def match_features(feature_coords1, feature_coords2, image1, image2):
"""
Computer Vision 600.461/661 Assignment 2
Args:
feature_coords1 (list of tuples): list of (row,col) tuple feature coordinates from image1
feature_coords2 (list of tuples): list of (row,col) tuple feature coordinates from image2
image1 (numpy.ndarray): The input image corresponding to features_coords1
image2 (numpy.ndarray): The input image corresponding to features_coords2
Returns:
matches (list of tuples): list of index pairs of possible matches. For example, if the 4-th feature in feature_coords1 and the 0-th feature
in feature_coords2 are determined to be matches, the list should contain (4,0).
"""
patch_size = 30
features_1 = filter_valid_features(feature_coords1, image1, patch_size)
features_2 = filter_valid_features(feature_coords2, image2, patch_size)
patches_1 = fetch_patches(image1, features_1, patch_size=patch_size)
patches_2 = fetch_patches(image2, features_2, patch_size=patch_size)
best_matches_1 = find_best_matches_one_way(patches_1, patches_2)
best_matches_2 = find_best_matches_one_way(patches_2, patches_1)
matches = find_best_matches_two_way(best_matches_1, best_matches_2)
return matches
def match_features(feature_coords1, feature_coords2, image1, image2): """ Computer Vision 600.461/661 Assignment 2 Args: feature_coords1 (list of tuples): list of (row,col) tuple feature coordinates from image1 feature_coords2 (list of tuples): list of (row,col) tuple feature coordinates from image2 image1 (numpy.ndarray): The input image corresponding to features_coords1 image2 (numpy.ndarray): The input image corresponding to features_coords2 Returns: matches (list of tuples): list of index pairs of possible matches. For example, if the 4-th feature in feature_coords1 and the 0-th feature in feature_coords2 are determined to be matches, the list should contain (4,0). """ patch_size = 30 features_1 = filter_valid_features(feature_coords1, image1, patch_size) features_2 = filter_valid_features(feature_coords2, image2, patch_size) patches_1 = fetch_patches(image1, features_1, patch_size=patch_size) patches_2 = fetch_patches(image2, features_2, patch_size=patch_size) best_matches_1 = find_best_matches_one_way(patches_1, patches_2) best_matches_2 = find_best_matches_one_way(patches_2, patches_1) matches = find_best_matches_two_way(best_matches_1, best_matches_2) return matches
def ssift_descriptor(feature_coords,image):
"""
Computer Vision 600.461/661 Assignment 2
Args:
feature_coords (list of tuples): list of (row,col) tuple feature coordinates from image
image (numpy.ndarray): The input image to compute ssift descriptors on. Note: this is NOT the image name or image path.
Returns:
descriptors (dictionary{(row,col): 128 dimensional list}): the keys are the feature coordinates (row,col) tuple and
the values are the 128 dimensional ssift feature descriptors.
"""
patch_size = 40
grid_number = 4
features = filter_valid_features(feature_coords, image, patch_size)
patches = fetch_patches(image, features, patch_size=patch_size)
patches_1_sift = map(lambda x: simple_sift(x, grid_number), patches)
output = {}
for ind, patch_sift in enumerate(patches_1_sift):
output[feature_coords[ind]] = patch_sift
return output
def ssift_descriptor(feature_coords, image):
"""
Computer Vision 600.461/661 Assignment 2
Args:
feature_coords (list of tuples): list of (row,col) tuple feature coordinates from image
image (numpy.ndarray): The input image to compute ssift descriptors on. Note: this is NOT the image name or image path.
Returns:
descriptors (dictionary{(row,col): 128 dimensional list}): the keys are the feature coordinates (row,col) tuple and
the values are the 128 dimensional ssift feature descriptors.
"""
patch_size = 40
grid_number = 4
features = filter_valid_features(feature_coords, image, patch_size)
patches = fetch_patches(image, features, patch_size=patch_size)
patches_1_sift = map(lambda x: simple_sift(x, grid_number), patches)
output = {}
for ind, patch_sift in enumerate(patches_1_sift):
output[feature_coords[ind]] = patch_sift
return output
