Develop ML code for oyster image analysis with computer vision

[Copilot]

Overview

This PR implements a complete machine learning pipeline for automatically counting oysters in photographs using computer vision techniques. The system was developed and optimized using the 8 labeled images in data/images/ (juvenile-99 through juvenile-116).

What's New

A new code/count-automation/ directory containing a fully functional oyster counting system:

Core Components

• oyster_counter.py - Main counting algorithm implementing:

  • Adaptive thresholding to handle varying lighting conditions
  • Morphological operations (opening/closing) for noise reduction
  • Contour detection with shape filtering (area, circularity, aspect ratio)
  • Configurable parameters for different scenarios
  • Command-line interface for single images and batch evaluation

• optimize_parameters.py - Parameter optimization framework:

  • Grid search across parameter combinations
  • Performance tracking and comparison
  • Exports optimized parameters to JSON

• batch_process.py - Batch processing utility:

  • Process multiple images efficiently
  • Generate CSV and JSON reports
  • Summary statistics and visualizations

• example_usage.py - Comprehensive examples demonstrating:

  • Default and custom parameter usage
  • Loading optimized parameters from files
  • Programmatic evaluation
  • Parameter experimentation

• requirements.txt - Python dependencies with security-patched versions

• README.md - Comprehensive documentation including:

  • Installation and usage instructions
  • Algorithm description with detailed parameter documentation
  • Performance metrics and analysis
  • Improvement recommendations and data collection strategy
  • Troubleshooting guide
  • Changelog section with timestamps as requested

Performance Results

The system was optimized through grid search testing 96 parameter combinations:

Baseline (default parameters):

• Mean Absolute Error: 67.0 oysters
• Mean Percentage Error: 63.4%

Optimized:

• Mean Absolute Error: 40.6 oysters (39% improvement)
• Mean Percentage Error: 38.5% (39% improvement)
• Detection Rate: ~61.5% of actual oysters

The algorithm currently shows consistent undercounting, with performance ranging from 29-48% error across the 8 test images, indicating room for improvement with additional data and advanced techniques.

Output Files

The system generates visualization images showing detected oysters with bounding boxes and labels, along with JSON reports containing:

• Detailed per-image predictions and errors
• Optimized parameters ready for reuse
• Complete optimization trial history

Future Improvements

The README documents a comprehensive improvement plan:

1. Short-term (current dataset): Enhanced preprocessing, advanced segmentation (watershed, distance transform)
2. Long-term (with more data):
   • Expand dataset to 30+ images with varying conditions (lighting, density, angles)
   • Implement deep learning approaches (YOLO, Faster R-CNN)
   • Develop active learning pipeline for efficient labeling
   • Create annotation tool for team collaboration

Security

• ✅ All dependencies verified against GitHub Advisory Database
• ✅ Security vulnerabilities fixed (opencv-python-headless ≥4.8.1.78, pillow ≥10.3.0)
• ✅ CodeQL security scan passed with 0 vulnerabilities

Usage Example

# Count oysters in a single image with optimized parameters
python oyster_counter.py --input ../../data/images/juvenile-102.jpeg \
  --params ../../output/count-automation/best_params.json

# Evaluate on all labeled images
python oyster_counter.py --input ../../data/images --evaluate \
  --output ../../output/count-automation

# Batch process images
python batch_process.py --input ../../data/images \
  --output results --params best_params.json

This implementation provides a solid foundation for oyster counting automation and a clear path forward for improving accuracy as more labeled training data becomes available.

Original prompt

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This section details on the original issue you should resolve

<issue_title>Develop ML code for oyster image analysis</issue_title>
<issue_description>Starting with images in data/images develop a machine learning code base (in code directory) that could be used to count the number of oysters in photographs. The 8 images in the data/images have number after "-" that indicate the actual number of oysters in the image. Optimize the model based on this and offer a plan to move forward to add more images with know oysters counts to improve the algorithm. Do all of this develop in a subdirectory in the code directory named count-automation. Create a readme in this directory that describes the procedure as well as a section that records all changes in the codebase with time stamps. </issue_description>

Comments on the Issue (you are @copilot in this section)

Fixes #3

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[Copilot]

Pull Request Overview

This PR implements a machine learning pipeline for automatically counting oysters in photographs using computer vision techniques. The solution uses adaptive thresholding, morphological operations, and contour detection to identify oysters, achieving a mean absolute error of 40.6 oysters (38.5% mean percentage error) on 8 labeled test images.

Key Changes:

• Developed core oyster detection algorithm with configurable parameters for preprocessing, thresholding, and contour filtering
• Created parameter optimization framework that tested 96 parameter combinations to identify optimal settings
• Built batch processing and evaluation utilities with JSON/CSV output formats

Reviewed Changes

Copilot reviewed 11 out of 20 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
code/count-automation/requirements.txt	Specifies Python dependencies including OpenCV, NumPy, scikit-image, and pandas
code/count-automation/oyster_counter.py	Implements OysterCounter class with detection pipeline, evaluation framework, and CLI interface
code/count-automation/optimize_parameters.py	Provides grid search optimization to find best parameter combinations
code/count-automation/example_usage.py	Demonstrates programmatic usage of the oyster counter with various parameter configurations
code/count-automation/batch_process.py	Enables batch processing of multiple images with summary statistics and CSV/JSON exports
code/count-automation/README.md	Comprehensive documentation covering installation, usage, algorithm details, performance metrics, and improvement recommendations
output/count-automation/best_params.json	Contains optimal parameter values identified during optimization
output/count-automation/evaluation_results.json	Stores detailed evaluation metrics for each test image
output/count-automation/optimization_results.json	Records all 96 parameter combinations tested with their performance metrics
output/count-automation/batch/batch_results.json	JSON output from batch processing of test images
output/count-automation/batch/batch_results.csv	CSV output from batch processing of test images

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[Copilot]

Version requirement inconsistency: README specifies >= 4.8.0 while requirements.txt specifies >=4.8.1.78. These should match to avoid confusion.

Suggested change

	- opencv-python-headless >= 4.8.0
	- opencv-python-headless >= 4.8.1.78

[Copilot]

Version requirement inconsistency: README specifies >= 10.0.0 while requirements.txt specifies >=10.3.0. These should match to avoid confusion.

Suggested change

	- pillow >= 10.0.0
	- pillow >= 10.3.0