{"id":120186,"date":"2024-03-16T18:00:32","date_gmt":"2024-03-16T12:30:32","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=120186"},"modified":"2024-03-16T18:13:44","modified_gmt":"2024-03-16T12:43:44","slug":"canny-edge-detection-using-opencv","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/canny-edge-detection-using-opencv\/","title":{"rendered":"Canny Edge Detection using OpenCV"},"content":{"rendered":"

Step into the realm where images reveal their hidden secrets. Welcome to the world of ‘OpenCV Canny Edge Detection.’<\/p>\n

In this journey, we’ll delve into the wizardry of algorithms that transform seemingly ordinary pictures into landscapes of edges and contours. Just as a skilled artist highlights every brushstroke, Canny Edge Detection illuminates the intricate details of objects, opening a gateway to a new dimension of image interpretation.<\/p>\n

Prepare to be captivated as we unravel the magic behind this technique and harness the power of OpenCV to see the unseen.<\/p>\n

Let’s first define edge detection, shall we?<\/h2>\n

A fundamental method of image processing called edge detection looks for boundaries inside a picture. In essence, it’s the process of highlighting abrupt changes in intensity or color between neighboring pixels. These changes often correspond to significant transitions in the underlying objects or structures present in the image.<\/p>\n

Edge detection algorithms work by analyzing the gradient of intensity or color in an image. They identify regions where the gradient is the steepest, indicating potential edges. The output of an edge detection process is typically a binary image where edge pixels are marked, providing a clear visual representation of object boundaries.<\/p>\n

One of the most popular and effective edge detection techniques is the Canny edge detection algorithm. It’s celebrated for its ability to accurately locate edges while minimizing false positives and negatives. Through careful processing involving blurring, gradient calculation, non-maximum suppression, and thresholding, the Canny algorithm transforms raw pixel data into a map of pronounced edges.<\/p>\n

Let\u2019s Know Hysteresis Thresholding<\/h3>\n

At this stage, it is determined which edges are actually edges and which ones are not. We require the threshold values minVal and maxVal for this. Any edges with gradients of intensity more than maxVal are certain to be edges, and any below minVal are certain to be non-edges, so they should be disregarded. Based on their connectedness, those who fall between these two thresholds are categorized as edges or non-edges. They are regarded as being a part of edges if they are linked to “sure-edge” pixels. If not, they are likewise thrown away.<\/p>\n

Look at the picture below:<\/strong><\/p>\n

\"Hysteresis<\/a><\/p>\n

The edge A is a “sure-edge” because it is above the maximum value. The whole curve is obtained even though edge C is below maxVal since it connects to edge A, which is likewise regarded as a legitimate edge. Although edge B is in the same region as edge C and is above minVal, it is not related to any other “sure-edges” and is therefore disregarded. Therefore, it is crucial that we choose minVal and maxVal appropriately in order to obtain the desired outcome.<\/p>\n

On the grounds that edges are long lines, this stage also eliminates noise from little pixels.<\/p>\n

Suppression :<\/h4>\n

After determining the gradient’s size and direction, the entire image is scanned to weed out any extraneous pixels that might not be the edge. For this, it is determined whether each pixel is a local maximum in its neighborhood in the gradient’s direction. See the illustration below:<\/p>\n

\"Hysteresis<\/a><\/p>\n

In a vertical sense, Point A is at the edge. The edge’s gradient direction is typical. Points B and C are on a gradient. So, points A, B, and C are examined to determine whether they constitute a local maximum. If so, it is taken into account for the following step; if not, it is suppressed (set to zero).<\/p>\n

A binary image with “thin edges” is the end outcome, to put it briefly.<\/p>\n

Canny Edge Detection in OpenCV: Revealing Hidden Structures<\/h3>\n

Unveil the magic of edge detection with the precision of OpenCV’s Canny algorithm. This section brings you into the heart of image interpretation, where edges are more than meets the eye. Dive into the workings of Canny edge detection, explore its key steps, and master its implementation through cv2.Canny().<\/p>\n

Working of Canny Edge Detection:<\/h4>\n

1. Smoothing:<\/strong><\/p>\n