54 lines
1.8 KiB
Markdown
54 lines
1.8 KiB
Markdown
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## OpenCV Hierarchical Feature Selection for Efficient Image Segmentation module
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Author and maintainers: Yujun Shi (shiyujun1016@gmail.com), Yun Liu (nk12csly@mail.nankai.edu.cn).
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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/
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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.
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In our implementation, we wrapped these stages into one single member function of the interface class.
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Since this module used cuda in some part of the implementation, it has to be compiled with cuda support
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For more details about the algorithm, please refer to the original paper: [1]
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### usage
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c++ interface:
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```c++
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// read a image
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Mat img = imread(image_path), res;
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int _h = img.rows, _w = img.cols;
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// create engine
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Ptr<HfsSegment> seg = HfsSegment::create( _h, _w );
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// perform segmentation
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// now "res" is a matrix of indices
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// change the second parameter to "True" to get a rgb image for "res"
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res = seg->performSegmentGpu(img, false);
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```
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python interface:
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```python
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import cv2
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import numpy as np
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img = cv2.imread(image_path)
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# create engine
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engine = cv2.hfs.HfsSegment_create(img.shape[0], img.shape[1])
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# perform segmentation
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# now "res" is a matrix of indices
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# change the second parameter to "True" to get a rgb image for "res"
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res = engine.performSegmentGpu(img, False)
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```
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### Reference
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[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.
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