ruurd-photos-ml 0.2.2

Some image related machine learning methods, to be used by Ruurd Photos.

Ruurd Photos ML

A Python package providing a suite of machine learning tools for image analysis, designed to be the backbone of the Ruurd Photos project, a self-hosted Google Photos alternative. This package is intended to be called from Rust using PyO3.

✨ Features

This library offers a selection of pre-trained models for various image analysis tasks:

Image Captioning

Generate descriptive captions for images and ask questions about their content.

• InstructBLIP: A powerful model for both generating detailed descriptions and answering questions about an image.
• Salesforce BLIP: A robust model for generating high-quality image captions.

😀 Facial Recognition

Detect and analyze faces within images.

• InsightFace: A comprehensive toolkit for face analysis that can:
  • Detect multiple faces in an image.
  • Estimate age and gender.
  • Identify key facial landmarks (eyes, nose, mouth).
  • Generate facial embeddings for clustering and recognition.

🖼️ Object Detection

Identify and locate various objects within an image.

• ResNet: Utilizes a ResNet-based model to detect a wide range of common objects, returning their labels and bounding boxes.

🔤 Optical Character Recognition (OCR)

Detect and extract text from images.

• ResNet & Tesseract: A two-stage process that first uses a ResNet model to determine if an image contains legible text, and then employs Tesseract to extract the text and its bounding boxes.

🚀 Installation

This package will be available on PyPI. You can install it using pip:

pip install ruurd-photos-ml

💻 Usage

The library is designed to be simple to use. Here are some examples for each of the main functionalities.

First, you'll need to load an image using Pillow:

from PIL import Image

# Load your image
image = Image.open("path/to/your/image.jpg")

Image Captioning

from ruurd_photos_ml import get_captioner, CaptionerProvider

# Initialize the captioner
captioner = get_captioner(CaptionerProvider.BLIP_INSTRUCT)

# Generate a simple caption
caption = captioner.caption(image)
print(f"Caption: {caption}")

# Ask a question about the image
question = "What color is the main object?"
answer = captioner.caption(image, instruction=question)
print(f"Answer: {answer}")

Facial Recognition

from ruurd_photos_ml import get_facial_recognition, FacialRecognitionProvider

# Initialize the facial recognition model
face_detector = get_facial_recognition(FacialRecognitionProvider.INSIGHT)

# Get faces from the image
faces = face_detector.get_faces(image)

for face in faces:
    print(f"Found a face at position {face.position} with confidence {face.confidence}")
    print(f"  - Age: {face.age}")
    print(f"  - Gender: {face.sex}")
    print(f"  - Embedding: {face.embedding[:5]}...")  # Showing first 5 values

Object Detection

from ruurd_photos_ml import get_object_detection, ObjectDetectionProvider

# Initialize the object detector
object_detector = get_object_detection(ObjectDetectionProvider.RESNET)

# Detect objects in the image
objects = object_detector.detect_objects(image)

for obj in objects:
    print(f"Detected '{obj.label}' with confidence {obj.confidence}")

Optical Character Recognition (OCR)

from ruurd_photos_ml import get_ocr, OCRProvider

# Initialize the OCR model
ocr = get_ocr(OCRProvider.RESNET_TESSERACT)

# Check for legible text
if ocr.has_legible_text(image):
    # Extract text (specify languages for better accuracy)
    text = ocr.get_text(image, languages=("eng", "nld"))
    print(f"Extracted Text: {text}")

    # Get text with bounding boxes
    boxes = ocr.get_boxes(image, languages=("eng", "nld"))
    for box in boxes:
        print(f"Found text: '{box.text}' at position {box.position}")

🛠️ Development

To contribute to this project, you can set up a local development environment.

1. Clone the repository:

   git clone https://github.com/RuurdBijlsma/ruurd-photos-ml.git
   cd ruurd-photos-ml
   

2. Install dependencies using uv:

   uv sync --all-extras --dev
   

3Run tests:

uv run pytest

3Quality checks:

pre-commit run -a

🔗 Project Links

• **Homepage **: https://github.com/RuurdBijlsma/ruurd-photos-ml
• **Repository **: https://github.com/RuurdBijlsma/ruurd-photos-ml
• **Documentation **: https://ruurdbijlsma.github.io/ruurd-photos-ml

📜 License

This project is licensed under the MIT License.