def retarget_motion_in_db(db_url,
retargeting,
motion_id,
motion_name,
collection,
skeleton_model_name,
is_aligned=False,
session=None):
motion_data = get_motion_by_id_from_remote_db(db_url,
motion_id,
is_processed=is_aligned)
if motion_data is None:
print("Error: motion data is empty")
return
meta_info_str = get_annotation_by_id_from_remote_db(
db_url, motion_id, is_processed=is_aligned)
motion_vector = MotionVector()
motion_vector.from_custom_db_format(motion_data)
motion_vector.skeleton = retargeting.src_skeleton
new_frames = retargeting.run(motion_vector.frames, frame_range=None)
target_motion = MotionVector()
target_motion.frames = new_frames
target_motion.skeleton = retargeting.target_skeleton
target_motion.frame_time = motion_vector.frame_time
target_motion.n_frames = len(new_frames)
m_data = target_motion.to_db_format()
upload_motion_to_db(db_url,
motion_name,
m_data,
collection,
skeleton_model_name,
meta_info_str,
is_processed=is_aligned,
session=session)
def slot_accept(self):
skeleton_name = str(self.skeletonModelComboBox.currentText())
col = self.get_collection()
if col is not None:
c_id, c_name, c_type = col
self.success = True
is_processed = bool(self.isProcessedCheckBox.isChecked())
for c in self.controller_list:
name = c.scene_object.name
motion_data = c.get_json_data()
print("upload motion clip ", name, "to ", c_id, c_name, c_type, skeleton_name)
semantic_annotation = c._motion._semantic_annotation
meta_info = dict()
if len(semantic_annotation) >0:
meta_info["sections"] = create_section_dict_from_annotation(semantic_annotation)
time_function = c._motion._time_function
if time_function is not None and is_processed:
meta_info["time_function"] = time_function
else:
is_processed = False
if len(meta_info) > 0:
meta_info_str = json.dumps(meta_info)
else:
meta_info_str = ""
upload_motion_to_db(self.db_url, name, motion_data, c_id, skeleton_name, meta_info_str, is_processed, session=self.session)
self.close()
def align_motions_in_db(db_url, skeleton_name, c_id, session=None):
motion_data = get_motion_vectors(db_url, c_id, skeleton_name, is_aligned=0)
# delete old data
old_aligned_motions = get_motion_list_from_remote_db(db_url, c_id, skeleton_name, is_processed=True)
for motion in old_aligned_motions:
delete_motion_by_id_from_remote_db(db_url, motion[0], is_processed=True, session=session)
skeleton = load_skeleton_from_db(db_url, skeleton_name)
n_motions = len(motion_data)
if n_motions > 1:
print("start alignment", n_motions)
aligned_data = align_motion_data(skeleton, motion_data)
print("finished alignment")
for key, data in aligned_data.items():
frames = data["frames"]
name = motion_data[key]["name"] + "_aligned"
mv = get_motion_vector(skeleton, frames)
m_data = mv.to_db_format()
try:
meta_data = json.loads(data["meta_info"])
except:
meta_data = dict()
meta_data["time_function"] = data["time_function"]
meta_data_str = json.dumps(meta_data)
upload_motion_to_db(db_url, name, m_data, c_id, skeleton_name, meta_data_str, is_processed=True, session=session)
print("uploaded aligned data")
elif n_motions == 1:
first_key = list(motion_data.keys())[0]
name = motion_data[first_key]["name"] + "_aligned"
mdata = motion_data[first_key]["data"]
meta_data_str = get_annotation_by_id_from_remote_db(db_url, first_key)
try:
meta_data = json.loads(meta_data_str)
except:
meta_data = dict()
print("process",name)
motion = create_motion_vector_from_json(mdata)
time_function = list(range(motion.n_frames))
meta_data["time_function"] = time_function
meta_data_str = json.dumps(meta_data)
upload_motion_to_db(db_url, name, mdata, c_id, skeleton_name, meta_data_str, is_processed=True, session=session)
print("Need more than 1 motion, found", n_motions)
else:
print("No motions found")