DigitalImageProcessing

Histogram Of Oriented Gradients Application for Pedestrian Detection

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. The platform used is Linux

Prerequisites

Python 3.6

Numpy

Scikit-learn

Open-CV

Project Directory Structure

.
├── dataset                    
│   ├── detect        #images for detection   
│   ├── hardneg       #images for hard negative training  
│   └── train         #positive training images
│       ├──neg
│       └──pos
└── testdataset       #images for testing the performance of the classifier
│       ├──neg
│       └──pos
│
└──output              #consists of result images

Usage

Clone the repository using the following command

git clone https://github.com/sourabhkumar0308/DigitalImageProcessing.git

cd DigitalImageProcessing

To run the Project there are two ways

1.) either use '.py' files
2.) or use 'ipynb' files (requires jupyter notebook)(recommended)

Run extract features.py or extract features.ipynb

Run detect.py or detect.ipynb

Run test.py or test.ipynb

person_final_hard.pkl is the pre-trained model. You can skip running 'extract features' if you are planning to use this model