Visão Geral

O Curso Computer Vision with OpenCV and Python introduz os fundamentos da visão computacional, capacitando os participantes a desenvolver aplicações que entendem e interpretam imagens e vídeos. Usando Python e a biblioteca OpenCV, o curso cobre desde manipulação básica de imagens até técnicas avançadas como detecção de faces, rastreamento de objetos e reconhecimento de padrões.

Objetivo

Após realizar este curso Computer Vision with OpenCV and Python, você será capaz de:

• Compreender os fundamentos da visão computacional e do OpenCV
• Processar e analisar imagens digitalmente
• Implementar algoritmos de detecção de bordas, objetos e rostos
• Trabalhar com vídeo em tempo real e câmeras
• Criar aplicações práticas de visão computacional usando Python

Publico Alvo

• Desenvolvedores, cientistas de dados, engenheiros de software e estudantes interessados em inteligência artificial e visão computacional, que desejam aplicar algoritmos de processamento de imagem em projetos reais.

Pre-Requisitos

• Conhecimento básico de Python
• Noções de matemática (álgebra linear e estatística)
• Familiaridade com Jupyter Notebook e bibliotecas NumPy e Matplotlib

Materiais

Conteúdo Programatico

Introduction to Computer Vision

1. What is computer vision?
2. Applications in industry and research
3. Overview of OpenCV and its capabilities

Setting Up the Environment

1. Installing Python, OpenCV, and dependencies
2. Configuring Jupyter Notebook for image processing

Image Basics and Manipulation

1. Reading, displaying, and saving images
2. Understanding color spaces (RGB, HSV, Grayscale)
3. Image resizing, cropping, and transformations

Drawing and Geometric Transformations

1. Drawing shapes and text on images
2. Scaling, rotation, and affine transformations
3. Perspective warping and image alignment

Image Filtering and Enhancement

1. Blurring and smoothing
2. Edge detection (Sobel, Canny)
3. Histogram equalization and contrast adjustments

Morphological Operations

1. Erosion, dilation, opening, and closing
2. Removing noise and enhancing image structures

Contours and Object Detection

1. Finding and drawing contours
2. Shape analysis and object measurement
3. Detecting objects based on color and shape

Working with Video and Real-Time Processing

1. Capturing video from a webcam
2. Frame-by-frame processing
3. Motion detection and tracking

Face and Feature Detection

1. Using Haar Cascades and DNN models
2. Detecting facial landmarks
3. Real-time face recognition with OpenCV

Object Tracking and Recognition

1. Tracking algorithms (KCF, CSRT, MOSSE)
2. Feature detection (SIFT, SURF, ORB)
3. Matching keypoints between images

Integration with Machine Learning

1. Introduction to deep learning for computer vision
2. Using pre-trained models with OpenCV DNN module
3. Building a simple image classifier with scikit-learn

Practical Projects

1. Real-time face recognition system
2. Object detection using pretrained models
3. Image-based measurement application

Future of Computer Vision

1. Introduction to neural networks and CNNs
2. Overview of frameworks (TensorFlow, PyTorch)
3. Ethics and limitations in computer vision applications