Open traffic cam (with YOLO)

This project is offline lightweight DIY solution to monitor urban landscape. After installing this software on the specified hardware (Nvidia Jetson board + Logitech webcam), you will be able to count cars, pedestrians, motorbikes from your webcam live stream.

Behind the scenes, it feeds the webcam stream to a neural network (YOLO darknet) and make sense of the generated detections.

It is very alpha and we do not provide any guarantee that this will work for your use case, but we conceived it as a starting point from where you can build-on & improve.

💻 Hardware pre-requisite

Nvidia Jetson TX2
Webcam Logitech C222 (or any usb webcam compatible with Ubuntu 16.04)
A smartphone / tablet / laptop that you will use to operate the system

💾 Exports documentation

Counter data export

This export gives you the counters results along with the unique id of each object counted.

"Timestamp","Counter","ObjectClass","UniqueID"
"2018-08-23T09:25:18.946Z","turquoise","car",9
"2018-08-23T09:25:19.073Z","green","car",14
"2018-08-23T09:25:19.584Z","yellow","car",1
"2018-08-23T09:25:20.350Z","green","car",13
"2018-08-23T09:25:20.600Z","turquoise","car",6
"2018-08-23T09:25:20.734Z","yellow","car",32
"2018-08-23T09:25:21.737Z","green","car",11
"2018-08-23T09:25:22.890Z","turquoise","car",40
"2018-08-23T09:25:23.145Z","green","car",7
"2018-08-23T09:25:24.423Z","turquoise","car",4
"2018-08-23T09:25:24.548Z","yellow","car",0
"2018-08-23T09:25:24.548Z","turquoise","car",4

Tracker data export

This export gives you the raw data of all objects tracked with frame timestamps and positionning.

[
  // 1 Frame
  {
    "timestamp": "2018-08-23T08:46:59.677Z" // Time of the frame
    // Objects in this frame
    "objects": [{
      "id": 13417, // unique id of this object
      "x": 257, // position and size on a 1280x720 canvas
      "y": 242,
      "w": 55,
      "h": 44,
      "bearing": 230,
      "name": "car"
    },{
      "id": 13418,
      "x": 312,
      "y": 354,
      "w": 99,
      "h": 101,
      "bearing": 230,
      "name": "car"
    }]
  },
  //...
  // Other frames ...
}

⚙ System overview

See technical architecture for a more detailed overview

Edit schema

🛠 Step by Step install guide

NOTE: lots of those steps needs to be automated by integrating them in a docker image or something similar, for now need to follow the full procedure

⚡️Flash Jetson Board:

Download JetPack to Flash your Jetson board with the linux base image and needed dependencies
Follow the install guide provided by NVIDIA

NOTE: You also can find a detailed video tutorial for flashing the Jetson board here.

🛩Prepare Jetson Board

Update packages
```
sudo apt-get update
```
Install cURL
```
sudo apt-get install curl
```
install git-core
```
sudo apt-get install git-core
```

Install nodejs (v8):

curl -sL https://deb.nodesource.com/setup_8.x | sudo -E bash -
sudo apt-get install -y nodejs
sudo apt-get install -y build-essential

Install ffmpeg (v3)

sudo add-apt-repository ppa:jonathonf/ffmpeg-3
# sudo add-apt-repository ppa:jonathonf/tesseract (ubuntu 14.04 only!!)
sudo apt update && sudo apt upgrade
sudo apt-get install ffmpeg

Optional: Install nano
```
sudo apt-get install nano
```

📡Configure Ubuntu to turn the jetson into a wifi access point

enable SSID broadcast

add the following line to /etc/modprobe.d/bcmdhd.conf
```
options bcmdhd op_mode=2
```
further infos: here
Configure hotspot via UI

follow this guide: https://askubuntu.com/a/762885

Define Address range for the hotspot network

Go to the file named after your Hotspot SSID in /etc/NetworkManager/system-connections
```
cd /etc/NetworkManager/system-connections
sudo nano <YOUR-HOTSPOT-SSID-NAME>
```

Add the following line to this file:

[ipv4]
dns-search=
method=shared
address1=192.168.2.1/24,192.168.2.1 <--- this line

Restart the network-manager
```
sudo service network-manager restart
```

🚀Configure jetson to start in overclocking mode:

Add the following line to /etc/rc.local before exit 0:

#Maximize performances
( sleep 60 && /home/ubuntu/jetson_clocks.sh )&

Enable rc.local.service

chmod 755 /etc/init.d/rc.local
sudo systemctl enable rc-local.service

👁Install Darknet-net:

IMPORTANT Make sure that openCV (v2) and CUDA will be installed via JetPack (post installation step) if not: (fallback :openCV 2: install script, CUDA: no easy way yet)

Install libwsclient:

git clone https://github.com/PTS93/libwsclient
cd libwsclient
./autogen.sh
./configure && make && sudo make install

Install liblo:

wget https://github.com/radarsat1/liblo/releases/download/0.29/liblo-0.29.tar.gz --no-check-certificate
tar xvfz liblo-0.29.tar.gz
cd liblo-0.29
./configure && make && sudo make install

Install json-c:

git clone https://github.com/json-c/json-c.git
cd json-c
sh autogen.sh
./configure && make && make check && sudo make install

Install darknet-net:

git clone https://github.com/meso-unimpressed/darknet-net.git

Download weight files:

link: yolo.weight-files

Copy yolo-voc.weights to darknet-net repository path (root level)

e.g.:

darknet-net
  |-cfg
  |-data
  |-examples
  |-include
  |-python
  |-scripts
  |-src
  |# ... other files
  |yolo-voc.weights <--- Weight file should be in the root directory

wget https://pjreddie.com/media/files/yolo-voc.weights --no-check-certificate

Make darknet-net
```
cd darknet-net
make
```

🎥Install the open-data-cam node app

Install pm2 and next globally
```
sudo npm i -g pm2
sudo npm i -g next
```

Clone open_data_cam repo:

git clone https://github.com/moovel/lab-open-data-cam.git

Specify ABSOLUTE PATH_TO_YOLO_DARKNET path in lab-open-data-cam/config.json (open data cam repo)

e.g.:
```
{
	"PATH_TO_YOLO_DARKNET" : "/home/nvidia/darknet-net"
}
```

Install open data cam

cd <path/to/open-data-cam>
npm install
npm run build

Run open data cam on boot

cd <path/to/open-data-cam>
# launch pm2 at startup
# this command gives you instructions to configure pm2 to
# start at ubuntu startup, follow them
sudo pm2 startup

# Once pm2 is configured to start at startup
# Configure pm2 to start the Open Traffic Cam app
sudo pm2 start npm --name "open-data-cam" -- start
sudo pm2 save

🏁 Restart the jetson board and open `http://IP-OF-THE-JETSON-BOARD:8080/`

Connect you device to the jetson

💡 We should maybe set up a "captive portal" to avoid people needing to enter the ip of the jetson, didn't try yet 💡

When the jetson is started you should have a wifi "YOUR-HOTSPOT-NAME" available.

Connect you device to the jetson wifi
Open you browser and open http://IPOFTHEJETSON:8080
In our case, IPOFJETSON is: http://192.168.2.1:8080

You are done 👌

🚨 This alpha version of december is really alpha and you might need to restart ubuntu a lot as it doesn't clean up process well when you switch between the counting and the webcam view 🚨

You should be able to monitor the jetson from the UI we've build and count 🚗 🏍 🚚 !

‼️Automatic installation (experimental)

The install script for autmatic installation

Setting up the access point is not automated yet! **follow this guide: https://askubuntu.com/a/762885 ** to set up the hotspot.

run the install.sh script directly from GitHub

wget -O - https://raw.githubusercontent.com/moovel/lab-opendatacam/master/install/install.sh | bash

Troubleshoothing

To debug the app log onto the jetson board and inspect the logs from pm2 or stop the pm2 service (sudo pm2 stop <pid>) and start the app by using sudo npm start to see the console output directly.

Error: please specify the path to the raw detections file

Make sure that ffmpeg is installed and is above version 2.8.11
Error: Could *not* find a valid build in the '.next' directory! Try building your app with '*next* build' before starting the server

Run npm build before starting the app
Could not find darknet. Be sure to make darknet without sudo otherwise it will abort mid installation.
Error: cannot open shared object file: No such file or directory

Try reinstalling the liblo package.
Error: Error: Cannot stop process that is not running.

It is possible that a process with the port 8090 is causing the error. Try to kill the process and restart the board:
```
sudo netstat -nlp | grep :8090
sudo kill <pid>
```

🗃 Run open data cam on a video file instead of the webcam feed:

It is possible to run Open Data Cam on a video file instead of the webcam feed.

Before doing this you should be aware that the neural network (YOLO) will run on all the frames of the video file at ~7-8 FPS (best jetson speed) and do not play the file in real-time. If you want to simulate a real video feed you should drop the framerate of your video down to 7 FPS (or whatever frame rate your jetson board can run YOLO).

To switch the Open Data Cam to "video file reading" mode, you should go to the open-data-cam folder on the jetson.

cd <path/to/open-data-cam>
Then open YOLO.js, and uncomment those lines:

YOLO.process = new forever.Monitor(
  [
    "./darknet",
    "detector",
    "demo",
    "cfg/voc.data",
    "cfg/yolo-voc.cfg",
    "yolo-voc.weights",
    "-filename",
    "YOUR_FILE_PATH_RELATIVE_TO_DARK_NET_FOLDER.mp4",
    "-address",
    "ws://localhost",
    "-port",
    "8080"
  ],
  {
    max: 1,
    cwd: config.PATH_TO_YOLO_DARKNET,
    killTree: true
  }
);

Copy the video file you want to run open data cam on in the darknet-net folder on the Jetson (if you did auto-install, it is this path: ~/darknet-net)

// For example, your file is `video-street-moovelab.mp4`, you will end up with the following in the darknet-net folder:

darknet-net
  |-cfg
  |-data
  |-examples
  |-include
  |-python
  |-scripts
  |-src
  |# ... other files
  |video-street-moovellab.mp4 <--- Video file

Then replace YOUR_FILE_PATH_RELATIVE_TO_DARK_NET_FOLDER.mp4 placeholder in YOLO.js with your file name, in this case video-street-moovellab.mp4

// In our example you should end up with the following:

YOLO.process = new forever.Monitor(
  [
    "./darknet",
    "detector",
    "demo",
    "cfg/voc.data",
    "cfg/yolo-voc.cfg",
    "yolo-voc.weights",
    "-filename",
    "video-street-moovellab.mp4",
    "-address",
    "ws://localhost",
    "-port",
    "8080"
  ],
  {
    max: 1,
    cwd: config.PATH_TO_YOLO_DARKNET,
    killTree: true
  }
);

After doing this you should re-build the Open Data Cam node app.

npm run build

You should be able to use any video file that are readable by OpenCV, which is what YOLO implementation use behind the hoods to decode the video stream

🎛 Advanced settings

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 .

For example, here is a way to only track buses and person:

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

If you change this config option, you will need to re-build the project by running npm run build.

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 and ignoring the class switch while tracking we saw that we can avoid losing some objects, this is discussed here

🛠 Development notes

Technical architecture

Edit schema

Miscellaneous dev tips

Mount jetson filesystem as local filesystem on mac for dev

sshfs -o allow_other,defer_permissions [email protected]:/home/nvidia/Desktop/lab-traffic-cam /Users/tdurand/Documents/ProjetFreelance/Moovel/remote-lab-traffic-cam/

SSH jetson

ssh [email protected]

Install it and run:

yarn install
yarn run dev

Name		Name	Last commit message	Last commit date
Latest commit History 384 Commits
components		components
install		install
pages		pages
scripts		scripts
server		server
statemanagement		statemanagement
static		static
utils		utils
.gitignore		.gitignore
README.md		README.md
config.json		config.json
package-lock.json		package-lock.json
package.json		package.json
server.js		server.js
yarn.lock		yarn.lock

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Repository files navigation

Open traffic cam (with YOLO)

Table of Contents

💻 Hardware pre-requisite

💾 Exports documentation

Counter data export

Tracker data export

⚙ System overview

🛠 Step by Step install guide

⚡️Flash Jetson Board:

🛩Prepare Jetson Board

📡Configure Ubuntu to turn the jetson into a wifi access point

🚀Configure jetson to start in overclocking mode:

👁Install Darknet-net:

🎥Install the open-data-cam node app

🏁 Restart the jetson board and open `http://IP-OF-THE-JETSON-BOARD:8080/`

Connect you device to the jetson

You are done 👌

‼️Automatic installation (experimental)

Troubleshoothing

🗃 Run open data cam on a video file instead of the webcam feed:

🎛 Advanced settings

Track only specific classes

🛠 Development notes

Technical architecture

Miscellaneous dev tips

Mount jetson filesystem as local filesystem on mac for dev

SSH jetson

Install it and run:

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Repository files navigation

Open traffic cam (with YOLO)

Table of Contents

💻 Hardware pre-requisite

💾 Exports documentation

Counter data export

Tracker data export

⚙ System overview

🛠 Step by Step install guide

⚡️Flash Jetson Board:

🛩Prepare Jetson Board

📡Configure Ubuntu to turn the jetson into a wifi access point

🚀Configure jetson to start in overclocking mode:

👁Install Darknet-net:

🎥Install the open-data-cam node app

🏁 Restart the jetson board and open http://IP-OF-THE-JETSON-BOARD:8080/

Connect you device to the jetson

You are done 👌

‼️Automatic installation (experimental)

Troubleshoothing

🗃 Run open data cam on a video file instead of the webcam feed:

🎛 Advanced settings

Track only specific classes

🛠 Development notes

Technical architecture

Miscellaneous dev tips

Mount jetson filesystem as local filesystem on mac for dev

SSH jetson

Install it and run:

About

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🏁 Restart the jetson board and open `http://IP-OF-THE-JETSON-BOARD:8080/`

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