ourcustompkg 0.14
A robust YOLOv7-based package designed for efficient toy car detection and comprehensive dataset management.
Requires Python: >=3.6
Keywords: YOLOv7, toy car detection, dataset management, computer vision, deep learning


OurCustomPkg: YOLOv7-based Toy Car Detection
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Welcome to **OurCustomPkg**, a cutting-edge Python package designed for the detection of toy cars using the powerful YOLOv7 model. Additionally, the package integrates hand-tracking capabilities via MediaPipe, allowing for interactive and dynamic detection experiences.

Features
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*   **YOLOv7 Integration:** Utilize the state-of-the-art YOLOv7 model for accurate and efficient toy car detection.
*   **Multi-Source Input:** Supports images, video files, and real-time webcam feeds as input sources.
*   **Hand Tracking:** Employ MediaPipe's hand tracking to interact with detected objects in real-time.
*   **Highly Customizable:** Easily adjust detection parameters and extend functionalities according to your project needs.

Installation
------------

To get started with **OurCustomPkg**, you can install it directly from PyPI:

    pip install ourcustompkg

This command will install the package along with all the necessary dependencies, including PyTorch, OpenCV, and MediaPipe.

Getting Started
---------------

### Basic Usage

The primary script for toy car detection is `detect_car.py`, located in the `ourcustompkg/yolov7/` directory. Here's how to use it:

    python -m ourcustompkg.yolov7.detect_car --source <input_source> --weights <path_to_weights>

#### Example Commands

**Detect cars in an image:**

    python -m ourcustompkg.yolov7.detect_car --source data/images/car.jpg --weights yolov7.pt

**Detect cars from a video file:**

    python -m ourcustompkg.yolov7.detect_car --source data/videos/car_video.mp4 --weights yolov7.pt

**Real-time detection using a webcam:**

    python -m ourcustompkg.yolov7.detect_car --source 0 --weights yolov7.pt

### Hand Tracking Interaction

One of the standout features of **OurCustomPkg** is its integration of hand-tracking functionality using MediaPipe. When running the `detect_car.py` script, you can interact with detected cars using hand gestures tracked by your webcam.

### Command-Line Arguments

*   `--source`: Specifies the input source, which can be an image file, video file, or webcam feed.
*   `--weights`: Path to the YOLOv7 weights file. You can download pretrained weights from the official YOLOv7 repository.
*   `--img-size`: Sets the size of the input image for detection (default: 640).
*   `--conf-thres`: Confidence threshold for filtering weak detections (default: 0.25).
*   `--iou-thres`: IoU threshold for non-maximum suppression (default: 0.45).
*   `--device`: Specifies the device to run the model on (`cpu` or `cuda`).

### Advanced Usage

For advanced users, the `detect_car.py` script offers additional options to customize your detection pipeline:

*   Save detection results to a specific directory.
*   Toggle between different models or weight files.
*   Modify input processing techniques or adjust the output format.

Documentation
-------------

For more detailed information, including how to extend the package or contribute to its development, please visit our [official documentation](https://pypi.org/project/ourcustompkg/).

Contributing
------------

We welcome contributions to **OurCustomPkg**! If you have ideas for new features, enhancements, or bug fixes, please open an issue or submit a pull request on our [GitHub repository](https://github.com/NeuroLeapTeam/gesture_recognition).

License
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This project is licensed under the MIT License. You can view the full license [here](https://github.com/NeuroLeapTeam/gesture_recognition/blob/main/LICENSE).

Acknowledgments
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*   **YOLOv7**: Our package is built upon the innovative YOLOv7 model, which has significantly advanced the field of real-time object detection.
*   **MediaPipe**: MediaPipe's hand tracking technology has enabled us to create a more interactive and user-friendly detection experience.

Contact
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For any questions, feedback, or support, feel free to reach out to the authors:

*   Brandon: [brandon@neuroleapmail.com](mailto:brandon@neuroleapmail.com)
*   Moshiur: [moshiur@neuroleapmail.com](mailto:moshiur@neuroleapmail.com)

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Thank you for using **OurCustomPkg**! We hope it serves your toy car detection needs and inspires further innovation in your projects.