def main():
logging.basicConfig(filename='preproces_dateset.log', level=logging.DEBUG)
logging.info("Scanning content of dataset")
content = file.scan_content(config.set_path)
logging.info("Dividing data into groups")
divided_content = divide_dataset.divide(content)
bounding_boxes = BoundingBox.get_bounding_boxes(config.bounding_boxes_path)
logging.info("Starting image preprocessing")
counter = 0
for key in divided_content.keys():
augment = False
if key == "training":
augment = True
logging.info("Training data will be augmented.")
database = h5py.File(config.get_convolution_datasets_path(key), 'w')
for (cls_name, img_name) in divided_content[key]:
counter += 1
if counter % config.take_every_nth_sample != 0:
continue
if cls_name not in database.keys():
database.create_group(cls_name)
cls_path = file.add_folder(config.set_path, cls_name)
if augment:
augmented_data = augment_images(
load_and_preprocess_img(cls_path, img_name,
bounding_boxes))
augmented_files = [
file.remove_extension(img_name) + "_" + str(i)
for i in range(len(augmented_data))
]
for img, name in zip(augmented_data, augmented_files):
database[cls_name].create_dataset(name, data=img)
else:
database[cls_name].create_dataset(
file.remove_extension(img_name),
data=load_and_preprocess_img(cls_path, img_name,
bounding_boxes))
database.close()
logging.info("Image loading finished")
for photo in split_ids[group_name]['data']:
class_name = photo[0]
if class_name not in group_db.keys():
group_db.create_group(class_name)
for i in range(0, config.data_multiplication_factor):
photo_name = photo[1] + "_" + str(i)
group_db[class_name].create_dataset(
photo_name, data=features_db[class_name][photo_name])
group_db.close()
if __name__ == "__main__":
logging.basicConfig(filename="sift.log", level=logging.DEBUG)
features_db = h5py.File(config.features_db_path, "w")
bounding_boxes = BoundingBox.get_bounding_boxes(config.bounding_boxes_path)
counter = 0
logging.info("Starting extraction")
for class_path in file.gen_subdir_path(config.set_path):
class_descriptors = features_db.create_group(
file.get_folder(class_path))
for photo_path, photo_name in file.gen_file_path(class_path):
counter += 1
if counter % config.take_every_nth_sample != 0:
continue
# removes file extension
photo_name_hash = file.remove_extension(photo_name)
bb = bounding_boxes[photo_name_hash]
photo_desc = execute_sift_extraction(photo_path, bb, 1)
for i, pic in enumerate(photo_desc):