You can study the SIFT keypoints and descriptors in further detail here. The gradient at each pixel is regarded as a sample of a 3-D elementary feature vector, formed by the pixel location and the gradient orientation. A SIFT descriptor is a 3-D spatial histogram of the image gradients, characterizing the appearance of a keypoint. Its orientation (an angle expressed in radians)Ī SIFT detector searches for keypoints of different sizes at different positions in the image.Įach SIFT keypoint has a descriptor associated with it.The keypoint center coordinates x and y.img_1 = cv2.imread('STB_0032.JPG') plt.imshow(img_1) plt.show() img_2 = cv2.imread('STA_0031.JPG') plt.imshow(img_2) plt.show()Ĭompute the SIFT keypoints and descriptorsĪ Scale Invariant Feature Transform (SIFT) feature or keypoint, is a selected circular region of an image with an orientation. We shall be working on first two images from Case 17 of Dataset 2. So let’s quickly read the images from it. You can choose any two images you clicked yourself, or you can download any of the 3 datasets from here. Now that the libraries have been imported successfully, let’s move on to loading the images. import cv2 import matplotlib.pyplot as plt import numpy as np Hence, you need to install 'opencv-contrib-python=3.4.2.16'. Note: SIFT function, that we will be using in this tutorial, is patented. Numpy : Numerical Python works on an N-dimensional array object and provides basic and complex mathematical functionality for it.Matplotlib : It provides data visualization functionality.
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