Animation: Gaussian Smooth operator for Graph Editor #105635
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@ -394,20 +394,28 @@ void blend_to_default_fcurve(PointerRNA *id_ptr, FCurve *fcu, const float factor
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}
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/* ---------------- */
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void get_1d_gauss_kernel(const int filter_width, const float sigma, double *kernel)
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void get_1d_gauss_kernel(const float sigma, const int kernel_size, double *r_kernel)
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{
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dr.sybren marked this conversation as resolved
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double norm = 1.0 / (M_2_SQRTPI * sigma);
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double sig_sq = 2.0 * sigma * sigma;
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double sigma_sq = 2.0 * sigma * sigma;
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double sum = 0.0;
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for (int i = -filter_width; i <= filter_width; i++) {
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kernel[i + filter_width] = norm * exp(-i * i / sig_sq);
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sum += kernel[i + filter_width];
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for (int i = 0; i < kernel_size; i++) {
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const double normalized_index = (double)i / (kernel_size - 1);
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r_kernel[i] = norm * exp(-normalized_index * normalized_index / sigma_sq);
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if (i == 0) {
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sum += r_kernel[i];
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}
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else {
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/* We only calculate half the kernel,
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* the normalization needs to take that into account. */
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sum += r_kernel[i] * 2;
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}
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}
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/* Normalize kernel values. */
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for (int i = 0; i < filter_width * 2 + 1; i++) {
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kernel[i] = kernel[i] / sum;
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for (int i = 0; i < kernel_size; i++) {
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r_kernel[i] = r_kernel[i] / sum;
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dr.sybren marked this conversation as resolved
Sybren A. Stüvel
commented
```c
r_kernel[i] /= sum;
```
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}
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}
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@ -415,17 +423,17 @@ void smooth_fcurve_segment(FCurve *fcu,
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FCurveSegment *segment,
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float *samples,
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const float factor,
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const int filter_order,
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const int kernel_size,
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double *kernel)
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{
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for (int i = segment->start_index; i < segment->start_index + segment->length; i++) {
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dr.sybren marked this conversation as resolved
Sybren A. Stüvel
commented
`segment->start_index + segment->length` and `fcu->bezt[segment->start_index].vec[1][0]` don't change during the loop, so you can store them in a constant outside of the loop and use that instead.
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const int sample_index = (int)(fcu->bezt[i].vec[1][0] -
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fcu->bezt[segment->start_index].vec[1][0]) +
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filter_order;
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kernel_size;
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double filter_result = 0;
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/* Apply the kernel. */
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for (int j = -filter_order; j <= filter_order; j++) {
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filter_result += samples[sample_index + j] * kernel[filter_order + j];
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for (int j = -kernel_size; j <= kernel_size; j++) {
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filter_result += samples[sample_index + j] * kernel[abs(j)];
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}
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fcu->bezt[i].vec[1][1] = interpf((float)filter_result, samples[sample_index], factor);
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}
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@ -424,12 +424,16 @@ void blend_to_neighbor_fcurve_segment(struct FCurve *fcu,
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struct FCurveSegment *segment,
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float factor);
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void breakdown_fcurve_segment(struct FCurve *fcu, struct FCurveSegment *segment, float factor);
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void get_1d_gauss_kernel(int filter_width, const float sigma, double *kernel);
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/** Get a 1D gauss kernel. Since the kernel is symmetrical, only calculates the positive side.
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* \param sigma The shape of the gauss distribution.
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* \param kernel_size How long the kernel array is.
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*/
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void get_1d_gauss_kernel(const float sigma, int kernel_size, double *r_kernel);
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void smooth_fcurve_segment(struct FCurve *fcu,
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struct FCurveSegment *segment,
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float *samples,
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float factor,
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int filter_order,
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int kernel_size,
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double *kernel);
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void ease_fcurve_segment(struct FCurve *fcu, struct FCurveSegment *segment, float factor);
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bool decimate_fcurve(struct bAnimListElem *ale, float remove_ratio, float error_sq_max);
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@ -1065,12 +1065,11 @@ static void gauss_smooth_graph_keys(bAnimContext *ac,
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ListBase anim_data = {NULL, NULL};
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ANIM_animdata_filter(ac, &anim_data, OPERATOR_DATA_FILTER, ac->data, ac->datatype);
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bAnimListElem *ale;
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const int kernel_size = filter_width + 1;
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double *kernel = MEM_callocN(sizeof(double) * kernel_size, "Gauss Kernel");
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get_1d_gauss_kernel(sigma, kernel_size, kernel);
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double *kernel = MEM_callocN(sizeof(double) * (filter_width * 2 + 1), "Gauss Kernel");
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get_1d_gauss_kernel(filter_width, sigma, kernel);
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for (ale = anim_data.first; ale; ale = ale->next) {
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LISTBASE_FOREACH (bAnimListElem *, ale, &anim_data) {
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FCurve *fcu = (FCurve *)ale->key_data;
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ListBase segments = find_fcurve_segments(fcu);
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@ -1078,7 +1077,7 @@ static void gauss_smooth_graph_keys(bAnimContext *ac,
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BezTriple left_bezt = fcu->bezt[segment->start_index];
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BezTriple right_bezt = fcu->bezt[segment->start_index + segment->length - 1];
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const int sample_count = (int)(right_bezt.vec[1][0] - left_bezt.vec[1][0]) +
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(filter_width * 2) + 1;
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(filter_width * 2 + 1);
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float *samples = MEM_callocN(sizeof(float) * sample_count, "Smooth FCurve Op Samples");
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sample_fcurve_segment(fcu, left_bezt.vec[1][0] - filter_width, samples, sample_count);
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smooth_fcurve_segment(fcu, segment, samples, factor, filter_width, kernel);
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@ -1094,6 +1093,38 @@ static void gauss_smooth_graph_keys(bAnimContext *ac,
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ANIM_animdata_freelist(&anim_data);
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}
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static void gauss_smooth_modal_update(bContext *C, wmOperator *op)
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{
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tGraphSliderOp *gso = op->customdata;
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ease_draw_status_header(C, gso);
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/* Reset keyframes to the state at invoke. */
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reset_bezts(gso);
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const float factor = slider_factor_get_and_remember(op);
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const float sigma = RNA_float_get(op->ptr, "sigma");
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const int filter_width = RNA_int_get(op->ptr, "filter_width");
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gauss_smooth_graph_keys(&gso->ac, factor, sigma, filter_width);
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WM_event_add_notifier(C, NC_ANIMATION | ND_KEYFRAME | NA_EDITED, NULL);
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}
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static int gauss_smooth_invoke(bContext *C, wmOperator *op, const wmEvent *event)
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{
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const int invoke_result = graph_slider_invoke(C, op, event);
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if (invoke_result == OPERATOR_CANCELLED) {
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return invoke_result;
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}
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tGraphSliderOp *gso = op->customdata;
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gso->modal_update = gauss_smooth_modal_update;
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gso->factor_prop = RNA_struct_find_property(op->ptr, "factor");
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ED_slider_allow_overshoot_set(gso->slider, false);
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ease_draw_status_header(C, gso);
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return invoke_result;
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}
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static int gauss_exec(bContext *C, wmOperator *op)
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{
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bAnimContext ac;
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@ -1120,8 +1151,8 @@ void GRAPH_OT_gauss_smooth(wmOperatorType *ot)
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ot->description = "Smooth the curve using a Gauss filter";
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/* API callbacks. */
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/* ot->invoke = fft_invoke; */
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/* ot->modal = fft_modal; */
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ot->invoke = gauss_smooth_invoke;
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ot->modal = graph_slider_modal;
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ot->exec = gauss_exec;
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ot->poll = graphop_editable_keyframes_poll;
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@ -1140,7 +1171,7 @@ void GRAPH_OT_gauss_smooth(wmOperatorType *ot)
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RNA_def_float(ot->srna,
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"sigma",
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2.0f,
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0.33f,
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0.001f,
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FLT_MAX,
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"Sigma",
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@ -1150,7 +1181,7 @@ void GRAPH_OT_gauss_smooth(wmOperatorType *ot)
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RNA_def_int(ot->srna,
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"filter_width",
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8,
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6,
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1,
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64,
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"Filter Width",
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Reference in New Issue
You could add
to document the assumptions of the code. If
sigma == 0
it'll cause some nice division issues.