def batch_feature(folder_name):
gs = GraphSearch()
# TODO Remove all hard links.
print(len(files))
feature_store = []
df = DeepFeatures(feature_type={
'model': 'custom', 'input_layer': 'default', 'output_layer': 'fc2'})
# TODO Remove hardcoded value.
file_indexes = np.random.choice(len(files), 25000)
print(file_indexes.size)
# TODO Move batchsize to properties file.
batch_size = 128
image_batch = []
for idx, filenumber in enumerate(file_indexes):
print(idx)
image_decoded = scipy.ndimage.imread(
files[filenumber], flatten=False, mode=None)
# TODO Move hardcoded values to properties file.
image_decoded = transform.resize(image_decoded, [224, 224, 3])
image_decoded = np.expand_dims(image_decoded, axis=0)
if image_batch == []:
image_batch = image_decoded
else:
image_batch = np.concatenate((image_batch, image_decoded), axis=0)
if (not (idx) % (batch_size)) or (idx >= len(file_indexes) - 1):
print(image_batch.shape)
feature_store.extend(df.get_feature(image_batch))
image_batch = []
print('feature_store shape', np.array(feature_store).shape)
gs.create_index(np.array(feature_store, np.float32), file_indexes)
gs.save_index()
query = gs.knn(feature_store[0])
print(query)
def single_feature():
gs = GraphSearch()
# TODO Move to command line arguments
folder_name = '/Users/midhun/Downloads/kagglecatsanddogs_3367a/PetImages'
files = glob.glob(os.path.join(folder_name, '**/*.jpg'))
print(len(files))
feature_store = []
df = DeepFeatures()
file_indexes = np.random.choice(len(files), 10000)
print(file_indexes.size)
for idx, filenumber in enumerate(file_indexes):
print(idx)
image_decoded = scipy.ndimage.imread(
files[filenumber], flatten=False, mode=None)
image_decoded = transform.resize(image_decoded, [224, 224, 3])
image_decoded = np.expand_dims(image_decoded, axis=0)
feature_store.append(df.get_feature(image_decoded).ravel())
print(np.array(feature_store).shape)
gs.create_index(np.array(feature_store, np.float32), file_indexes)
gs.save_index()
query = gs.knn(feature_store[0])
print(query)