PTZ APP

This is an application for sending images of specific objects autonomously using PTZ cameras.

How It Works

The algorithm performs the following steps:

1. Initialization: Sets up object detection model (YOLO or Florence) based on user parameters.

2. Area Scanning: Systematically scans the environment by rotating the PTZ camera in pan steps (default: 15 degrees) through a full 360° rotation at the specified tilt and zoom level.

3. Object Detection: At each camera position, captures an image and runs object detection to identify specified objects (e.g., person, car, dog).

4. Filtering: Filters detections based on confidence threshold (default: 0.1).

5. Object Tracking: When an object of interest is detected with sufficient confidence, the algorithm:

   • Centers the camera on the detected object
   • Adjusts zoom to maximize the object in the frame

6. Image Publishing: Saves and publishes the optimized images of detected objects.

7. Iteration: Repeats the process for the specified number of iterations with configurable delay between scans.

Build the container

sudo docker buildx build --platform=linux/amd64,linux/arm64/v8 -t your_docker_hub_user_name/ptzapp -f Dockerfile --push .

Then pull the container from dockerhub in the node:

sudo docker image pull your_docker_hub_user_name/ptzapp

Run the container on a dell blade

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest -ki -it 5 -un camera_user_name -pw camera_password -ip camera_ip_address -obj person,car

Run the container on a waggle node

sudo docker run -it --rm your_docker_hub_user_name/ptzapp:latest -ki -it 5 -un camera_user_name -pw camera_password -ip camera_ip_address -obj person,car

Example with Florence model

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest --model Florence-base --iterations 5 --username username --password 'password' --cameraip 130.202.23.92 --objects 'person,car'

Using Different Object Detection Models

YOLO (Default)

By default, the application uses the YOLO model (yolo11n) for object detection. Specify objects by name:

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest --objects 'person,car,dog'

Florence Models

When using Florence models, you have more powerful detection capabilities:

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest --model Florence-base --objects 'person,car'

Detecting All Objects with Florence

To detect all objects using Florence models, use the asterisk:

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest --model Florence-base --objects '*'

Saving multiple images per discrete sweep location

To save multiple images when there are multiple detections in a single frame, use the --multiple flag:

sudo docker run --gpus all -it --rm your_docker_hub_user_name/ptzapp:latest --model Florence-base --objects '*' --multiple

Note: When using '*' with Florence models, the application runs in the <OD> task mode, which enables general object detection without filtering for specific classes. This can be useful for inventorying all objects in a scene but may produce more diverse results than when targeting specific objects.

Command Line Arguments

Argument	Short	Description	Default
--model	-m	Model to use (e.g., 'yolo11n', 'Florence-base')	yolo11n
--iterations	-it	Number of iterations (PTZ rounds) to run	5
--username	-un	PTZ camera username	""
--password	-pw	PTZ camera password	""
--cameraip	-ip	PTZ camera IP address	""
--objects	-obj	Objects to detect (comma-separated or '*' for everything)	"person"
--keepimages	-ki	Keep collected images in persistent folder	False
--panstep	-ps	Step of pan in degrees	15
--tilt	-tv	Tilt value in degrees	0
--zoom	-zm	Zoom value	1
--confidence	-conf	Confidence threshold (0-1)	0.1
--iterdelay	-id	Minimum delay in seconds between iterations	60.0
--debug		Enable debug level logging	False
--multiple		Save multiple images for multiple detections in a single frame	False