def test_single_map_returns_sorted(self):
subject = {(v * 7 + 3) % 10: str(v)
for v in range(10)} # A disordered map
result = tum_loader.associate_data(subject)
self.assertEqual([[0, '1'], [1, '4'], [2, '7'], [3, '0'], [4, '3'],
[5, '6'], [6, '9'], [7, '2'], [8, '5'], [9, '8']],
result)
def test_associate_data_same_keys(self):
desired_result = sorted([
np.random.uniform(0, 100),
np.random.randint(0, 1000),
np.random.uniform(-100, 100), "test-{0}".format(
np.random.randint(0, 1000))
] for _ in range(20))
int_map = {stamp: int_val for stamp, int_val, _, _ in desired_result}
float_map = {
stamp: float_val
for stamp, _, float_val, _ in desired_result
}
str_map = {stamp: str_val for stamp, _, _, str_val in desired_result}
self.assertEqual(
desired_result,
tum_loader.associate_data(int_map, float_map, str_map))
def test_same_keys_associates(self):
root_map = {(t * 7 + 3) % 10: str(t) for t in range(10)}
map_float_1 = {
t: t * 1.2215 - 0.2234 * t * t + 0.115
for t in range(10)
}
map_float_2 = {t: 4.462 * t * t - 1000.212 for t in range(10)}
map_filename = {
t: "{0}.png".format(t * 4999936 + 1403638128940097024)
for t in range(10)
}
result = tum_loader.associate_data(root_map, map_float_1, map_float_2,
map_filename)
self.assertEqual(
[[t, root_map[t], map_float_1[t], map_float_2[t], map_filename[t]]
for t in range(10)], result)
def test_different_keys_associates_keys(self):
expected_result = []
root_map = {}
map_float_1 = {}
map_float_2 = {}
map_filename = {}
for t in range(10):
root_time = t * 10 + np.random.uniform(-0.1, 0.1)
float_1 = t * 1.2215 - 0.2234 * t * t + 0.115
float_2 = 4.462 * t * t - 1000.212
filename = "{0}.png".format(t * 4999936 + 1403638128940097024)
expected_result.append([root_time, t, float_1, float_2, filename])
root_map[root_time] = t
map_float_1[t * 10 + np.random.uniform(-0.1, 0.1)] = float_1
map_float_2[t * 10 + np.random.uniform(-0.1, 0.1)] = float_2
map_filename[t * 10 + np.random.uniform(-0.1, 0.1)] = filename
result = tum_loader.associate_data(root_map, map_float_1, map_float_2,
map_filename)
self.assertEqual(expected_result, result)
def test_associate_data_missing_keys(self):
random = np.random.RandomState()
original_data = sorted([
idx / 2 + random.uniform(0, 0.01),
random.randint(0, 1000),
random.uniform(-100, 100), "test-{0}".format(
random.randint(0, 1000))
] for idx in range(20))
int_map = {stamp: int_val for stamp, int_val, _, _ in original_data}
float_map = {
stamp + random.uniform(-0.02, 0.02): float_val
for stamp, _, float_val, _ in original_data if stamp > 2
}
str_map = {
stamp + random.uniform(-0.02, 0.02): str_val
for stamp, _, _, str_val in original_data if stamp < 8
}
self.assertEqual(
[inner for inner in original_data if 2 < inner[0] < 8],
tum_loader.associate_data(int_map, float_map, str_map))
def test_associate_data_noisy_keys(self):
random = np.random.RandomState(1531)
desired_result = sorted([
time + random.uniform(0, 0.5),
random.randint(0, 1000),
random.uniform(-100, 100), "test-{0}".format(
random.randint(0, 1000))
] for time in range(20))
int_map = {stamp: int_val for stamp, int_val, _, _ in desired_result}
float_map = {
stamp + random.uniform(-0.02, 0.02): float_val
for stamp, _, float_val, _ in desired_result
}
str_map = {
stamp + random.uniform(-0.02, 0.02): str_val
for stamp, _, _, str_val in desired_result
}
self.assertEqual(
desired_result,
tum_loader.associate_data(int_map, float_map, str_map))
def verify_dataset(image_collection: ImageCollection,
root_folder: typing.Union[str, Path],
dataset_name: str,
repair: bool = False):
"""
Load a TUM RGB-D sequence into the database.
:return:
"""
root_folder = Path(root_folder)
dataset_name = str(dataset_name)
repair = bool(repair)
valid = True
irreparable = False
image_group = dataset_name
# Check the root folder to see if it needs to be extracted from a tarfile
delete_when_done = None
if not root_folder.is_dir():
if (root_folder.parent / dataset_name).is_dir():
# The root was a tarball, but the extracted data already exists, just use that as the root
root_folder = root_folder.parent / dataset_name
else:
candidate_tar_file = root_folder.parent / (dataset_name + '.tgz')
if candidate_tar_file.is_file() and tarfile.is_tarfile(
candidate_tar_file):
# Root is actually a tarfile, extract it. find_roots with handle folder structures
with tarfile.open(candidate_tar_file) as tar_fp:
tar_fp.extractall(root_folder.parent / dataset_name)
root_folder = root_folder.parent / dataset_name
delete_when_done = root_folder
else:
# Could find neither a dir nor a tarfile to extract from
raise NotADirectoryError(
"'{0}' is not a directory".format(root_folder))
# Check the image group on the image collection
if image_collection.image_group != image_group:
if repair:
image_collection.image_group = image_group
image_collection.save()
logging.getLogger(__name__).info(
f"Fixed incorrect image group for {image_collection.sequence_name}"
)
else:
logging.getLogger(__name__).warning(
f"{image_collection.sequence_name} has incorrect image group {image_collection.image_group}"
)
valid = False
# Find the relevant metadata files
root_folder, rgb_path, depth_path, trajectory_path = tum_loader.find_files(
root_folder)
# Step 2: Read the metadata from them
image_files = tum_loader.read_image_filenames(rgb_path)
trajectory = tum_loader.read_trajectory(trajectory_path,
image_files.keys())
depth_files = tum_loader.read_image_filenames(depth_path)
# Step 3: Associate the different data types by timestamp
all_metadata = tum_loader.associate_data(image_files, trajectory,
depth_files)
# Step 3: Load the images from the metadata
total_invalid_images = 0
total_fixed_images = 0
with arvet.database.image_manager.get().get_group(image_group,
allow_write=repair):
for img_idx, (timestamp, image_file, camera_pose,
depth_file) in enumerate(all_metadata):
changed = False
img_valid = True
img_path = root_folder / image_file
depth_path = root_folder / depth_file
rgb_data = image_utils.read_colour(img_path)
depth_data = image_utils.read_depth(depth_path)
depth_data = depth_data / 5000 # Re-scale depth to meters
img_hash = bytes(xxhash.xxh64(rgb_data).digest())
# Load the image from the database
try:
_, image = image_collection[img_idx]
except (KeyError, IOError, RuntimeError):
logging.getLogger(__name__).exception(
f"Error loading image object {img_idx}")
valid = False
total_invalid_images += 1
continue
# First, check the image group
if image.image_group != image_group:
if repair:
image.image_group = image_group
changed = True
logging.getLogger(__name__).warning(
f"Image {img_idx} has incorrect group {image.image_group}")
valid = False
img_valid = False
# Load the pixels from the image
try:
actual_pixels = image.pixels
except (KeyError, IOError, RuntimeError):
actual_pixels = None
try:
actual_depth = image.depth
except (KeyError, IOError, RuntimeError):
actual_depth = None
# Compare the loaded image data to the data read from disk
if actual_pixels is None or not np.array_equal(
rgb_data, actual_pixels):
if repair:
image.store_pixels(rgb_data)
changed = True
else:
logging.getLogger(__name__).error(
f"Image {img_idx}: Pixels do not match data read from {img_path}"
)
valid = False
img_valid = False
if img_hash != bytes(image.metadata.img_hash):
if repair:
image.metadata.img_hash = img_hash
changed = True
else:
logging.getLogger(__name__).error(
f"Image {img_idx}: Image hash does not match metadata")
valid = False
img_valid = False
if actual_depth is None or not np.array_equal(
depth_data, actual_depth):
if repair:
image.store_depth(depth_data)
changed = True
else:
logging.getLogger(__name__).error(
f"Image {img_idx}: Depth does not match data read from {depth_path}"
)
valid = False
img_valid = False
if changed and repair:
logging.getLogger(__name__).warning(
f"Image {img_idx}: repaired")
image.save()
total_fixed_images += 1
if not img_valid:
total_invalid_images += 1
if irreparable:
# Images are missing entirely, needs re-import
logging.getLogger(__name__).error(
f"Image Collection {image_collection.pk} for sequence {dataset_name} "
"is IRREPARABLE, invalidate and re-import")
elif repair:
# Re-save the modified image collection
logging.getLogger(__name__).info(
f"{image_collection.sequence_name} repaired successfully "
f"({total_fixed_images} image files fixed).")
elif valid:
logging.getLogger(__name__).info(
f"Verification of {image_collection.sequence_name} successful.")
else:
logging.getLogger(__name__).error(
f"Verification of {image_collection.sequence_name} ({image_collection.pk}) "
f"FAILED, ({total_invalid_images} images failed)")
if delete_when_done is not None and delete_when_done.exists():
# We're done and need to clean up after ourselves
shutil.rmtree(delete_when_done)
return valid