Skip to main content

Automatic Graph Classification and Data Extraction

Project description

GraphVision AI 📊👁️

GraphVision AI is a lightweight, powerful computer vision library for automatic graph classification and structured data extraction.

Built with PyTorch and EasyOCR, it is designed to look at an image of a chart, instantly recognize what kind of graph it is, and extract its labels and values into a clean, developer-friendly JSON format.


✨ Key Features

🚀 Zero-Configuration

Models and weights are automatically downloaded from Hugging Face the first time you run it. No manual weight management required.

🧠 Intelligent Routing

Automatically classifies the input image (Pie, Vertical Bar, Horizontal Bar, Line, etc.) and routes it to the correct extraction algorithm.

🖼 Robust Input Handling

Pass a file path (String), an OpenCV image (NumPy array), or a PIL Image directly into the analyzer.

🔍 Smart OCR Masking

Uses contrast filtering and spatial mapping to accurately match text labels with their corresponding graphical data points.


📦 Installation

Install directly from PyPI:

pip install graphvision-ai

🚀 Quick Start

Extracting data from a graph takes less than 5 lines of code:

from graphvision.extractor import GraphExtractor

try:
    # 1. Initialize your engine (this will download weights if needed)
    vision_engine = GraphExtractor()
    
    # Path to your test image
    image_to_test = "hbar2.png" 
    
    # 2. Run the extraction using the new method name
    print(f"\n🚀 Extracting data from {image_to_test}...")
    result_json_string = vision_engine.extract(image_to_test)
    
    # 3. Print the result (it's already a nicely formatted string!)
    print("\n✅ Extraction Successful!")
    print(result_json_string)

except Exception as e:
    print(f"\n❌ Error during testing: {e}")

📄 Example Output

{
    "type": "pie",
    "title": "Favorite Programming Languages",
    "data": {
        "Python": 45.2,
        "JavaScript": 25.1,
        "C++": 15.4,
        "Java": 14.3
    }
}

📈 Supported Graph Types

Currently, GraphVision AI supports high-accuracy extraction for:

  • pie — Pie Charts
  • vbar_categorical — Vertical Bar Charts
  • hbar_categorical — Horizontal Bar Charts

Line and Dot-Line charts coming soon.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

graphvision_ai-0.2.2.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

graphvision_ai-0.2.2-py3-none-any.whl (10.7 kB view details)

Uploaded Python 3

File details

Details for the file graphvision_ai-0.2.2.tar.gz.

File metadata

  • Download URL: graphvision_ai-0.2.2.tar.gz
  • Upload date:
  • Size: 11.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for graphvision_ai-0.2.2.tar.gz
Algorithm Hash digest
SHA256 73a0151424d6e9e413e99118b4e9a2ed0f4fd6fd6c6cdda615aca0a0d1a57bbe
MD5 4ae579107c67bf97d6e985ab22b49426
BLAKE2b-256 b4fa6d0dac573b0f966e5a47a9a66d71adbebbcfbadda7d429b92cd26e390218

See more details on using hashes here.

File details

Details for the file graphvision_ai-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: graphvision_ai-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 10.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for graphvision_ai-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 4a61e7b41dad9bcc97c9fc24a76ae92cddeb5e3fa93a65da82fdd9a86426fd6e
MD5 396c197d21ffaad76b17b9625e5b1d25
BLAKE2b-256 3e48c025aa154c44d04970c6512729b328003351277d812f561b75daa1fce888

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page