-
To use in a project, include fmm.hpp and use
fmm::fmm<image_data_type>(image, seeds, weight_map_type[, image_segmentation_threshold])This is a single-file header-only library without any dependencies. The CMakeLists.txt is only for building the sample program (which I encourage you to try). -
If you are using OpenCV and only need support for OpenCV Mat's, there is the (almost) drop-in replacement
fmmcv.hppwhich should be faster since it uses some of OpenCV's built-in functions. This, and the sample program, depend on OpenCV. -
fmm.hppIncludes a bare-bones Image struct which allows you to use any row-major contiguous matrix type:Image<field_type>(rows, cols, data_ptr)or create a image managing its own memory:Image<field_type>(rows, cols). The functions are also templated to support any Image, Point data types, and is by default compatible with OpenCV Mat/Point as well. To support a custom image type, you will have to specialize the__create_liketemplate function which should create a new uninitialized image of the same type and size as the input.
Computing distances/segmenting grayscale image (OpenCV sample image: fruit)
Computing distances/segmenting depth image (picture of me captured by Azure Kinect)
Comment on top of fmm.hpp copied here:
/** Fast marching method implementation
* usage: fmm::fmm(image, seeds, weight_map_type = IDENTITY,
* segmentation_threshold = disabled,
* normalize_output_geodesic_distances = true,
* output = nullptr)
* > returns an image, either geodesic distance map or, if
* segmentation_threshold is given, a segmentation mask
* image: input image (can be OpenCV Mat or fmm::Image<T>)
* seeds: std::vector of points (each can be OpenCV Point or fmm::Point)
* weight_map_type: transformation to apply to input image to use as FMM
* weight function. Can be one of:
* fmm::weight::IDENTITY no transformation (avoids a copy, faster)
* fmm::weight::GRADIENT recommended: image gradient magnitude (using Sobel gradients)
* fmm::weight::ABSDIFF absolute difference from average
* grayscale value of seeds
* fmm::weight::LAPLACIAN absolute value of image Laplacian
* segmentation_threshold: if specified, sets pixels with geodesic value less
* than or equal to this threshold to 1 and others to 0
* in the output image. If not given,the geodesic
* distances map will be returned.
* normalize_output_geodesic_distances: if true, normalizes geodesic distances
* values to be in the interval [0, 1].
* If segmentation_threshold is specified,
* this occurs prior to segmentation.
* Default true.
* max_visits: maximum number of points to visit. Can help speed up the computation
* if objects larger than a certain area are eliminated (e.g. background)
* output: optionally, a pointer to an already-allocated output image.
* This allows you to avoid a copy if you already have one
* allocated. By default a new image is created, and this
* is not necessary. Note: for fmmcv.hpp, this should just be
* a cv::Mat instead of a pointer.
*
* fmm::Image<T> usage (optional)
* - To make owning image fmm::Image<T>(rows, cols)
* - To make non-owning image that maps to row-major data (of same type, or char/uchar):
* fmm::Image<T>(rows, cols, data_ptr)
* */Apache 2.0
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