This course will start your Computer Vision journey. You will learn how a computer extracts high-level understanding of what happens in a video. This will all be done by combining theory directly with hands on projects to speed up your learning curve. ComputerVision is one of most interesting areas in computer science. For obvious reasons: How can a computer understand what happens in an image or a video?It is simple for you and I to understand what happens in an image or a video. but it is not trivial for computers to gain that understandingAt the end of this course you will create two interactive Computer Vision games that extract high level understanding from a real-time webcam flow. All this will be achieved with no prior Computer Vision knowledge. We learn and built along the way. Combining Computer Vision theory immediately by implementing it in useful scenarios. This is a entertaining way to learn Computer Vision with practical projects at each stage in your learning journey. Most Computer Vision courses focus on covering a broad basis, with the cost of given a overload of information, which the student will not fully master. This course focuses on learning what is needed to make full interactive games, and it will cover the theory when needed to keep the student engaged and applying the concepts immediately. This will ensure the best learning experience. When you master something in depth, it will be easier to expand your basis to make more complex projects later. This is the best way to learn a new area. To make fully working projects based on a full understanding of the underlying theory. This is what this course gives you. Why learn Computer Vision with OpenCV and Python?If you want to use the strongest Computer Vision library supported by broad set of languages and most platformsOpenCV is a Computer Vision library and is highly optimized with focus on real-time applications. OpenCV integrates with C++, Python and Java interfaces on Linux, MacOS, Windows, iOS, and AndroidPython combines the power of being easy to learn and leaves the heavy processing in libraries (like OpenCV)The best learning practices applied in this courseNew concepts need to be applied immediately after you learn them, otherwise you will forget themYou need to understand why you need new concepts in order to be engaged in the learning processThis course has short learning cycles with motivated concepts that are immediately applied in projects. finally, if you want to build something entertaining, then you are highly motivatedHow will you benefit from this course?You will master Computer Vision approaches for real-time video applications. Have full projects with OpenCV in Python using your webcamMaster real-time processing of a video stream with OpenCV and PythonPractical programming experience on how Computer Vision extracts high level understanding of a live webcam streamHow to extract moving parts from a frameIf you want to become a comfortable with Computer Vision you need to have some basic understanding of the underlying concepts. This course will teach you the main principles in real-time Computer Vision and you will create two interactive games with your webcam stream. In this course we will cover all concepts for real-time application, like noise tolerant motion detection, inserting objects, interact with objects from webcam to the frames, and combining that to interactive games. This course covers the following. Update or install the newest Python and PyCharm (one of the best environment to develop Python code in).Install OpenCV and ensure you have correct version running. Understand how webcam can be configured and the limitations. Measure Frames-per-seconds and understand the process flow from webcam to screen. Understand how Python interacts with OpenCV and keeps processing speed high. Learn how frames are represented in Numpy and how they are processed. Basic Numpy understandning for OpenCV needs. Modifying frames: resize, gray scale, Gaussian blur. Working with region of interest (ROI) and inserting objects in framesHow motion detection works. Implementing a simple and noise tolerant motion detection. Optimizing processing for noise tolerant motion detection. Creating games where you interact through the webcam. The course is structured in an easy understandable wayStarting with the simple webcam processing flow with OpenCV and PythonAdding concepts and processing as we go along with each example having visual explanation and coding examplesStructure the code to easily expand the concepts and make more advanced processing Adding pieces together in a simple way - focus on keeping things understandableYou code along - you only learn by trying yourself - 40 coding lecturesAt each step you make the implementation along with me. You implement it in all stages to increase your understanding of Computer Vision with OpenCV and Python. Basically, we learn along the way with 40 coding lectures that adds further knowledge at each step. What is needed to fully understand this course?You have ba
