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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
|
/*
* 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,
ccl_global TilesInfo *tiles,
int x, int y,
ccl_global float *unfilteredA,
ccl_global float *unfilteredB,
ccl_global float *sampleVariance,
ccl_global float *sampleVarianceV,
ccl_global float *bufferVariance,
int4 rect,
int buffer_pass_stride,
int buffer_denoising_offset,
bool use_split_variance)
{
int xtile = (x < tiles->x[1])? 0: ((x < tiles->x[2])? 1: 2);
int ytile = (y < tiles->y[1])? 0: ((y < tiles->y[2])? 1: 2);
int tile = ytile*3+xtile;
int offset = tiles->offsets[tile];
int stride = tiles->strides[tile];
const ccl_global float *ccl_restrict center_buffer = (ccl_global float*) tiles->buffers[tile];
center_buffer += (y*stride + x + offset)*buffer_pass_stride;
center_buffer += buffer_denoising_offset + 14;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
unfilteredA[idx] = center_buffer[1] / max(center_buffer[0], 1e-7f);
unfilteredB[idx] = center_buffer[4] / max(center_buffer[3], 1e-7f);
float varA = center_buffer[2];
float varB = center_buffer[5];
int odd_sample = (sample+1)/2;
int even_sample = sample/2;
if(use_split_variance) {
varA = max(0.0f, varA - unfilteredA[idx]*unfilteredA[idx]*odd_sample);
varB = max(0.0f, varB - unfilteredB[idx]*unfilteredB[idx]*even_sample);
}
varA /= (odd_sample - 1);
varB /= (even_sample - 1);
sampleVariance[idx] = 0.5f*(varA + varB) / sample;
sampleVarianceV[idx] = 0.5f * (varA - varB) * (varA - varB) / (sample*sample);
bufferVariance[idx] = 0.5f * (unfilteredA[idx] - unfilteredB[idx]) * (unfilteredA[idx] - unfilteredB[idx]);
}
/* 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.
* - m_offset, v_offset: Render Buffer Pass offsets of mean and variance of the feature.
* - x, y: Current pixel
* - mean, variance: Target denoise buffers.
* - rect: The prefilter area (lower pixels inclusive, upper pixels exclusive).
*/
ccl_device void kernel_filter_get_feature(int sample,
ccl_global TilesInfo *tiles,
int m_offset, int v_offset,
int x, int y,
ccl_global float *mean,
ccl_global float *variance,
int4 rect, int buffer_pass_stride,
int buffer_denoising_offset,
bool use_split_variance)
{
int xtile = (x < tiles->x[1])? 0: ((x < tiles->x[2])? 1: 2);
int ytile = (y < tiles->y[1])? 0: ((y < tiles->y[2])? 1: 2);
int tile = ytile*3+xtile;
ccl_global float *center_buffer = ((ccl_global float*) tiles->buffers[tile]) + (tiles->offsets[tile] + y*tiles->strides[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);
mean[idx] = center_buffer[m_offset] / sample;
if(use_split_variance) {
variance[idx] = max(0.0f, (center_buffer[v_offset] - mean[idx]*mean[idx]*sample) / (sample * (sample-1)));
}
else {
variance[idx] = center_buffer[v_offset] / (sample * (sample-1));
}
}
ccl_device void kernel_filter_detect_outliers(int x, int y,
ccl_global float *image,
ccl_global float *variance,
ccl_global float *depth,
ccl_global float *out,
int4 rect,
int pass_stride)
{
int buffer_w = align_up(rect.z - rect.x, 4);
int n = 0;
float values[25];
for(int y1 = max(y-2, rect.y); y1 < min(y+3, rect.w); y1++) {
for(int x1 = max(x-2, rect.x); x1 < min(x+3, rect.z); x1++) {
int idx = (y1-rect.y)*buffer_w + (x1-rect.x);
float L = average(make_float3(image[idx], image[idx+pass_stride], image[idx+2*pass_stride]));
/* Find the position of L. */
int i;
for(i = 0; i < n; i++) {
if(values[i] > L) break;
}
/* Make space for L by shifting all following values to the right. */
for(int j = n; j > i; j--) {
values[j] = values[j-1];
}
/* Insert L. */
values[i] = L;
n++;
}
}
int idx = (y-rect.y)*buffer_w + (x-rect.x);
float L = average(make_float3(image[idx], image[idx+pass_stride], image[idx+2*pass_stride]));
float ref = 2.0f*values[(int)(n*0.75f)];
float fac = 1.0f;
if(L > ref) {
/* If the pixel is an outlier, negate the depth value to mark it as one.
* Also, scale its brightness down to the outlier threshold to avoid trouble with the NLM weights. */
depth[idx] = -depth[idx];
fac = ref/L;
variance[idx ] *= fac*fac;
variance[idx + pass_stride] *= fac*fac;
variance[idx+2*pass_stride] *= fac*fac;
}
out[idx ] = fac*image[idx];
out[idx + pass_stride] = fac*image[idx + pass_stride];
out[idx+2*pass_stride] = fac*image[idx+2*pass_stride];
}
/* Combine A/B buffers.
* Calculates the combined mean and the buffer variance. */
ccl_device void kernel_filter_combine_halves(int x, int y,
ccl_global float *mean,
ccl_global float *variance,
ccl_global float *a,
ccl_global 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
|