StopChildAbuse👶🏻

👩‍💻2021 NIA 인공지능 데이터 활용 경진대회 장려상 수상작

The 3rd prize of the 2021 NIA Artificial Intelligence Data Contest

'ChildKeeper' is an Automation system for automating mosaic and encryption processing to take out CCTV footage to prevent child abuse in daycare centers.

⚡TEAM _ NEXT LEVEL⚡

ChildKeeper started with NEXT LEVEL's hope that child abuse would no longer occur using this automation system.

🙈 Hyewon Kang(Team Leader) : https://github.com/HyewonKkang

🕵🏼‍♀️ Yujung Gil(Team Member) : https://github.com/fairyroad

🎅🏻 Minjeong Kim(Team Member) : https://github.com/kimminje0ng

👩🏻‍🚀 Su-A Jang(Team Member) : https://github.com/sua1223

🏊🏼‍♂️Tech Stack

Version Control

Git/Github

Frontend

Hybrid App : React

Backend

Framework : Django

DB

Image Data : MySQL

Development Environment

Visual studio code
Google colab
Mac OS Apple Silicon(M1)

Image Processing

Deep Sort
Yolo v5
PyTorch 1.7.0

Encryption

OpenCV-python 4.1.2
Pycryptodome

❤System Architecture❤

The service development environment is as above. The frontend of the web page used React, the backend used Django, and the database used MySQL. Face detection and tracking were performed for the image processing part using Yolo version 5 and Deepsort, and Pyroptodome and OpenCV were used for encryption, decryption, and mosaic parts.

📢How to test our face detection model?

• You can download a pt file here

  https://drive.google.com/drive/u/0/folders/1_MzpXoRL14ijcz9ESEaEfOveUHjxN0pP

• clone YOLOv5

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

• Download the dataset(facedataset -> save your local environment)

https://drive.google.com/file/d/1F70qUfhkLA4lAVOJvlGbdRM5QwlAUQT0/view?usp=sharing

• Modify data.yaml

[Exist Version]
train: ../train/images
val: ../valid/images

[Modification Version]
train: {your saved path}/yolov5/train.txt
val: {your saved path}/yolov5/test.txt
--------------------
[for using GPU server]
train: D:/11_23_yolo/yolov5/train.txt
val: D:/11_23_yolo/yolov5/test.txt

• Move maketxt.py into yolov5 folder and run it --> Being made train.txt, test.txt

$ python maketxt.py

• Run

$ python train.py --img 416 --batch 16 --epochs 100 --data data.yaml --weights yolov5s.pt --name result --cfg ./models/yolov5s.yaml

Google Colab

📢How to test the detect and tracking code?

$ git clone https://github.com/2021-NEXT-LEVEL/StopChildAbuse
$ cd detection_and_tracking
$ pip install -qr requirements.txt

#Enter the commands below using colab or jupyternotebook
import torch
from IPython.display import Image, clear_output  # to display images
clear_output()
print(f"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

#Enter the commands below
$ python track.py --yolo_weights [folder location]/Yolov5_DeepSort_Pytorch/yolov5/weights/best.pt --source heart_Trim_7.mp4[your video name] --save-vid

#You can check out put here
txt file for bounding box : Yolov5_DeepSort_Pytorch/inference/output/[your video name].txt
output mp4 file : Yolov5_DeepSort_Pytorch/inference/output/[your video name].mp4

📢How to run the website?

$ git clone https://github.com/2021-NEXT-LEVEL/StopChildAbuse.git

// backend
$ cd backend
$ python3 -m venv django_venv
$ source django_venv/bin/activate
(django_venv) $ pip install -r requirements.txt
(django_venv) $ python manage.py migrate
(django_venv) $ python manage.py createsuperuser
(django_venv) $ python manage.py runserver
# Load the site at http://127.0.0.1:8000/

// frontend
$ cd frontend
$ npm install
$ npm start

📌Service Prototype

You can watch the video through the YouTube link below.
Demo

Thanks, NIA and mikel-brostrom.