We need a Jupyter Notebook that can process a demo video to generate and display counts of objects (e.g., fish) detected in the video. This notebook will serve as a demonstration of our video analysis capabilities and should be user-friendly for new users to understand our process and results.

Requirements

• The notebook should take a demo video as input. For testing, you can use the video located at /Users/aus10powell/Documents/Projects/MIT-Fishery-Counter/data/gold_dataset/videos/irwa/1_2016-04-22_12-36-58.mp4.
• Utilize the existing inference pipeline as implemented in InferenceCounter for processing the video.
• Display the total count of objects detected, differentiating between objects entering and exiting the frame if applicable.
• Plot a graph showing the count of objects over time to visualize the flow within the video.
  Include comments and explanations in the notebook to guide new users through the analysis process.

Additional Context

• The analysis should leverage the tracking configuration specified in /Users/aus10powell/Documents/Projects/MIT-Fishery-Counter/code/src/utils/tracking_configs/botsort.yaml.
• The model for detection is located at /Users/aus10powell/Documents/Projects/MIT-Fishery-Counter/code/notebooks/runs/detect/train133/weights/best.pt.
• It would be beneficial to include a section on how to adjust parameters for different videos or scenarios.
• The output should be easily interpretable, with visualizations where possible.

Expected Outcome

• A Jupyter Notebook that can be run end-to-end, providing insights into the object counts within a given video.
  Documentation within the notebook that explains each step of the process and how users can adapt it to their needs.
• This notebook will be a valuable addition to our documentation, helping to showcase our video analysis technology and providing a practical tool for users to experiment with.

Investigate next appropriate Yolo version

Differences between YOLOv2 (current) and YOLOv3 (also considered YOLOv6):

Architectural Changes: YOLOv3 introduced several architectural changes compared to YOLOv2:

• YOLOv3 uses three different scales or sizes of the detection grid, whereas YOLOv2 had only one scale. This allows YOLOv3 to detect objects of different sizes more effectively.

• YOLOv3 utilizes residual blocks inspired by the ResNet architecture, enabling better feature extraction and representation.

• YOLOv3 introduces skip connections, which facilitate the flow of information from earlier layers to later layers, aiding in the detection of objects at different scales.

• Improved Accuracy and Performance: YOLOv3 (or YOLOv6) generally offers improved accuracy and performance compared to YOLOv2:

• YOLOv3 achieves higher average precision and better localization accuracy due to its architectural improvements and multi-scale detection strategy.

• YOLOv3 has a higher number of convolutional layers and more parameters, allowing it to capture more intricate features and improve detection accuracy.

• YOLOv3 maintains real-time performance, although it may be slightly slower than YOLOv2 due to its increased complexity.

Pros and Cons of Upgrading from YOLOv2 to YOLOv3 (or YOLOv6):

Pros of upgrading from YOLOv2 to YOLOv3 (or YOLOv6):

• Improved detection accuracy: YOLOv3 offers better localization accuracy and higher average precision, which can result in improved object detection performance.
• Better handling of objects at different scales: YOLOv3's multi-scale detection strategy and skip connections enable more effective detection of objects of various sizes.
• Support for more intricate features: YOLOv3's deeper architecture and residual blocks allow it to capture more complex and detailed features, potentially improving the detection of challenging objects.

Cons of upgrading from YOLOv2 to YOLOv3 (or YOLOv6):

• Training time and computational requirements: YOLOv3 has a higher number of layers and more parameters, which may result in increased training time and computational resource requirements compared to YOLOv2.
• Potential need for reannotation: Upgrading to YOLOv3 (or YOLOv6) might require revisiting the annotation process to ensure compatibility with the multi-scale detection approach.

Fishery Counter API

System Design Outline

1. Data Collection

• Camera setup at fishery locations
• Video/image capture of fish passages

2. Data Processing

• Object detection and tracking pipeline
  • Phase 1: Detection (every N frames)
  • Phase 2: Tracking (between detections)
• Image labeling and annotation

3. Machine Learning Model

• YOLOv8 model for object detection
• Custom trained on herring images

4. API Backend

• REST API endpoints
• Fish counting logic
• Data storage and retrieval

5. Frontend Interface

• User dashboard for non-programmers
• Visualization of fish counts and statistics

6. Deployment and Infrastructure

• Cloud hosting (e.g. AWS, GCP)
• Containerization with Docker

Prediction REST API Details

1. Endpoints

• POST /predict
  • Accepts image/video upload
  • Returns fish count and species classification
• GET /stats
  • Returns summary statistics (e.g. daily/weekly counts)
• GET /health
  • API health check

2. Request/Response Format

• JSON for metadata
• Binary for image/video data

3. Authentication

• API key for access control

4. Processing Pipeline

• Image preprocessing
• YOLOv8 model inference
• Post-processing of detections
• Counting logic (e.g. directional filtering)

5. Output

• Fish count
• Species classification (River Herring vs Not River Herring)
• Confidence scores
• Timestamp

6. Additional Features

• Batch processing for historical data
• Real-time processing for live video feeds
• Integration with environmental sensors (temperature, current speed, etc.)

7. Scalability Considerations

• Load balancing for multiple concurrent requests
• Caching of frequent queries
• Asynchronous processing for long-running tasks

8. Monitoring and Logging

• Performance metrics
• Error tracking
• Usage statistics

Getting Started

[Instructions for setting up and running the API locally or in a development environment]

Deployment

[Instructions for deploying the API to production]

Contributing

[Guidelines for contributing to the project]

License

[License information]