Object Detection using Tensorflow.

This python application performs object detection using Tensorflow.

Getting Started

0. Virtual Environment(Optional) python3 -m venv [target_folder] source ./[target_folder]/bin/activate

1. Requirements pip install -r requirements.txt

2. Usage

python3 ./nn_objdet/main.py [args]

List of arguments are (with default value []):

"-d", "--display", [0] Whether or not frames should be displayed
"-o", "--output", [0] Whether or not modified videos shall be writen
"-op", "--output-path", ["./output"] Name of the output video file
"-i", "--input-source", "./" Path to videos input, overwrite device input if used
'-w', '--num-workers', [2], Number of workers
'-q-size', '--queue-size', [5] Size of the queue.
'-l', '--logger-debug', [0], Print logger debug
"-pg", "--graph_path",["./model"] Path to the frozen graph
"-pl", "--label_path", ["./model"],Path to the object labels

Work in progress...

Application structure

The aim is to take advantage of the concurrent execution to speed up the object detection routine.

I still have to check the assumption, but the idea is that the data flow is more I/O bound, whilst the NN jobs are CPU bound.

Due to the GIL problem under python, CPU bound activities won't get the expected speed up if managed with multithreading. A possible solution is to deploy the routines in separate processes, with separate memory spaces, to avoid the GIL issue.

The structure of the application is shown in the following Figure.

Work in progress...