def test_FileJson_interface():
# Given the following directory and dictionary data
test_file, test_data = generate_file_and_data(data_type="dict")
# When perform write it to file and read
file_io.FileJson().write(test_data, test_file)
read_data = file_io.FileJson().read(test_file)
# Then it should be equal to the original test_data
assert test_data == read_data
# Remove test files and directory
shutil.rmtree(os.path.dirname(test_file))
"--n_test_image",
help="Number of Test Images",
default=DEFAULT_N_TEST_IMAGE,
type=int)
parser.add_argument('-n',
"--nms",
help="Non Maxima Suppresiion",
default=DEFAULT_NMS,
type=int)
parser.add_argument('-s',
"--show_operation",
help="Show Detect Running Operation",
default=DEFAULT_SHOW_OP,
type=int)
args = vars(parser.parse_args())
conf = file_io.FileJson().read(args["config"])
test_image_files = file_io.list_files(conf["dataset"]["pos_data_dir"])
test_image_files = test_image_files[:args["n_test_image"]]
# 2. Build detector and save it
detector = factory.Factory.create_detector(
conf["descriptor"]["algorithm"], conf["descriptor"]["parameters"],
conf["classifier"]["algorithm"], conf["classifier"]["parameters"],
conf["classifier"]["output_file"])
# 3. Run detector on Test images
for image_file in test_image_files:
test_image = cv2.imread(image_file)
#test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
#-*- coding: utf-8 -*-
import subprocess
import object_detector.file_io as file_io
CONFIG_FILE = "conf/car_side.json"
#CONFIG_FILE = "conf/faces.json"
N_RUNNING_TEST_IMAGE = 1
N_EVALUATION_TEST_IMAGE = "all"
SHOW_RUNNING_OPERATION = 0
if __name__ == "__main__":
conf = file_io.FileJson().read(CONFIG_FILE)
print "\n[Step 1] Getting average size of the target object.\n\
You can set or edit your detector's window size referring this result.\n\
config_file.json[\"detector\"][\"window_dim\"]\n\
[INFO] current setting is {}".format(
conf["detector"]["window_dim"])
subprocess.call("python 1_get_object_size.py -c {}".format(CONFIG_FILE))
print "\n[Step 2] Extracting features"
subprocess.call("python 2_extract_features.py -c {}".format(CONFIG_FILE))
print "\n[Step 3] Training classifier. (Note: It can be time-consuming task)"
subprocess.call("python 3_train.py -c {} -i {}".format(CONFIG_FILE, False))
print "\n[Step 4] Gathering Hard Negative Samples"
subprocess.call("python 4_hard_negative_mine.py -c {}".format(CONFIG_FILE))
if __name__ == "__main__":
# 1. Load configuration file and test images
parser = ap.ArgumentParser()
parser.add_argument('-c',
"--config",
help="Configuration File",
default=DEFAULT_CONFIG_FILE)
parser.add_argument('-t',
"--n_test_image",
help="Number of Test Images",
default=DEFAULT_N_TEST_IMAGE,
type=str)
args = vars(parser.parse_args())
conf = file_io.FileJson().read(DEFAULT_CONFIG_FILE)
test_image_files = file_io.list_files(conf["dataset"]["pos_data_dir"])
if args["n_test_image"] == "all":
pass
else:
n_test_imgs = int(args["n_test_image"])
test_image_files = test_image_files[:n_test_imgs]
print len(test_image_files)
# 2. Build detector
detector = factory.Factory.create_detector(
conf["descriptor"]["algorithm"], conf["descriptor"]["parameters"],
conf["classifier"]["algorithm"], conf["classifier"]["parameters"],
conf["classifier"]["output_file"])