supervision/examples/time_in_zone

time in zone

👋 hello

Practical demonstration on leveraging computer vision for analyzing wait times and monitoring the duration that objects or individuals spend in predefined areas of video frames. This example project, perfect for retail analytics or traffic management applications.

https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-d38b86334c39

💻 install

• clone repository and navigate to example directory

  git clone https://github.com/roboflow/supervision.git
  cd supervision/examples/time_in_zone
  

• setup python environment and activate it [optional]

  python3 -m venv venv
  source venv/bin/activate
  

• install required dependencies

  pip install -r requirements.txt
  

🛠 scripts

download_from_youtube

This script allows you to download a video from YouTube.

• --url: The full URL of the YouTube video you wish to download.
• --output_path (optional): Specifies the directory where the video will be saved.
• --file_name (optional): Sets the name of the saved video file.

python scripts/download_from_youtube.py \
--url "https://www.youtube.com/watch?v=-8zyEwAa50Q" \
--output_path "data/checkout" \
--file_name "video.mp4"

python scripts/download_from_youtube.py \
--url "https://www.youtube.com/watch?v=MNn9qKG2UFI" \
--output_path "data/traffic" \
--file_name "video.mp4"

stream_from_file

This script allows you to stream video files from a directory. It's an awesome way to mock a live video stream for local testing. Video will be streamed in a loop under rtsp://localhost:8554/live0.stream URL. This script requires docker to be installed.

• --video_directory: Directory containing video files to stream.
• --number_of_streams: Number of video files to stream.

python scripts/stream_from_file.py \
--video_directory "data/checkout" \
--number_of_streams 1

python scripts/stream_from_file.py \
--video_directory "data/traffic" \
--number_of_streams 1

draw_zones

If you want to test zone time in zone analysis on your own video, you can use this script to design custom zones and save results as a JSON file. The script will open a window where you can draw polygons on the source image or video file. The polygons will be saved as a JSON file.

• --source_path: Path to the source image or video file for drawing polygons.

• --zone_configuration_path: Path where the polygon annotations will be saved as a JSON file.

• enter - finish drawing the current polygon.

• escape - cancel drawing the current polygon.

• q - quit the drawing window.

• s - save zone configuration to a JSON file.

python scripts/draw_zones.py \
--source_path "data/checkout/video.mp4" \
--zone_configuration_path "data/checkout/config.json"

python scripts/draw_zones.py \
--source_path "data/traffic/video.mp4" \
--zone_configuration_path "data/traffic/config.json"

https://github.com/roboflow/supervision/assets/26109316/9d514c9e-2a61-418b-ae49-6ac1ad6ae5ac

🎬 video & stream processing

inference_file_example

Script to run object detection on a video file using the Roboflow Inference model.

• --zone_configuration_path: Path to the zone configuration JSON file.
• --source_video_path: Path to the source video file.
• --model_id: Roboflow model ID.
• --classes: List of class IDs to track. If empty, all classes are tracked.
• --confidence_threshold: Confidence level for detections (0 to 1). Default is 0.3.
• --iou_threshold: IOU threshold for non-max suppression. Default is 0.7.

python inference_file_example.py \
--zone_configuration_path "data/checkout/config.json" \
--source_video_path "data/checkout/video.mp4" \
--model_id "yolov8x-640" \
--classes 0 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

https://github.com/roboflow/supervision/assets/26109316/d051cc8a-dd15-41d4-aa36-d38b86334c39

python inference_file_example.py \
--zone_configuration_path "data/traffic/config.json" \
--source_video_path "data/traffic/video.mp4" \
--model_id "yolov8x-640" \
--classes 2 5 6 7 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

https://github.com/roboflow/supervision/assets/26109316/5ec896d7-4b39-4426-8979-11e71666878b

inference_stream_example

Script to run object detection on a video stream using the Roboflow Inference model.

• --zone_configuration_path: Path to the zone configuration JSON file.
• --rtsp_url: Complete RTSP URL for the video stream.
• --model_id: Roboflow model ID.
• --classes: List of class IDs to track. If empty, all classes are tracked.
• --confidence_threshold: Confidence level for detections (0 to 1). Default is 0.3.
• --iou_threshold: IOU threshold for non-max suppression. Default is 0.7.

python inference_stream_example.py \
--zone_configuration_path "data/checkout/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--model_id "yolov8x-640" \
--classes 0 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

python inference_stream_example.py \
--zone_configuration_path "data/traffic/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--model_id "yolov8x-640" \
--classes 2 5 6 7 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

👉 show ultralytics examples

ultralytics_file_example

Script to run object detection on a video file using the Ultralytics YOLOv8 model.

• --zone_configuration_path: Path to the zone configuration JSON file.
• --source_video_path: Path to the source video file.
• --weights: Path to the model weights file. Default is 'yolov8s.pt'.
• --device: Computation device ('cpu', 'mps' or 'cuda'). Default is 'cpu'.
• --classes: List of class IDs to track. If empty, all classes are tracked.
• --confidence_threshold: Confidence level for detections (0 to 1). Default is 0.3.
• --iou_threshold: IOU threshold for non-max suppression. Default is 0.7.

python ultralytics_file_example.py \
--zone_configuration_path "data/checkout/config.json" \
--source_video_path "data/checkout/video.mp4" \
--weights "yolov8x.pt" \
--device "cpu" \
--classes 0 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

python ultralytics_file_example.py \
--zone_configuration_path "data/traffic/config.json" \
--source_video_path "data/traffic/video.mp4" \
--weights "yolov8x.pt" \
--device "cpu" \
--classes 2 5 6 7 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

ultralytics_stream_example

Script to run object detection on a video stream using the Ultralytics YOLOv8 model.

• --zone_configuration_path: Path to the zone configuration JSON file.
• --rtsp_url: Complete RTSP URL for the video stream.
• --weights: Path to the model weights file. Default is 'yolov8s.pt'.
• --device: Computation device ('cpu', 'mps' or 'cuda'). Default is 'cpu'.
• --classes: List of class IDs to track. If empty, all classes are tracked.
• --confidence_threshold: Confidence level for detections (0 to 1). Default is 0.3.
• --iou_threshold: IOU threshold for non-max suppression. Default is 0.7.

python ultralytics_stream_example.py \
--zone_configuration_path "data/checkout/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--weights "yolov8x.pt" \
--device "cpu" \
--classes 0 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

python ultralytics_stream_example.py \
--zone_configuration_path "data/traffic/config.json" \
--rtsp_url "rtsp://localhost:8554/live0.stream" \
--weights "yolov8x.pt" \
--device "cpu" \
--classes 2 5 6 7 \
--confidence_threshold 0.3 \
--iou_threshold 0.7

© license

This demo integrates two main components, each with its own licensing:

• ultralytics: The object detection model used in this demo, YOLOv8, is distributed under the AGPL-3.0 license. You can find more details about this license here.

• supervision: The analytics code that powers the zone-based analysis in this demo is based on the Supervision library, which is licensed under the MIT license. This makes the Supervision part of the code fully open source and freely usable in your projects.