detected and saved to the file. Then it matches all loaded and newly found keypoints and writes the output project.
If you don't need the keyfile longer, the can be deleted automatic by
cpfind --clean input.pto
For speed reasons cpfind is using images, which are scaled to their half width and height, to find keypoints. With the switch --fullscale cpfind is working on the full scale images. This takes longer but can provide "better" and/or more control points.
The feature description step can be fine-tuned by the parameters:
--sieve1width <int> Sieve 1: Number of buckets on width (default: 10)
--sieve1height <int> Sieve 1: Number of buckets on height (default: 10)
--sieve1size <int> Sieve 1: Max points per bucket (default: 100)
--kdtreesteps <int> KDTree: search steps (default: 200)
--kdtreeseconddist <double> KDTree: distance of 2nd match (default: 0.25)
Cpfind stores maximal sieve1width * sieve1height * sieve1size keypoints per image. If you have only a small overlap, e.g. for 360 degree panorama shoot with fisheye images, you can get better results if you increase sieve1size. You can also try to increase sieve1width and/or sieve1height.
Effectively cpfind splits your image in rectangles or buckets: sieve1width horizontally by sieve1height vertically. It will try to find sieve1size keypoints per bucket. This ensures a reasonably uniform distribution of interest points over your image. These features will be matched in the matching step. With the default parameters, up to 10000 interest points will be used in the feature matching step.
Fine-tuning of the matching step by the following parameters:
--ransaciter <int> Ransac: iterations (default: 1000)
--ransacdist <int> Ransac: homography estimation distance threshold (pixels) (default: 25)
--ransacmode <string> Select the mode used in the ransac step. Possible values: auto, hom, rpy, rpyv, rpyb (default: auto)
- hom: Assume a homography. Only applicable for non-wide angle views. Uses the original panomatic code. It is also more flexible than required and can generate false matches, particularly if most of the matches are located on a single line.
- rpy: Align images using roll, pitch and yaw. This requires a good estimate for the horizontal field of view (and distortion, for heavily distorted images). It is the preferred mode if a calibrated lens is used, or the HFOV could be read successfully from the EXIF data.
- rpyv: Align pair by optimizing roll, pitch, yaw and field of view. Should work without prior knowledge of the field of view, but might fail more often, due to error function used in the panotools optimizer, it tends to shrink the fov to 0.
- rpyvb: Align pair by optimizing roll, pitch, yaw, field of view and the "b" distortion parameter. Probably very fragile, just implemented for testing.
- auto: Use homography for images with hfov < 65 degrees and rpy otherwise.
--minmatches <int> Minimum matches (default: 4)
--sieve2width <int> Sieve 2: Number of buckets on width (default: 5)
--sieve2height <int> Sieve 2: Number of buckets on height (default: 5)
--sieve2size <int> Sieve 2: Max points per bucket (default: 1)
Cpfind generates between minmatches and sieve2width * sieve2height * sieve2size control points between an image pair. (Default setting is between 4 and 25 (=5*5*1) control points per image pair.) If less then minmatches control points are found for a given image pairs these control points are disregarded and this image pair is considered as not connected. For narrow overlaps you can try to decrease minmatches, but this increases the risk of getting wrong control points. This filtering step (sieve2) works similar like sieve1 step described above: The rectangular area, which is covered by control points, is divided into sieve2width * sieve2height rectangles/buckets - sieve1width horizontally by sieve1height vertically. For each bucket only sieve2size control points are saved in the project file. This step is intended to get an uniform distribution of the control points.