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
|
/*
**
** Copyright 2014, The Android Open Source Project
**
** 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.
*/
//#define LOG_NDEBUG 0
#define LOG_TAG "ClockEstimator"
#include <utils/Log.h>
#include <math.h>
#include <media/stagefright/ClockEstimator.h>
#include <media/stagefright/foundation/ADebug.h>
namespace android {
WindowedLinearFitEstimator::WindowedLinearFitEstimator(
size_t headLength, double headFactor, size_t mainLength, double tailFactor)
: mHeadFactorInv(1. / headFactor),
mTailFactor(tailFactor),
mHistoryLength(mainLength + headLength),
mHeadLength(headLength) {
reset();
mXHistory.resize(mHistoryLength);
mYHistory.resize(mHistoryLength);
mFirstWeight = pow(headFactor, mHeadLength);
}
WindowedLinearFitEstimator::LinearFit::LinearFit() {
reset();
}
void WindowedLinearFitEstimator::LinearFit::reset() {
mX = mXX = mY = mYY = mXY = mW = 0.;
}
double WindowedLinearFitEstimator::LinearFit::size() const {
double s = mW * mW + mX * mX + mY * mY + mXX * mXX + mXY * mXY + mYY * mYY;
if (s > 1e72) {
// 1e72 corresponds to clock monotonic time of about 8 years
ALOGW("estimator is overflowing: w=%g x=%g y=%g xx=%g xy=%g yy=%g",
mW, mX, mY, mXX, mXY, mYY);
}
return s;
}
void WindowedLinearFitEstimator::LinearFit::add(double x, double y, double w) {
mW += w;
mX += w * x;
mY += w * y;
mXX += w * x * x;
mXY += w * x * y;
mYY += w * y * y;
}
void WindowedLinearFitEstimator::LinearFit::combine(const LinearFit &lf) {
mW += lf.mW;
mX += lf.mX;
mY += lf.mY;
mXX += lf.mXX;
mXY += lf.mXY;
mYY += lf.mYY;
}
void WindowedLinearFitEstimator::LinearFit::scale(double w) {
mW *= w;
mX *= w;
mY *= w;
mXX *= w;
mXY *= w;
mYY *= w;
}
double WindowedLinearFitEstimator::LinearFit::interpolate(double x) {
double div = mW * mXX - mX * mX;
if (fabs(div) < 1e-5 * mW * mW) {
// this only should happen on the first value
return x;
// assuming a = 1, we could also return x + (mY - mX) / mW;
}
double a_div = (mW * mXY - mX * mY);
double b_div = (mXX * mY - mX * mXY);
ALOGV("a=%.4g b=%.4g in=%g out=%g",
a_div / div, b_div / div, x, (a_div * x + b_div) / div);
return (a_div * x + b_div) / div;
}
double WindowedLinearFitEstimator::estimate(double x, double y) {
/*
* TODO: We could update the head by adding the new sample to it
* and amplifying it, but this approach can lead to unbounded
* error. Instead, we recalculate the head at each step, which
* is computationally more expensive. We could balance the two
* methods by recalculating just before the error becomes
* significant.
*/
const bool update_head = false;
if (update_head) {
// add new sample to the head
mHead.scale(mHeadFactorInv); // amplify head
mHead.add(x, y, mFirstWeight);
}
/*
* TRICKY: place elements into the circular buffer at decreasing
* indices, so that we can access past elements by addition
* (thereby avoiding potentially negative indices.)
*/
if (mNumSamples >= mHeadLength) {
// move last head sample from head to the main window
size_t lastHeadIx = (mSampleIx + mHeadLength) % mHistoryLength;
if (update_head) {
mHead.add(mXHistory[lastHeadIx], mYHistory[lastHeadIx], -1.); // remove
}
mMain.add(mXHistory[lastHeadIx], mYHistory[lastHeadIx], 1.);
if (mNumSamples >= mHistoryLength) {
// move last main sample from main window to tail
mMain.add(mXHistory[mSampleIx], mYHistory[mSampleIx], -1.); // remove
mTail.add(mXHistory[mSampleIx], mYHistory[mSampleIx], 1.);
mTail.scale(mTailFactor); // attenuate tail
}
}
mXHistory.editItemAt(mSampleIx) = x;
mYHistory.editItemAt(mSampleIx) = y;
if (mNumSamples < mHistoryLength) {
++mNumSamples;
}
// recalculate head unless we were using the update method
if (!update_head) {
mHead.reset();
double w = mFirstWeight;
for (size_t headIx = 0; headIx < mHeadLength && headIx < mNumSamples; ++headIx) {
size_t ix = (mSampleIx + headIx) % mHistoryLength;
mHead.add(mXHistory[ix], mYHistory[ix], w);
w *= mHeadFactorInv;
}
}
if (mSampleIx > 0) {
--mSampleIx;
} else {
mSampleIx = mHistoryLength - 1;
}
// return estimation result
LinearFit total;
total.combine(mHead);
total.combine(mMain);
total.combine(mTail);
return total.interpolate(x);
}
void WindowedLinearFitEstimator::reset() {
mHead.reset();
mMain.reset();
mTail.reset();
mNumSamples = 0;
mSampleIx = mHistoryLength - 1;
}
}; // namespace android
|
