LeafVeinExtraction

To extract leaf veins from scanned leaf groups and save some valuable data, like curvatures.

Done:

1. Use K-means to get the order of each leaf in current group, then use Radon Transformation to straighten the leaf images.
2. Use an improved dynamic Canny + Region Growth with two direction to extract leaf vein from the scanned leaf groups.
3. Use DFT(Discrete Fourier Transformation) to evaluate the curvatures of the discrete vein points.
4. Use curve-fitting to calculate the angles between the main-vein and sub-veins.

Author: Peng Zheng.

Project duration: 6/2017~12/2017, while some data formatting jobs still need to be done.

Required_packages:

• numpy
• scipy
• opencv-python
• scikit-image
• scikit-learn
• matplotlib
• xlsxwriter

Essential methods:

1. Preprocessing:
   1. Radon transformation.
   2. FloodFill.
   3. K-means
2. Extraction:
   1. Improved Canny.
   2. Region growth.
3. Data formatting:
   1. Discrete Fourier Transformation.
   2. Skeletonization.
   3. Curve fitting.

Usage:

1. Put the scanned leaf group image in the "split_before" folder.
2. python main.py.

Result:

TODO:

• Use superpixel to adapt canny theshold locally.

• Upgrade Region Growth with Kalman Filter.

If you've ever met any confusion or bug in related algorithms or code, please be not mean about your issue:)