display-detections

Display detections on higher resolution frames

Object detection models usually require smaller frame for inferencing (eg. 512x288). Instead of displaying bounding boxes on such small frames, you could also stream higher resolution frames and display bounding boxes on these frames. There are several approaches to achieving that, and this example will go over some of them. It uses YOLOv6 Nano model for object detection.

Demo

1. Passthrough

Simplest approach is to just stream the small inferencing frame. This example uses passthrough frame of ParsingNeuralNetwork's output so bounding boxes are in sync with the frame.

2. Crop high resolution frame

A simple solution to low resolution frame is to use higher resolution frames and crop them to the correct size of the NN input. This example crops 640x480 frame to 512x288.

3. Stretch or crop the frame before inferencing but keep the high resolution frame

Another solution is to stretch the frame to the correct aspect ratio and size of the NN. For more information, see Resolution Techniques for NNs. This example stretches 1920x1440 frame to 512x288 before inferencing.

Usage

Running this example requires a Luxonis device connected to your computer. Refer to the documentation to setup your device if you haven't done it already.

You can run the example fully on device (STANDALONE mode) or using your computer as host (PERIPHERAL mode).

Here is a list of all available parameters:

-d DEVICE, --device DEVICE
                    Optional name, DeviceID or IP of the camera to connect to. (default: None)
-fps FPS_LIMIT, --fps_limit FPS_LIMIT
                    FPS limit for the model runtime. (default: 30)

Peripheral Mode

Installation

You need to first prepare a Python 3.10 environment with the following packages installed:

• DepthAI,
• DepthAI Nodes.

You can simply install them by running:

pip install -r requirements.txt

Running in peripheral mode requires a host computer and there will be communication between device and host which could affect the overall speed of the app. Below are some examples of how to run the example.

Examples

python3 passthrough.py

This will run the Display Detections example with the default device and camera input and use passthrough frame.

python3 crop_highres.py

This will run the Display Detections example with the default device and camera input and crop 640x480 frame to 512x288.

python3 stretch_before_inferencing.py -fps 10

This will run the Display Detections example with the default device at 10 FPS and stretch 1920x1440 frame to 512x288.

Standalone Mode (RVC4 only)

Running the example in the standalone mode, app runs entirely on the device. To run the example in this mode, first install the oakctl tool using the installation instructions here.

The app can then be run with:

oakctl connect <DEVICE_IP>
oakctl app run .

This will run the example with default argument values. If you want to change these values you need to edit the oakapp.toml file (refer here for more information about this configuration file).