io_scene_fbx/fbx_utils.py

@@ -1314,42 +1314,88 @@ class AnimationCurveNodeWrapper:
         min_reldiff_fac = fac * 1.0e-3  # min relative value evolution: 0.1% of current 'order of magnitude'.
         min_absdiff_fac = 0.1  # A tenth of reldiff...
 
-        are_keyed = []
         for values, frame_write_mask in zip(self._frame_values_array, self._frame_write_mask_array):
             # Initialise to no frames written.
             frame_write_mask[:] = False
 
-            # Create views of the 'previous' and 'current' mask and values. The memoryview, .data, of each array is used
+            abs_values = np.abs(values)
-            # for its iteration and indexing performance compared to the array.
-            key = values[1:].data
-            p_key = values[:-1].data
-            key_write = frame_write_mask[1:].data
-            p_key_write = frame_write_mask[:-1].data
 
-            p_keyedval = values[0]
+            # Create views of the 'previous' and 'current' mask and values.
-            is_keyed = False
+            p_val = values[:-1]
-            for idx, (val, p_val) in enumerate(zip(key, p_key)):
+            abs_p_val = abs_values[:-1]
-                if val == p_val:
+            p_write_mask = frame_write_mask[:-1]
-                    # Never write keyframe when value is exactly the same as prev one!
+            val = values[1:]
-                    continue
+            abs_val = abs_values[1:]
-                # This is contracted form of relative + absolute-near-zero difference:
+            write_mask = frame_write_mask[1:]
-                #     absdiff = abs(a - b)
+
-                #     if absdiff < min_reldiff_fac * min_absdiff_fac:
+            # This is contracted form of relative + absolute-near-zero difference:
-                #         return False
+            #     absdiff = abs(a - b)
-                #     return (absdiff / ((abs(a) + abs(b)) / 2)) > min_reldiff_fac
+            #     if absdiff < min_reldiff_fac * min_absdiff_fac:
-                # Note that we ignore the '/ 2' part here, since it's not much significant for us.
+            #         return False
-                if abs(val - p_val) > (min_reldiff_fac * max(abs(val) + abs(p_val), min_absdiff_fac)):
+            #     return (absdiff / ((abs(a) + abs(b)) / 2)) > min_reldiff_fac
-                    # If enough difference from previous sampled value, key this value *and* the previous one!
+            # Note that we ignore the '/ 2' part here, since it's not much significant for us.
-                    key_write[idx] = True
+            enough_diff_p_val_mask = (
-                    p_key_write[idx] = True
+                    np.abs(val - p_val) > (min_reldiff_fac * np.maximum(abs_val + abs_p_val, min_absdiff_fac))
-                    p_keyedval = val
+            )
-                    is_keyed = True
+            # If enough difference from previous sampled value, key this value *and* the previous one!
-                elif abs(val - p_keyedval) > (min_reldiff_fac * max((abs(val) + abs(p_keyedval)), min_absdiff_fac)):
+            # Unless it is forcefully keyed later, this is the only way that the first value can be keyed.
-                    # Else, if enough difference from previous keyed value, key this value only!
+            p_write_mask[enough_diff_p_val_mask] = True
-                    key_write[idx] = True
+            write_mask[enough_diff_p_val_mask] = True
-                    p_keyedval = val
+
-                    is_keyed = True
+            # The other case where we key a value is if there is enough difference between it and the previous keyed
-            are_keyed.append(is_keyed)
+            # value.
+            # Values that equal their previous value can be skipped, so the remaining values to check are those which
+            # are currently not keyed.
+            not_keyed_mask = ~write_mask
+            check_diff_mask = np.logical_and(not_keyed_mask, p_val != val)
+            val_check_idx = np.flatnonzero(check_diff_mask)
+            val_check = val[val_check_idx]
+            abs_val_check = abs_val[val_check_idx]
+
+            # For each current value, get the index of the previous keyed value in `values`.
+            p_keyed_idx = np.arange(1, len(values))
+            # The first 'previous keyed value' defaults to values[0], even if it's not actually keyed.
+            p_keyed_idx[not_keyed_mask] = 0
+            # Accumulative maximum fills in the zeroed indices with the closest previous non-zero index because the
+            # indices must be increasing.
+            p_keyed_idx = np.maximum.accumulate(p_keyed_idx)
+            # Extract only the indices that need checking.
+            p_keyed_idx_check = p_keyed_idx[val_check_idx]
+            p_keyed_val_check = values[p_keyed_idx_check]
+            abs_p_keyed_val_check = np.abs(p_keyed_val_check)
+
+            # We check the relative + absolute-near-zero difference again, but against the previous keyed value this
+            # time.
+            enough_diff_p_keyed_val_mask = (
+                np.abs(val_check - p_keyed_val_check)
+                > (min_reldiff_fac * np.maximum(abs_val_check + abs_p_keyed_val_check, min_absdiff_fac))
+            )
+            if np.any(enough_diff_p_keyed_val_mask):
+                # If there are any that are different enough from the previous keyed value, then we have to check them
+                # all iteratively because keying a new value can change the previous keyed value of some elements, which
+                # changes their relative + absolute-near-zero difference.
+                new_p_keyed_idx = -1
+                new_p_keyed_val = -1
+                new_p_keyed_val_abs = -1
+                # Accessing .data, the memoryview of the array, iteratively or by individual index is faster than doing
+                # the same with the array itself.
+                key_write_mv = write_mask.data
+                zipped = zip(val_check_idx.data, val_check.data, abs_val_check.data, p_keyed_idx_check.data,
+                             enough_diff_p_keyed_val_mask.data)
+                for cur_idx, cur_val, abs_cur_val, old_p_keyed_idx, enough_diff in zipped:
+                    if new_p_keyed_idx > old_p_keyed_idx:
+                        # The previous keyed value is new and was not included when enough_diff_p_keyed_val_mask was
+                        # calculated, so whether the current value is different enough needs to be calculated.
+                        # Check if the relative + absolute-near-zero difference is enough to key this value.
+                        enough_diff = (abs(cur_val - new_p_keyed_val)
+                                       > (min_reldiff_fac * max(abs_cur_val + new_p_keyed_val_abs, min_absdiff_fac)))
+                    if enough_diff:
+                        # The current value needs to be keyed.
+                        key_write_mv[cur_idx] = True
+                        # Update the index and values for this newly keyed value.
+                        new_p_keyed_idx = cur_idx
+                        new_p_keyed_val = cur_val
+                        new_p_keyed_val_abs = abs_cur_val
 
         # If we write nothing (action doing nothing) and are in 'force_keep' mode, we key everything! :P
         # See T41766.
@@ -1358,7 +1404,9 @@ class AnimationCurveNodeWrapper:
         # one key in this case.
         # See T41719, T41605, T41254...
         if self.force_keying or (force_keep and not self):
-            are_keyed[:] = [True] * len(are_keyed)
+            are_keyed = [True] * len(self._frame_write_mask_array)
+        else:
+            are_keyed = np.any(self._frame_write_mask_array, axis=1)
 
         # If we did key something, ensure first and last sampled values are keyed as well.
         if self.force_startend_keying: