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Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems for detecting human behavior Key Features Understand OpenCV and select the right algorithm to solve real-world problems Discover techniques for image and video processing Learn how to apply face recognition in videos to automatically extract key information Book Description Computer Vision (CV) has become an important aspect of AI technology. From driverless cars to medical diagnostics and monitoring the health of crops to fraud detection in banking, computer vision is used across all domains to automate tasks. The Computer Vision Workshop will help you understand how computers master the art of processing digital images and videos to mimic human activities. Starting with an introduction to the OpenCV library, you'll learn how to write your first script using basic image processing operations. You'll then get to grips with essential image and video processing techniques such as histograms, contours, and face processing. As you progress, you'll become familiar with advanced computer vision and deep learning concepts, such as object detection, tracking, and recognition, and finally shift your focus from 2D to 3D visualization. This CV course will enable you to experiment with camera calibration and explore both passive and active canonical 3D reconstruction methods. By the end of this book, you'll have developed the practical skills necessary for building powerful applications to solve computer vision problems. What you will learn Access and manipulate pixels in OpenCV using BGR and grayscale images Create histograms to better understand image content Use contours for shape analysis, object detection, and recognition Track objects in videos using a variety of trackers available in OpenCV Discover how to apply face recognition tasks using computer vision techniques Visualize 3D objects in point clouds and polygon meshes using Open3D