A transformer which can randomly augment VOC format dataset (both image and bbox) online.

Overview

VocAug

It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it is hard to use. Or, it is offline but not online so it needs very very large disk volume.

Here, is a simple transformer which can randomly augment VOC format dataset online! It can work with only numpy and cv2 packages!

The highlight is,

  1. it augments both image and b-box!!!
  2. it only use cv2 & numpy, means it could be used simply without any other awful packages!!!
  3. it is an online transformer!!!

It contains methods of:

  1. Random HSV augmentation
  2. Random Cropping augmentation
  3. Random Flipping augmentation
  4. Random Noise augmentation
  5. Random rotation or translation augmentation

All the methods can adjust abundant arguments in the constructed function of class VocAug.voc_aug.

Here are some visualized examples:

(click to enlarge)

e.g. #1 e.g. #2
eg1 eg2

More

This script was created when I was writing YOLOv1 object detectin algorithm for learning and entertainment. See more details at https://github.com/BestAnHongjun/YOLOv1-pytorch

Quick Start

1. Download this repo.

git clone https://github.com/BestAnHongjun/VOC-Augmentation.git

or you can download the zip file directly.

2. Enter project directory

cd VOC-Augmentation

3. Install the requirements

pip install -r requirements.txt

For some machines with mixed environments, you need to use pip3 but not pip.

Or you can install the requirements by hand. The default version is ok.

pip install numpy
pip install opencv-python
pip install opencv-contrib-python
pip install matplotlib

4.Create your own project directory

Create your own project directory, then copy the VocAug directory to yours. Or you can use this directory directly.

5. Create your own demo.py file

Or you can use my demo.py directly.

Thus, you should have a project directory with structure like this:

Project_Dir
  |- VocAug (dir)
  |- demo.py

Open your demo.py.

First, import some system packages.

import os
import matplotlib.pyplot as plt

Second, import my VocAug module in your project directory.

from VocAug.voc_aug import voc_aug
from VocAug.transform.voc2vdict import voc2vdict
from VocAug.utils.viz_bbox import viz_vdict

Third, Create two transformer.

voc2vdict_transformer = voc2vdict()
augmentation_transformer = voc_aug()

For the class voc2vdict, when you call its instance with args of xml_file_path and image_file_path, it can read the xml file and the image file and then convert them to VOC-format-dict, represented by vdict.

What is vdict? It is a python dict, which has a structure like:

vdict = {
    "image": numpy.array([[[....]]]),   # Cv2 image Mat. (Shape:[h, w, 3], RGB format)
    "filename": 000048,                 # filename without suffix
    "objects": [{                       # A list of dicts representing b-boxes
        "class_name": "house",
        "class_id": 2,                  # index of self.class_list
        "bbox": (x_min, y_min, x_max, y_max)
    }, {
        ...
    }]
}

For the class voc_aug, when you call its instance by args of vdict, it can augment both image and bbox of the vdict, then return a vdict augmented.

It will randomly use augmentation methods include:

  1. Random HSV augmentation
  2. Random Cropping augmentation
  3. Random Flipping augmentation
  4. Random Noise augmentation
  5. Random rotation or translation augmentation

Then, let's augment the vdict.

# prepare the xml-file-path and the image-file-path
filename = "000007"
file_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "dataset")
xml_file_path = os.path.join(file_dir, "Annotations", "{}.xml".format(filename))
image_file_path = os.path.join(file_dir, "JPEGImages", "{}.jpg".format(filename))

# Firstly convert the VOC format xml&image path to VOC-dict(vdict), then augment it.
src_vdict = voc2vdict_transformer(xml_file_path, image_file_path)
image_aug_vdict = augmentation_transformer(src_vdict)

The 000007.jpg and 000007.xml is in the dataset directory under Annotations and JPEGImages separately.

Then you can visualize the vdict. I have prepare a tool for you. That is viz_vdict function in VocAug.utils.viz_bbox module. It will return you a cv2 image when you input a vdict into it.

You can use it like:

image_src = src_vdict.get("image")
image_src_with_bbox = viz_vdict(src_vdict)

image_aug = image_aug_vdict.get("image")
image_aug_with_bbox = viz_vdict(image_aug_vdict)

Visualize them by matplotlib.

plt.figure(figsize=(15, 10))
plt.subplot(2, 2, 1)
plt.title("src")
plt.imshow(image_src)
plt.subplot(2, 2, 3)
plt.title("src_bbox")
plt.imshow(image_src_with_bbox)
plt.subplot(2, 2, 2)
plt.title("aug")
plt.imshow(image_aug)
plt.subplot(2, 2, 4)
plt.title("aug_bbox")
plt.imshow(image_aug_with_bbox)
plt.show()

Then you will get a random result like this. eg1

For more detail see demo.py .

Detail of Algorithm

I am writing this part...

Owner
Coder.AN
Researcher, CoTAI Lab, Dalian Maritime University. Focus on Computer Vision, Moblie Vision, and Machine Learning. Contact me at
Coder.AN
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022
The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Patient Selection for Diabetes Drug Testing Project Overview EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical ind

Omar Laham 1 Jan 14, 2022
This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

Yue Yu 58 Dec 21, 2022
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)

Feedback Network for Image Super-Resolution [arXiv] [CVF] [Poster] Update: Our proposed Gated Multiple Feedback Network (GMFN) will appear in BMVC2019

Zhen Li 539 Jan 06, 2023
Add-on for importing and auto setup of character creator 3 character exports.

CC3 Blender Tools An add-on for importing and automatically setting up materials for Character Creator 3 character exports. Using Blender in the Chara

260 Jan 05, 2023
🏆 The 1st Place Submission to AICity Challenge 2021 Natural Language-Based Vehicle Retrieval Track (Alibaba-UTS submission)

AI City 2021: Connecting Language and Vision for Natural Language-Based Vehicle Retrieval 🏆 The 1st Place Submission to AICity Challenge 2021 Natural

82 Dec 29, 2022
Wenet STT Python

Wenet STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using WeNet models for sp

David Zurow 33 Feb 21, 2022
Instance-level Image Retrieval using Reranking Transformers

Instance-level Image Retrieval using Reranking Transformers Fuwen Tan, Jiangbo Yuan, Vicente Ordonez, ICCV 2021. Abstract Instance-level image retriev

UVA Computer Vision 87 Jan 03, 2023
Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

Nest Protect integration for Home Assistant Custom component for Home Assistant to interact with Nest Protect devices via an undocumented and unoffici

Mick Vleeshouwer 175 Dec 29, 2022
Cweqgen - The CW Equation Generator

The CW Equation Generator The cweqgen (pronouced like "Queck-Jen") package provi

2 Jan 15, 2022
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

ETSformer - Pytorch Implementation of ETSformer, state of the art time-series Transformer, in Pytorch Install $ pip install etsformer-pytorch Usage im

Phil Wang 121 Dec 30, 2022
This git repo contains the implementation of my ML project on Heart Disease Prediction

Introduction This git repo contains the implementation of my ML project on Heart Disease Prediction. This is a real-world machine learning model/proje

Aryan Dutta 1 Feb 02, 2022
Official Implementation of SWAD (NeurIPS 2021)

SWAD: Domain Generalization by Seeking Flat Minima (NeurIPS'21) Official PyTorch implementation of SWAD: Domain Generalization by Seeking Flat Minima.

Junbum Cha 97 Dec 20, 2022
The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

Object-Placement-Assessment-Dataset-OPA Object-Placement-Assessment (OPA) is to verify whether a composite image is plausible in terms of the object p

BCMI 53 Nov 15, 2022
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Pavel Izmailov 25 Sep 21, 2022
QilingLab challenge writeup

qiling lab writeup shielder 在 2021/7/21 發布了 QilingLab 來幫助學習 qiling framwork 的用法,剛好最近有用到,順手解了一下並寫了一下 writeup。 前情提要 Qiling 是一款功能強大的模擬框架,和 qemu user mode

Yuan 17 Nov 17, 2022
Training DALL-E with volunteers from all over the Internet using hivemind and dalle-pytorch (NeurIPS 2021 demo)

Training DALL-E with volunteers from all over the Internet This repository is a part of the NeurIPS 2021 demonstration "Training Transformers Together

<a href=[email protected]"> 19 Dec 13, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022