WhatFishDo: A Behavior Annotation Tool for Videos Collected from the Field

Overview

WhatFishDo is a tool designed to assist researchers in annotating behavior from video footage. The tool supports detection, tracking, and annotation of animals in videos, leveraging YOLO for detection and OpenCV for tracking. It also supports GPU acceleration for faster processing.

Features

• Detection: Uses YOLO model to detect fish in video frames.
• Tracking: Tracks detected fish across frames using OpenCV trackers.
• Annotation: Allows users to annotate fish behavior by drawing bounding boxes and entering data.
• GPU Acceleration: Supports CUDA for faster processing on compatible hardware.
• Session Management: Supports resuming from previous sessions.

Installation

Prerequisites

• Python 3.13 or higher
• pip (Python package installer)

Steps

1. Clone the repository:

   sh git clone https://github.com/yourusername/WhatFishDo.git cd WhatFishDo

2. Install dependencies:

   sh pip install -r requirements.txt

   Note for GPU users: You will need to compile OpenCV with CUDA support. Instructions can be found here.

3. Download YOLO model files:

   • Download config and weights for a pretrained model of your choosing.
   • Place them in the model/ directory of the project.
   • Rename them to model.cfg and model.weights respectively.

   I recommend YOLO-Fish for videos of fish in heterogeneous environments.

Usage

Command Line Arguments

• -g, --gpu: Run detection model with CUDA.
• -d, --detect: Run with detection model.
• -t, --track: Run with tracking algorithm.
• -p, --project: Specify project file.

Running the Tool

I recommend using uv package and environment manager. With uv you can simply use the following command to run the application:

sh uv run app.py

If you are on Windows, you can use the following command:

sh uv run app.py --with PyQt5 --with opencv-python-headless

If you are not using uv, you can run the application using the following command:

```sh

create a virtual environment

python -m venv venv

activate the virtual environment

source venv/bin/activate

install the dependencies

pip install -r requirements.txt

run the application

python app.py ```

Key Bindings

• Press [space] to pause the video.
• Press LEFT to skip backward 1 second.
• Press RIGHT to skip forward 1 second.
• Press UP to skip forward 10 seconds.
• Press DOWN to skip backward 10 seconds.
• Press j to increase speed.
• Press k to decrease speed.
• Press z to stop the observation.
• Press p to record a predator in frame.

Data and Images

Data and images are saved automatically in the data folder.

Annotation

Click and drag to draw a bounding box around the fish and start an observation. Use the number keys to record the fish's behavior.

Project Files

When creating a new project, you will be prompted to choose a model. This project supports two project models:

1. Individual: Each video in your dataset is a separate individual. This is appropriate for experimental studies and focal follows.
2. Plot or Treatment: Each folder in your dataset is a separate plot or treatment. This is appropriate for observational studies and in-situ experiments.

Project properties are stored in .json files in the project directory. You can edit these files to change the project settings.

Subsampling

Having large datasets can be computationally expensive. You can specify the number of subsamples to use in the project settings. This will create random non-overlapping subsamples of the specified duration which are stored in project propertes file.

Behavior Codes

You can specify an ethogram using a .json file. The file should cotain the key bindings follwed by names and types for each behavior. You can optionally add a description for each behavior. See the below example:

json { "1": { "name": "Feeding", "type": "Behavior", "description": "Fish is feeding on the substrate." }, "2": { "name": "Resting", "type": "Behavior", "description": "Fish is resting on the substrate." }, "3": { "name": "Swimming", "type": "Behavior", "description": "Fish is swimming in the water column." } }

In the above example, 1, 2, and 3 are the key bindings for the behaviors. The names are the names of the behaviors, the type is the type of behavior, and the description is an optional description of the behavior.

Size classes

You can specify size classes for the indiviudals in the dataset. This is useful for studies that require size-based analysis. You can specify the size classes in a .json file. See the below example:

json {"sizes": ["0-10", "10-20", "20-30", "30-40", "40-50", "50<"]}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• YOLO-Fish for the pretrained model.
• OpenCV for the computer vision library.
• Ocean icons created by Freepik - Flaticon