Automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure

Overview

fwhr-calc-website

This project is to automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure. Used in

Built with

  • Python 3.6
  • Dlib
  • Opencv
  • Flask

Getting started

Prerequisites

  1. python version 3.6 with Anaconda distribution (no guarantee for other versions)
    • You can download Anaconda Individual Edition in [here] (https://www.anaconda.com/products/individual)
    • Check your anaconda installation by conda -V
    • Create a virtual environment by conda create -n [name] python=3.6 and activate the venv by conda activate [name]
  2. Clone this repo.
    • git clone https://github.com/haileypark-kr/fwhr-calc-website.git
  3. Microsoft Azure Face Api Key
    1. Create an Azure account and a Cognitive Service Face API resource in Azure Portal. Read [this] (https://docs.microsoft.com/en-us/azure/cognitive-services/face/) documentation.
    2. Generate keys to access your API. (Resource Management > Keys and Endpoint)
    3. Make a file named azure_faceapi_key.conf and paste the first key in the file. (you can change the file name if you want, but make sure you also change .gitignore and config.py) Do not upload this file to GitHub.
    4. Replace the variable FACE_API_ENDPOINT in config.py with your endpoint.
      # config.py
      
      FACE_API_ENDPOINT = "https://eastasia.api.cognitive.microsoft.com"
      

Installation

Install python libraries in this project's root directory.

  • pip install -r requirements.txt
  • Some libraries (dlib) cannot be installed by pip - should be installed using conda with conda install -y -c conda-forge dlib

Usage

There are two ways to run this application.

  • Running a flask web server: If you want to analyze a few facial images with GUI.
  • Running fWHR calcaculating script: If you want to analyze thousands of images

Running a flask web server

  1. Command: python app.py
  2. Open a Chrome browser and enter 127.0.0.1:5001
  3. Select some images and press Submit button.
  4. Wait and do not reload the browser.
  5. Anlysis result will be downloaded shortly (in xlsx format)

Running fWHR calcaculating script

  1. Command: python fWHR_main.py --dataroot [path to the image directory]
  2. Wait
  3. Go to data/output direcetory and get the analysis result file.
Owner
SoohyunPark
Soohyun Park. Interests in computer vision and backend
SoohyunPark
CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.

CausalNLP CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. Install pip install -U

Arun S. Maiya 95 Jan 03, 2023
A PyTorch re-implementation of Neural Radiance Fields

nerf-pytorch A PyTorch re-implementation Project | Video | Paper NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall

Krishna Murthy 709 Jan 09, 2023
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

Nicola Dall'Asen 10 May 24, 2022
An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Retina Blood Vessels Segmentation This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional

Srijarko Roy 23 Aug 20, 2022
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representa

Bats Research 94 Nov 21, 2022
Research code for the paper "Variational Gibbs inference for statistical estimation from incomplete data".

Variational Gibbs inference (VGI) This repository contains the research code for Simkus, V., Rhodes, B., Gutmann, M. U., 2021. Variational Gibbs infer

Vaidotas Šimkus 1 Apr 08, 2022
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur

FFCV 92 Dec 31, 2022
OBBDetection: an oriented object detection toolbox modified from MMdetection

OBBDetection note: If you have questions or good suggestions, feel free to propose issues and contact me. introduction OBBDetection is an oriented obj

MIXIAOXIN_HO 3 Nov 11, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an

VITA 112 Nov 07, 2022
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch

CoCa - Pytorch Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch. They were able to elegantly fit in contras

Phil Wang 565 Dec 30, 2022
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project

This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create

M Faber 769 Dec 08, 2022
22 Oct 14, 2022
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

LMFD-PAD Note This is the official repository of the paper: LMFD-PAD: Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechani

28 Dec 02, 2022
Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming

Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming. Outperforming `GPT-3` on SuperGLUE Few-Shot text classification.

YerevaNN 75 Nov 06, 2022
N-RPG - Novel role playing game da turfu

N-RPG Ce README sera la page de garde du projet. Contenu Il contiendra la présen

4 Mar 15, 2022
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
Label-Free Model Evaluation with Semi-Structured Dataset Representations

Label-Free Model Evaluation with Semi-Structured Dataset Representations Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch

8 Oct 06, 2022
Conformer: Local Features Coupling Global Representations for Visual Recognition

Conformer: Local Features Coupling Global Representations for Visual Recognition (arxiv) This repository is built upon DeiT and timm Usage First, inst

Zhiliang Peng 378 Jan 08, 2023