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https://github.com/marshalx/inbody-to-sqlite

A Python tool that processes InBody body composition scan images using OpenAI GPT Vision to extract structured data and store it in a local SQLite database.
https://github.com/marshalx/inbody-to-sqlite

ai analysis bia body composition inbody

Last synced: about 1 month ago
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A Python tool that processes InBody body composition scan images using OpenAI GPT Vision to extract structured data and store it in a local SQLite database.

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README 

          
# InBody to SQLite



A Python tool that processes InBody body composition scan images using OpenAI GPT Vision to extract structured data and store it in a local SQLite database. **Now includes a PDF Report Generator for beautiful progress tracking!**



## Features



### Core Features

- **AI-powered text extraction**: Uses OpenAI GPT-4 Vision to analyze InBody scan images

- **Unified data model**: Combines data from different InBody machine models into a single schema

- **Smart caching**: Avoids reprocessing the same images using SHA-256 file hashes

- **SQLite storage**: Stores all extracted data in a local SQLite database

- **Multi-language support**: Handles InBody scans in different languages (English, Polish, etc.)

- **Structured output**: Uses JSON schema validation for consistent data extraction

- **Batch processing**: Process entire folders of images at once

- **Export functionality**: Export results to JSON for analysis



### 🆕 NEW: PDF Report Generator

- **📊 Beautiful Charts**: Generate colorful progress reports with multiple chart types

- **📅 Time Range Filtering**: Create reports for any date range (entire history by default)

- **🎨 Professional Design**: High-quality PDF reports with modern styling

- **💡 Smart Insights**: Automatic analysis of your fitness progress and achievements

- **🔧 Multiple Interfaces**: Both command-line tool and Python API

- **📈 Comprehensive Analysis**: Weight, body composition, health metrics, and segmental analysis



## Installation



1. **Clone the repository**:

   ```bash

   git clone 

   cd inbody-to-sqlite

   ```



2. **Install dependencies using UV**:

   ```bash

   uv sync

   ```



3. **Set up environment variables**:

   ```bash

   cp .env.example .env

   # Edit .env and add your OpenAI API key

   ```



## Configuration



You need an OpenAI API key to use GPT-4 Vision. Get one from [OpenAI Platform](https://platform.openai.com/api-keys).



Create a `.env` file:

```bash

OPENAI_API_KEY=your-api-key-here

```



## Usage



### Core Data Processing



Process a folder of InBody images:

```bash

python -m inbody_to_sqlite /path/to/images

```



### 🆕 PDF Report Generation



Generate beautiful progress reports from your processed data:



```bash

# Generate a complete report

uv run python -m inbody_reports



# Custom report with title and filename

uv run python -m inbody_reports --output "my_fitness_journey.pdf" --title "My Progress Report"



# Report for specific date range

uv run python -m inbody_reports --start-date 2024-01-01 --end-date 2024-12-31



# Check available data

uv run python -m inbody_reports --list-data-range

```



Options:

- `--force`: Reprocess images even if already cached

- `--export`: Export results to JSON after processing

- `--export-only`: Only export existing results, don't process new images



Examples:

```bash

# Process all images in a folder

python -m inbody_to_sqlite ./my_inbody_scans



# Force reprocess all images and export results

python -m inbody_to_sqlite ./my_inbody_scans --force --export



# Export existing results only

python -m inbody_to_sqlite ./my_inbody_scans --export-only

```



### Python API



#### Data Processing

```python

from inbody_to_sqlite.main import InBodyProcessor



# Initialize processor

processor = InBodyProcessor(api_key="your-api-key")



# Process a folder

stats = processor.process_folder("/path/to/images")



# Export results

output_file = processor.export_results()

```



#### 🆕 Report Generation

```python

from inbody_reports import InBodyReportGenerator

from datetime import datetime, timedelta



# Initialize report generator

generator = InBodyReportGenerator('inbody_results.db')



# Generate complete report

report_path = generator.generate_report(

    output_path="progress_report.pdf",

    title="My Fitness Journey"

)



# Generate report for specific time range

six_months_ago = datetime.now() - timedelta(days=180)

recent_report = generator.generate_report(

    start_date=six_months_ago,

    title="Recent Progress"

)

```



## Supported Data Fields



The tool extracts and unifies the following data from InBody scans:



### Basic Information

- **scan_date**: Date and time of the scan

- **height**: Height in cm

- **weight**: Weight in kg

- **age**: Age in years

- **gender**: Gender (Male/Female)



### Body Composition

- **muscle_mass**: Total muscle mass in kg

- **body_fat_mass**: Total body fat mass in kg

- **body_fat_percentage**: Body fat percentage

- **total_body_water**: Total body water in liters

- **fat_free_mass**: Fat-free mass in kg



### Health Metrics

- **bmi**: Body Mass Index

- **bmr**: Basal Metabolic Rate in kcal

- **visceral_fat_level**: Visceral fat level

- **pbf**: Percent body fat

- **whr**: Waist-hip ratio



### Scores & Recommendations

- **inbody_score**: InBody score

- **fitness_score**: Fitness score

- **muscle_control**: Muscle control recommendation in kg

- **fat_control**: Fat control recommendation in kg



### Segmental Analysis

- **segmental**: Body part analysis for arms, trunk, and legs

  - Lean mass for each body part

  - Fat mass for each body part



## Database Schema



The tool creates two main tables:



### `processed_images`

- Tracks which images have been processed

- Stores file hash for cache validation

- Records processing success/failure



### `inbody_results`

- Stores extracted InBody data

- Links to processed images via file hash

- Contains all the structured data fields



## Supported Image Formats



- JPEG (.jpg, .jpeg)

- PNG (.png)

- BMP (.bmp)

- TIFF (.tiff)

- WebP (.webp)



## InBody Models Supported



The tool has been tested with multiple InBody models and handles:

- Different languages (English, Polish, etc.)

- Varying data layouts



## PDF Report Features



The new report generator creates comprehensive PDF reports with:



### 📊 Chart Types

- **Weight Progression**: Timeline with trend analysis

- **Body Composition**: Muscle vs fat mass over time

- **Health Metrics**: BMI, BMR, visceral fat levels

- **Segmental Analysis**: Body part measurements

- **Progress Dashboard**: Key metrics overview

- **Before/After Comparison**: First vs latest scans



### 🎨 Report Sections

1. **Summary Dashboard** - Key progress indicators

2. **Weight Progression** - Detailed weight analysis with trends

3. **Body Composition** - Muscle and fat mass changes

4. **Health Metrics** - BMI, BMR, visceral fat tracking

5. **Progress Comparison** - First vs latest scan analysis

6. **Segmental Analysis** - Body part distribution

7. **Insights & Recommendations** - AI-generated progress insights



### 📈 Sample Results

Your reports will show insights like:

- 📉 Weight decreased by 17.6kg over 773 days

- 💪 Body fat decreased by 14.0% - Great progress!

- ✅ Current BMI is in the healthy range

- 👍 Good tracking consistency - 19 scans over 773 days

- Different measurement units (automatically normalized)

- Missing fields in different models



## Error Handling



- **Caching**: Images are cached by hash to avoid reprocessing

- **Validation**: Pydantic models ensure data consistency

- **Error logging**: Failed extractions are logged with error messages

- **Graceful degradation**: Missing fields are handled as optional



## Examples



### Processing Results



```

🔍 Scanning folder: ./images

📁 Found 5 image files



[1/5] 🔍 Processing inbody_scan_1.jpg...

✅ Successfully processed inbody_scan_1.jpg

   Date: 2023-06-03 06:53:00

   Weight: 93.4kg, Height: 181.0cm

   BMI: 28.5



[2/5] ⏭️  Skipping inbody_scan_2.jpg (already processed)



==================================================

📊 Processing Summary:

   Total files: 5

   Successfully processed: 3

   Failed: 1

   Skipped (cached): 1



📈 Database Statistics:

   Total processed images: 10

   Successful extractions: 9

   Failed extractions: 1

   Total InBody results: 9

```



## Contributing



1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests if applicable

5. Submit a pull request



## License



This project is licensed under the MIT License.



## Troubleshooting



### Common Issues



1. **"OPENAI_API_KEY environment variable is required"**

   - Make sure you've created a `.env` file with your OpenAI API key



2. **"No image files found"**

   - Check that your folder contains supported image formats

   - Verify the folder path is correct



3. **Processing failures**

   - Check your OpenAI API key is valid and has sufficient credits

   - Ensure images are clear and readable InBody scans



### Getting Help



- Check the processing logs for specific error messages

- Verify your OpenAI API key has access to GPT-4 Vision

- Ensure images are high quality and clearly show InBody data