⚙️ Customize OpenDataCam

We offer several customization options:

Table of content


For a standard install of OpenDataCam

All settings are in the config.json file that you will find in the same directory you run the install script.

When you modify a setting, you will need to restart the docker container, you can do so by:

# List containers
sudo docker container list

# Restart container (find id from previous command)
sudo docker restart <containerID>

# Or if you have stopped it
sudo ./

For a non-docker install of OpenDataCam

You need to modify the config.json file located the opendatacam folder.


Once modified, you just need to restart the node.js app (npm run start), no need to re-build it, it loads the config file at runtime.

Run opendatacam on a video file

NB: if you are running with nvidiadocker and want to use an usbcam, please see Run from an usbcam (step 4)

By default, opendatacam will try to pickup the usb webcam connected to your jetson. The settings is VIDEO_INPUT in the config.json file.

"VIDEO_INPUT": "usbcam"

You can change this to run it on a pre-recorded file.

If you installed opendatacam through the default setup process you should have a opendatacam_videos folder where you ran the install script. Inside this folder you should also find a demo video: demo.mp4

You will need to copy the videos you want inside this folder. (this folder gets mounted when running the container and docker has access to it)

Once you do have the video file inside the opendatacam_videos folder, you can modify the config.json the following way:

  1. Change VIDEO_INPUT to "file"
"VIDEO_INPUT": "file"
  1. Change VIDEO_INPUTS_PARAMS > file with the path to your file
  "file": "opendatacam_videos/demo.mp4"

Once config.json is saved, you only need to restart the docker container or restart your jetson and next time you access opendatacam, it will run on this file.

Learn more about the others video inputs available (IP camera, Rasberry Pi in the Advanced use section)

For a non-docker install of OpenDataCam:

Follow the same instruction but note the path you will put in VIDEO_INPUTS_PARAMS > file if relative to your darknet directory.

For example if you have a myvideo.mp4 file in your darknet directory, the settings should be:

  "file": "myvideo.mp4"

Specificities of running on a file

Change neural network weights

You can change YOLO weights files depending on what objects you want to track and which hardware your are running opendatacam on.

Lighters weights file results in speed improvements, but loss in accuracy, for example yolov3 run at ~1-2 FPS on Jetson Nano, ~5-6 FPS on Jetson TX2, and ~22 FPS on Jetson Xavier, and yolov2-voc runs at ~4-5 FPS on Jetson Nano, ~11-12 FPS on Jetson TX2, and realtime on Jetson Xavier.

In order to have good enough tracking accuracy for cars and mobility objects, from our experiments we found out that the sweet spot was to be able to run YOLO at least at 8-9 FPS.

For a standard install of opendatacam, these are the default weights we pick depending on your hardware:

We allow you to change those settings, here is how to do it:

For a docker (standard install) of OpenDataCam:

We ship inside the docker container those three YOLO weights: yolov3-tiny, yolov2-voc, yolov3

In order to switch to another one, you need to change the setting NEURAL_NETWORK in config.json.

  "NEURAL_NETWORK": "yolov2-voc"

The settings available are: "yolov3" , "yolov3-tiny", "yolov2-voc", if you want to run from others weights like yolov3-openimages, yolov3-spp.. or custom trained ones, please refer to the advanced use section below.

For a non-docker install of opendatacam:

The settings are the same as with the docker install, but you can also run from other weights file, see advanced use section below

Track only specific classes

By default, the opendatacam will track all the classes that the neural network is trained to track. In our case, YOLO is trained with the VOC dataset, here is the complete list of classes

You can restrict the opendatacam to some specific classes with the VALID_CLASSES option in the config.json file .

Find which classes YOLO is tracking depending on the weights you are running. For example yolov3 trained on COCO dataset classes

Here is a way to only track buses and person:

  "VALID_CLASSES": ["bus","car"]

In order to track all the classes (default value), you need to set it to:

  "VALID_CLASSES": ["*"]

Extra note: the tracking algorithm might work better by allowing all the classes, in our test we saw that for some classes like Bike/Motorbike, YOLO had a hard time distinguishing them well, and was switching between classes across frames for the same object. By keeping all the detections classes we saw that we can avoid losing some objects, this is discussed here

Display custom classes

By default we are displaying the mobility classes:

Display classes

If you want to customize it you should modify the DISPLAY_CLASSES config.

  { "class": "bicycle", "icon": "1F6B2.svg"},
  { "class": "person", "icon": "1F6B6.svg"},
  { "class": "truck", "icon": "1F69B.svg"},
  { "class": "motorbike", "icon": "1F6F5.svg"},
  { "class": "car", "icon": "1F697.svg"},
  { "class": "bus", "icon": "1F683.svg"}

You can associate any icon that are in the /static/icons/openmojis folder. (they are from, you can search the unicode icon name directly there)

For example:

    { "class": "dog", "icon": "1F415.svg"},
    { "class": "cat", "icon": "1F431.svg"}

Display classes custom

LIMITATION: You can display a maximum of 6 classes, if you add more, it will just display the first 6 classes

Customize pathfinder colors

You can change the PATHFINDER_COLORS variable in the config.json. The app picks randomly for each new tracked object a color inside it. The colors need to be in HEX format.


For example, with only 2 colors:


Demo 2 colors

Customize Counter colors

You can change the COUNTER_COLORS variable in the config.json. As you draw counter lines, the app will pick the colors in the order you specified them.

You need to add “key”:”value” for counter lines, the key should be the label of the color (without space, numbers or special characters), and the value the color in HEX.

For example, you can modify the default from:

  "yellow": "#FFE700",
  "turquoise": "#A3FFF4",
  "green": "#a0f17f",
  "purple": "#d070f0",
  "red": "#AB4435"


  "white": "#fff"

And after restarting OpenDataCam you should get a white line when defining a counter area:

Screenshot 2019-05-24 at 21 03 44

NOTE: If you draw more line than COUNTER_COLORS defined, the lines will be black

Advanced settings

Video input

OpenDataCam is capable to take in input several video streams: pre-recorded file, usbcam, raspberry cam, remote IP cam etc etc..

This is configurable via the VIDEO_INPUT ans VIDEO_INPUTS_PARAMS settings.

  "file": "opendatacam_videos/demo.mp4",
  "usbcam": "v4l2src device=/dev/video0 ! video/x-raw, framerate=30/1, width=640, height=360 ! videoconvert ! appsink",
  "experimental_raspberrycam_docker": "v4l2src device=/dev/video2 ! video/x-raw, framerate=30/1, width=640, height=360 ! videoconvert ! appsink",
  "raspberrycam_no_docker": "nvarguscamerasrc ! video/x-raw(memory:NVMM),width=1280, height=720, framerate=30/1, format=NV12 ! nvvidconv ! video/x-raw, format=BGRx, width=640, height=360 ! videoconvert ! video/x-raw, format=BGR ! appsink",
  "remote_cam": "YOUR IP CAM STREAM (can be .m3u8, MJPEG ...), anything supported by opencv"

With the default installation, OpenDataCam will have VIDEO_INPUT set to usbcam. See below how to change this

Technical note:

Behind the hoods, this config input becomes the input of the darknet process which then get fed into OpenCV VideoCapture().

As we compile OpenCV with Gstreamer support when installing OpenDataCam, we can use any Gstreamer pipeline as input + other VideoCapture supported format like video files / IP cam streams.

You can add your own gstreamer pipeline for your needs by adding an entry to "VIDEO_INPUTS_PARAMS"

Run from an usbcam
  1. Verify if you have an usbcam detected
ls /dev/video*
# Output should be: /dev/video0
  1. Change VIDEO_INPUT to "usbcam"
"VIDEO_INPUT": "usbcam"
  1. (Optional) If your device is on video1 or video2 instead of default video0, change VIDEO_INPUTS_PARAMS > usbcam to your video device, for example if /dev/video1
  "usbcam": "v4l2src device=/dev/video1 ! video/x-raw, framerate=30/1, width=640, height=360 ! videoconvert ! appsink"
  1. (If running with nvidiadocker container) You need allow docker to access your device when starting the container, to do this modify the script that should have been downloaded by the install script and set:
docker run --runtime=nvidia --device=/dev/video0:/dev/video0 -p 8080:8080 -p 8090:8090 -p 8070:8070 $DOCKER_VOLUMES -v /data/db:/data/db $@
Run from a file

See Run opendatacam on a video file

Run from IP cam
  1. Change VIDEO_INPUT to "file"
"VIDEO_INPUT": "remote_cam"
  1. Change VIDEO_INPUTS_PARAMS > remote_cam to your IP cam stream, for example
  "remote_cam": ""

NB: this IP cam won’t work, it is just an example. Only use IP Cam you own yourself, see CODE OF CONDUCT (TODO @b-g)

Run from Raspberry Pi cam (Jetson nano)

See dedicated documention for Jetson nano

Change webcam resolution

As explained on the Technical note above, you can modify the Gstreamer pipeline as you like, by default we use a 640x360 feed from the webcam.

If you want to change this, you need to:

NOTE: Increasing webcam resolution won’t increase OpenDataCam accuracy, the input of the neural network is 400x400 max, and it might cause the UI to have logs as the MJPEG stream becomes very slow for higher resolution

Use Custom Neural Network weights

In order to use other weights like yolov3-openimages, yolov3-spp, custom trained ones or “third party” weights you need to install OpenDataCam without Docker (we will enable this for docker install at some point #97).

For example, if you want to use yolov3-openimages, you need to:

"yolov3-openimages": {
  "data": "cfg/",
  "cfg": "cfg/yolov3.cfg",
  "weights": "yolov3-openimages.weights"
"NEURAL_NETWORK": "yolov3-openimages"


If you want to persist the data on a remote mongodb instance, you can change the setting MONGODB_URL .

By default the Mongodb will be persisted in the /data/db directory of your host machine

Tracker accuracy display

The tracker accuracy layer shows a heatmap like this one:

Screenshot 2019-06-12 at 18 59 54

This heatmap highlights the areas where the tracker accuracy isn’t really good to help you:

Behind the hoods, it displays a metric of the tracker called “zombies” which represent the predicted bounding box when the tracked isn’t able to asign a bounding box from the YOLO detections.

You can tweak all the settings of this display with the TRACKER_ACCURACY_DISPLAY setting.

nbFrameBuffer Number of previous frames displayed on the heatmap
radius Radius of the points displayed on the heatmap (in % of the width of the canvas)
blur Blur of the points displayed on the heatmap (in % of the width of the canvas)
step For each point displayed, how much the point should contribute to the increase of the heapmap value (the range is between 0-1), increasing this will cause the heatmap to reach the higher values of the gradient faster
gradient Colors gradient, insert as many values as you like between 0-1 (hex value supported, ex: “#fff” or “white”)
canvasResolutionFactor In order to improve performance, the tracker accuracy canvas resolution is downscaled by a factor of 10 by default (set a value between 0-1)
  "nbFrameBuffer": 300,
  "settings": {
    "radius": 3.1,
    "blur": 6.2,
    "step": 0.1,
    "gradient": {
    "canvasResolutionFactor": 0.1

For example, if you change the gradient with:

"gradient": {

Other gradient

Limitation with docker setup