https://github.com/zhengpeng7/leafveinextraction

To extract leaf veins from scanned leaf groups.
https://github.com/zhengpeng7/leafveinextraction

computer-vision curvature edge-detection extract-leaf-veins leaf region-growth veins

Last synced: 3 months ago
JSON representation

To extract leaf veins from scanned leaf groups.

• Host: GitHub
• URL: https://github.com/zhengpeng7/leafveinextraction
• Owner: ZhengPeng7
• License: bsd-2-clause
• Created: 2018-05-11T15:18:03.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-06-07T12:12:18.000Z (over 6 years ago)
• Last Synced: 2023-10-20T23:59:49.623Z (over 1 year ago)
• Topics: computer-vision, curvature, edge-detection, extract-leaf-veins, leaf, region-growth, veins
• Language: Python
• Size: 7.43 MB
• Stars: 17
• Watchers: 2
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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:

![cannied_edges](./images/Cannied_Edges.jpg)

![veins](./images/Veins.jpg)

![main_veins](./images/Main_Veins.jpg)

![color_veins_with_angles](./images/Colored_Veins.jpg)

![find_tops_bottoms](./images/Leaves_with_tops_and_bottoms.jpg)

## 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:)