When we are using opencv to match multiple objects from an image, we usually do not know how many objects in image. In order to detect correct object numbers, we can use a threshold when detecting. In this tutorial, we will introduce you how to do.
We have introduced how to detect object using python opencv. However, it only can detect one object each time from an image. In this tutorial, we will introduce how to detect multiple objects from an image.
We have introduced six object detection algorithms in opencv. In this tutorial, we will use these six algorithms to detect object from an image.
In this tutorial, we will introduce how to replace a part of big image using an small image or numpy array, which is very useful when you plan to recognize objects from images.
In python opencv, we can use cv2.matchTemplate() to match images. Here are six template matching algorithms we can use. In this tutorial, we will discuss these algorithms.
OpenCV cv2.minMaxLoc() is often used to find the maximum and minimum value in a numpy array. In this tutorial, we will use an example to show you how to use this function.
OpenCV is a powerful tool to process images. In this tutorial, we will introduce how to read an image to numpy ndarray.