def cal_chamfer(dataset, class_name, instance_name, experiment_directory, checkpoint, data_dir):
reconstructed_mesh_filename = ws.get_reconstructed_mesh_filename(
experiment_directory,
checkpoint,
dataset,
class_name,
instance_name,
)
ground_truth_samples_filename = os.path.join(
data_dir,
"SurfaceSamples",
dataset,
class_name,
instance_name + ".ply",
)
normalization_params_filename = os.path.join(
data_dir,
"NormalizationParameters",
dataset,
class_name,
instance_name + ".npz",
)
ground_truth_points = trimesh.load(
ground_truth_samples_filename
)
reconstruction = trimesh.load(reconstructed_mesh_filename)
normalization_params = np.load(normalization_params_filename)
chamfer_dist = deep_sdf.metrics.chamfer.compute_trimesh_chamfer(
ground_truth_points,
reconstruction,
normalization_params["offset"],
normalization_params["scale"],
)
print(instance_name, chamfer_dist)
return os.path.join(dataset, class_name, instance_name), chamfer_dist
def evaluate(experiment_directory, checkpoint, data_dir, split_filename):
with open(split_filename, "r") as f:
split = json.load(f)
chamfer_results = []
for dataset in split:
for class_name in split[dataset]:
for instance_name in split[dataset][class_name]:
logging.debug("evaluating " +
os.path.join(dataset, class_name, instance_name))
reconstructed_mesh_filename = ws.get_reconstructed_mesh_filename(
experiment_directory, checkpoint, dataset, class_name,
instance_name)
logging.debug('reconstructed mesh is "' +
reconstructed_mesh_filename + '"')
ground_truth_samples_filename = os.path.join(
data_dir,
"SurfaceSamples",
dataset,
class_name,
instance_name + ".npy",
)
logging.debug("ground truth samples are " +
ground_truth_samples_filename)
# normalization_params_filename = os.path.join(
# data_dir,
# "NormalizationParameters",
# dataset,
# class_name,
# instance_name + ".npz",
# )
# logging.debug(
# "normalization params are " + ground_truth_samples_filename
# )
ground_truth_points = np.load(ground_truth_samples_filename)
ground_truth_points = ground_truth_points.reshape(-1, 2)
reconstruction = np.load(reconstructed_mesh_filename)
reconstruction = reconstruction.reshape(-1, 2)
# normalization_params = np.load(normalization_params_filename)
chamfer_dist = deep_sdf.metrics.chamfer.compute_trimesh_chamfer(
ground_truth_points, reconstruction)
logging.debug("chamfer distance: " + str(chamfer_dist))
chamfer_results.append(
(os.path.join(dataset, class_name,
instance_name), chamfer_dist))
with open(
os.path.join(
ws.get_evaluation_dir(experiment_directory, checkpoint, True),
"chamfer.csv"),
"w",
) as f:
f.write("shape, chamfer_dist\n")
for result in chamfer_results:
f.write("{}, {}\n".format(result[0], result[1]))
def evaluate_one_instance(dataset, class_name, instance_name, experiment_directory, checkpoint, data_dir):
logging.debug(
"evaluating " + os.path.join(dataset, class_name, instance_name)
)
reconstructed_mesh_filename = ws.get_reconstructed_mesh_filename(
experiment_directory, checkpoint, dataset, class_name, instance_name
)
logging.debug(
'reconstructed mesh is "' + reconstructed_mesh_filename + '"'
)
if not os.path.isfile(reconstructed_mesh_filename):
print('[WARNING] Skipping %s as it doesn\'t exists' % reconstructed_mesh_filename)
return "", 0
ground_truth_samples_filename = os.path.join(
data_dir,
"SurfaceSamples",
dataset,
class_name,
instance_name + ".ply",
)
logging.debug(
"ground truth samples are " + ground_truth_samples_filename
)
normalization_params_filename = os.path.join(
data_dir,
"NormalizationParameters",
dataset,
class_name,
instance_name + ".npz",
)
logging.debug(
"normalization params are " + ground_truth_samples_filename
)
ground_truth_points = trimesh.load(ground_truth_samples_filename)
reconstruction = trimesh.load(reconstructed_mesh_filename)
normalization_params = np.load(normalization_params_filename)
chamfer_dist = deep_sdf.metrics.chamfer.compute_trimesh_chamfer(
ground_truth_points,
reconstruction,
normalization_params["offset"],
normalization_params["scale"],
)
earthmover_dist = deep_sdf.metrics.emd.compute_trimesh_emd(
ground_truth_points,
reconstruction,
normalization_params["offset"],
normalization_params["scale"],
)
logging.debug("chamfer distance: " + str(chamfer_dist))
return os.path.join(dataset, class_name, instance_name), chamfer_dist, earthmover_dist