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
GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images

GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-

VITA 298 Dec 12, 2022
Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

Dynamic VAE frame Automatic feature extraction can be achieved by probability di

10 Oct 07, 2022
Session-aware Item-combination Recommendation with Transformer Network

Session-aware Item-combination Recommendation with Transformer Network 2nd place (0.39224) code and report for IEEE BigData Cup 2021 Track1 Report EDA

Tzu-Heng Lin 6 Mar 10, 2022
Pytorch implementation of VAEs for heterogeneous likelihoods.

Heterogeneous VAEs Beware: This repository is under construction 🛠️ Pytorch implementation of different VAE models to model heterogeneous data. Here,

Adrián Javaloy 35 Nov 29, 2022
🔊 Audio and fastai v2

Fastaudio An audio module for fastai v2. We want to help you build audio machine learning applications while minimizing the need for audio domain expe

152 Dec 28, 2022
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
Experiments for Neural Flows paper

Neural Flows: Efficient Alternative to Neural ODEs [arxiv] TL;DR: We directly model the neural ODE solutions with neural flows, which is much faster a

54 Dec 07, 2022
This is a Keras implementation of a CNN for estimating age, gender and mask from a camera.

face-detector-age-gender This is a Keras implementation of a CNN for estimating age, gender and mask from a camera. Before run face detector app, expr

Devdreamsolution 2 Dec 04, 2021
一个多语言支持、易使用的 OCR 项目。An easy-to-use OCR project with multilingual support.

AgentOCR 简介 AgentOCR 是一个基于 PaddleOCR 和 ONNXRuntime 项目开发的一个使用简单、调用方便的 OCR 项目 本项目目前包含 Python Package 【AgentOCR】 和 OCR 标注软件 【AgentOCRLabeling】 使用指南 Pytho

AgentMaker 98 Nov 10, 2022
This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on table detection and table structure recognition.

WTW-Dataset This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on ICCV 2021. Here, you can download the

109 Dec 29, 2022
Code for our CVPR2021 paper coordinate attention

Coordinate Attention for Efficient Mobile Network Design (preprint) This repository is a PyTorch implementation of our coordinate attention (will appe

Qibin (Andrew) Hou 726 Jan 05, 2023
Weight estimation in CT by multi atlas techniques

maweight A Python package for multi-atlas based weight estimation for CT images, including segmentation by registration, feature extraction and model

György Kovács 0 Dec 24, 2021
[NeurIPS 2021 Spotlight] Code for Learning to Compose Visual Relations

Learning to Compose Visual Relations This is the pytorch codebase for the NeurIPS 2021 Spotlight paper Learning to Compose Visual Relations. Demo Imag

Nan Liu 88 Jan 04, 2023
A library for building and serving multi-node distributed faiss indices.

About Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don't fit into a single server memory. It fol

Meta Research 170 Dec 30, 2022
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"

Viewpoint Invariant Dense Matching for Visual Geolocalization: PyTorch implementation This is the implementation of the ICCV21 paper: G Berton, C. Mas

Gabriele Berton 44 Jan 03, 2023
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
[ACM MM 2021] TSA-Net: Tube Self-Attention Network for Action Quality Assessment

Tube Self-Attention Network (TSA-Net) This repository contains the PyTorch implementation for paper TSA-Net: Tube Self-Attention Network for Action Qu

ShunliWang 18 Dec 23, 2022
Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network.

face-mask-detection Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network. It contains 3 scr

amirsalar 13 Jan 18, 2022
Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

S-multi-SNE Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification A repository containing the code to reproduce the findings

Theodoulos Rodosthenous 3 Apr 15, 2022
The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Dr. Sander Ali Khowaja 1 May 08, 2022