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# Running the Live Camera Detection Demo Up next we have a realtime object detection camera demo available for C++ and Python: - [`detectnet-camera.cpp`](../examples/detectnet-camera/detectnet-camera.cpp) (C++) - [`detectnet-camera.py`](../python/examples/detectnet-camera.py) (Python) Similar to the previous [`detectnet-console`](detectnet-console.md) example, these camera applications use detection networks, except that they process a live video feed from a camera. `detectnet-camera` accepts 4 optional command-line parameters: - `--network` flag setting the classification model (default is PedNet) - See [Pre-trained Detection Models Available](detectnet-console.md#pre-trained-detection-models-available) for the networks available to use. - `--camera` flag setting the camera device to use - MIPI CSI cameras are used by specifying the sensor index (`0` or `1`, ect.) - V4L2 USB cameras are used by specifying their `/dev/video` node (`/dev/video0`, `/dev/video1`, ect.) - The default is to use MIPI CSI sensor 0 (`--camera=0`) - `--width` and `--height` flags setting the camera resolution (default is `1280x720`) - The resolution should be set to a format that the camera supports. - Query the available formats with the following commands: ``` bash $ sudo apt-get install v4l-utils $ v4l2-ctl --list-formats-ext ``` You can combine the usage of these flags as needed, and there are additional command line parameters available for loading custom models. Launch the application with the `--help` flag to recieve more info, or see the [`Examples`](../README.md#code-examples) readme. Below are some typical scenarios for launching the program: #### C++ ``` bash $ ./detectnet-camera # using PedNet, default MIPI CSI camera (1280x720) $ ./detectnet-camera --network=facenet # using FaceNet, default MIPI CSI camera (1280x720) $ ./detectnet-camera --camera=/dev/video0 # using PedNet, V4L2 camera /dev/video0 (1280x720) $ ./detectnet-camera --width=640 --height=480 # using PedNet, default MIPI CSI camera (640x480) ``` #### Python ``` bash $ ./detectnet-camera.py # using PedNet, default MIPI CSI camera (1280x720) $ ./detectnet-camera.py --network=facenet # using FaceNet, default MIPI CSI camera (1280x720) $ ./detectnet-camera.py --camera=/dev/video0 # using PedNet, V4L2 camera /dev/video0 (1280x720) $ ./detectnet-camera.py --width=640 --height=480 # using PedNet, default MIPI CSI camera (640x480) ``` > **note**: for example cameras to use, see these sections of the Jetson Wiki:
>              - Nano:  [`https://eLinux.org/Jetson_Nano#Cameras`](https://elinux.org/Jetson_Nano#Cameras)
>              - Xavier: [`https://eLinux.org/Jetson_AGX_Xavier#Ecosystem_Products_.26_Cameras`](https://elinux.org/Jetson_AGX_Xavier#Ecosystem_Products_.26_Cameras)
>              - TX1/TX2: developer kits include an onboard MIPI CSI sensor module (0V5693)
#### Visualization Displayed in the OpenGL window are the live camera stream overlayed with the bounding boxes of the detected objects. Note that the SSD-based models currently have the highest performance. Here is one using the `coco-dog` model: ``` bash # C++ $ ./detectnet-camera --network=coco-dog # Python $ ./detectnet-camera.py --network=coco-dog ```

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