258 lines
11 KiB
Python
258 lines
11 KiB
Python
from .data import *
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from ..helpers import *
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from typing import Dict
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class PsaBuilderOptions(object):
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def __init__(self):
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self.sequence_source = 'ACTIONS'
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self.actions = []
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self.marker_names = []
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self.bone_filter_mode = 'ALL'
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self.bone_group_indices = []
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self.should_use_original_sequence_names = False
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self.should_trim_timeline_marker_sequences = True
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self.sequence_name_prefix = ''
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self.sequence_name_suffix = ''
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class PsaBuilderPerformance:
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def __init__(self):
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self.frame_set_duration = datetime.timedelta()
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self.key_build_duration = datetime.timedelta()
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self.key_add_duration = datetime.timedelta()
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class PsaBuilder(object):
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def __init__(self):
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pass
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def build(self, context, options: PsaBuilderOptions) -> Psa:
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performance = PsaBuilderPerformance()
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active_object = context.view_layer.objects.active
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if active_object.type != 'ARMATURE':
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raise RuntimeError('Selected object must be an Armature')
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armature = active_object
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if armature.animation_data is None:
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raise RuntimeError('No animation data for armature')
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# Ensure that we actually have items that we are going to be exporting.
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if options.sequence_source == 'ACTIONS' and len(options.actions) == 0:
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raise RuntimeError('No actions were selected for export')
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elif options.sequence_source == 'TIMELINE_MARKERS' and len(options.marker_names) == 0:
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raise RuntimeError('No timeline markers were selected for export')
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psa = Psa()
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bones = list(armature.data.bones)
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# The order of the armature bones and the pose bones is not guaranteed to be the same.
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# As as a result, we need to reconstruct the list of pose bones in the same order as the
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# armature bones.
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bone_names = [x.name for x in bones]
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pose_bones = [(bone_names.index(bone.name), bone) for bone in armature.pose.bones]
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pose_bones.sort(key=lambda x: x[0])
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pose_bones = [x[1] for x in pose_bones]
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# Get a list of all the bone indices and instigator bones for the bone filter settings.
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export_bone_names = get_export_bone_names(armature, options.bone_filter_mode, options.bone_group_indices)
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bone_indices = [bone_names.index(x) for x in export_bone_names]
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# Make the bone lists contain only the bones that are going to be exported.
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bones = [bones[bone_index] for bone_index in bone_indices]
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pose_bones = [pose_bones[bone_index] for bone_index in bone_indices]
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# No bones are going to be exported.
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if len(bones) == 0:
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raise RuntimeError('No bones available for export')
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# Build list of PSA bones.
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for bone in bones:
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psa_bone = Psa.Bone()
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psa_bone.name = bytes(bone.name, encoding='utf-8')
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try:
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parent_index = bones.index(bone.parent)
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psa_bone.parent_index = parent_index
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psa.bones[parent_index].children_count += 1
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except ValueError:
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psa_bone.parent_index = -1
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if bone.parent is not None:
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rotation = bone.matrix.to_quaternion()
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rotation.x = -rotation.x
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rotation.y = -rotation.y
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rotation.z = -rotation.z
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quat_parent = bone.parent.matrix.to_quaternion().inverted()
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parent_head = quat_parent @ bone.parent.head
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parent_tail = quat_parent @ bone.parent.tail
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location = (parent_tail - parent_head) + bone.head
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else:
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location = armature.matrix_local @ bone.head
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rot_matrix = bone.matrix @ armature.matrix_local.to_3x3()
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rotation = rot_matrix.to_quaternion()
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psa_bone.location.x = location.x
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psa_bone.location.y = location.y
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psa_bone.location.z = location.z
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psa_bone.rotation.x = rotation.x
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psa_bone.rotation.y = rotation.y
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psa_bone.rotation.z = rotation.z
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psa_bone.rotation.w = rotation.w
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psa.bones.append(psa_bone)
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# Populate the export sequence list.
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class NlaState:
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def __init__(self):
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self.frame_min = 0
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self.frame_max = 0
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self.action = None
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class ExportSequence:
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def __init__(self):
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self.name = ''
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self.nla_state = NlaState()
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export_sequences = []
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if options.sequence_source == 'ACTIONS':
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for action in options.actions:
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if len(action.fcurves) == 0:
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continue
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export_sequence = ExportSequence()
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export_sequence.nla_state.action = action
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export_sequence.name = get_psa_sequence_name(action, options.should_use_original_sequence_names)
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frame_min, frame_max = [int(x) for x in action.frame_range]
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export_sequence.nla_state.frame_min = frame_min
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export_sequence.nla_state.frame_max = frame_max
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export_sequences.append(export_sequence)
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pass
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elif options.sequence_source == 'TIMELINE_MARKERS':
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sequence_frame_ranges = self.get_timeline_marker_sequence_frame_ranges(armature, context, options)
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for name, (frame_min, frame_max) in sequence_frame_ranges.items():
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export_sequence = ExportSequence()
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export_sequence.name = name
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export_sequence.nla_state.action = None
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export_sequence.nla_state.frame_min = frame_min
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export_sequence.nla_state.frame_max = frame_max
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export_sequences.append(export_sequence)
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else:
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raise ValueError(f'Unhandled sequence source: {options.sequence_source}')
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# Add prefixes and suffices to the names of the export sequences and strip whitespace.
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for export_sequence in export_sequences:
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export_sequence.name = f'{options.sequence_name_prefix}{export_sequence.name}{options.sequence_name_suffix}'.strip()
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# Now build the PSA sequences.
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# We actually alter the timeline frame and simply record the resultant pose bone matrices.
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frame_start_index = 0
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for export_sequence in export_sequences:
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armature.animation_data.action = export_sequence.nla_state.action
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context.view_layer.update()
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psa_sequence = Psa.Sequence()
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frame_min = export_sequence.nla_state.frame_min
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frame_max = export_sequence.nla_state.frame_max
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frame_count = frame_max - frame_min + 1
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psa_sequence.name = bytes(export_sequence.name, encoding='windows-1252')
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psa_sequence.frame_count = frame_count
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psa_sequence.frame_start_index = frame_start_index
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psa_sequence.fps = context.scene.render.fps
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frame_count = frame_max - frame_min + 1
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for frame in range(frame_count):
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with Timer() as t:
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context.scene.frame_set(frame_min + frame)
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performance.frame_set_duration += t.duration
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for pose_bone in pose_bones:
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with Timer() as t:
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key = Psa.Key()
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pose_bone_matrix = pose_bone.matrix
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if pose_bone.parent is not None:
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pose_bone_parent_matrix = pose_bone.parent.matrix
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pose_bone_matrix = pose_bone_parent_matrix.inverted() @ pose_bone_matrix
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location = pose_bone_matrix.to_translation()
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rotation = pose_bone_matrix.to_quaternion().normalized()
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if pose_bone.parent is not None:
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rotation.x = -rotation.x
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rotation.y = -rotation.y
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rotation.z = -rotation.z
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key.location.x = location.x
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key.location.y = location.y
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key.location.z = location.z
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key.rotation.x = rotation.x
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key.rotation.y = rotation.y
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key.rotation.z = rotation.z
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key.rotation.w = rotation.w
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key.time = 1.0 / psa_sequence.fps
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performance.key_build_duration += t.duration
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with Timer() as t:
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psa.keys.append(key)
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performance.key_add_duration += t.duration
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psa_sequence.bone_count = len(pose_bones)
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psa_sequence.track_time = frame_count
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frame_start_index += frame_count
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psa.sequences[export_sequence.name] = psa_sequence
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print(f'frame set duration: {performance.frame_set_duration}')
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print(f'key build duration: {performance.key_build_duration}')
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print(f'key add duration: {performance.key_add_duration}')
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return psa
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def get_timeline_marker_sequence_frame_ranges(self, object, context, options: PsaBuilderOptions) -> Dict:
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# Timeline markers need to be sorted so that we can determine the sequence start and end positions.
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sequence_frame_ranges = dict()
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sorted_timeline_markers = list(sorted(context.scene.timeline_markers, key=lambda x: x.frame))
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sorted_timeline_marker_names = list(map(lambda x: x.name, sorted_timeline_markers))
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for marker_name in options.marker_names:
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marker = context.scene.timeline_markers[marker_name]
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frame_min = marker.frame
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# Determine the final frame of the sequence based on the next marker.
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# If no subsequent marker exists, use the maximum frame_end from all NLA strips.
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marker_index = sorted_timeline_marker_names.index(marker_name)
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next_marker_index = marker_index + 1
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frame_max = 0
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if next_marker_index < len(sorted_timeline_markers):
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# There is a next marker. Use that next marker's frame position as the last frame of this sequence.
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frame_max = sorted_timeline_markers[next_marker_index].frame
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if options.should_trim_timeline_marker_sequences:
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nla_strips = get_nla_strips_in_timeframe(object, marker.frame, frame_max)
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frame_max = min(frame_max, max(map(lambda nla_strip: nla_strip.frame_end, nla_strips)))
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frame_min = max(frame_min, min(map(lambda nla_strip: nla_strip.frame_start, nla_strips)))
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else:
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# There is no next marker.
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# Find the final frame of all the NLA strips and use that as the last frame of this sequence.
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for nla_track in object.animation_data.nla_tracks:
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if nla_track.mute:
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continue
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for strip in nla_track.strips:
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frame_max = max(frame_max, strip.frame_end)
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if frame_min == frame_max:
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continue
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sequence_frame_ranges[marker_name] = int(frame_min), int(frame_max)
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return sequence_frame_ranges
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