def get_similarity_value(self, images_list, dataset_images_features):
feature = self.model_name
features_images = FeaturesImages(feature)
model = features_images.get_model()
test_image_features = dataset_images_features[self.test_image_id]
similarity_value = 0
for sub_image_id in images_list:
subject_image_features = dataset_images_features[sub_image_id]
similarity_value = similarity_value + model.similarity_fn(
test_image_features, subject_image_features)
return similarity_value
def get_similar_images(self,
test_folder=None,
decomposition=None,
reduced_dimension=False,
metadata_pickle=None):
test_folder_path = os.path.join(
Path(os.path.dirname(__file__)).parent, test_folder)
test_image_path = os.path.join(test_folder_path, self.test_image_id)
try:
# Image is present
misc.read_image(test_image_path)
except FileNotFoundError:
print('ImageId is not in the folder specified.')
return
test_image_features, dataset_images_features = self.get_database_image_features(
test_folder, decomposition, reduced_dimension, metadata_pickle)
test_folder_path = os.path.join(
Path(os.path.dirname(__file__)).parent, test_folder)
features_images = FeaturesImages(self.model_name)
model = features_images.get_model()
ranking = {}
for image_id, feature_vector in tqdm(dataset_images_features.items()):
if image_id != self.test_image_id:
distance = model.similarity_fn(test_image_features,
feature_vector)
ranking[image_id] = distance
sorted_results = collections.OrderedDict(
sorted(ranking.items(),
key=lambda val: val[1],
reverse=model.reverse_sort))
top_k_items = {
item: sorted_results[item]
for item in list(sorted_results)[:self.k + 1]
}
plot_images = {}
for image_id in top_k_items.keys():
if image_id != self.test_image_id:
image_path = os.path.join(test_folder_path, image_id)
plot_images[image_path] = top_k_items[image_id]
print('Plotting Similar Images')
misc.plot_similar_images(plot_images)
class Decomposition:
def __init__(self, decomposition_name, k_components, feature_extraction_model_name, test_folder_path,
metadata_images_list=None, metadata_label=None):
self.decomposition_name = decomposition_name
self.k_components = k_components
self.decomposition_model = None
self.feature_extraction_model_name = feature_extraction_model_name
self.test_folder_path = test_folder_path
self.feature_extraction_object = FeaturesImages(self.feature_extraction_model_name, self.test_folder_path)
self.feature_extraction_model = self.feature_extraction_object.get_model()
self.database_matrix = []
self.database_image_id = []
self.reduced_pickle_file_folder = os.path.join(os.path.dirname(__file__), 'pickle_files')
self.metadata_images_list = metadata_images_list
self.metadata_label = metadata_label or ''
self.set_database_matrix()
def set_database_matrix(self):
parent_directory_path = Path(os.path.dirname(__file__)).parent
pickle_file_directory = os.path.join(parent_directory_path, 'Phase1')
print('Getting the Model Features from Phase1')
self.feature_extraction_object.compute_features_images_folder()
database_images_features = misc.load_from_pickle(pickle_file_directory, self.feature_extraction_model_name)
if self.metadata_images_list is not None:
print("Taking images based on metadata")
for image_id in self.metadata_images_list:
self.database_matrix.append(database_images_features[image_id])
self.database_image_id.append(image_id)
else:
for image_id, feature_vector in database_images_features.items():
self.database_matrix.append(feature_vector)
self.database_image_id.append(image_id)
def dimensionality_reduction(self):
# self.set_database_matrix()
# Note : when we have number of images <=20 or features <=20 , we are getting an error
# this is because the database_matrix has <=20 images and the reduction models,
# should have n_components parameters <= n,m
# Hence, we have to take the min(min(len(self.database_matrix[0]),len(self.database_matrix)),20)
if self.decomposition_name == 'PCA':
self.decomposition_model = PCAModel(self.database_matrix, self.k_components, self.database_image_id)
elif self.decomposition_name == 'SVD':
self.decomposition_model = SVD(self.database_matrix, self.k_components, self.database_image_id)
elif self.decomposition_name == 'NMF':
self.decomposition_model = NMFModel(self.database_matrix, self.k_components, self.database_image_id)
elif self.decomposition_name == 'LDA':
self.decomposition_model = LDAModel(self.database_matrix, self.k_components, self.database_image_id)
self.decomposition_model.decompose()
print('Decomposition Complete')
decomposed_database_matrix = self.decomposition_model.get_decomposed_data_matrix()
reduced_dimension_folder_images_dict = {}
for image_id, reduced_feature_vector in zip(self.database_image_id, decomposed_database_matrix):
reduced_dimension_folder_images_dict[image_id] = reduced_feature_vector
if self.metadata_label != '':
misc.save2pickle(reduced_dimension_folder_images_dict, self.reduced_pickle_file_folder,
feature=(self.feature_extraction_model_name+'_'+self.decomposition_name+
'_' + self.metadata_label))
else:
misc.save2pickle(reduced_dimension_folder_images_dict, self.reduced_pickle_file_folder,
feature=(self.feature_extraction_model_name + '_' + self.decomposition_name))