https://github.com/rybakov-ks/particleanalyzer

A Computer Vision-based tool for automatic segmentation and size analysis of particles in Scanning Electron Microscope (SEM) images.
https://github.com/rybakov-ks/particleanalyzer

computer-vision data-analysis deep-learning detectron2 electron-microscopy image-segmentation materials-characterization microscopy-images nanotechnology object-detection particle-analysis scanning-electron-microscopy scientific-research sem sem-image-analysis yolo

Last synced: 30 days ago
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A Computer Vision-based tool for automatic segmentation and size analysis of particles in Scanning Electron Microscope (SEM) images.

• Host: GitHub
• URL: https://github.com/rybakov-ks/particleanalyzer
• Owner: rybakov-ks
• License: gpl-3.0
• Created: 2025-05-03T19:46:57.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-13T19:57:15.000Z (12 months ago)
• Last Synced: 2025-06-15T03:01:47.264Z (12 months ago)
• Topics: computer-vision, data-analysis, deep-learning, detectron2, electron-microscopy, image-segmentation, materials-characterization, microscopy-images, nanotechnology, object-detection, particle-analysis, scanning-electron-microscopy, scientific-research, sem, sem-image-analysis, yolo
• Language: Python
• Homepage: https://sem.rybakov-k.ru
• Size: 104 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## 📑 Table of Contents

1. 🔎 [ParticleAnalyzer](#particleanalyzer)

2. ✨ [Key Features](#-key-features)

3. 📥 [Installation Guide](#-installation-guide)

4. 🛠 [Segmentation Optimization Guide](#-segmentation-optimization-guide)

5. 📊 [Analysis Outputs](#-analysis-outputs)

6. ⚙️ [Advanced Settings](#-advanced-settings)

7. 📏 [Scale Calibration](#-scale-calibration)

8. 📧 [Contributors](#-contributors)

## ParticleAnalyzer

[![Try Online](https://img.shields.io/badge/TRY%20ONLINE-Available%20at%20sem.rybakov--k.ru-brightgreen)](https://sem.rybakov-k.ru/)

[![Download from PyPI](https://img.shields.io/pypi/v/particleanalyzer?label=Download%20from%20PyPI)](https://pypi.org/project/particleanalyzer/)

[![Downloads per month](https://static.pepy.tech/badge/particleanalyzer/month)](https://pepy.tech/project/particleanalyzer)

A Computer Vision Tool for Automatic Particle Segmentation and Size Analysis in Scanning Electron Microscope (SEM) Images.



  Video demonstrations:


  Local video (MP4) | 

  YouTube demonstration





  



*If you encounter any errors while using ParticleAnalyzer, please open an issue in our GitHub repository or contact us directly at rybakov-ks@ya.ru for support.

If the model fails to segment your images correctly, please send them to rybakov-ks@ya.ru. Your submissions will be used to retrain and improve the model’s performance.*

## ✨ Key Features

- Automated particle segmentation in SEM images

- SAHI mode enables accurate detection of small particles in high-resolution images via a sliding window method

- Comprehensive statistical analysis of particle characteristics

- Interactive visualization of size distributions

- Dual unit support — switch between pixels and micrometers (µm)

- Supports multiple AI models: YOLOv11, YOLOv12, and Detectron2

- Advanced configuration options for fine-tuning detection accuracy

- Multi-language interface: English, Russian, Simplified Chinese, Traditional Chinese (en, ru, zh-CN, zh-TW)

- Try it online: [sem.rybakov-k.ru](https://sem.rybakov-k.ru/)

## 🛠 Installation Guide

 ### 1. 📥 Install PyTorch with CUDA support

Make sure your system has an NVIDIA GPU with CUDA. Install [PyTorch](https://pytorch.org/get-started/locally/) using the appropriate CUDA version (e.g., CUDA 11.8):

   ```python

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

   ```

If you do not have a CUDA-capable GPU, use the CPU version instead:

   ```python

   pip install torch torchvision torchaudio

   ```

### 🧪 2. Install Detectron2 (Optional)

If you want to enable advanced instance segmentation, install Detectron2:

```python

pip install 'git+https://github.com/facebookresearch/detectron2.git'

```

⚠️ *There may be problems installing Detectron2. Use the official [documentation](https://detectron2.readthedocs.io/en/latest/tutorials/install.html).*

### 📦 3. Install ParticleAnalyzer

Finally, install ParticleAnalyzer from PyPI:

```python

pip install ParticleAnalyzer

```

✅ Now you're ready to run the application:

```python

ParticleAnalyzer run

```

Open in browser: http://127.0.0.1:8000

## 🛠 Segmentation Optimization Guide

🔧 Core Parameters:

   - Model Selection

   - Detection Confidence Threshold (0-1)

     - Increase (e.g., 0.7→0.85) to reduce false positives

     - Decrease (e.g., 0.5→0.3) to detect faint particles

   - IoU Threshold (0-1)

     - Increase (e.g., 0.5→0.7) to eliminate duplicate detections

     - Decrease for dense particle fields

   - Enable SAHI Processing (split-analyze-merge)

🧩 SAHI Configuration (for large images):

   - Slice Size: Start with 400×400

   - Overlap Ratio: 0.2-0.3 (prevents edge artifacts)\

*SAHI mode helps detect small objects in high-resolution images by using a sliding window approach*

🔄 Model Selection:



   

| Model       | Best For                   | Speed     | Recommended Use Case               |

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

| **YOLOv11** | General use (balanced)      | ⚡⚡⚡ Fast | Quick analysis of standard samples |

| **YOLOv12** | High precision detection    | ⚡⚡ Medium | Critical measurements              |

| **Cascade_X152** | Challenging morphology   | ⚡ Slow    | Irregular/overlapping particles    |




## 📊 Analysis Outputs

### Statistical Data Table



  



*Comprehensive metrics including mean, median, min/max, standard deviation values for:*

- Area (px² or µm²)

- Perimeter (px or µm)

- Equivalent diameter (px or µm)

- Eccentricity (unitless)

- Intensity values (grayscale units)

### Size Distribution Visualization



  



*Normal distribution fitting for all measured parameters showing particle population characteristics*

## Advanced Settings Panel



  



*Configuration options include:*

- **Model Selection**: YOLOv11, YOLOv12, Detectron2

- **SAHI Mode**: Enable/disable sliced inference for large images



  



- **Detection Threshold**: Confidence level (0-1)

- **IOU Threshold**: Overlap threshold for NMS (0-1)

- **Max Detections**: Maximum number of particles to detect

- **Scaling Mode**: Pixel/µm unit selection

- **Image Resolution**: Output resolution control

- **Result Rounding**: Decimal places for metrics

- **Single Particle Mode**: Detailed individual analysis

- **Histogram Bins**: Number of intervals for distribution plots

## 📐 Scale Calibration



  



Micrometer values are calculated by:

1. Identifying the SEM image's scale bar using two marker points

2. Manually specifying the known real-world distance between markers

3. Automatically computing the pixel-to-µm conversion ratio



  



*Note: For accurate µm measurements, please ensure:*

- The scale bar is clearly visible in your image

- You input the correct reference distance when prompted

- The scale bar was created at the same magnification as your particles

## 📧 Contributors

Rybakov Kirill (Saratov State University): rybakov-ks@ya.ru