Fix for root bone being incorrectly oriented if it wasn't at the identity rotation in the bind pose

io_scene_psk_psa/__init__.py

@@ -3,7 +3,7 @@ from bpy.app.handlers import persistent
 bl_info = {
     'name': 'PSK/PSA Importer/Exporter',
     'author': 'Colin Basnett, Yurii Ti',
-    'version': (6, 2, 1),
+    'version': (6, 2, 0),
     'blender': (4, 0, 0),
     'description': 'PSK/PSA Import/Export (.psk/.psa)',
     'warning': '',

io_scene_psk_psa/psa/import_/properties.py

@@ -71,8 +71,7 @@ class PSA_PG_import(PropertyGroup):
             ('EXACT', 'Exact', 'Bone names must match exactly.', 'EXACT', 0),
             ('CASE_INSENSITIVE', 'Case Insensitive', 'Bones names must match, ignoring case (e.g., the bone PSA bone '
              '\'root\' can be mapped to the armature bone \'Root\')', 'CASE_INSENSITIVE', 1),
-        ),
-        default='CASE_INSENSITIVE'
+        )
     )
     fps_source: EnumProperty(name='FPS Source', items=(
         ('SEQUENCE', 'Sequence', 'The sequence frame rate matches the original frame rate', 'ACTION', 0),

io_scene_psk_psa/psa/importer.py

@@ -2,7 +2,7 @@ import typing
 from typing import List, Optional
 
 import bpy
-import numpy as np
+import numpy
 from bpy.types import FCurve, Object, Context
 from mathutils import Vector, Quaternion
 
@@ -64,12 +64,12 @@ class PsaImportResult:
 
 
 def _get_armature_bone_index_for_psa_bone(psa_bone_name: str, armature_bone_names: List[str], bone_mapping_mode: str = 'EXACT') -> Optional[int]:
-    """
+    '''
     @param psa_bone_name: The name of the PSA bone.
     @param armature_bone_names: The names of the bones in the armature.
     @param bone_mapping_mode: One of 'EXACT' or 'CASE_INSENSITIVE'.
     @return: The index of the armature bone that corresponds to the given PSA bone, or None if no such bone exists.
-    """
+    '''
     for armature_bone_index, armature_bone_name in enumerate(armature_bone_names):
         if bone_mapping_mode == 'CASE_INSENSITIVE':
             if armature_bone_name.lower() == psa_bone_name.lower():
@@ -80,52 +80,6 @@ def _get_armature_bone_index_for_psa_bone(psa_bone_name: str, armature_bone_name
     return None
 
 
-def _resample_sequence_data_matrix(sequence_data_matrix: np.ndarray, time_step: float = 1.0) -> np.ndarray:
-    '''
-    Resamples the sequence data matrix to the target frame count.
-    @param sequence_data_matrix: FxBx7 matrix where F is the number of frames, B is the number of bones, and X is the
-    number of data elements per bone.
-    @param target_frame_count: The number of frames to resample to.
-    @return: The resampled sequence data matrix, or sequence_data_matrix if no resampling is necessary.
-    '''
-    def get_sample_times(source_frame_count: int, time_step: float) -> typing.Iterable[float]:
-        # TODO: for correctness, we should also emit the target frame time as well (because the last frame can be a
-        #  fractional frame).
-        time = 0.0
-        while time < source_frame_count - 1:
-            yield time
-            time += time_step
-        yield source_frame_count - 1
-
-    if time_step == 1.0:
-        # No resampling is necessary.
-        return sequence_data_matrix
-
-    source_frame_count, bone_count = sequence_data_matrix.shape[:2]
-    sample_times = list(get_sample_times(source_frame_count, time_step))
-    target_frame_count = len(sample_times)
-    resampled_sequence_data_matrix = np.zeros((target_frame_count, bone_count, 7), dtype=float)
-
-    for sample_index, sample_time in enumerate(sample_times):
-        frame_index = int(sample_time)
-        if sample_time % 1.0 == 0.0:
-            # Sample time has no fractional part, so just copy the frame.
-            resampled_sequence_data_matrix[sample_index, :, :] = sequence_data_matrix[frame_index, :, :]
-        else:
-            # Sample time has a fractional part, so interpolate between two frames.
-            next_frame_index = frame_index + 1
-            for bone_index in range(bone_count):
-                source_frame_1_data = sequence_data_matrix[frame_index, bone_index, :]
-                source_frame_2_data = sequence_data_matrix[next_frame_index, bone_index, :]
-                factor = sample_time - frame_index
-                q = Quaternion((source_frame_1_data[:4])).slerp(Quaternion((source_frame_2_data[:4])), factor)
-                q.normalize()
-                l = Vector(source_frame_1_data[4:]).lerp(Vector(source_frame_2_data[4:]), factor)
-                resampled_sequence_data_matrix[sample_index, bone_index, :] = q.w, q.x, q.y, q.z, l.x, l.y, l.z
-
-    return resampled_sequence_data_matrix
-
-
 def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object, options: PsaImportOptions) -> PsaImportResult:
     result = PsaImportResult()
     sequences = [psa_reader.sequences[x] for x in options.sequence_names]
@@ -144,7 +98,7 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
         if armature_bone_index is not None:
             # Ensure that no other PSA bone has been mapped to this armature bone yet.
             if armature_bone_index not in armature_to_psa_bone_indices:
-                psa_to_armature_bone_indices[psa_bone_index] = armature_bone_index
+                psa_to_armature_bone_indices[psa_bone_index] = armature_bone_names.index(psa_bone_name)
                 armature_to_psa_bone_indices[armature_bone_index] = psa_bone_index
             else:
                 # This armature bone has already been mapped to a PSA bone.
@@ -173,7 +127,7 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
 
     # Create intermediate bone data for import operations.
     import_bones = []
-    psa_bone_names_to_import_bones = dict()
+    import_bones_dict = dict()
 
     for (psa_bone_index, psa_bone), psa_bone_name in zip(enumerate(psa_reader.bones), psa_bone_names):
         if psa_bone_index not in psa_to_armature_bone_indices:
@@ -183,22 +137,15 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
         import_bone = ImportBone(psa_bone)
         import_bone.armature_bone = armature_data.bones[psa_bone_name]
         import_bone.pose_bone = armature_object.pose.bones[psa_bone_name]
-        psa_bone_names_to_import_bones[psa_bone_name] = import_bone
+        import_bones_dict[psa_bone_name] = import_bone
         import_bones.append(import_bone)
 
-    bones_with_missing_parents = []
-
     for import_bone in filter(lambda x: x is not None, import_bones):
         armature_bone = import_bone.armature_bone
-        has_parent = armature_bone.parent is not None
-        if has_parent:
-            if armature_bone.parent.name in psa_bone_names:
-                import_bone.parent = psa_bone_names_to_import_bones[armature_bone.parent.name]
-            else:
-                # Add a warning if the parent bone is not in the PSA.
-                bones_with_missing_parents.append(armature_bone)
+        if armature_bone.parent is not None and armature_bone.parent.name in psa_bone_names:
+            import_bone.parent = import_bones_dict[armature_bone.parent.name]
         # Calculate the original location & rotation of each bone (in world-space maybe?)
-        if has_parent:
+        if import_bone.parent is not None:
             import_bone.original_location = armature_bone.matrix_local.translation - armature_bone.parent.matrix_local.translation
             import_bone.original_location.rotate(armature_bone.parent.matrix_local.to_quaternion().conjugated())
             import_bone.original_rotation = armature_bone.matrix_local.to_quaternion()
@@ -210,12 +157,6 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
 
         import_bone.post_rotation = import_bone.original_rotation.conjugated()
 
-    # Warn about bones with missing parents.
-    if len(bones_with_missing_parents) > 0:
-        count = len(bones_with_missing_parents)
-        message = f'{count} bone(s) have parents that are not present in the PSA:\n' + str([x.name for x in bones_with_missing_parents])
-        result.warnings.append(message)
-
     context.window_manager.progress_begin(0, len(sequences))
 
     # Create and populate the data for new sequences.
@@ -246,9 +187,12 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
             case _:
                 raise ValueError(f'Unknown FPS source: {options.fps_source}')
 
+        keyframe_time_dilation = target_fps / sequence.fps
+
         if options.should_write_keyframes:
-            # Remove existing f-curves.
-            action.fcurves.clear()
+            # Remove existing f-curves (replace with action.fcurves.clear() in Blender 3.2)
+            while len(action.fcurves) > 0:
+                action.fcurves.remove(action.fcurves[-1])
 
             # Create f-curves for the rotation and location of each bone.
             for psa_bone_index, armature_bone_index in psa_to_armature_bone_indices.items():
@@ -282,25 +226,19 @@ def import_psa(context: Context, psa_reader: PsaReader, armature_object: Object,
                     # Calculate the local-space key data for the bone.
                     sequence_data_matrix[frame_index, bone_index] = _calculate_fcurve_data(import_bone, key_data)
 
-            # Resample the sequence data to the target FPS.
-            # If the target frame count is the same as the source frame count, this will be a no-op.
-            resampled_sequence_data_matrix = _resample_sequence_data_matrix(sequence_data_matrix,
-                                                                            time_step=sequence.fps / target_fps)
-
             # Write the keyframes out.
-            # Note that the f-curve data consists of alternating time and value data.
-            target_frame_count = resampled_sequence_data_matrix.shape[0]
-            fcurve_data = np.zeros(2 * target_frame_count, dtype=float)
-            fcurve_data[0::2] = range(0, target_frame_count)
+            fcurve_data = numpy.zeros(2 * sequence.frame_count, dtype=float)
 
+            # Populate the keyframe time data.
+            fcurve_data[0::2] = [x * keyframe_time_dilation for x in range(sequence.frame_count)]
             for bone_index, import_bone in enumerate(import_bones):
                 if import_bone is None:
                     continue
                 for fcurve_index, fcurve in enumerate(import_bone.fcurves):
                     if fcurve is None:
                         continue
-                    fcurve_data[1::2] = resampled_sequence_data_matrix[:, bone_index, fcurve_index]
-                    fcurve.keyframe_points.add(target_frame_count)
+                    fcurve_data[1::2] = sequence_data_matrix[:, bone_index, fcurve_index]
+                    fcurve.keyframe_points.add(sequence.frame_count)
                     fcurve.keyframe_points.foreach_set('co', fcurve_data)
                     for fcurve_keyframe in fcurve.keyframe_points:
                         fcurve_keyframe.interpolation = 'LINEAR'