My personal project for the final assignment of Class Machine Vision Application in SCUT.
- opencv-python
- opencv-contrib-python <= 3.4.0.10 (for SIFT & SURF algorithem can be used.)
- numpy
- matplotlib
Here is the structure of the whole system:
We use the traditional ways to process the images, rather than the deep-learning methods.
After your running vein_main.py
, you should see the histogram of the scores between inter-class and in-class.
So we can set the threshold value to 60 for classification.
python vein_main.py
Besides, you should dive into the file vein_main.py
, and adjust the comments for many other usages.
I didn' t upload all of my own vein data for individual privacy.
You should place your own vein data in the ./data/600/2
folder and name it like the format below.
├──data
│ ├── 600 // A Person's vein image
│ │ ├── 1 // the first machine
│ │ ├── 2 // the second machine
│ │ │ ├── 600-1-1-1.bmp
│ │ │ ├── 600-1-2-1.bmp
│ │ │ ├── 600-1-3-1.bmp
│ │ │ ├── ...
│ │ │ ├── 600-2-1-1.bmp
│ │ │ ├── 600-2-2-1.bmp
│ │ │ ├── 600-2-3-1.bmp
│ │ │ ├── ...
│ ├── roi_600_2_all_320240 //saved ROI
│ │ ├── 600-1-1-1.bmp
│ │ ├── 600-1-2-1.bmp
│ │ ├── 600-1-3-1.bmp
│ │ ├── ...
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