def sub_16_map(self, data_frame, dataset_images_features):
metadata_arr_list = list()
metadata_arr_list.append(['dorsal left', 1, 'male'])
metadata_arr_list.append(['dorsal left', 0, 'male'])
metadata_arr_list.append(['dorsal right', 1, 'male'])
metadata_arr_list.append(['dorsal right', 0, 'male'])
metadata_arr_list.append(['palmar left', 1, 'male'])
metadata_arr_list.append(['palmar left', 0, 'male'])
metadata_arr_list.append(['palmar right', 1, 'male'])
metadata_arr_list.append(['palmar right', 0, 'male'])
metadata_arr_list.append(['dorsal left', 1, 'female'])
metadata_arr_list.append(['dorsal left', 0, 'female'])
metadata_arr_list.append(['dorsal right', 1, 'female'])
metadata_arr_list.append(['dorsal right', 0, 'female'])
metadata_arr_list.append(['palmar left', 1, 'female'])
metadata_arr_list.append(['palmar left', 0, 'female'])
metadata_arr_list.append(['palmar right', 1, 'female'])
metadata_arr_list.append(['palmar right', 0, 'female'])
features_image = FeaturesImages('SIFT')
metadata_vectors_16_map = {}
count = 0
for metadata_arr in metadata_arr_list:
count = count + 1
sift_cluster_vector = self.get_metadata_sift_feature_vector(data_frame, metadata_arr, dataset_images_features)
metadata_vectors_16_map['combination'+str(count)] = sift_cluster_vector
metadata_vectors_16_map = features_image.compute_sift_new_features(metadata_vectors_16_map)
return metadata_vectors_16_map
def get_image_dataset_features(self):
features_obj = FeaturesImages(self.feature_name, self.test_folder_path)
features_obj.compute_features_images_folder()
self.image_feature_map = misc.load_from_pickle(self.pickle_file_folder,
self.feature_name)
self.images_list = list(self.image_feature_map.keys())
self.original_feature_map = copy.deepcopy(self.image_feature_map)
self.original_image_list = copy.deepcopy(self.images_list)
def get_database_image_features(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)
if not reduced_dimension:
path = os.path.dirname(__file__)
feature = self.model_name
features_images = FeaturesImages(self.model_name, test_folder_path)
# if not(os.path.exists(os.path.join(path, feature+'.pkl'))):
features_images.compute_features_images_folder()
test_image_features = features_images.compute_image_features(
test_image_path)
dataset_images_features = misc.load_from_pickle(
os.path.dirname(__file__), feature)
return test_image_features, dataset_images_features
# return dataset_images_features[self.test_image_id], dataset_images_features
else:
feature = self.model_name
reduced_dimension_pickle_path = os.path.join(
Path(os.path.dirname(__file__)).parent, 'Phase2',
'pickle_files')
if metadata_pickle:
dataset_image_features = misc.load_from_pickle(
reduced_dimension_pickle_path, metadata_pickle)
test_image_features = dataset_image_features[
self.test_image_id]
return test_image_features, dataset_image_features
if not (os.path.exists(
os.path.join(
reduced_dimension_pickle_path, feature + '_' +
decomposition.decomposition_name + '.pkl'))):
print(
'Pickle file not found for the Particular (model,Reduction)'
)
print(
'Runnning Task1 for the Particular (model,Reduction) to get the pickle file'
)
decomposition.dimensionality_reduction()
dataset_images_features = misc.load_from_pickle(
reduced_dimension_pickle_path,
feature + '_' + decomposition.decomposition_name, self.k)
test_image_features = dataset_images_features[self.test_image_id]
return test_image_features, dataset_images_features
def get_unlabelled_classification_image_features(self, image_id,
unlabelled_folder_path):
test_image_path = os.path.join(
Path(os.path.dirname(__file__)).parent, unlabelled_folder_path,
image_id)
features_images = FeaturesImages(
self.feature_name,
os.path.join(
Path(os.path.dirname(__file__)).parent,
unlabelled_folder_path))
unlabelled_image_features = features_images.compute_image_features(
test_image_path)
return [unlabelled_image_features]
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 __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 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)
def get_unlabelled_images_decomposed_features(self):
test_dataset_folder_path = os.path.abspath(
os.path.join(
Path(os.getcwd()).parent, self.unlabelled_dataset_path))
images_list = list(
misc.get_images_in_directory(test_dataset_folder_path).keys())
images_decomposed_features = {}
for image_id in images_list:
features_images = FeaturesImages(self.feature_name,
test_dataset_folder_path)
test_image_path = os.path.join(test_dataset_folder_path, image_id)
test_image_features = list()
test_image_features.append(
features_images.compute_image_features(test_image_path))
if self.decomposition_name != '':
decomposed_features = self.decomposition.decomposition_model.get_new_image_features_in_latent_space(
test_image_features)
images_decomposed_features[image_id] = decomposed_features
else:
images_decomposed_features[image_id] = test_image_features
return images_decomposed_features
def set_features(self):
if self.decomposition_name != '':
self.decomposition = Decomposition(self.decomposition_name, 100,
self.feature_name,
self.labelled_dataset_path)
self.decomposition.dimensionality_reduction()
else:
test_dataset_folder_path = os.path.abspath(
os.path.join(
Path(os.getcwd()).parent, self.labelled_dataset_path))
print('Getting the Model Features from Phase1')
features_obj = FeaturesImages(self.feature_name,
test_dataset_folder_path)
features_obj.compute_features_images_folder()
self.unlabelled_dataset_features = self.get_unlabelled_images_decomposed_features(
)
misc.save2pickle(self.unlabelled_dataset_features,
self.reduced_pickle_file_folder,
feature='unlabelled_' + self.decomposed_feature)
print("Getting features for dorsal_images ")
self.dorsal_features = self.get_features('dorsal')
print("Getting features for palmar images")
self.palmar_features = self.get_features('palmar')
import sys
from features_images import FeaturesImages
from similar_images import Similarity
task = input("Please specify the task number: ")
model = input("1.CM\n2.LBP\n3.HOG\n4.SIFT\nSelect model: ")
if task == '1':
image_id = input("Please specify the test image file name: ")
features_image = FeaturesImages(model)
features_image.compute_image_features(image_id, print_arr=True)
elif task == '2':
folder_path = input("Please specify test folder path: ")
features_folder = FeaturesImages(model, folder_path)
features_folder.compute_features_images_folder()
elif task == '3':
image_id = input("Please specify the test image file name: ")
k = int(input("Please specify the value of K: "))
test_dataset_path = input("Please specify test folder path: ")
similarity = Similarity(model, image_id, k)
similarity.get_similar_images(test_dataset_path)
def get_main_features(feature_name, dataset_folder_path):
folder = os.path.join(Path(os.path.dirname(__file__)).parent, 'Phase1')
feature_extraction_object = FeaturesImages(feature_name,
dataset_folder_path)
feature_extraction_object.compute_features_images_folder()
return misc.load_from_pickle(folder, feature_name)
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))