https://github.com/ophiase/microorganism-computer-vision

Computer Vision 🧫 - Analysis of the motion of microorganisms 🦠 | Work in progress
https://github.com/ophiase/microorganism-computer-vision

computer-vision microorganism motion-analysis

Last synced: 3 months ago
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Computer Vision 🧫 - Analysis of the motion of microorganisms 🦠 | Work in progress

• Host: GitHub
• URL: https://github.com/ophiase/microorganism-computer-vision
• Owner: Ophiase
• License: apache-2.0
• Created: 2024-12-06T21:43:17.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-02-15T22:06:34.000Z (4 months ago)
• Last Synced: 2025-02-15T23:19:29.500Z (4 months ago)
• Topics: computer-vision, microorganism, motion-analysis
• Language: Python
• Homepage:
• Size: 57.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Microorganism Computer Vision 🧫

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

[![Python 3.11.5+](https://img.shields.io/badge/Python-3.11.5%2B-yellow.svg)](https://www.python.org/)

A comprehensive computer vision pipeline for analyzing microorganism 🦠 motility patterns and diffusion characteristics.

Deadline: 28th February 2025



  

  

  



## 🌟 Features

- **Multi-modal Tracking**

  - Optical flow-based motion estimation

  - Kalman-filter enhanced object detection

  - Synthetic trajectory generation

- **Advanced Diffusion Analysis**

  - 6 statistical hypothesis tests for motility patterns

  - Automated trajectory classification

  - Comprehensive visualization toolkit

- **Scalable Architecture**

  - Modular pipeline design

  - Configurable hypothesis parameters

  - Batch processing capabilities

## 🧩 Diffusion Hypothesis Tests

Our framework implements sophisticated statistical tests to characterize microbial motion:

| Hypothesis Test | Key Mechanism | Biological Relevance |

|-----------------|---------------|----------------------|

| **Circular Motion** 
 `CircularMotionTest` | Curvature analysis + rotation angle detection | Magnetotactic bacteria, spiral-seeking organisms |

| **Directional Switching** 
 `DirectionalSwitchTest` | Fourier analysis of angular changes | Run-and-tumble motility (e.g., *E. coli*) |

| **Gaussian Random Walk** 
 `GaussianRandomWalkTest` | Kolmogorov-Smirnov normality test | Passive diffusion in isotropic environments |

| **MSD Linear Fit** 
 `MSDLinearTest` | R² evaluation of mean squared displacement | Normal diffusion processes |

| **Persistent Motion** 
 `PersistentMotionTest` | Velocity autocorrelation analysis | Active transport mechanisms |

| **Subdiffusion** 
 `SubDiffusionTest` | Power law exponent ($\alpha < 1$) detection | Crowded environments, viscoelastic media |

## 🚀 Installation

```bash

# Clone repository

git clone [email protected]:Ophiase/Microorganism-Computer-Vision.git

cd Microorganism-Computer-Vision

# Install dependencies

make pip

# Download sample dataset

make extract

```

## 🧪 Usage

Process real videos:

```bash

# Full pipeline execution

make transform      # Preprocess videos

make detection      # Track microorganisms

make render         # Generate gifs

make analysis       # Run statistical tests and graphics

```

Generate synthetic data:

```bash

make synthetic      # Create trajectory datasets

python3 -m script.main --task analysis --video synthetic_brownian

# "brownian" can be replaced by directed, sinusoidal, confined, ctrw, ...

```

Key Makefile Targets:

```makefile

detection     # Track objects in videos

render        # Generate trajectory visualizations

analysis      # Perform statistical testing

synthetic     # Generate synthetic trajectories

```

## 📊 Results Interpretation

- 


  

  



- 


  

  



## 📂 Data Structure

```bash

microorganism-cv/

├── data/                           # Raw and processed data

│   ├── preprocessed/               # Normalized video tensors

│   └── tracking/                   # Trajectory datasets

├── logic/                          # Core algorithms

│   ├── diffusion/                  # Statistical tests

│   ├── filters/                    # Image processing

│   └── structure/                  # Data models

├── script/                         # Pipeline components

└── visualization/                  # Plotting utilities

```

## 📚 Methodology

Our pipeline implements a multi-stage analysis process:

### Preprocessing

- Optical flow estimation

- Spatial normalization

- Noise reduction

### Object Detection

- Adaptive thresholding (❌ not implemented)

- Connected component analysis

- Kalman-filter based tracking

### Trajectory Analysis

- [MSD](https://en.wikipedia.org/wiki/Mean_squared_displacement) calculations

- Velocity autocorrelation

- Directional persistence metrics

### Statistical Classification

- Hypothesis testing

- Confidence interval estimation (❌ not implemented)

- [Motility](https://en.wikipedia.org/wiki/Motility) pattern classification