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CPFIND(1)			     HUGIN			     CPFIND(1)

NAME
       cpfind -	Feature	matching for panoramic stitching

SYNOPSIS
       cpfind [options]	-o output_project project.pto

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

       cpfind [options]	--kall project.pto

DESCRIPTION
       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.

USAGE
   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
       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 annother 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	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

EXTENDED OPTIONS
   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
       sieve1height.

   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:
	   25)

       --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
	   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: 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.

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

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

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

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

       --clean
	   Clean up cached keyfiles

       -c, --cache
	   Caches keypoints to external	file

       --kall
	   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

       --fullscale
	   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)

       --multirow
	   Enable heuristic multi row matching (default: off)

       --linearmatch
	   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.

       --version
	   Displays version information	and exits.

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

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

"Version: 2019.2.0"		  2019-01-26			     CPFIND(1)

NAME | SYNOPSIS | DESCRIPTION | USAGE | EXTENDED OPTIONS | OPTIONS | AUTHORS

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