OpenCV: Advanced Image Operations

OpenCV 4.5    |    Intermediate
  • 9 videos | 1h 8m 48s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 12 users Rating 4.4 of 12 users (12)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 2m 45s
    In this video, you’ll learn more about the course and your instructor. In this course, you’ll learn mathematical operations that can be applied to images.  You’ll also learn a variety of blur operations that can be performed from OpenCV. You’ll also learn how to perform a number of image transformations to detect the edges present in your image. Finally, you’ll cover the use of pre-trained classifiers to detect people in images. FREE ACCESS
  • 9m 22s
    In this video, you’ll watch a demo. In this demo, you’ll learn how the addition of noise and blur and smoothing operations can be performed in OpenCV. You’ll start with the import statements cv2, pyplot, and NumPy. Then, you’ll read in the file containing the image you’ll be working with. The resulting image is a NumPy ndarray and you’ll examine its shape and its color information. You’ll display this in your Jupyter notebook using pyplot. FREE ACCESS
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    3.  Using Gaussian and Median Blurs in OpenCV
    7m 30s
    In this video, you’ll watch a demo. In this demo, you’ll explore other types of blur functions. Following along onscreen, you’ll see the cv2.GaussianBlur. You’ll learn the basic idea of GaussianBlur remains the same. Every pixel's value is taken by averaging out the values of nearby pixels. With GaussianBlur, we apply values drawn from a normal distribution. This means it’s high at the center and becomes smaller as you move towards the edges. FREE ACCESS
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    4.  Detecting Edges in an Image Using OpenCV
    12m 46s
    In this video, you’ll watch a demo. In this demo, you’ll learn about edge detection. First, you’ll define what an edge is. An edge is a curve that connects points where the intensity of the RGB values of the pixels is different from surrounding pixels. The pixels on the edge are different from pixels close to the edge, but not on the edge. On the edge itself, the pixels are similar to each other. FREE ACCESS
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    5.  Drawing Shapes in Images Using OpenCV
    9m 58s
    In this video, you’ll watch a demo. In this demo, you’ll learn about drawing and adding text and shapes into images using OpenCV. You’ll begin with the import statements cv2, matplotlib's pyplot module, and NumPy. Next, you’ll read in the image you’ll be working with in this demo, moon.jpg, and you’ll read it in with cv2.imread. It will be displayed using pyplot. Next, you’ll convert it from BGR2RGB and examine the shape of the image. FREE ACCESS
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    6.  Adding Polygons, Arrows, and Text to an OpenCV Image
    3m 49s
    In this video, you’ll watch a demo. In this demo, you’ll continue to experiment with adding geometric shapes to images in OpenCV. In this demo, you’ll learn how to draw a polygon. First, you’ll define an array containing all of the vertexes of the polygon. You’ll do so using the NumPy np.array function, which is a two dimensional NumPy ndarray. FREE ACCESS
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    7.  Using OpenCV for People Detection in Images
    12m 3s
    In this video, you’ll watch a demo. In this demo, you’ll see how OpenCV supports the use of advanced pre-trained machine learning models, which can be used to quickly and easily detect people, faces, and eyes. You’ll follow along with several examples of the output of a pre-trained model from OpenCV. You’ll see the pre-trained models have correctly highlighted the faces of the people in the photographs. Then, you’ll learn how these can be used. FREE ACCESS
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    8.  Applying Morphological Transformations with OpenCV
    8m 34s
    In this video, you’ll watch a demo. In this demo, you’ll learn about morphological operations, erode, and dilate. You’ll learn the term morphological operations is a catch-all for various operations that apply a structuring element such as a kernel to an input image and generate an output image. Erosion and dilation are useful for noise reduction, for the isolation of individual elements in an image, and also for identifying bumps or holes. FREE ACCESS
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    9.  Course Summary
    2m 2s
    In this video, you’ll review what you’ve learned in the course. You’ve learned a variety of blur operations that can be performed with OpenCV. Those ranged from simple blurs to Gaussian and median blurs. You also performed a number of image transformations to detect the edges present in your image. You then learned to add shapes and objects of your own into your images. You also covered the use of pre-trained classifiers. FREE ACCESS

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