CPFIND(1)                            HUGIN                           CPFIND(1)

       cpfind - Feature matching for panoramic stitching

       cpfind [options] -o output_project project.pto

       cpfind [options] -k i0 -k i1 [...] project.pto

       cpfind [options] --kall project.pto

       cpfind cpfind is a control-point detector for Hugin. It expects a
       project file as input and writes a project file with control-points on
       success.  It depends on reasonable lens information in the input
       project file.

       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 destinctive features in the image. cpfind uses 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.

   Using celeste
       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.

   Matching strategy
       All pairs

       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.

       Linear match

       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.

       Multirow matching

       This is an optimized matching strategy for single and multi-row

          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 annother directory use the --keypath

       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 save
       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

   Feature description
       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

   Feature matching
       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:

       --ransacmode (auto, hom, rpy, rpyv, rpyb)
           Select the model used in the ransac step.

           hom: Assume a homography. Only applicable for non-wide angle
                views. Uses the original panomatic code. It is also more
                than required and can generate false matches, particularly if
                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
                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
                 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

       --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: 2)

           Cpfind generates between minmatches and sieve2width * sieve2height
           * sieve2size control points between an image pair. (Default setting
           is between 4 and 50 (=5*5*2) 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
           considers as not connected. For narrow overlaps you can try to
           decrease minmatches, but this increases the risk of getting wrong
           control points.

       --celesteRadius <int>
           Radius for celeste (default 20)

       --celesteThreshold <double>
           Threshold for celeste (default 0.5)

           Run celeste sky identification after loading images, this ignores
           all features associated with 'clouds'.

       -p <string, --keypath <string>
           Path to cache keyfiles

           Clean up cached keyfiles

       -c, --cache
           Caches keypoints to external file

           Write keyfiles for all images

       -k <int>, --writekeyfile <int>
           Write a keyfile for this image number (accepted multiple times)

       -o <string>, --output <string>
           Output file, required

       -n <int>, --ncores <int>
           Number of CPU/Cores (default:autodetect)

       -t, --test
           Enables test mode

           Uses full scale image to detect keypoints (default:false)

       --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 : 0.15)

           Enable heuristic multi row matching (default: off)

           Enable linear images matching (default : all pairs)

       --linearmatchlen <int>
           Number of images to match in linear matching (default:1)

       --minmatches <int>
           Minimum matches (default : 4)

       --ransaciter <int>
           Ransac : iterations (default : 1000)

       --ransacdist <int>
           Ransac : homography estimation distance threshold (pixels) (default
           : 25)

       --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 : 2)

       --, --ignore_rest
           Ignores the rest of the labeled arguments following this flag.

           Displays version information and exits.

       -h, --help
           Displays usage information and exits.

       Anael Orlinski, Pablo d'Angelo, Antoine Deleforge, Thomas Modes

"Version: 2019.2.0"               2020-05-12                         CPFIND(1)