Image Segmentation in OpenCV with Python and Contours

In this video tutorial, you'll learn how to perform image segmentation using Python OpenCV and contour detection. Image segmentation is a fundamental task in computer vision that involves dividing an image into multiple regions or segments in order to simplify its representation and make it easier to analyze. You'll start by loading an image and converting it to grayscale. Then, you'll apply a thresholding technique to convert the grayscale image into a binary image, where each pixel is either black or white based on a threshold value. Next, you'll use OpenCV's findContours() function to detect the contours in the binary image. Finally, you'll merge the detected contours with the original color image to produce an image where the segmented areas are shown in the original color, while the rest of the areas are shown as black. By the end of this tutorial, you'll have a solid understanding of how to perform image segmentation with Python OpenCV and contour detection, which can be applied to a wide range of computer vision tasks.

Code for this video: https://github.com/feitgemel/Open-CV/tree/main/Image-Segmentation-using-Contour-Detection

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