Initial commit for push folder

recognization/code/suft.py

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+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