def convert_results(results_path, out_csv_path, method):
predictions = torch.load(results_path)['predictions']
predictions = predictions[method]
print("Predictions from:", results_path)
print("Method:", method)
print("Number of predictions: ", len(predictions))
preds = []
for n in tqdm(range(len(predictions))):
TCO_n = predictions.poses[n]
t = TCO_n[:3, -1] * 1e3 # m -> mm conversion
R = TCO_n[:3, :3]
row = predictions.infos.iloc[n]
obj_id = int(row.label.split('_')[-1])
score = row.score
time = row.time
pred = dict(scene_id=row.scene_id,
im_id=row.view_id,
obj_id=obj_id,
score=score,
t=t, R=R, time=time)
preds.append(pred)
print("Wrote:", out_csv_path)
inout.save_bop_results(out_csv_path, preds)
return out_csv_path
def evaluate(self, res_file, out_dir=None, renderer_type='python'):
"""Evaluates BOP metrics given a result file.
Args:
res_file: Path to the result file.
out_dir: Path to the output directory.
renderer_type: Renderer type. 'python' or 'cpp'.
Returns:
A dictionary holding the results.
Raises:
RuntimeError: If BOP evaluation failed.
"""
if out_dir is None:
out_dir = self._out_dir
ests = inout.load_bop_results(res_file)
ests = [self._convert_pose_to_bop(est) for est in ests]
res_name = os.path.splitext(os.path.basename(res_file))[0]
bop_res_name = 'bop-{}_{}-{}'.format(res_name.replace('_', '-'),
self._setup, self._split)
bop_res_file = os.path.join(out_dir, "{}.csv".format(bop_res_name))
inout.save_bop_results(bop_res_file, ests)
eval_cmd = [
'python',
os.path.join('scripts', 'eval_bop19.py'),
'--renderer_type={}'.format(renderer_type),
'--result_filenames={}'.format(bop_res_file),
'--results_path={}'.format(out_dir),
'--eval_path={}'.format(out_dir),
]
cwd = "bop_toolkit"
env = os.environ.copy()
env['PYTHONPATH'] = "."
env['BOP_PATH'] = self._bop_dir
if subprocess.run(eval_cmd, cwd=cwd, env=env).returncode != 0:
raise RuntimeError('BOP evaluation failed.')
log_file = os.path.join(
out_dir, "bop_eval_{}_{}.log".format(self._name, res_name))
logger = get_logger(log_file)
results = {}
results['all'] = self._derive_bop_results(out_dir, bop_res_name, False,
logger)
results['grasp_only'] = self._derive_bop_results(
out_dir, bop_res_name, True, logger)
logger.info('Evaluation complete.')
return results
def tc_to_csv(predictions, csv_path):
preds = []
for n in range(len(predictions)):
TCO_n = predictions.poses[n]
t = TCO_n[:3, -1] * 1e3 # m -> mm conversion
R = TCO_n[:3, :3]
row = predictions.infos.iloc[n]
obj_id = int(row.label.split('_')[-1])
score = row.score
time = -1.0
pred = dict(scene_id=row.scene_id,
im_id=row.view_id,
obj_id=obj_id,
score=score,
t=t,
R=R,
time=time)
preds.append(pred)
inout.save_bop_results(csv_path, preds)
time_spend =time.time()-t1 #ends time for the computation
total_inst=0
n_inst = np.sum(inst_counts)
else:
continue
for result_id in sorted_id:
total_inst+=1
if(task_type=='2' and total_inst>n_inst): #for vivo task
break
obj_id = result_objid[result_id]
R = result_R[result_id].flatten()
t = (result_t[result_id]).flatten()
score = result_score[result_id]
obj_gt_no = obj_id_targets.index(obj_id)
inst_count_est[obj_gt_no]+=1
if(task_type=='2' and inst_count_est[obj_gt_no]>inst_counts[obj_gt_no]):
#skip if the result exceeds the amount of taget instances for vivo task
continue
result_temp ={'scene_id':scene_id,'im_id': im_id,'obj_id':obj_id,'score':score,'R':R,'t':t,'time':time_spend }
result_dataset.append(result_temp)
if(dataset=='tless'):
output_path = os.path.join(output_dir,"pix2pose-iccv19_"+dataset+"-test-primesense.csv")
else:
output_path = os.path.join(output_dir,"pix2pose-iccv19_"+dataset+"-test.csv")
print("Saving the result to ",output_path)
inout.save_bop_results(output_path,result_dataset)
aae_start = time.time()
pose_est = mp_pose_estimator.process(det, img_masked, cam_K, mm=True)
aae_time += (time.time() - aae_start)
#pose refinement
if pose_refiner_method:
# cv2.imshow('depth_mask', depth_masked / depth_img.max())
# cv2.imshow('depth_img', depth_img / depth_img.max())
# cam_K[0,2] = depth_img.shape[1] / 2
# cam_K[1,2] = depth_img.shape[0] / 2
icp_start = time.time()
pose_est = pose_refiner.process(pose_est,
depth_img=depth_img,
camK=cam_K,
masks=inst_mask)
icp_time += (time.time() - icp_start)
if pose_est:
img_bop_res.append(
convert_rmc2bop(pose_est[0], det[0], scene_id, im_id))
img_pose_ests += pose_est
img_dets += det
# except:
# print((im_id,'not found'))
res_path = os.path.join(
result_folder, 'sundermeyer-{}_{}-{}.csv'.format(args.eval_name,
dataset_name, split))
inout.save_bop_results(res_path, bop_results)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Model folder.
model_dir = os.path.join(config.TF_MODELS_PATH, FLAGS.model)
# Update flags with parameters loaded from the model folder.
common.update_flags(os.path.join(model_dir, common.PARAMS_FILENAME))
# Print the flag values.
common.print_flags()
# Folder from which the latest model checkpoint will be loaded.
checkpoint_dir = os.path.join(model_dir, 'train')
# Folder for the inference output.
infer_dir = os.path.join(model_dir, 'infer')
tf.gfile.MakeDirs(infer_dir)
# Folder for the visualization output.
vis_dir = os.path.join(model_dir, 'vis')
tf.gfile.MakeDirs(vis_dir)
# TFRecord files used for training.
tfrecord_names = FLAGS.infer_tfrecord_names
if not isinstance(FLAGS.infer_tfrecord_names, list):
tfrecord_names = [FLAGS.infer_tfrecord_names]
# Stride of the final output.
if FLAGS.upsample_logits:
# The stride is 1 if the logits are upsampled to the input resolution.
output_stride = 1
else:
assert (len(FLAGS.decoder_output_stride) == 1)
output_stride = FLAGS.decoder_output_stride[0]
with tf.Graph().as_default():
return_gt_orig = np.any([
FLAGS.task_type == common.LOCALIZATION,
FLAGS.vis_gt_poses])
return_gt_maps = np.any([
FLAGS.vis_pred_obj_labels,
FLAGS.vis_pred_obj_confs,
FLAGS.vis_pred_frag_fields])
# Dataset provider.
dataset = datagen.Dataset(
dataset_name=FLAGS.dataset,
tfrecord_names=tfrecord_names,
model_dir=model_dir,
model_variant=FLAGS.model_variant,
batch_size=1,
max_height_before_crop=FLAGS.infer_max_height_before_crop,
crop_size=list(map(int, FLAGS.infer_crop_size)),
num_frags=FLAGS.num_frags,
min_visib_fract=None,
gt_knn_frags=1,
output_stride=output_stride,
is_training=False,
return_gt_orig=return_gt_orig,
return_gt_maps=return_gt_maps,
should_shuffle=False,
should_repeat=False,
prepare_for_projection=FLAGS.project_to_surface,
data_augmentations=None)
# Initialize a renderer for visualization.
renderer = None
if FLAGS.vis_gt_poses or FLAGS.vis_pred_poses:
tf.logging.info('Initializing renderer for visualization...')
renderer = bop_renderer.Renderer()
renderer.init(dataset.crop_size[0], dataset.crop_size[1])
model_type_vis = 'eval'
dp_model = dataset_params.get_model_params(
config.BOP_PATH, dataset.dataset_name, model_type=model_type_vis)
for obj_id in dp_model['obj_ids']:
path = dp_model['model_tpath'].format(obj_id=obj_id)
renderer.add_object(obj_id, path)
tf.logging.info('Renderer initialized.')
# Inputs.
samples = dataset.get_one_shot_iterator().get_next()
# A map from output type to the number of associated channels.
outputs_to_num_channels = common.get_outputs_to_num_channels(
dataset.num_objs, dataset.model_store.num_frags)
# Options of the neural network model.
model_options = common.ModelOptions(
outputs_to_num_channels=outputs_to_num_channels,
crop_size=list(map(int, FLAGS.infer_crop_size)),
atrous_rates=FLAGS.atrous_rates,
encoder_output_stride=FLAGS.encoder_output_stride)
# Construct the inference graph.
predictions = model.predict(
images=samples[common.IMAGE],
model_options=model_options,
upsample_logits=FLAGS.upsample_logits,
image_pyramid=FLAGS.image_pyramid,
num_objs=dataset.num_objs,
num_frags=dataset.num_frags,
frag_cls_agnostic=FLAGS.frag_cls_agnostic,
frag_loc_agnostic=FLAGS.frag_loc_agnostic)
# Global step.
tf.train.get_or_create_global_step()
# Get path to the model checkpoint.
if FLAGS.checkpoint_name is None:
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
else:
checkpoint_path = os.path.join(checkpoint_dir, FLAGS.checkpoint_name)
time_str = time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime())
tf.logging.info('Starting inference at: {}'.format(time_str))
tf.logging.info('Inference with model: {}'.format(checkpoint_path))
# Scaffold for initialization.
scaffold = tf.train.Scaffold(
init_op=tf.global_variables_initializer(),
saver=tf.train.Saver(var_list=misc.get_variable_dict()))
# TensorFlow configuration.
if FLAGS.cpu_only:
tf_config = tf.ConfigProto(device_count={'GPU': 0})
else:
tf_config = tf.ConfigProto()
# tf_config.gpu_options.allow_growth = True # Only necessary GPU memory.
tf_config.gpu_options.allow_growth = False
# Nodes that can use multiple threads to parallelize their execution will
# schedule the individual pieces into this pool.
tf_config.intra_op_parallelism_threads = 10
# All ready nodes are scheduled in this pool.
tf_config.inter_op_parallelism_threads = 10
poses_all = []
first_im_poses_num = 0
session_creator = tf.train.ChiefSessionCreator(
config=tf_config,
scaffold=scaffold,
master=FLAGS.master,
checkpoint_filename_with_path=checkpoint_path)
with tf.train.MonitoredSession(
session_creator=session_creator, hooks=None) as sess:
im_ind = 0
while not sess.should_stop():
# Estimate object poses for the current image.
poses, run_times = process_image(
sess=sess,
samples=samples,
predictions=predictions,
im_ind=im_ind,
crop_size=dataset.crop_size,
output_scale=(1.0 / output_stride),
model_store=dataset.model_store,
renderer=renderer,
task_type=FLAGS.task_type,
infer_name=FLAGS.infer_name,
infer_dir=infer_dir,
vis_dir=vis_dir)
# Note that the first image takes longer time (because of TF init).
tf.logging.info(
'Image: {}, prediction: {:.3f}, establish_corr: {:.3f}, '
'fitting: {:.3f}, total time: {:.3f}'.format(
im_ind, run_times['prediction'], run_times['establish_corr'],
run_times['fitting'], run_times['total']))
poses_all += poses
if im_ind == 0:
first_im_poses_num = len(poses)
im_ind += 1
# Set the time of pose estimates from the first image to the average time.
# Tensorflow takes a long time on the first image (because of init).
time_avg = 0.0
for pose in poses_all:
time_avg += pose['time']
if len(poses_all) > 0:
time_avg /= float((len(poses_all)))
for i in range(first_im_poses_num):
poses_all[i]['time'] = time_avg
# Save the estimated poses in the BOP format:
# https://bop.felk.cvut.cz/challenges/bop-challenge-2020/#formatofresults
if FLAGS.save_estimates:
suffix = ''
if FLAGS.infer_name is not None:
suffix = '_{}'.format(FLAGS.infer_name)
poses_path = os.path.join(
infer_dir, 'estimated-poses{}.csv'.format(suffix))
tf.logging.info('Saving estimated poses to: {}'.format(poses_path))
inout.save_bop_results(poses_path, poses_all, version='bop19')
time_str = time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime())
tf.logging.info('Finished inference at: {}'.format(time_str))