# VIsualSizer
* [Award](#award)
* [Inspiration](#inspiration)
* [Demonstration](#demonstration)
* [What it does](#what-it-does)
* [Calculation logic](#calculation-logic)
* [Future Plans](#future-plans)
* [Technologies](#technologies)
## Award
This groundbreaking project ascended to the prestigious third place ð¥ at the Hackathon GreatUniHack 2023, held in Manchester, United Kingdom.
Link: https://devpost.com/software/body-measurement
## Inspiration
Imagine the thrill of discovering a stunning piece of clothing online, the anticipation as you wait for the package to arrive, and the excitement of wearing it for the first time. Now, imagine the crushing disappointment when that garment doesnât fit and is likely to be thrown away or not used.
The online fashion industry is worth $770 Billion. That is more money than the GDP of many countries...
This project is designed with the purpose to help you avoid the frustration of recieving an ill-fitting garment by revolutionizing the way we choose sizes for our clothes. We aim to automate the process, ensuring a perfect fit every time, transforming the online shopping experience, as well as reduce the quantity of clothing material wasted.
# Demonstration
![Demo](Demo.png)
## What it does
With a given picture and person`s height, it can meassure the length of the following body parts: head, shoulders, hands, torse, chest and legs.
## Calculation logic
Used Python powered by OpenCV, OpenPose, Pytorch, YOLO. Main idea is to use OpenPose to detect the human body points and combine some of them into vectors representing different body parts. Aftwerwards calculate the magnitude of vectors and using a proportion of the given input of the body`s height to the amount of pixels representing the height.
For the chest measurement we used the following approach: Turn the background of the picture into black and the body to white. Iterate over the pixels horizontally to check the difference between white pixels and detect the the beginning of the chest. Do this from the left and right parts, hence we completely eliminate hands from the picture. Calculate the horizontal length of the output image representing the lenght of the chest in pixels. Use the proportion described above to turn the lenght in pixels into cantimeters.
## Future Plans
1. Continue development and create an API so that it can be easily integrated with online shops.
2. Improve the user experience by developing the own web site.
3. Create a Docker package in order to make the installation of the library as easy as possible for everyone.
4. Implement a new functionality for calucalation of other body parts.
5. Implement a functionality to determine which body type this is by turning the body contour into mathematical function and do a mathematical analysis using direvatives.
## Technologies
Project is created with:
* Python 3.10.12
* OpenCV 4.6.0-dev
* OpenPose v1.7.0
* Cuda 11.7.r11.7
* PyTorch 1.13.1+cu117
* YOLO v5
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基于Python的人体测量信息分析应用程序(基于OpenCV、OpenPose、YOLO、PyTorch)
共42个文件
py:15个
pyc:8个
png:6个
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2024-12-08
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更智能地购物,减少浪费:计算机视觉满足您的完美需求。基于 Python 的人体测量信息分析应用程序(基于 OpenCV、OpenPose、YOLO、PyTorch) 它能做什么 根据给定的图片和人的身高,它可以测量以下身体部位的长度:头部、肩部、手部、躯干、胸部和腿部。 计算逻辑 使用由 OpenCV、OpenPose、Pytorch、YOLO 提供支持的 Python。主要思想是使用 OpenPose 检测人体点,并将其中一些点组合成代表不同身体部位的向量。Aftwerwards 计算向量的大小,并使用物体高度的给定输入与表示高度的像素数量的比例。 对于胸部测量,我们使用了以下方法:将图片的背景变为黑色,将身体变为白色。水平迭代像素以检查白色像素之间的差异并检测胸部的开头。从左侧和右侧部分执行此操作,因此我们完全从图片中消除了手。计算输出图像的水平长度,表示胸部的长度(以像素为单位)。使用上述比例将长度(以像素为单位)转换为高度计。
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VisualSizer-Great-Uni-Hack-main_2.zip (42个子文件)
VisualSizer-Great-Uni-Hack-main
mecd.jpg 97KB
additional-scripts
pytorch_testing.py 5KB
mecd.jpg 97KB
GraphToEquation_GH.py 4KB
output.png 13KB
pytorch_working_01.py 3KB
mecd_resized.jpg 28KB
find_head.py 489B
contour-detection-01.py 764B
mask_research_1.py 11KB
mask_research.py 11KB
json_data_out.py 275B
trackbar.py 1KB
__pycache__
find_head.cpython-310.pyc 789B
json_data_out.cpython-310.pyc 540B
roifind.cpython-310.pyc 2KB
roifind.py 3KB
mecd_keypoints.json 866B
output.png 13KB
pytorch_working_01.py 3KB
mecd_rendered_rendered_keypoints.json 866B
mecd_rendered_keypoints.json 871B
Demo.png 294KB
main_working.py 7KB
haarcascades
haarcascade_frontalface_alt.xml 661KB
haarcascade_frontalface_default.xml 908KB
haarcascade_frontalface_alt_tree.xml 2.56MB
haarcascade_frontalface_alt2.xml 528KB
mask_research.py 5KB
json_data_out.py 275B
images
mecd.jpg 97KB
mecd_rendered_rendered_rendered.png 547KB
mecd_rendered.png 548KB
mecd_rendered_rendered.png 549KB
__pycache__
mask_research.cpython-310.pyc 3KB
pytorch_working_01.cpython-310.pyc 3KB
main_working.cpython-310.pyc 4KB
json_data_out.cpython-310.pyc 521B
roifind.cpython-310.pyc 2KB
roifind.py 2KB
mecd_keypoints.json 866B
README.md 3KB
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