Aspire     Pythonista to Python Master     Python Master Track 5: Resource Optimization with Python

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Many image processing operations involve complex math, but when using OpenCV, much of that is abstracted from the developer. In this course, you'll gain a high-level understanding of advanced image operations in OpenCV.
You'll begin by recognizing how to apply different blur operations to an image. These range from simple blurs to Gaussian and median blurs. While doing so, you'll examine their specific advantages and disadvantages and how to distinguish between them.
Moving on, you'll outline how to highlight objects in an image using edge detection and augment images by adding shapes and objects to them.
Finally, you'll discover how to work with pre-trained classifiers to detect people in an image and perform morphological transformations to emphasize or suppress specific parts of an image.

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Objectives

OpenCV: Advanced Image Operations discover the key concepts covered in this course apply and blur noise in an image perform Gaussian and median blur operations to smoothen an image apply the Laplacian, Sobel, and Canny operators to detect edges in an image plot a circle, line, rectangle, and ellipse in an image introduce a text element, polygon, and arrow to an OpenCV image use trained classifiers to detect eyes, faces, and people in images apply morphological transformations such as erosion and dilation to emphasize specific features of an image summarize the key concepts covered in this course