def compute_image_features(self, image, print_arr=False):
image_feature = []
try:
image_path = os.path.join(os.path.dirname(__file__), image)
image = misc.read_image(image_path)
converted_image = misc.convert2gray(image)
if self.model_name == 'CM':
converted_image = misc.convert2yuv(image)
if self.model_name == 'HOG':
converted_image = misc.resize_image(converted_image,
(120, 160))
if self.split_windows:
windows = misc.split_into_windows(converted_image, 100, 100)
for window in windows:
window_pattern = self.model.compute(window)
if len(image_feature) == 0:
image_feature = window_pattern
else:
image_feature = np.concatenate(
[image_feature, window_pattern])
else:
image_feature = self.model.compute(converted_image)
except OSError as e:
print("Features_image", e.strerror)
sys.exit()
finally:
if not print_arr:
return image_feature
else:
print(image_feature)
def decode(path):
"""extract hidden message from image"""
mat = read_image(path)
height = len(mat)
width = len(mat[0])
seed(get_seed(mat))
gen_coo = gen_no_doubles(gen_random_coo(height, width))
for i in gen_message(gen_coo, mat):
yield i
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)
