843b6ede6a
Get CUDA code to compile with clang CUDA and without CUDA #3800 Changelist: - there are some syntactic changes: `<< <` -> `<<<`. For some reason, I do not need to change all those in the code. - `::min` -> `std::min` in `__host__` code - `modules/cudaimgproc/src/moments.cpp` needs to have the CUDA code in the `#ifdef` - The signature of `cv::cuda::swapChannels` is not exactly the same as the C++ one in `modules/cudaimgproc/src/color.cpp` - `cv::cuda::FarnebackOpticalFlow::create` needs to be explicit about which FarnebackOpticalFlow it returns ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch
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:
// 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:
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.