ORB algorithm

ORB Algorithm/ORB_Algorithm.py

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+import cv2
+import numpy as np
+
+# Load the image
+path=input('Enter the path of the image: ')
+image = cv2.imread(path)
+path2=input('Enter the path for testing image: ')
+test_image=cv2.imread(path2)
+
+#Resizing the image
+image=cv2.resize(image,(600,600))
+test_image=cv2.resize(test_image,(600,600))
+
+# Convert the image to gray scale
+gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+test_gray = cv2.cvtColor(test_image, cv2.COLOR_RGB2GRAY)
+
+#Display the given and test image
+image_stack = np.concatenate((image, test_image), axis=1)
+cv2.imshow('image VS test_image', image_stack)
+
+#Implementing the ORB alogorithm
+orb = cv2.ORB_create()
+
+train_keypoints, train_descriptor = orb.detectAndCompute(gray, None)
+test_keypoints, test_descriptor = orb.detectAndCompute(test_gray, None)
+
+keypoints = np.copy(image)
+
+cv2.drawKeypoints(image, train_keypoints, keypoints, color = (0, 255, 0))
+
+# Display image with keypoints
+cv2.imshow('keypoints',keypoints)
+# Print the number of keypoints detected in the given image
+print("Number of Keypoints Detected In The Image: ", len(train_keypoints))
+
+# Create a Brute Force Matcher object.
+bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True)
+
+# Perform the matching between the ORB descriptors of the training image and the test image
+matches = bf.match(train_descriptor, test_descriptor)
+
+# The matches with shorter distance are the ones we want.
+matches = sorted(matches, key = lambda x : x.distance)
+
+result = cv2.drawMatches(image, train_keypoints, test_image, test_keypoints, matches, test_gray, flags = 2)
+
+# Display the best matching points
+cv2.imshow('result',result)
+
+#Naming the output image
+image_name = path.split(r'/')
+image_path = image_name[-1].split('.')
+output  = r"./ORB Algorithm/"+ image_path[0] + "(featureMatched).jpg"
+cv2.imwrite(output,result)
+
+# Print total number of matching points between the training and query images
+print("\nNumber of Matching Keypoints Between The input image and Test Image: ", len(matches))
+cv2.waitKey(0)
+cv2.destroyAllWindows()

ORB Algorithm/Readme.md

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+## ORB Algorithm
+In this script, we would use the **ORB(Oriented FAST Rotated Brief)** algorithm of `Open CV` for recognition and matching the features of image.
+
+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.
+
+## Setup Instructions
+- You need to install `OpenCV` and `Python` in your machine.
+
+## Output
+<img src="https://i.ibb.co/6Y8Z04s/Robert-input.png" width=400/> <img src="https://i.ibb.co/J7z8nqT/Robert-feature.png" width=400/>
+<img src="https://i.ibb.co/XCJjYYW/output-ORB.png"/>
+
+## Author
+[Shubham Gupta](https://github.com/ShubhamGupta577)