diff --git a/examples/README.md b/examples/README.md index d004688c..02b6c4ed 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,16 +1,17 @@ -## 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: diff --git a/examples/count_people_in_zone/README.md b/examples/count_people_in_zone/README.md index d0fb14aa..2703e759 100644 --- a/examples/count_people_in_zone/README.md +++ b/examples/count_people_in_zone/README.md @@ -33,7 +33,22 @@ video or display it live on the screen. ./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 \ diff --git a/examples/tracking/README.md b/examples/tracking/README.md index b15a2c98..c1e2c671 100644 --- a/examples/tracking/README.md +++ b/examples/tracking/README.md @@ -5,9 +5,41 @@ detection and Supervision for tracking and annotation. ## 💻 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: diff --git a/examples/traffic_analysis/README.md b/examples/traffic_analysis/README.md index 6ce5bf72..9746a561 100644 --- a/examples/traffic_analysis/README.md +++ b/examples/traffic_analysis/README.md @@ -33,6 +33,22 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c- ```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