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examples/README.md
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## Supervision Examples
## examples
This repository is packed with real-world use-cases, provided through Python scripts or
interactive notebooks. Browse through to understand how the Supervision library
interfaces with diverse applications.
| **Title** | **Contributor** |
|:--------------------------------------:|:----------------------------------------:|
| [Tracking](./tracking) | [@SkalskiP](https://github.com/SkalskiP) |
| [Traffic Analysis](./traffic_analysis) | [@SkalskiP](https://github.com/SkalskiP) |
| **Title** | **Contributor** |
|:----------------------------------------------:|:----------------------------------------:|
| [Tracking](./tracking) | [@SkalskiP](https://github.com/SkalskiP) |
| [Count People in Zone](./count_people_in_zone) | [@SkalskiP](https://github.com/SkalskiP) |
| [Traffic Analysis](./traffic_analysis) | [@SkalskiP](https://github.com/SkalskiP) |
## Guidelines for Contributing
## contributing
We welcome contributions from the community in the form of examples, applications, and
guides. To contribute, please follow these steps:
examples/count_people_in_zone/README.md
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./setup.sh
```
## ⚙️ run
## 🛠️ script arguments
- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
configurations. This file defines the polygonal areas in the video where objects will
be counted.
- `--source_weights_path` (optional): The path to the YOLO model's weights file.
Defaults to `"yolov8x.pt"` if not specified.
- `--source_video_path`: The path to the source video file that will be analyzed.
- `--target_video_path` (optional): The path to save the output video with annotations.
If not provided, the processed video will be displayed in real-time.
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
to filter detections. Default is `0.3`.
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
for the model. Default is `0.7`.
## ⚙️ run example
```bash
python script.py \
examples/tracking/README.md
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## 💻 install
```bash
pip install -r requirements.txt
```
- clone repository and navigate to example directory
```bash
git clone https://github.com/roboflow/supervision.git
cd supervision/examples/tracking
```
- setup python environment and activate it [optional]
```bash
python3 -m venv venv
source venv/bin/activate
```
- install required dependencies
```bash
pip install -r requirements.txt
```
## 🛠️ script arguments
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
file, which is essential for the object detection process. This file contains the data
that the model uses to identify objects in the video.
- `--source_video_path`: Required. The path to the source video file to be processed.
This is the video on which object detection and annotation will be performed.
- `--target_video_path`: Required. The path where the processed video, with annotations
added, will be saved. This is your output video file.
- `--confidence_threshold` (optional): Sets the confidence level at which the model
identifies objects in the video. Default is `0.3`. A higher threshold makes the model
more selective, while a lower threshold makes it more inclusive in identifying objects.
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
for the model, defaulting to `0.7`. This parameter helps in differentiating between
distinct objects, especially in crowded scenes.
## ⚙️ run
@@ -18,17 +50,6 @@ python script.py \
--target_video_path tracking_result.mp4
```
## ⚙️ parameters
| parameter | required | description |
|:-------------------------|:--------:|:----------------------------------------------------------------------------------|
| `--source_weights_path` | ✓ | Path to the source weights file for YOLOv8. |
| `--source_video_path` | ✓ | Path to the source video file to be processed. |
| `--target_video_path` | ✓ | Path to the target video file (output). |
| `--confidence_threshold` | ✗ | Confidence threshold for YOLO model detection. Default is 0.3. |
| `--iou_threshold` | ✗ | IOU (Intersection over Union) threshold for YOLO model detection. Default is 0.7. |
## © license
This demo integrates two main components, each with its own licensing:
examples/traffic_analysis/README.md
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```bash
./setup.sh
```
## 🛠️ script arguments
- `--source_weights_path`: Required. Specifies the path to the weights file for the
YOLO model. This file contains the trained model data necessary for object detection.
- `--source_video_path`: Required. The path to the source video file that will be
analyzed. This is the input video on which traffic flow analysis will be performed.
- `--target_video_path` (optional): The path to save the output video with annotations.
If not specified, the processed video will be displayed in real-time without being
saved.
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
to filter detections. Default is `0.3`. This determines how confident the model should
be to recognize an object in the video.
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
for the model. Default is 0.7. This value is used to manage object detection accuracy,
particularly in distinguishing between different objects.
## ⚙️ run