ORB is a fusion of FAST keypoint detector and BRIEF descriptor with some added features to improve the performance.FAST isFeatures from 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 as the BRIEF performs poorly with rotation. So what ORB does is to rotate the BRIEF according to the orientation of keypoints. 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 its use. But ORB is not patented.
In this tutorial, we are going to learn how to find the features in an image and match them with the other images in a continuous Video.
Algorithm
- Take the query image and convert it to grayscale.
- Now Initialize the ORB detector and detect the keypoints in query image and scene.
- Compute the descriptors belonging to both the images.
- Match the keypoints using Brute Force Matcher.
- Show the matched images.
Below is the implementation.
Input image:
Python3importnumpyasnpimportcv2# 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.jpgquery_img=cv2.imread('query.jpg')train_img=cv2.imread('train.jpg')# Convert it to grayscalequery_img_bw=cv2.cvtColor(query_img,cv2.COLOR_BGR2GRAY)train_img_bw=cv2.cvtColor(train_img,cv2.COLOR_BGR2GRAY)# Initialize the ORB detector algorithmorb=cv2.ORB_create()# Now detect the keypoints and compute# the descriptors for the query image# and train imagequeryKeypoints,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# keypointsmatcher=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 imagefinal_img=cv2.drawMatches(query_img,queryKeypoints,train_img,trainKeypoints,matches[:20],None)final_img=cv2.resize(final_img,(1000,650))# Show the final imagecv2.imshow("Matches",final_img)cv2.waitKey(3000)Output:
