Visual Traffic Surveillance and Analytics System

Introduction

A complete system for the analysis of Indian Traffic Patterns

Provides a novel approach to acquiring traffic counts and link speeds

Prerequisite Installation

The code was tested on Ubuntu 18.04
First it is suggested to have your nvidia-drivers to be installed properly This can be checked by:
```
nvidia-smi
```
Install cuda-10.0 and cudnn (latest for cuda-10.0)
```
nvcc -V
```

Create a virtualenvironment using virtualenv and virtualenvwrapper

pip install virtualenv virtualenvwrapper
mkvirtualenv all
workon all

Install opencv-3.4.4 (pyimagesearch website tutorial) While compiling OpenCV use this command:

cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_LIBV4L=OFF \
-D WITH_FFMPEG=ON \
-D WITH_CUDA=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D OPENCV_ENABLE_NONFREE=ON \
-D BUILD_EXAMPLES=ON ..

To test opencv for python :-

# In python3
import cv2
cv2.__version__

Install tensorflow-gpu==1.14.0 Does not work with tensorflow-gpu==1.15.0 (You might Engine load failure even though engine is generated correctly)
```
import tensorflow as tf
tf.__version__
```
Install pytorch (pip installation, cuda-10.0,10.1)
```
import torch
import torchvision
```
Install nvidia tensorrt 5.1.5. To check tensorrt installation
```
dpkg -l | grep nvinfer
```
Install boost and all its dependencies
```
sudo apt-get install libboost-all-dev
```

Installation

Create a build folder
```
mkdir build && cd build
```
Compile and install
```
cmake ..
make -j8
make install
cd ..
```

Creating model

Run the following command after setting up: shell ./install/createEngine --caffemodel=./models/yolov3_traffic_final.caffemodel --prototxt=./models/yolov3_traffic_final.prototxt --input=./test/test.jpg --W=416 --H=416 --class=9

if you want batching, then run shell ./install/createEngine --caffemodel=./models/yolov3_traffic_final.caffemodel --prototxt=./models/yolov3_traffic_final.prototxt --input=./test/test.jpg --W=416 --H=416 --class=9 --batchsize=2

This will create an engine file namely yolov3_fp32.engine.

Move this engine file into the models folder

Adjust Config

NOT IMPLEMENTED YET

Run

For RTMP streams run main.sh

 ./main.sh --engine=./models/yolov3_fp32.engine --pipe-path=/tmp/fifopipe1
 --segment-path=./segments/test_cam/
 --line-coord=./configs/line_coord/test_cam.json
 --intrinsics=./configs/cam_intrinsic/test_cam.json
 --qlength_conf=./configs/qlen_conf/coord.json
 --rtsp_strm=rtmp://aaa.bbb.cc.ddd/0

For RTSP streams run main.sh

 ./main.sh --engine=./models/yolov3_fp32.engine --pipe-path=/tmp/fifopipe1
 --segment-path=./segments/test_cam/
 --line-coord=./configs/line_coord/test_cam.json
 --intrinsics=./configs/cam_intrinsic/test_cam.json
 --qlength_conf=./configs/qlen_conf/coord.json
 --rtsp_strm=rtsp://aaa.bbb.cc.ddd/0

Results

Soon to be out

Authors/Maintainers

Prajwal Rao - prajwaljpj@gmail.com, prajwalrao@iisc.ac.in
Sadgun Srinivas - sadgun.srinivas@gmail.com, sadguns@iisc.ac.in
Armaan Puri - puri.armaan17@gmail.com

gmayday1997 / VisTraSAS