Feature Matching
ORB is a fusion of the FAST keypoint detector and BRIEF descriptor with some added features to improve the performance. FAST is Features from the Accelerated Segment Test used to detect features from the provided image. It also uses a pyramid to produce multiscale features. Now it doesn’t compute the orientation and descriptors for the features, so this is where BRIEF comes in the role.
ORB uses BRIEF descriptors but the BRIEF performs poorly with rotation. So what ORB does is rotate the BRIEF according to the orientation of key points. Using the orientation of the patch, its rotation matrix is found and rotates the BRIEF to get the rotated version. ORB is an efficient alternative to SIFT or SURF algorithms used for feature extraction, in computation cost, matching performance, and mainly the patents. SIFT and SURF are patented and you are supposed to pay them for their use. But ORB is not patented.
Python3
import numpy as np import cv2 # Read the query image as query_img # and train image This query image # is what you need to find in train image # Save it in the same directory # with the name image.jpg query_img = cv2.imread('geeks.png') train_img = cv2.imread('geeks.png') # Convert it to grayscale query_img_bw = cv2.cvtColor(query_img,cv2.COLOR_BGR2GRAY) train_img_bw = cv2.cvtColor(train_img, cv2.COLOR_BGR2GRAY) # Initialize the ORB detector algorithm orb = cv2.ORB_create() # Now detect the keypoints and compute # the descriptors for the query image # and train image queryKeypoints, queryDescriptors = orb.detectAndCompute(query_img_bw,None) trainKeypoints, trainDescriptors = orb.detectAndCompute(train_img_bw,None) # Initialize the Matcher for matching # the keypoints and then match the # keypoints matcher = cv2.BFMatcher() matches = matcher.match(queryDescriptors,trainDescriptors) # draw the matches to the final image # containing both the images the drawMatches() # function takes both images and keypoints # and outputs the matched query image with # its train image final_img = cv2.drawMatches(query_img, queryKeypoints, train_img, trainKeypoints, matches[:20],None) final_img = cv2.resize(final_img, (1000,650)) # Show the final image cv2.imshow("Matches", final_img) cv2.waitKey(0) cv2.destroyAllWindows() |
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Getting Started with Python OpenCV
Computer Vision is one of the techniques from which we can understand images and videos and can extract information from them. It is a subset of artificial intelligence that collects information from digital images or videos.
Python OpenCV is the most popular computer vision library. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it is integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis.
In this article, we will discuss Python OpenCV in detail along with some common operations like resizing, cropping, reading, saving images, etc with the help of good examples.