nonpersons_data

Overview

This repository contains code for downloading 2000 non-person images from Google open image V6 dataset for object detection applications (https://storage.googleapis.com/openimages/web/index.html).

Usage

Step 1: Clone repository

Open a linux terminal and clone this repository to your workspace using(copy paste entire thing):

git clone https://github.com/prchinmay/nonpersons_data.git &
cd nonpersons_data

Step 2: Create new virtual environment

Before intalling dependencies, let us first create a new virtual environment using(copy paste the entire thing):

python3 -m pip install --user virtualenv &
python3 -m virtualenv env &
source env/bin/activate

Step 3: Install Dependencies

Install dependiencies using:

pip install -r requirements.txt

Step 4: Run Code

After dependencies are installed, run this command in you linux terminal to generate non-persons ImageIDs, download those Images and generate labels in YOLOv3 format.

bash script.sh

Results

There should be 2000 images in data/images folder and 2000 annotation files in data/labels. The annotation files are in the format: Row format: image_file_path box1 box2 ... boxN; Box format: x_min,y_min,x_max,y_max,class_id

Customizing

Change split

The code is written assuming that you downloaded validation set annotations. If you want to download images from training set/test set, download the appropriate annotation files from https://storage.googleapis.com/openimages/web/download.html. Refer to the image below to know which files to download. In this example, I have downloaded only validation annotations.

Once downloaded, make sure that you also change the respective file names in the imports section of file filter.py

annot = pd.read_csv (r'validation-annotations-bbox.csv')
v_annot = pd.read_csv (r'validation-annotations-human-imagelabels-boxable.csv')

Select which class to remove

Select the classes to remove in filter.py

class_to_remove = ['Person','Man','Boy','Girl', 'Mammal','Woman', 'Human mouth', 
                   'Human body', 'Human foot', 'Human leg', 
                   'Human ear', 'Human hair', 'Human head',
                   'Human arm','Human face', 'Human nose',
                   'Human hand', 'Human eye', 'Human beard']

Change number of images to download

Select the number of images to download in filter.py

n_images = 2000