Added PSA resampling

Fixed PSA import resampling logic

io_scene_psk_psa/psa/importer.py

@@ -2,7 +2,7 @@ import typing
 from typing import List, Optional
 
 import bpy
-import numpy
+import numpy as np
 from bpy.types import FCurve, Object, Context
 from mathutils import Vector, Quaternion
 
@@ -80,6 +80,52 @@ 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]
@@ -187,12 +233,9 @@ 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 (replace with action.fcurves.clear() in Blender 3.2)
-            while len(action.fcurves) > 0:
-                action.fcurves.remove(action.fcurves[-1])
+            # Remove existing f-curves.
+            action.fcurves.clear()
 
             # 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():
@@ -226,19 +269,25 @@ 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)
 
-            # Write the keyframes out.
-            fcurve_data = numpy.zeros(2 * sequence.frame_count, dtype=float)
+            # 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)
 
-            # 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] = sequence_data_matrix[:, bone_index, fcurve_index]
-                    fcurve.keyframe_points.add(sequence.frame_count)
+                    fcurve_data[1::2] = resampled_sequence_data_matrix[:, bone_index, fcurve_index]
+                    fcurve.keyframe_points.add(target_frame_count)
                     fcurve.keyframe_points.foreach_set('co', fcurve_data)
                     for fcurve_keyframe in fcurve.keyframe_points:
                         fcurve_keyframe.interpolation = 'LINEAR'