Visão Geral

O curso OpenCV for Deep Learning explora o uso da biblioteca OpenCV em conjunto com técnicas de aprendizado profundo (Deep Learning) para criar sistemas poderosos de visão computacional. Os participantes aprenderão como utilizar OpenCV para manipulação e processamento de imagens e integrar modelos de deep learning, como redes neurais convolucionais (CNNs), em projetos práticos de visão computacional. O curso aborda desde a classificação de imagens até a detecção de objetos e o reconhecimento facial, usando frameworks como TensorFlow e Keras.

Objetivo

Após realizar este Curso OpenCV for Deep Learning, você será capaz de:

• Integrar OpenCV com frameworks de deep learning para resolver problemas de visão computacional
• Criar e treinar redes neurais para reconhecimento de padrões em imagens
• Implementar projetos de classificação de imagens, detecção de objetos e reconhecimento facial
• Desenvolver aplicações em tempo real utilizando deep learning com OpenCV

Publico Alvo

• Cientistas de dados e engenheiros de machine learning
• Desenvolvedores de visão computacional
• Profissionais interessados em aprender deep learning aplicado à visão computacional
• Estudantes e pesquisadores da área de IA e visão computacional

Pre-Requisitos

• Conhecimento intermediário de Python
• Familiaridade básica com OpenCV
• Experiência prévia com deep learning e frameworks como TensorFlow ou Keras é recomendada

Materiais

Conteúdo Programatico

Introduction to OpenCV and Deep Learning

1. Overview of OpenCV and its capabilities
2. Introduction to neural networks and deep learning concepts
3. OpenCV integration with deep learning frameworks (TensorFlow, Keras)

Setting Up the Development Environment

1. Installing and configuring OpenCV and deep learning libraries
2. Overview of necessary dependencies (NumPy, TensorFlow, Keras)
3. Verifying the installation with a sample project

Image Preprocessing Techniques in OpenCV

1. Resizing, normalization, and data augmentation with OpenCV
2. Converting images between different color spaces
3. Preparing images for deep learning models

Deep Learning Model Integration

1. Introduction to pretrained models for deep learning
2. Loading and using deep learning models in OpenCV
3. TensorFlow and Keras integration with OpenCV

Convolutional Neural Networks (CNNs) for Image Classification

1. Understanding the architecture of CNNs
2. Using pretrained CNNs for image classification tasks
3. Building custom image classification models using Keras and TensorFlow

Object Detection with OpenCV

1. Introduction to object detection algorithms (YOLO, SSD)
2. Using YOLO and SSD models in OpenCV
3. Implementing real-time object detection systems

Face Detection and Recognition

1. Face detection using OpenCV's Haar Cascades and DNN module
2. Implementing face recognition using deep learning models
3. Building a face recognition system with OpenCV

Image Segmentation Techniques

1. Understanding semantic and instance segmentation
2. Using OpenCV with deep learning models for segmentation tasks
3. Applying pretrained segmentation models with OpenCV

Real-Time Deep Learning Applications

1. Deploying deep learning models for real-time image processing
2. Using OpenCV for video processing and live camera feeds
3. Optimizing deep learning models for real-time performance

Model Optimization and Deployment

1. Techniques for model optimization (quantization, pruning)
2. Using OpenCV to deploy optimized models on edge devices
3. Running deep learning models on resource-constrained environments

Final Project: Building a Real-Time Deep Learning Application

1. Developing a real-time deep learning application using OpenCV
2. Implementing an end-to-end solution for object detection or image classification
3. Deploying the project for live camera or video stream processing