84 lines
No EOL
3.1 KiB
Python
84 lines
No EOL
3.1 KiB
Python
import cv2
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
def suft_function(image):
|
|
image1 = cv2.imread(image)
|
|
# 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)
|
|
|
|
|
|
# ## Detect keypoints and Create Descriptor
|
|
|
|
surf = cv2.xfeatures2d.SURF_create(800)
|
|
|
|
train_keypoints, train_descriptor = surf.detectAndCompute(training_gray, None)
|
|
test_keypoints, test_descriptor = surf.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))
|
|
|
|
|
|
# ## Matching Keypoints
|
|
|
|
# Create a Brute Force Matcher object.
|
|
bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck = False)
|
|
|
|
# Perform the matching between the SURF 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))
|
|
return keypoints_with_size |