/CascadeSORT

This is the official implementation of the paper "Cascade-SORT: A Robust Fruit Counting Approach Using Multiple Features Cascade Matching"

Primary LanguagePython

fruit counting

This is the official implementation of the paper "Cascade-SORT: A Robust Fruit Counting Approach Using Multiple Features Cascade Matching".

At present, This code have beeen verified on MacOS 10.15.6. More functions will be added in future versions, to be continued...

Dependencies

  • python 3.8
  • numpy 1.18.5
  • scipy 1.5.0
  • opencv-python 4.4.0.44
  • opencv-contrib-python 4.4.0.44
  • scikit learn 0.23.1

Quick Start

  1. Check all dependencies installed

  2. Clone this repository

git clone git@github.com:ZQPei/deep_sort_pytorch.git
  1. Download the YOLO Weights from the followed links:
Google Drive:

https://drive.google.com/file/d/1lNvWKdFl36FrY-Cj2vEZrx-H8okXkcbT/view?usp=sharing

Baidu:

https://pan.baidu.com/s/1JA5lVb_BkQGbWy_u9bwdug  
Extract code: 5efn
  1. Set configuration, revise "yaml/apple.yaml", if the code is run on the custom videos and models
YOLO:
  CFG: "cfg/apple.cfg"
  WEIGHT: "checkpoints/apple_best.weights"
  CLASS_NAMES: "cfg/apple.names"
  SCORE_THRESH: 0.5
  NMS_THRESH: 0.5

TRACK:
  MODE: "cascade"
  VIDEO_DIR: "video/apple.mp4"
  SAVE_DIR: "results/apple.mp4"
  1. Run demo
python main.py