pax-detector 0.1.7

A vision pipeline for object and car make classification with CLI

Pax Case: Object and Car Make Classification

This project provides a vision pipeline for object and car make classification. It includes a command-line interface (CLI) for processing images.

Features

• Two-Stage Pipeline:
  • Object Detection: Uses a YOLOv8 model to detect objects (person, bicycle, car, truck).
  • Car Make Classification: Uses a Google SigLIP model fine-tuned on the Roboflow Car Make Model Recognition dataset to classify the make of detected cars.
• CLI Tool (detector):
  • Process single images or directories of images from the command line.
  • Supports configuration via a YAML file.
  • Adjustable confidence threshold for object detection.
• Scalability and Extensibility:
  • The architecture is designed to be scalable and extensible. See SCALING_STRATEGY.md for a detailed plan on scaling to 10M images/day and adding new classification types.

Package usage

1. Install PyPi package

pip install pax-detector

Local Setup and Installation

1. Clone the repository:

   git clone <repository-url>
   cd pax-case
   

2. Install dependencies: It is recommended to use a virtual environment. This project uses uv https://docs.astral.sh/uv/getting-started/installation/ for package management.

   uv venv 
   source .venv/bin/activate
   
   uv pip install -e . # To build package locally
   uv sync # If want to install local dependencies 
   

Usage

Command-Line Interface (CLI)

The CLI tool (detector) is used for processing individual images.

Basic Usage:

Using a Configuration File:

You can also run the CLI using a config.yml file to specify parameters.

Example config.yml:

image_path: '/Users/fnayres/Downloads/captura-de-tela-2022-12-08-as-13.36.55.webp'
images_dir: null
confidence: 0.5

pipeline:
  - name: object_detection
    class: classification.objects.object_classifier.ObjectClassfier
    params:
      model_version: yolov8l.pt

  - name: car_make_classification
    class: classification.makes.siglip_classifier.MakeClassifier
    depends_on: object_detection
    condition: "'car' in detection['class_name'] or 'truck' in detection['class_name']" # accept None condition
    

Run with config:

detector --config config.yml
# or
uv run detector --config config.yml

To process a single image:

detector --image_path /path/to/your/image.jpg
# or 
uv run detector --image_path /path/to/your/image.jpg

To process all images in a directory:

detector --images_dir /path/to/your/images/
# or
uv run detector --images_dir /path/to/your/images/

Arguments:

• --image_path: Path to a single input image.
• --images_dir: Path to a directory of images.
• --config: Path to a YAML configuration file.
• --confidence: Confidence threshold for object detection (default: 0.5).

This will process the image and print the JSON output to the console.

Extensibility

For details on how to add more classification types (e.g., helmet color, t-shirt color), please refer to the SCALING_STRATEGY.md document, which outlines the proposed architecture for extending the pipeline.