https://github.com/slowy07/transport-counting
simple counting transportation with object detection and tensorflow
https://github.com/slowy07/transport-counting
Last synced: 3 months ago
JSON representation
simple counting transportation with object detection and tensorflow
- Host: GitHub
- URL: https://github.com/slowy07/transport-counting
- Owner: slowy07
- Created: 2022-05-14T01:23:40.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-05-18T17:02:47.000Z (about 3 years ago)
- Last Synced: 2025-01-23T06:12:07.124Z (5 months ago)
- Language: Python
- Size: 6.74 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# transport-counting
simple counting transportation with object detection and tensorflow
## general capabilities
this sample project has more than just counting vehicles, here are the additional capabilites of it:
- detection and classification of the vehicle (car, truck, biycles, motorcycle, bus)
- Recognition of approximate vehicle color
- detection of vehicle direction of travel
- prediction the speed of the vehicle
- prediction of approximate vehicle size
## theory
- vehicle detection and classification have been developed using TensorFlow object detection API
- vehicle speed predicition has been developed using OpenCVA via image pixel manipulation and calculation
- vehicle color prediction has been developed using OpenCV via k-nearest neighbours machine learning classification algorithm trained color histogram features
## installation
**docker setup with nvdia gpu**
run the demo in the GPU without installing anything, just nvidia docker
```
docker-compose up
```
alternative for nvidia-docker
1. python and pip
```
sudo apt-get install python3-pip python3-dev
```
2. OpenCV
See required command to install openCV on ubuntu [here](https://gist.github.com/dynamicguy/3d1fce8dae65e765f7c4)
3. TensorFlow
install the tensorflow by invoking one of the following commands
```
pip3 install tensorflow
pip3 install tensorflow-gpu
```
current program is compatible with tensorflow 1.5.0
## running program
program tekes an input argument ``imshow`` or ``imwrite``
```
python3 vehicle_detection_main.py imshow
python3 vehicle_detection_main.py imwrite
```
- ``imshow``: show the processsed frames as an video on screen.
- ``imwrite``: saves the processed frames as an output video in the project root folder.