def plot_k_similar(self):
count = 1
m_image_rank_map = {}
for image, rank in self.page_ranking.items():
image_path = os.path.join(
Path(os.path.dirname(__file__)).parent, self.test_folder_path,
image)
m_image_rank_map[image_path] = rank
count += 1
if count > self.m:
break
# print(m_image_rank_map)
misc.plot_similar_images(m_image_rank_map, text='\nRanking: ')
def plot_subjects(self, main_subject, sub_sub_list,
test_image_directory_path):
sub_ids_list = [main_subject]
sub_sub_similarity_pairs = {}
for subject_pair in sub_sub_list:
sub_ids_list += [subject_pair[0]]
sub_sub_similarity_pairs[subject_pair[0]] = subject_pair[1]
if self.images_metadata is None:
self.set_images_metadata()
filtered_images_metadata = self.images_metadata
if self.test_images_list is not None:
filtered_images_metadata = filtered_images_metadata[(
filtered_images_metadata['imageName'].isin(
self.test_images_list))]
subject_map = {}
for sub_id in sub_ids_list:
is_subject_id = filtered_images_metadata['id'] == sub_id
subject_map[sub_id] = filtered_images_metadata[is_subject_id]
subject_images_list = {}
count = 0
for sub_id, data_frame in subject_map.items():
if sub_id == main_subject:
subject_images_list[sub_id] = {
'imageList': [
os.path.join(test_image_directory_path, image)
for image in data_frame['imageName'].tolist()
],
'value':
0
}
else:
subject_images_list[sub_id] = {
'imageList': [
os.path.join(test_image_directory_path, image)
for image in data_frame['imageName'].tolist()
],
'value':
sub_sub_similarity_pairs[sub_id]
}
count += 1
if count > 3:
break
misc.plot_similar_images(subject_images_list,
subject_subject_similarity=True)
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)
dataset_features = pickle.load(infile)
metadata = Metadata(metadatapath='Data/HandInfo.csv')
images_dop_dict = metadata.getimagesdop_dict()
for image_id, feature in dataset_features.items():
lsh.add_to_index_structure(input_feature=feature, image_id=image_id)
ret_val, no_of_images, unique_images = lsh.query(
dataset_features[q_image_id], num_results=t)
print('Query Image:', q_image_id, images_dop_dict[q_image_id])
print("Total Number of Images:", no_of_images)
print("Unique Images:", unique_images)
result = {}
for val in ret_val:
result[val[0]] = val[1]
misc.plot_similar_images(result)
lsh.save_result(result)
elif task == '6':
r = int(input('Number Of Images you would like to label as Relevant:'))
ir = int(input('Number of Images you would like to label as Irrelevant:'))
final_path = '../Phase2/pickle_files/HOG_SVD_11k.pkl'
print('loading from pickle file path', final_path)
infile = open(final_path, 'rb')
dataset_features = pickle.load(infile)
feedback = Feedback()
task5_result = feedback.task5_result
base_id = None
num_image = {}
count = 1
rorir_map = {}