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import pandas as pd
import numpy as np
from pathlib import Path
import contextlib
import fbx
from typing import List, Union
# Import custom data.
import globals
class MarkerData:
# TODO: Model is currently built for training. Add testing mode.
def __init__(self, fbx_file: Path):
"""
Class that stores references to important nodes in an FBX file.
Offers utility functions to quickly load animation data.
:param fbx_file: `str` Path to the file to load.
"""
self.time_modes = globals.get_time_modes()
self.marker_names = globals.get_marker_names()
self.markers = []
self.actor_names = []
self.actors = []
self.volume_dim_x = 10.
self.volume_dim_y = 4.
self.fbx_file = fbx_file
self.valid_frames = []
self.__init_scene()
self.__init_anim()
self.__init_actors()
self.__init_markers()
def __init_scene(self):
# Create an FBX manager and importer
manager = fbx.FbxManager.Create()
importer = fbx.FbxImporter.Create(manager, '')
# Import the FBX file
importer.Initialize(str(self.fbx_file))
self.scene = fbx.FbxScene.Create(manager, '')
importer.Import(self.scene)
self.root = self.scene.GetRootNode()
self.time_mode = self.scene.GetGlobalSettings().GetTimeMode()
# Destroy importer to remove reference to imported file.
# This will allow us to delete the uploaded file.
importer.Destroy()
def __init_anim(self):
# Get the animation stack and layer.
anim_stack = self.scene.GetCurrentAnimationStack()
self.anim_layer = anim_stack.GetSrcObject(fbx.FbxCriteria.ObjectType(fbx.FbxAnimLayer.ClassId), 0)
# Find the total number of frames to expect from the local time span.
local_time_span = anim_stack.GetLocalTimeSpan()
self.num_frames = int(local_time_span.GetDuration().GetFrameCount(self.time_mode))
def __init_actors(self):
# Find all parent nodes (/System, /_Unlabeled_Markers, /Actor1, etc).
gen1_nodes = [self.root.GetChild(i) for i in range(self.root.GetChildCount())]
for gen1_node in gen1_nodes:
gen2_nodes = [gen1_node.GetChild(i) for i in
range(gen1_node.GetChildCount())] # Actor nodes (/Mimi/Hips, /Mimi/ARIEL, etc)
# If the first 3 marker names are children of this parent, it must be an actor.
if all(name in [node.GetName() for node in gen2_nodes] for name in self.marker_names[:4]):
self.actor_names.append(gen1_node.GetName())
self.actors.append(gen1_node)
self.actor_count = len(self.actors)
self.valid_frames = [[] for _ in range(self.actor_count)]
def __init_markers(self):
for actor_node in self.actors:
actor_markers = {}
for marker_name in self.marker_names:
for actor_idx in range(actor_node.GetChildCount()):
child = actor_node.GetChild(actor_idx)
child_name = child.GetName()
if child_name == marker_name:
actor_markers[child_name] = child
assert len(actor_markers) == len(self.marker_names), f'{actor_node.GetName()} does not have all markers.'
self.markers.append(actor_markers)
def _check_actor(self, actor: int = 0):
assert 0 <= actor <= self.actor_count, f'Actor number must be between 0 and {self.actor_count - 1}. ' \
f'It is {actor}.'
def _set_valid_frames_for_actor(self, actor: int = 0):
self._check_actor(actor)
frames = list(range(self.num_frames))
for marker_name in self.marker_names:
marker = self.markers[actor][marker_name]
t_curve = marker.LclTranslation.GetCurve(self.anim_layer, 'X')
keys = [t_curve.KeyGet(i).GetTime().GetFrameCount(self.time_mode) for i in range(t_curve.KeyGetCount())]
for frame in frames:
if frame not in keys:
with contextlib.suppress(ValueError):
frames.remove(frame)
self.valid_frames[actor] = frames
def _check_valid_frames(self, actor: int = 0):
if not len(self.valid_frames[actor]):
self._set_valid_frames_for_actor(actor)
def _modify_pose(self, actor, frame) -> List[float]:
# Set new frame to evaluate at.
time = fbx.FbxTime()
time.SetFrame(frame)
# Prepare arrays for each axis.
x, y, z = [], [], []
# For each marker, store the x, y and z global position.
for n, m in self.markers[actor].items():
t = m.EvaluateGlobalTransform(time).GetRow(3)
x += [t[0] * 0.01]
y += [t[1] * 0.01]
z += [t[2] * 0.01]
# Move the point cloud to the center of the x and y axes. This will put the actor in the middle.
x = self.center_axis(x)
z = self.center_axis(z)
# Move the actor to the middle of the volume floor by adding volume_dim_x/2 to x and z.
x += self.volume_dim_x / 2.
z += self.volume_dim_x / 2.
# Squeeze the actor into the 1x1 plane for the neural network by dividing the axes.
x /= self.volume_dim_x
z /= self.volume_dim_x
y = np.array(y) / self.volume_dim_y
# TODO: Optionally: Add any extra modifications to the point cloud here.
# Append all values to a new array, one axis at a time.
# This way it will match the column names order.
pose = []
for i in range(len(x)):
pose += [x[i]]
pose += [y[i]]
pose += [z[i]]
return pose
def get_marker_by_name(self, actor: int, name: str):
self._check_actor(actor)
return self.markers[actor][name]
def get_valid_frames_for_actor(self, actor: int = 0):
self._check_valid_frames(actor)
return self.valid_frames[actor]
def print_valid_frames_stats_for_actor(self, actor: int = 0):
self._check_actor(actor)
self._check_valid_frames(actor)
len_valid = len(self.valid_frames[actor])
ratio = (len_valid / self.num_frames) * 100
print(f'Actor {self.actor_names[actor]}: Total: {self.num_frames}, valid: {len_valid}, missing: '
f'{self.num_frames - len_valid}, ratio: {ratio:.2f}% valid.')
return self.actor_names[actor], self.num_frames, len_valid, ratio
def columns_from_joints(self):
columns = []
for name in self.marker_names:
columns += [f'{name}x', f'{name}y', f'{name}z']
return columns
@staticmethod
def center_axis(a) -> np.array:
a = np.array(a)
_min = np.min(a)
_max = np.max(a)
_c = _max - _min
a -= _c
return a
def extract_translations_per_actor(self, actor: int = 0):
self._check_actor(actor)
self._check_valid_frames(actor)
poses = []
# Go through all valid frames for this actor.
# Note that these frames can be different per actor.
for frame in self.valid_frames[actor]:
# Get the centered point cloud as an array.
pose_at_frame = self._modify_pose(actor, frame)
poses.append(pose_at_frame)
return poses
def extract_all_translations(self) -> pd.DataFrame:
columns = self.columns_from_joints()
all_poses = []
for i in range(self.actor_count):
all_poses.extend(self.extract_translations_per_actor(i))
return pd.DataFrame(all_poses, columns=columns)
def export(self, t: str = 'csv', output_file: Path = None) -> Union[bytes, Path]:
# Get the dataframe with all animation data.
df = self.extract_all_translations()
if t == 'string':
return df.to_csv(index=False).encode('utf-8')
if output_file is None:
output_file = self.fbx_file.with_suffix('.csv')
if output_file.suffix != '.csv':
raise ValueError(f'{output_file} needs to be a .csv file.')
df.to_csv(output_file, index=False)
return output_file
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