/manacus-dynamics

Camera Trap video processing for Manacus dynamics assessment

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0

Manacus Dynamics

Camera Trap video processing for Manacus dynamics assessment

Tasks [Vision]

  1. Dataset Preparation
    • Extract video frames given the metadata sheet using ./dataset/fcat/locate_dataset.py
    • Organize the dataset for bird detection
      • Stage 1: ebird high resolution images in coco format ./dataset/DATASET.md
      • Stage 2: camera trap image sampling [TODO]
    • Optical flow or Background subtraction to determine the most likely frames to contain birds for detection
    • Run the videos through Marco's audio processing pipeline for likely frames (eg: Bird snaps right after flying off or copulation)
  2. Manacus detection
    • Detect bird/manacus in the video frames - bird, background [Deprecated]
    • Stage 1 model to detect manacus in image frames (male/female should be present)
      • male (white chest, black wings) [only male displays aand makes snap noise]
      • female (green) [low representation, camouflages with the foliage]
      • unknown (juvenile: between male and female) [male bird can be practicing infront of juveniles ]
    • Stage 2 model trained via transfer learning on camera trap manacus dataset
  3. Dynamics
    • Estimate copulation/visitation in the video based on heuristics
      • frame by frame position (bounding box) for male and female

Environment Setup

  • Python 3.8.18 Installation
    • Instructions via Miniconda v23.1.0 - https://docs.conda.io/projects/miniconda/en/latest/
    • Create a virtual environment named "mothra" for this analysis 
      conda env remove -n mana
conda create -n mana python=3.9 jupyterlab -c conda-forge
conda activate mana
pip install -r requirements.txt
    • Clone the current codebase - git clone https://github.com/karmarv/manacus-dynamics.git && cd manacus-dynamics
    • Install pre-requisite packages in the activated python virtual environment using - pip install -r requirements.txt

Dataset

Model