ORB Feature Detection

import cv2
import matplotlib.pyplot as plt
import numpy as np
 
# Load the image
image1 = cv2.imread('pexels-bestbe-models-2412691.jpg')
 
# Convert the training image to RGB
training_image = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
 
# Convert the training image to gray scale
training_gray = cv2.cvtColor(training_image, cv2.COLOR_RGB2GRAY)
 
# Create test image by adding Scale Invariance and Rotational Invariance
test_image = cv2.pyrDown(training_image)
test_image = cv2.pyrDown(test_image)
num_rows, num_cols = test_image.shape[:2]
 
rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), 30, 1)
test_image = cv2.warpAffine(test_image, rotation_matrix, (num_cols, num_rows))
 
test_gray = cv2.cvtColor(test_image, cv2.COLOR_RGB2GRAY)
 
# Display traning image and testing image
fx, plots = plt.subplots(1, 2, figsize=(20,10))
 
plots[0].set_title("Training Image")
plots[0].imshow(training_image)
 
plots[1].set_title("Testing Image")
plots[1].imshow(test_image)
 
 
orb = cv2.ORB_create()
 
train_keypoints, train_descriptor = orb.detectAndCompute(training_gray, None)
test_keypoints, test_descriptor = orb.detectAndCompute(test_gray, None)
 
keypoints_without_size = np.copy(training_image)
keypoints_with_size = np.copy(training_image)
 
cv2.drawKeypoints(training_image, train_keypoints, keypoints_without_size, color = (0, 255, 0))
 
cv2.drawKeypoints(training_image, train_keypoints, keypoints_with_size, flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
 
# Display image with and without keypoints size
fx, plots = plt.subplots(1, 2, figsize=(20,10))
 
plots[0].set_title("Train keypoints With Size")
plots[0].imshow(keypoints_with_size, cmap='gray')
 
plots[1].set_title("Train keypoints Without Size")
plots[1].imshow(keypoints_without_size, cmap='gray')
 
# Print the number of keypoints detected in the training image
print("Number of Keypoints Detected In The Training Image: ", len(train_keypoints))
 
# Print the number of keypoints detected in the query image
print("Number of Keypoints Detected In The Query Image: ", len(test_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(training_image, train_keypoints, test_gray, test_keypoints, matches, test_gray, flags = 2)
 
# Display the best matching points
plt.rcParams['figure.figsize'] = [14.0, 7.0]
plt.title('Best Matching Points')
plt.imshow(result)
plt.show()
 
# Print total number of matching points between the training and query images
print("\nNumber of Matching Keypoints Between The Training and Query Images: ", len(matches))