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-0.2.4.tar.gz (12.1 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-0.2.4-py3-none-any.whl (11.1 kB view details)

Uploaded Python 3

File details

Details for the file graphvision_ai-0.2.4.tar.gz.

File metadata

  • Download URL: graphvision_ai-0.2.4.tar.gz
  • Upload date:
  • Size: 12.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for graphvision_ai-0.2.4.tar.gz
Algorithm Hash digest
SHA256 61f107fcb66492e232247f8d09feaadf13a6920ed88e7a4b6ed4c1d333781db9
MD5 ebe9eb4be5f4d908a1fb3f0f3da6a0e5
BLAKE2b-256 bf9ab151ccc423aa4cc87af24284562fd262adf01018cf912dc6a3e88fc3682e

See more details on using hashes here.

File details

Details for the file graphvision_ai-0.2.4-py3-none-any.whl.

File metadata

  • Download URL: graphvision_ai-0.2.4-py3-none-any.whl
  • Upload date:
  • Size: 11.1 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-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 1ff06cbf4bf687585af78de010ad7397432472343044e3a7521d62d2cb27b62c
MD5 0257d9c13c1489f7e855e1966354ccf7
BLAKE2b-256 29ae4df15bbc3afadbae5d33c5cb3a09661f8ba5ff0d7673fcc8af7ee1a7900b

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