In 2011, Opencv labs developed ORB which was an amazing alternative to SIFT and SURF. We control the number of features using the parameter MAX_FEATURES in the Python and C++ code. cv2.ORB() does not work, nor does cv2.xfeatures2d.ORB_create() or any other agglutination of commands. ORB stands for Oriented FAST and rotated BRIEF. Also, how do I initialize a numpy array with data type CV_8UC1 in python? Python+OpenCV：ORB: An efficient alternative to SIFT or SURF theory. # OpenCV Python program to detect cars in video frame # import libraries of python OpenCV import cv2 # capture frames from a video cap = cv2.VideoCapture('video.avi') # Trained XML classifiers describes some features of some object we want to detect car_cascade = cv2.CascadeClassifier('cars.xml') # loop runs if capturing has been initialized. In this post, we will learn how to perform feature-based image alignment using OpenCV. A keypoint is the position where the feature has been detected, while the descriptor is an array containing numbers to describe that feature. For binary string based descriptors like ORB, BRIEF, BRISK etc, cv2.NORM_HAMMING should be used, which used Hamming distance as measurement. In my previous question I learned that I had to install opencv-contrib in order to use OpenCV Python with external modules such as SIFT. Asked: 2015-07-29 11:43:41 -0500 Seen: 263 times Last updated: Jul 29 '15 Although we need only 4 features to compute the homography, typically hundreds of features are detected in the two images. In this tutorial, we will see what is ORB feature detector and how can we implement it in Python. Can I just use dtype=cv2.CV_8UC1. We will demonstrate the steps by way of an example in which we will align a photo of a form taken using a mobile phone to a template of the form. Lines 26-29 in the C++ code and Lines 16-19 in the Python code detect features and compute the descriptors using detectAndCompute. The […] # compute the homography matrix between the two sets of matched # points (H, mask) = cv2.findHomography(ptsA, ptsB, method=cv2.RANSAC) # use the homography matrix to align the images (h, w) = template.shape[:2] aligned = cv2.warpPerspective(image, H, (w, h)) # … Here is the code I am using currently, based on the assumptions I'm making above. It specifies the distance measurement to be used. Unlike SIFT and SURF, it … As an OpenCV enthusiast, the most important thing about the ORB is that it came from "OpenCV Labs". It’s faster and has less computation cost. But the issue is that I don't get any keypoints when I run detectAndCompute for either image. Stats. This algorithm was brought up by Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary R. Bradski in their paper ORB: An efficient alternative to SIFT or SURF in 2011. As SO knows, OpenCV has rather poor documentation for its Python API. In my project, however, I want to use ORB or something similar. def compute_orb_keypoints(filename): """ Takes in filename to read and computes ORB keypoints Returns image, keypoints and descriptors """ img = cv2.imread(filename) img = cv2.pyrDown(img) img = cv2.pyrDown(img) # img = cv2.pyrDown(img) # img = cv2.pyrDown(img) # create orb object orb = cv2.ORB_create() # set parameters orb.setScoreType(cv2.FAST_FEATURE_DETECTOR_TYPE_9_16) orb… sift = cv2.xfeatures2d.SIFT_create() surf = cv2.xfeatures2d.SURF_create() orb = cv2.ORB_create(nfeatures=1500) We find the keypoints and descriptors of each spefic algorythm. import cv2 import numpy as np input_image = cv2.imread('input.jpg') gray_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY) # Initiate ORB object orb = cv2.ORB() # find the keypoints with ORB keypoints = orb.detect(gray_image, None) # compute the descriptors with ORB keypoints, descriptors = orb.compute(gray_image, keypoints) # draw only the location of the keypoints … If ORB is using VTA_K == 3 or 4, cv2.NORM_HAMMING2 should be used. It is good for SIFT, SURF etc (cv2.NORM_L1 is also there). By default, it is cv2.NORM_L2. We will share code in both C++ and Python.