Uses OpenCV and Python Code to detect a face on the screen

Last update: Feb 12, 2022

Related tags

Deep Learning simple-face-detection

Overview

Simple-Face-Detection

This code uses OpenCV and Python Code to detect a face on the screen. This serves as an example program.

Important prerequisites

You'll need the haarcascade_frontalface_default.xml file, available in this repository.

You'll also need to specify the path to this file inside the code.

If you don't have OpenCV then you'll need to install that for Python as well pip install opencv-python.

Tutorial

This gitHub repository was created to aid the tutorial video that was made for it, so if you want the video tutorial then the link is here - https://youtu.be/1ReddmESOjo

Owner

Denis Woolley (CreepyD)

trying to get better at programming

GitHub Repository

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Uses OpenCV and Python Code to detect a face on the screen

Related tags

Overview

Simple-Face-Detection

Important prerequisites

Tutorial

Owner

Denis Woolley (CreepyD)

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Uses OpenCV and Python Code to detect a face on the screen

Related tags

Overview

Simple-Face-Detection

Important prerequisites

Tutorial

Owner

Denis Woolley (CreepyD)

ALBERT-pytorch-implementation - ALBERT pytorch implementation

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Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Learning Representations that Support Robust Transfer of Predictors

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

Distance-Ratio-Based Formulation for Metric Learning

This is an official implementation for "PlaneRecNet".

Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

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Face uncertainty quantification or estimation using PyTorch.

Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

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This repository is dedicated to developing and maintaining code for experiments with wide neural networks.

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, and Trevor Darrell. CVPR 2015 and PAMI 2016.