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
Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech

Meta-TTS: Meta-Learning for Few-shot SpeakerAdaptive Text-to-Speech This repository is the official implementation of "Meta-TTS: Meta-Learning for Few

Sung-Feng Huang 128 Dec 25, 2022
[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of

AccSrd 1 Sep 22, 2022
Very Deep Convolutional Networks for Large-Scale Image Recognition

pytorch-vgg Some scripts to convert the VGG-16 and VGG-19 models [1] from Caffe to PyTorch. The converted models can be used with the PyTorch model zo

Justin Johnson 217 Dec 05, 2022
Phy-Q: A Benchmark for Physical Reasoning

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

29 Dec 19, 2022
The ARCA23K baseline system

ARCA23K Baseline System This is the source code for the baseline system associated with the ARCA23K dataset. Details about ARCA23K and the baseline sy

4 Jul 02, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022
CvT-ASSD: Convolutional vision-Transformerbased Attentive Single Shot MultiBox Detector (ICTAI 2021 CCF-C 会议)The 33rd IEEE International Conference on Tools with Artificial Intelligence

CvT-ASSD including extra CvT, CvT-SSD, VGG-ASSD models original-code-website: https://github.com/albert-jin/CvT-SSD new-code-website: https://github.c

金伟强 -上海大学人工智能小渣渣~ 5 Mar 07, 2022
Wikidated : An Evolving Knowledge Graph Dataset of Wikidata’s Revision History

Wikidated Wikidated 1.0 is a dataset of Wikidata’s full revision history, which encodes changes between Wikidata revisions as sets of deletions and ad

Lukas Schmelzeisen 11 Aug 16, 2022
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

tflite2tensorflow Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite. 1. Supported Layers No. TFLite Layer TF

Katsuya Hyodo 214 Dec 29, 2022
This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust.

Demo BERT ONNX pipeline written in rust This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust. R

Xavier Tao 14 Dec 17, 2022
Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

Dynamic Vision and Learning Group 43 Dec 25, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
Contains supplementary materials for reproduce results in HMC divergence time estimation manuscript

Scalable Bayesian divergence time estimation with ratio transformations This repository contains the instructions and files to reproduce the analyses

Suchard Research Group 1 Sep 21, 2022
Reproducing code of hair style replacement method from Barbershorp.

Barbershorp Reproducing code of hair style replacement method from Barbershorp. Also reproduces II2S, an improved version of Image2StyleGAN. Requireme

1 Dec 24, 2021
Codes for "Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier"

Deep-RTC [project page] This repository contains the source code accompanying our ECCV 2020 paper. Solving Long-tailed Recognition with Deep Realistic

Gina Wu 16 May 26, 2022
Show-attend-and-tell - TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
FewBit — a library for memory efficient training of large neural networks

FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back

24 Oct 22, 2022
TensorFlow implementation of the algorithm in the paper "Decoupled Low-light Image Enhancement"

Decoupled Low-light Image Enhancement Shijie Hao1,2*, Xu Han1,2, Yanrong Guo1,2 & Meng Wang1,2 1Key Laboratory of Knowledge Engineering with Big Data

17 Apr 25, 2022
DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

Facebook Research 145 Dec 30, 2022
Examples of using f2py to get high-speed Fortran integrated with Python easily

f2py Examples Simple examples of using f2py to get high-speed Fortran integrated with Python easily. These examples are also useful to troubleshoot pr

Michael 35 Aug 21, 2022