ICS4U – Computer Vision Using Python
ICS4U Learning Goals
In this ICS4U Grade 12 Computer Science lesson you will be learning about
- Tasks that can be accomplished using computer vision
- Using OpenCV Python to make a Computer Vision Project
What is Computer Vision
Computer vision is a field of artificial intelligence (AI) and computer science that focuses on enabling computers to interpret and understand visual information from the real world. It involves developing algorithms and techniques that allow computers to analyze and extract meaningful insights from images or videos, similar to how humans perceive and interpret visual data.
Aspects of Computer Vision
Image Acquisition: The process of capturing visual data using cameras or other imaging devices. This may involve still images or video streams.
Preprocessing: Cleaning and enhancing raw image data to improve its quality and suitability for analysis. This may include tasks such as noise reduction, image stabilization, and color correction.
Feature Extraction: Identifying and extracting relevant features or patterns from the visual data, such as edges, shapes, textures, or keypoints. This step is crucial for representing the visual information in a format that can be analyzed and interpreted by computer algorithms.
Object Detection and Recognition: Identifying and locating specific objects or entities within an image or video. This may involve tasks such as object detection, classification, segmentation, and tracking.
Scene Understanding: Interpreting the overall context and semantics of a scene or environment depicted in visual data. This may include tasks such as scene classification, semantic segmentation, and depth estimation.
Image Understanding: Analyzing and interpreting the content and meaning of images or videos in a more holistic sense, beyond just object recognition. This may involve higher-level tasks such as image captioning, image retrieval, and visual question answering
Deep Learning with Computer Vision
Deep learning is a subfield of machine learning that utilizes neural networks with multiple layers (hence the term “deep”) to automatically learn hierarchical representations of data. In computer vision, deep learning has revolutionized the field by enabling the development of highly effective and accurate models for various tasks.
Here are some ways deep learning is used in computer vision
Image Classification: Deep learning models can classify images into predefined categories or labels. Convolutional Neural Networks (CNNs) are commonly used for image classification tasks, where the network learns to extract relevant features from input images and make predictions about their content.
Object Detection: Deep learning models can detect and localize objects within images, identifying their presence and locations. Techniques such as Region-based CNNs (R-CNNs), Faster R-CNN, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO) are commonly used for object detection tasks.
OpenCV
OpenCV, short for Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. It provides a comprehensive set of tools, algorithms, and functions for performing various tasks related to computer vision, image processing, and machine learning.
Originally developed by Intel in the late 1990s, OpenCV has since become one of the most widely used and popular libraries in the field of computer vision. It is written in C++ and optimized for performance, but it also provides interfaces and bindings for several programming languages, including Python, Java, and MATLAB, making it accessible to a broad community of developers and researchers.
OpenCV is one of the main tools that you would need to use in python to accomplish a lot of computer vision tasks