1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
|
/*
* Copyright 2011-2017 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
CCL_NAMESPACE_BEGIN
/* First step of the shadow prefiltering, performs the shadow division and stores all data
* in a nice and easy rectangular array that can be passed to the NLM filter.
*
* Calculates:
* unfiltered: Contains the two half images of the shadow feature pass
* sampleVariance: The sample-based variance calculated in the kernel. Note: This calculation is biased in general, and especially here since the variance of the ratio can only be approximated.
* sampleVarianceV: Variance of the sample variance estimation, quite noisy (since it's essentially the buffer variance of the two variance halves)
* bufferVariance: The buffer-based variance of the shadow feature. Unbiased, but quite noisy.
*/
ccl_device void kernel_filter_divide_shadow(int sample, float **buffers,
int x, int y,
int *tile_x, int *tile_y,
int *offset, int *stride,
float *unfiltered, float *sampleVariance,
float *sampleVarianceV, float *bufferVariance,
int4 rect, int buffer_pass_stride,
int buffer_denoising_offset, int num_frames,
bool use_gradients)
{
int xtile = (x < tile_x[1])? 0: ((x < tile_x[2])? 1: 2);
int ytile = (y < tile_y[1])? 0: ((y < tile_y[2])? 1: 2);
int tile = ytile*3+xtile;
float *center_buffer = buffers[tile] + (offset[tile] + y*stride[tile] + x)*buffer_pass_stride;
if(use_gradients && tile == 4) {
center_buffer[0] = center_buffer[1] = center_buffer[2] = center_buffer[3] = 0.0f;
}
center_buffer += buffer_denoising_offset;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
int Bofs = (rect.w - rect.y)*buffer_w*num_frames;
unfiltered[idx] = center_buffer[15] / max(center_buffer[14], 1e-7f);
unfiltered[idx+Bofs] = center_buffer[18] / max(center_buffer[17], 1e-7f);
float varFac = 1.0f / (sample * (sample-1));
sampleVariance[idx] = (center_buffer[16] + center_buffer[19]) * varFac;
sampleVarianceV[idx] = 0.5f * (center_buffer[16] - center_buffer[19]) * (center_buffer[16] - center_buffer[19]) * varFac * varFac;
bufferVariance[idx] = 0.5f * (unfiltered[idx] - unfiltered[idx+Bofs]) * (unfiltered[idx] - unfiltered[idx+Bofs]);
}
/* Load a regular feature from the render buffers into the denoise buffer.
* Parameters:
* - sample: The sample amount in the buffer, used to normalize the buffer.
* - buffers: 9-Element Array containing pointers to the buffers of the 3x3 tiles around the current one.
* - m_offset, v_offset: Render Buffer Pass offsets of mean and variance of the feature.
* - x, y: Current pixel
* - tile_x, tile_y: 4-Element Arrays containing the x/y coordinates of the start of the lower, current and upper tile as well as the end of the upper tile plus one.
* - offset, stride: 9-Element Arrays containing offset and stride of the RenderBuffers.
* - mean, variance: Target denoise buffers.
* - rect: The prefilter area (lower pixels inclusive, upper pixels exclusive).
*/
ccl_device void kernel_filter_get_feature(int sample, float **buffers,
int m_offset, int v_offset,
int x, int y,
int *tile_x, int *tile_y,
int *offset, int *stride,
float *mean, float *variance,
int4 rect, int buffer_pass_stride,
int buffer_denoising_offset, bool use_cross_denoising)
{
int xtile = (x < tile_x[1])? 0: ((x < tile_x[2])? 1: 2);
int ytile = (y < tile_y[1])? 0: ((y < tile_y[2])? 1: 2);
int tile = ytile*3+xtile;
float *center_buffer = buffers[tile] + (offset[tile] + y*stride[tile] + x)*buffer_pass_stride + buffer_denoising_offset;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
/* TODO: This is an ugly hack! */
if(m_offset >= 20 && m_offset <= 22 && use_cross_denoising) {
int odd_sample = sample/2;
mean[idx] = (center_buffer[m_offset] - center_buffer[m_offset+6]) / odd_sample;
variance[idx] = center_buffer[v_offset] / (odd_sample * (sample-1));
}
else if(m_offset >= 26) {
int even_sample = (sample+1)/2;
mean[idx] = center_buffer[m_offset] / even_sample;
variance[idx] = center_buffer[v_offset-6] / (even_sample * (sample-1));
}
else {
mean[idx] = center_buffer[m_offset] / sample;
variance[idx] = center_buffer[v_offset] / (sample * (sample-1));
}
}
/* Combine A/B buffers.
* Calculates the combined mean and the buffer variance. */
ccl_device void kernel_filter_combine_halves(int x, int y,
float *mean, float *variance,
float *a, float *b,
int4 rect, int r)
{
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
if(mean) mean[idx] = 0.5f * (a[idx]+b[idx]);
if(variance) {
if(r == 0) variance[idx] = 0.25f * (a[idx]-b[idx])*(a[idx]-b[idx]);
else {
variance[idx] = 0.0f;
float values[25];
int numValues = 0;
for(int py = max(y-r, rect.y); py < min(y+r+1, rect.w); py++) {
for(int px = max(x-r, rect.x); px < min(x+r+1, rect.z); px++) {
int pidx = (py-rect.y)*buffer_w + (px-rect.x);
values[numValues++] = 0.25f * (a[pidx]-b[pidx])*(a[pidx]-b[pidx]);
}
}
/* Insertion-sort the variances (fast enough for 25 elements). */
for(int i = 1; i < numValues; i++) {
float v = values[i];
int j;
for(j = i-1; j >= 0 && values[j] > v; j--)
values[j+1] = values[j];
values[j+1] = v;
}
variance[idx] = values[(7*numValues)/8];
}
}
}
CCL_NAMESPACE_END
|
