def get_data(input_txt_file, input_image_dir, mean_image, beaconmap,
mean_beacon, use_fixed_input_mean_std):
image_filenames = []
poses = []
images = []
beacons = []
with open(input_txt_file) as f:
# skip header
next(f)
next(f)
next(f)
for line in f:
fname, beaconstr, p0, p1, p2, p3, p4, p5, p6 = line.split()
quat = posenet_geom_utils.preprocess_quaternion(
[float(p3), float(p4),
float(p5), float(p6)])
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = quat[0]
p4 = quat[1]
p5 = quat[2]
p6 = quat[3]
poses.append((p0, p1, p2, p3, p4, p5, p6))
image_filenames.append(os.path.join(input_image_dir, fname))
beacon = posenet_beacon_utils.parse_beacon_string(
beaconstr, beaconmap)
beacons.append(beacon)
beacons = posenet_beacon_utils.preprocess_test_beacons(
beacons, mean_beacon, use_fixed_input_mean_std)
return datasource(image_filenames, beacons, poses), image_filenames
def get_data(input_txt_file, input_image_dir):
image_filenames = []
poses = []
with open(input_txt_file) as f:
# skip header
next(f)
next(f)
next(f)
for line in f:
fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
quat = posenet_geom_utils.preprocess_quaternion(
[float(p3), float(p4),
float(p5), float(p6)])
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = quat[0]
p4 = quat[1]
p5 = quat[2]
p6 = quat[3]
poses.append((p0, p1, p2, p3, p4, p5, p6))
image_filenames.append(os.path.join(input_image_dir, fname))
return datasource(image_filenames, poses), image_filenames
def get_beacon_data(input_txt_file, beaconmap, beacon_num,
use_fixed_input_mean_std, use_augmentation_beacon):
poses_index = []
beacons_index = []
poses_data = []
beacons_data = []
with open(input_txt_file) as f:
# skip header
next(f)
next(f)
next(f)
for line in f:
beaconstr, p0, p1, p2, p3, p4, p5, p6 = line.split()
quat = posenet_geom_utils.preprocess_quaternion(
[float(p3), float(p4),
float(p5), float(p6)])
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = quat[0]
p4 = quat[1]
p5 = quat[2]
p6 = quat[3]
poses_data.append((p0, p1, p2, p3, p4, p5, p6))
poses_index.append(len(poses_data) - 1)
beacon = posenet_beacon_utils.parse_beacon_string(
beaconstr, beaconmap)
beacons_data.append(beacon)
beacons_index.append(len(beacons_data) - 1)
if use_augmentation_beacon:
nonzero_idxs = np.nonzero(beacon)[0]
num_augment_beacon = int(
math.ceil(
len(nonzero_idxs) *
posenet_config.ratio_beacon_augmentation))
for idx in range(0, posenet_config.num_beacon_augmentation):
poses_index.append(len(poses_data) - 1)
augment_beacon = np.copy(beacon)
for idx_augment in range(0, num_augment_beacon):
random_idx = int(
np.random.uniform(0.0, 1.0) *
(len(nonzero_idxs) - 1))
augment_beacon_idx = nonzero_idxs[random_idx]
augment_beacon[augment_beacon_idx][0][0] = math.floor(
augment_beacon[augment_beacon_idx][0][0] *
np.random.uniform(0.0, 1.0))
beacons_data.append(augment_beacon)
beacons_index.append(len(beacons_data) - 1)
beacons_data, mean_beacon = posenet_beacon_utils.preprocess_beacons(
beacons_data, beacon_num, use_fixed_input_mean_std)
return beacon_datasource(beacons_index, poses_index, beacons_data,
poses_data), mean_beacon
def get_image_data(input_txt_file,
input_image_dir,
use_fixed_input_mean_std,
image_size=224):
poses_index = []
images_index = []
poses_data = []
images_data = []
with open(input_txt_file) as f:
# skip header
next(f)
next(f)
next(f)
for line in f:
fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
quat = posenet_geom_utils.preprocess_quaternion(
[float(p3), float(p4),
float(p5), float(p6)])
p0 = float(p0)
p1 = float(p1)
p2 = float(p2)
p3 = quat[0]
p4 = quat[1]
p5 = quat[2]
p6 = quat[3]
poses_data.append((p0, p1, p2, p3, p4, p5, p6))
poses_index.append(len(poses_data) - 1)
images_data.append(os.path.join(input_image_dir, fname))
images_index.append(len(images_data) - 1)
images_data, mean_image = posenet_image_utils.preprocess(
images_data, use_fixed_input_mean_std, image_size=image_size)
return image_datasource(images_index, poses_index, images_data,
poses_data), mean_image