vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/hfs/README.md

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+##   OpenCV Hierarchical Feature Selection for Efficient Image Segmentation module
+
+Author and maintainers: Yujun Shi (shiyujun1016@gmail.com), Yun Liu (nk12csly@mail.nankai.edu.cn).
+
+Hierachical Feature Selection (HFS) is a real-time system for image segmentation. It was originally proposed in [1]. Here is the original project website: http://mmcheng.net/zh/hfs/
+
+The algorithm is executed in 3 stages. In the first stage, it obtains an over-segmented image using SLIC(simple linear iterative clustering). In the last 2 stages, it iteratively merges the over-segmented  image with merging method mentioned in EGB(Efficient Graph-based Image Segmentation) and learned SVM parameters.
+
+In our implementation, we wrapped these stages into one single member function of the interface class.
+
+Since this module used cuda in some part of  the implementation, it has to be compiled with cuda support
+
+For more details about the algorithm, please refer to the original paper: [1]
+
+### usage
+
+c++ interface:
+
+```c++
+// read a image
+Mat img = imread(image_path), res;
+int _h = img.rows, _w = img.cols;
+
+// create engine
+Ptr<HfsSegment> seg = HfsSegment::create( _h, _w );
+
+// perform segmentation
+// now "res" is a matrix of indices
+// change the second parameter to "True" to get a rgb image for "res"
+res = seg->performSegmentGpu(img, false);
+```
+
+python interface:
+
+```python
+import cv2
+import numpy as np
+
+img = cv2.imread(image_path)
+
+# create engine
+engine = cv2.hfs.HfsSegment_create(img.shape[0], img.shape[1])
+
+# perform segmentation
+# now "res" is a matrix of indices
+# change the second parameter to "True" to get a rgb image for "res"
+res = engine.performSegmentGpu(img, False)
+```
+
+
+
+### Reference
+
+[1]: M. cheng, Y. Liu, Q. Hou, J. Bian, P. Torr, S. Hu, Z. Tu HFS: Hierarchical Feature Selection for Efficient Image Segmentation ECCV, Oct.2016.