def __get_attempt(self, exp, feature_vectors, which_metric):
# index reference for set creation
CMC = MetricRunner.CMC
STR = MetricRunner.STR
# index reference for cosine_distances which is in the format of [(car_id, cosine_distance)]
CHOSEN_CAR_INDEX = 0
COMP_CAR_INDEX = 1
COSINE_DISTANCE_INDEX = 2
logging.info("Generate set of images")
camsets = exp.generate()
logging.info("Match target car to its respective vector")
target_car_filename = utils.get_basename(exp.target_car.filepath)
logging.info("target {}".format(target_car_filename))
target_car_vector = feature_vectors[target_car_filename]
logging.info("Identify chosen set vs comparison set")
# chosen
chosen_set = {
CMC: [exp.target_car],
STR: camsets[0],
}.get(which_metric)
# comparison
comp_sets = {
CMC: camsets,
STR: camsets[1:],
}.get(which_metric)
logging.info("Calculate cosine distances between the sets")
cosine_distances = list()
for chosen_car in chosen_set:
logging.info(">> Match chosen car to its respective vector")
chosen_car_filename = utils.get_basename(chosen_car.filepath)
chosen_car_vector = feature_vectors[chosen_car_filename]
for comp_set in comp_sets:
for comp_car in comp_set:
logging.info(">> Match comparison car to its respective vector")
comp_car_filename = utils.get_basename(comp_car.filepath)
comp_car_vector = feature_vectors[comp_car_filename]
logging.info(">> Calculate the cosine distance")
cosine_distance = scipy.spatial.distance.cosine(chosen_car_vector, comp_car_vector)
cosine_distances.append((chosen_car.car_id, comp_car.car_id, cosine_distance))
logging.info(">> chosen {}, comp {}, cosine distance {}".format(chosen_car.filepath, comp_car.filepath, cosine_distance))
logging.info("Sort the cosine distances")
cosine_distances = sorted(cosine_distances, key=lambda tupl:tupl[COSINE_DISTANCE_INDEX])
logging.info("Determine how many times we have to go through the sorted list to find the matching target car")
attempt = {
CMC: utils.get_index_of_tuple(cosine_distances, COMP_CAR_INDEX, exp.target_car.car_id),
STR: utils.get_index_of_pairs(cosine_distances, CHOSEN_CAR_INDEX, COMP_CAR_INDEX, exp.target_car.car_id),
}.get(which_metric)
return attempt
def main(args):
# extract arguments from command line
dataset_path = args.dataset_path
set_type = args.set_type
dataset_type = args.dataset_type
dataset = chip.DatasetFactory.create_dataset(dataset_type, dataset_path,
set_type)
num_cams = args.num_cams
num_cars_per_cam = args.num_cars_per_cam
drop_percentage = args.drop_percentage
seed = args.seed
# create the generator
exp = ExperimentGenerator(dataset, num_cams, num_cars_per_cam,
drop_percentage, seed)
# generate the experiment
set_num = 1
print("=" * 80)
for camset in exp.generate():
print("Set #{}".format(set_num))
print("Target car: {}".format(
utils.get_basename(exp.target_car.filepath)))
print("-" * 80)
for image in camset:
print("filepath: {}".format(image.filepath))
print("car id: {}".format(image.car_id))
print("camera id: {}".format(image.cam_id))
print("timestamp: {}".format(utils.get_timestamp(image.time)))
if image.misc is not None:
for key, value in image.misc.items():
print("{}: {}".format(key, value))
print("-" * 80)
print("=" * 80)
set_num = set_num + 1
return
def test_get_basename():
TEST_FILEPATHS = (("/path/to/file/hello.py", "hello.py"), ("hello.py",
"hello.py"))
for test_input, answer in TEST_FILEPATHS:
assert answer == utils.get_basename(test_input)