speed-camera

SPEED CAMERA - Object Motion Tracker

Raspberry Pi, Unix Disto's and Windows Speed Camera Using python3, openCV, RPI camera module, USB Cam or IP/RTSP Cam

For Details See Program Features, Wiki Instructions and YouTube Videos.

Note re Bullseye
speed-cam.py will run using pi camera with libcamera, picamera2 under Raspberry Pi OS Bullseye, Bookworm,and later. Usbcam and IP/RTSP cameras are also supported. For picamera library support (on Bullseye only) Run sudo raspi-config, Interface Options, then enable Legacy Camera option and Reboot.

RPI Quick Install or Upgrade

IMPORTANT - A raspbian sudo apt-get update and sudo apt-get upgrade will NOT be performed as part of
speed-install.sh so it is highly recommended you run these prior to install to ensure your system is up-to-date.

Step 1

Press GitHub copy icon on right side of code box below. Copied! will be displayed.

curl -L https://raw.github.com/pageauc/speed-camera/master/source/speed-install.sh | bash

Step 2

On RPI putty SSH or terminal session right click, select paste then Enter to Download and Run speed-install.sh script.

To get started, see Instructions at the end of the install script. Initial default config.py setting is CALIBRATE_ON=True. You may also need to Align Camera using config.py ALIGN_CAM_ON=True setting. Once calibration is complete set CALIBRATE_ON=False

Mac or Windows Systems

See Windows 10/11 or Apple Mac Docker Install Quick Start
or Windows or Unix Distro Installs without Docker

Program Description

This project can run on a Raspberry Pi, Windows, Unix Distro computer. It is written in python3 and uses openCV to detect and track the x,y coordinates of the largest moving object in the camera view above a minimum pixel area and calculates speed based on calibration settings.

User variables are stored in the config.py file. Motion detection is restricted between MO_CROP_Y_UPPER, MO_CROP_Y_LOWER, MO_CROP_X_LEFT, MO_CROP_X_RIGHT variables (road or area of interest). MO_CROP_AUTO_ON = True overrides manual settings and will Auto calculate a rough crop area based on image size. Motion Tracking is controlled by the MO_TRACK_EVENT_COUNT variable in config.py. This sets the number of track events and the track length in pixels. This may need to be tuned for camera view, cpu speed, etc. Speed is calculated based on CAL_OBJ_PX_ and CAL_OBJ_MM_ variables for L2R and R2L motion direction. A video stream frame image will be captured and saved in media/images dated subfolders (optional) per variable IM_SUBDIR_MAX_FILES = 2000 For variable settings details see config.py file.

If LOG_DATA_TO_CSV = True then a speed-cam.csv file will be created/updated with event data stored in CSV (Comma Separated Values) format. This can be imported into a spreadsheet, database, Etc program for further processing. Release 8.9 adds a sqlite3 database to store speed data. Default is data/speed_cam.db with data in the speed table. Database setting can be managed from config.py. Database is automatically created from config.py settings. For more details see How to Manage Sqlite3 Database

Admin, Reports, Graphs and Utilities scripts

• menubox.sh script is a whiptail menu system to allow easier management of program settings and operation.
• speed-cam.py View operation and log messages in real time in terminal session. Can be used to monitor, troubleshoot and tune speed camera (see config.py for speed-cam.py settings)
• speed-cam.sh This bash script uses supervisorctl to manage start, stop, status, Etc of speed-cam.py Configured to autostart eg due to interruption of RTSP stream. See conf files in supervisor folder for details. Note: you must run ./speed-cam.sh install to initialize symbolic links to /etc/supervisor/conf.d folder. Stop running any speed-cam and/or websever processes before running ./speed-cam.sh start
• speed-web.py Allows viewing images, video and/or data from a web browser. This will display web server messages. (see config.py for speed-web.py settings)
• speed-web.sh This bash script uses supervisorctl to manage start, stop, status, Etc of speed-web.py. Configured to restart eg due to interruption See conf files in supervisor folder for details. Note: you must run ./speed-web.sh install to initialize symbolic links to /etc/supervisor/conf.d folder. Stop running any speed-cam and/or websever processes before running ./speed-web.sh start
• rclone Manage settings and setup for optional remote file sync to a remote storage service like google drive, DropBox and many others.
• Query sqlite database and Generate one or more matplotlib graph images and save to media/graphs folder. Graphs display counts by hour, day or month for specfied previous days and speed over. Multiple reports can be managed from the config.py variable under matplotlib image settings section.

Reference Links

• YouTube Tutorial Video https://www.youtube.com/watch?v=n2WT3Qb0SIU
• YouTube Speed Lapse Video https://youtu.be/-xdB_x_CbC8
• YouTube Speed Camera Video https://youtu.be/eRi50BbJUro
• YouTube motion-track video https://youtu.be/09JS7twPBsQ
• How to Build a Cheap Homemade Speed Camera
• Speed Camera RPI Forum post https://www.raspberrypi.org/forums/viewtopic.php?p=1004150#p1004150
• YouTube Channel https://www.youtube.com/user/pageaucp
• Speed Camera GitHub Repo https://github.com/pageauc/speed-camera

Requirements

Raspberry Pi computer and a RPI camera module installed or USB Camera plugged in. Make sure hardware is tested and works. Most RPI models will work OK. A quad core RPI will greatly improve performance due to threading. A recent version of Raspbian operating system is Recommended.
or
MS Windows or Unix distro computer with a USB Web Camera plugged in and a recent version of python installed For Details See Wiki details.

It is recommended you upgrade to OpenCV version 3.x.x For Easy compile of opencv 3.4.2 from source See https://github.com/pageauc/opencv3-setup

Windows or Non RPI Unix Installs

For Windows or Unix computer platforms (non RPI or Debian) ensure you have the most up-to-date python version. For Download and Install python and Opencv

The latest python3 versions includes numpy and recent opencv version that is required to run this code. You will also need a USB web cam installed and working. To install this program access the GitHub project page at https://github.com/pageauc/speed-camera Select the green Clone or download button. The files will be cloned or zipped to a speed-camera folder. You can run the code from python IDLE application (recommended), GUI desktop or command prompt terminal window. Note bash .sh shell scripts will not work with windows unless special support for bash is installed for windows Eg http://win-bash.sourceforge.net/ http://www.cygwin.com/ Note: I have Not tested these.

Docker Install Quick Start

speed camera supports a docker installation on
Apple Macintosh per System requirements and Instructions
and
Microsoft Windows 10/11 64 bit with BIOS Virtualization enabled and Microsoft Windows Subsystem for Linux WSL 2 per System requirements and Instructions.

1. Download and install Docker Desktop for your System
2. Clone the GitHub Speed Camera repository using green Clone button (top right)
3. Run docker-compose up from the directory you cloned the repo into.
4. The Docker container will likely exit because it is using a default config.
5. Edit the configuration file @ config/config.py
6. Run docker-compose up per documentation
7. Run