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# Running the Live Camera Detection Demo The [`detectnet.cpp`](../examples/detectnet/detectnet.cpp) / [`detectnet.py`](../python/examples/detectnet.py) sample that we used previously can also be used for realtime camera streaming. The types of supported cameras include: - MIPI CSI cameras (`csi://0`) - V4L2 cameras (`/dev/video0`) - RTP/RTSP streams (`rtsp://username:password@ip:port`) - WebRTC streams (`webrtc://@:port/stream_name`) For more information about video streams and protocols, please see the [Camera Streaming and Multimedia](aux-streaming.md) page. Run the program with `--help` to see a full list of options - some of them specific to detectNet include: - `--network` flag which changes the [detection model](detectnet-console-2.md#pre-trained-detection-models-available) being used (the default is SSD-Mobilenet-v2). - `--overlay` flag which can be comma-separated combinations of `box`, `labels`, `conf`, `track` and `none` - The default is `--overlay=box,labels,conf` which displays boxes, labels, and confidence values - `--alpha` value which sets the alpha blending value used during overlay (the default is `120`). - `--threshold` value which sets the minimum threshold for detection (the default is `0.5`). Below are some typical scenarios for launching the program on a camera feed: #### C++ ``` bash $ ./detectnet csi://0 # MIPI CSI camera $ ./detectnet /dev/video0 # V4L2 camera $ ./detectnet /dev/video0 output.mp4 # save to video file ``` #### Python ``` bash $ ./detectnet.py csi://0 # MIPI CSI camera $ ./detectnet.py /dev/video0 # V4L2 camera $ ./detectnet.py /dev/video0 output.mp4 # save to video file ``` > **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:

If the desired objects aren't being detected in the video feed or you're getting spurious detections, try decreasing or increasing the detection threshold with the `--threshold` parameter (the default is `0.5`). Next, we'll cover creating the code for a camera detection app in Python. ##

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