https://github.com/prodeveloper0/pydarknet

Darknet written by AlexeyAB Python Wrapper
https://github.com/prodeveloper0/pydarknet

darknet python yolo yolov4

Last synced: about 2 months ago
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Darknet written by AlexeyAB Python Wrapper

• Host: GitHub
• URL: https://github.com/prodeveloper0/pydarknet
• Owner: prodeveloper0
• License: mit
• Created: 2021-11-20T06:12:19.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2021-12-06T02:17:24.000Z (about 3 years ago)
• Last Synced: 2024-05-18T18:43:43.745Z (7 months ago)
• Topics: darknet, python, yolo, yolov4
• Language: Python
• Homepage:
• Size: 5.86 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pyDarknet

This is Python wrapper library for Darknet which is a deep neural network framework by AlexeyAB running YOLO object detector.

This is implemented by `ctypes` and you can use complied shared library file (`libdark.so` / `darknet.dll`) on Darknet directely in you Python project.

# Requirements

* opencv-python

```bash

pip install -r requirements.txt

```

# Usages

## How to import `pyDarknet` on your project.

1. Copy compiled shared library file to on your project.

2. Rename the shared library file `libdarknet-gpu.so` or `libdarknet.so`.

     * pyDarknet selects `libdarknet-gpu.so` supporting GPU acceleration first. If there's no library surpoting GPU acceleration, pyDarknet select `libdarknet.so` alternatively.  

    * pyDarknet can detect shared library extensions by operating systems.  

    e.g.) `libdarknet.so` is for Linux, `libdarknet.dll` is for Windows.

3. Just import `pydarknet` package on your project.

    ```python

    import pydarknet

    ```

## How to load `YOLOv4` by `pyDarknet`.

It's simple. Just create a `Detector`.

```python

detector = pydarknet.Detector('yolov4.cfg', 'yolov4.weights', 'coco.names')

```

`cfg_filename` and `weights_filename` assigned as each `yolov4.cfg` and `yolov4.weights` must be filled. On the other hand, `names_filename` assigned as `coco.names` can be keep empty by passing `None`.

## How to detect objects by `YOLOv4` created by `pyDarknet`.

1. Prepare or load image.  

    * The input must have **HWC** shape.  

    * The input must be 3 channel BGR (**NOT RGB**) image commonly used in OpenCV.  

    If you are using OpenCV mainly, you can use ```cv2.imread``` / ```cv2.imdecode```.  

    ```python

    import cv2

    img = cv2.imread('image.png', cv2.IMREAD_COLOR)

    ```  

2. Call `detect` method in `Detector`.  

    ```python

    bboxes = detector.detect(img)

    ```

    `detect` method has `obj_thres`, `hier_thresh`, `nms_thresh` and `obj_filter`.  

    Three threshold paramaters having a posfix as `_thresh` are object detection paramaters. You can refence original paper or repository.  

    `obj_filter` is an option filtering bounding boxes you want by object names. It is must be list of object names or indices.  

    ```python

    detector.detect(..., obj_filter=['car', 'bus'])

    ```

    or

    ```python

    detector.detect(..., obj_filter['1', '2'])

    ```

## Results of Detector

The detector returns list of tuples like `(name, prob, gebox)`.  

```python

for name, prob, geobox in bboxes:

    x, y, w, h = geobox

```

_What is `geobox`? To distinguish a **bounding box** including both **name** and **geometry informations** on image and a box including only **geometry informations** on image. I called the latter `geobox`._

`name` is detected object name defined in `.names` file. if you not set `.names` file in the detector, `name` is object index (_NOT A **NUMBER**, BUT A **STRING** PRESENTATION NUMBER_) defined in the model.