OpenCV: Introduction

IN THIS COURSE

• 1.  Course Overview
  2m 16s
  In this video, you’ll learn more about the course and your instructor. In this course, you’ll learn about OpenCV. You’ll start by reading images from your file system into your Python source in the form of arrays and writing image arrays from the file. You’ll then move on to some basic image operations such as adding two images and subtracting one image from another. FREE ACCESS
• 2.  Installing OpenCV
  5m 10s
  In this video, you’ll watch a demo that explores OpenCV. You’ll use Jupyter notebooks for these demos. You’ll start by creating a directory called OpenCV. Then, you’ll use the ls -n command to view the contents of the current directory. Then, you’ll run the Jupyter notebook command to launch the Jupyter notebook server. Following onscreen directions, you’ll copy and paste a URL into your browser. Finally, you’ll install OpenCV. FREE ACCESS
• 3.  Reading and Writing Images with OpenCV
  11m 54s
  In this video, you’ll watch a demo that shows you how to open an image using OpenCV. You’ll also learn about the interoperability between OpenCV and some other popular Python packages. You’ll start by opening your Jupyter notebook. Then, you’ll open Python 3 and rename the notebook. Next, you’ll import cv2. Then, OpenCV is available for you to use. Next, you’ll learn how to read in an image. FREE ACCESS
• 4.  Using OpenCV to Load Images in Grayscale
  10m 53s
  In this video, you’ll learn how to use OpenCV to load images in grayscale. You’ll use Jupyter notebook to read the file you created in the last demo. You’ll use cv2.imread, with two input arguments. Your first input argument is the file path and the second is the flag which denotes how to read this file. You’ll specify a value, cv2.IMREAD_GRAYSCALE. This means the image will be read as grayscale. Then you’ll save the image. FREE ACCESS
• 5.  Transforming Color Space of an Image in OpenCV
  7m 37s
  In this video, you’ll learn how to use OpenCV to transform the color space of an image. You’ll learn how to change the color of the sky in the image you uploaded in the last demo. To do this, you’ll use another Python library called Pillow. You’ll need to invoke the pip install Pillow command. Pillow is a free, open-source image processing library that you’ll use to contrast different ways of working with channels. FREE ACCESS
• 6.  Separating Channels of a Color Image in OpenCV
  9m 20s
  In this video, you’ll learn how to use OpenCV to explore other color space transformations. Following onscreen directions, you’ll convert your image from BGR to grayscale. You’ll use cv2.cvtColor. Just like in previous demos, your first input argument is the image_array. The second input argument gives the enum value for the exact transformation that you want to perform, which is BGR2GRAY.  Now, you’ll see the array is two-dimensional. Next, you’ll perform another color space transformation. FREE ACCESS
• 7.  Using OpenCV's Add Operation to Combine Images
  9m 10s
  In this video, you’ll learn how to use OpenCV to combine images. In this demo, you’ll learn to manipulate images using simple arithmetic operations. You’ll get started by performing all of the requisite import statements. First, you’ll begin by importing cv2 and then pyplot with the alias plt, and numpy with the alias np. You’ll use cv2.imread, to pass in the path of an image. Then, you’ll save the resulting numpy three-dimensional as a variable. FREE ACCESS
• 8.  Subtracting Images in OpenCV
  9m 32s
  In this video, you’ll learn how to use OpenCV to subtract images. On screen, you’ve read two images into two variables called image_round and image_stars. Both of these have been read in using cv2.imread, so they will be in the BGR representation. You’ll view these using pyplot, and change from BGR to RGB. Then, you’ll examine the image_round to view the black bounding area. This area is important when subtracting one image from another. FREE ACCESS
• 9.  Course Summary
  1m 38s
  In this video, you’ll review what you’ve learned in the course. In this course, you’ve learned the fundamental concepts around computer vision and some of the basic operations which can be performed on images. You read images from your file system into your Python source in the form of arrays and wrote image arrays out to file. You explored how color images are represented and how these can be converted to grayscale images. FREE ACCESS