Matching Features with ORB using OpenCV (Python code) Matching Features with ORB and Brute Force using OpenCV (Python code) Today I will explain how to detect and match feature points using OpenCV. videofacerec.py example help. I will be using OpenCV 2.4.9. For feature matching, we will use the Brute Force matcher and FLANN-based matcher. Different behaviour of OpenCV Python arguments in 32 and 64-bit systems And the result is shown below. 2. ORB in OpenCV . It makes use of OpenCV's ORB feature mapping function for key-point extraction. Today I will show you a simple script using the ORB (oriented BRIEF), see C++ documentation / OpenCV. ORB is a good choice in low-power devices for panorama stitching etc. Match Features: In Lines 31-47 in C++ and in Lines 21-34 in Python we find the matching features in the two images, sort them by goodness of match and keep only a small percentage of original matches. cv2.perspectiveTransform() with Python. Lowe's ratio test is used for mapping the key-points. As a minor sidenote, I used this concept when I wrote a workaround for drawMatches because for OpenCV 2.4.x, the Python wrapper to the C++ function does not exist, so I made use of the above concept in locating the spatial coordinates of the matching features between the two images to write my own implementation of it. Load the images using imread() function and pass the path or name of the image as a parameter. As usual, we have to create an ORB object with the function, cv.ORB() or using feature2d common interface. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are Getting single frames from video with python. Line detection and timestamps, video, Python. Funtions we will be using: - cv2.VideoCapture() Check it out if you like! Each detected key-point from the image at '(t-1)' interval is matched with a number of key-points from the 't' interval image. It has a number of optional parameters. 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. A keypoint is the position where the feature has been detected, while the descriptor is an array containing numbers to describe that feature. Best Features are selected by Ratio test based on Lowe's paper. We finally display the good matches on the images and write the pip install opencv-python Approach: Import the OpenCV library. So, lets begin with our code. I run SIFT, SURF, and ORB using OpenCV with Python. Create the ORB detector for detecting the features of the images. Using the ORB detector find the keypoints and descriptors for both of the images. In this post we are going to use two popular methods: Scale Invariant Feature Transform (SIFT), and Oriented FAST and Rotated BRIEF (ORB). Brute-Force Matching with ORB detector In this section, we will demonstrate how two image descriptors can be matched using the brute-force matcher of opencv.In this, a descriptor of a feature from one image is matched with all the features in another image (using some distance metric), and the closest one is returned. Then a FLANN based KNN Matching is done with default parameters and k=2 for KNN. The paper says ORB is much faster than SURF and SIFT and ORB descriptor works better than SURF. Python correctMatches. To detect the Four Keypoints, I spent some time in Understanding the keypoints object and DMatch Object with opencv documentations and .cpp files in opencv cv2 bindings incompatible with numpy.dstack function? box.pgm for testing. Matching with ORB features using brute-force matching with python-opencv. Python findFundamentalMat.