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For example, let’s say that we have the contour of a square with dimensions \(100\times100 \) pixels. It is important to set an experimental number that will determine the contour area. We will create a for loop and we will iterate through all detected contours. To do that we can use the function cv2.contourArea(). So, we need to calculate the area of all contours and to sort them from largest to smallest. Once we extracted our contours we need to find out which one we want to process. On the other hand, a parameter cv2.CHAIN_APPROX_SIMPLE is used to compress the contours to save space. If we pass the parameter cv2.CHAIN_APPROX_NONE all contour points are stored. Using this argument we can specify how many contour points we want to store. The third argument is the contour approximation method. Panorama stitcher for four images python open cv python full#RETR_TREE – It retrieves all the contours and creates a full family hierarchy list.External contours are placed in hierarchy 1, and the contours of holes inside the object are placed in hierarchy 2. RETR_CCOMP – Retrieves all the contours and arranges them to a 2-level hierarchy.RETR_EXTERNAL -It returns only extreme outer flags (parent contours).RETR_LIST – It retrieves all the contours, but doesn’t create any parent-child relationship (they all belongs to the same hierarchy level). ![]() To determine the type of hierarchy between contours we can pass four different flags. In such a case, we call the outer shape as a parent and the inner shape as a child. To better understand this hierarchy we need to imagine a case where some shapes are located inside other shapes. The second argument is Contour Retrieval Mode which is used to determine the hierarchy between contours. In our code, we will use the function cv2.findContours(). OpenCV provides us several methods for that. Then, we are ready to extract the contours. This function consists of two input parameters: source image and clone image that we created before. Panorama stitcher for four images python open cv python code#We will continue by organizing our code within the function get_contours(). If you want to learn the theory and math behind this method you can visit the following post. It is a feature extraction technique used in image processing for detecting simple shapes such as circles and lines. Let’s first remind ourselves what the Hough transform method is. Detecting lines and circles using Hough transform In our code, we will use the Hough Transform line detection method. There are several functions in OpenCV we can use for this purpose. Instead of trying to find an identical match, we need to compare the corresponding properties between the original and the reference shape. That’s why we need to choose a different approach. However, this method will not work if two objects have different sizes. When the reference image overlaps the corresponding object in the input image we will get a large matching percentage and we will be able to detect the object. We will move the reference image across the input image and in each position, we will calculate an inner product. The idea of this method is to find a correlation between the object in the reference image and the object in the original image. We take another image of that object as a reference and try to identify that same shape in our original image. How can we do that? One way to do that is to apply a method called template matching (brute search method). Let’s say that we want to identify the shape of the square. Here, in the image above we can see several different shapes. Let’s take a look at the following image. Hence, to detect contours we need to apply threshold or Canny edge detection. The easiest way to do that is to use binary images (the object that we need to detect should be white and the background should be black). ![]() So, to detect shapes we first need to analyze and understand the contours of that shape. We can use various algorithms to analyze the contours of a lot of shapes that we are dealing with in the real world. In the computer vision field, contours can be a very useful tool for shape analysis and object detection and recognition. So, what exactly is an edge? If we look carefully at the image, we will notice that the edge represents a change in pixel intensity.Ĭontours can be seen as a curved line joining all the continuous points along the edge. Therefore, we can conclude from this example that sometimes simplified objects like contours (shapes) can help us to recognize the content of an image. In the process, we lost colors but we keep the most important part of an image – edges, and contours. Although the image has been altered, we can still recognize what is on it. Now, we can see the silhouette of the same objects. ![]()
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