/usr/include/mia-2.2/mia/core/histogram.hh is in libmia-2.2-dev 2.2.2-1+b1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 | /* -*- mia-c++ -*-
*
* This file is part of MIA - a toolbox for medical image analysis
* Copyright (c) Leipzig, Madrid 1999-2014 Gert Wollny
*
* MIA is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with MIA; if not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef mia_core_histogram_hh
#define mia_core_histogram_hh
#include <mia/core/defines.hh>
#include <cmath>
#include <cassert>
#include <vector>
#include <mia/core/defines.hh>
#include <mia/core/msgstream.hh>
#include <boost/type_traits.hpp>
/** \file histogram.hh This file defined some classes to handle simple histograms */
NS_MIA_BEGIN
/**
\ingroup misc
\brief A class to normalize and quantizize input data to
a given histogram range with its given number of bins.
This class is used as a helpe class for simple histograms.
The class is responsible for scaling and quantizising the input values to
fit the histogram parameters.
\tparam the input data type to be fed into the instogram
*/
template <typename T>
class THistogramFeeder {
public:
/// typedef for generic programming
typedef T value_type;
/**
Initialize the histogram feeder for a histogram with values
in [min,max] with the given number of bins.
\param min
\param max
\param bins
*/
THistogramFeeder(T min, T max, size_t bins);
/// \returns the number of bins
size_t size() const;
/**
Evaluate the target bin of an input value
\param x input value
\returns the target bin index
\remark the index is the nearest neighbor of the scaled input value
*/
size_t index(T x) const;
/**
Evaluate the center value of a given bin in terms of the input data range
\param k bin index
\returns center value of bin
*/
T value(size_t k) const;
private:
T m_min;
T m_max;
size_t m_bins;
double m_step;
double m_inv_step;
};
/**
\ingroup helpers
\brief specialization of the THistogramFeeder for unsigned byte input data
This specialization always uses the full range [0,256) of the input data
and 256 bins.
\remark is this really a good idea to specialize the histogram like this,
because so it is not possible to use a different number of bins for unsigned byte
*/
template <>
class THistogramFeeder<unsigned char> {
public:
/// typedef for generic programming
typedef unsigned char value_type;
/// Construct the feeder, the parameters are ignored
THistogramFeeder(unsigned char min, unsigned char max, size_t bins);
/// \returns 256
size_t size() const;
/// \returns x
size_t index(unsigned char x) const;
/// \returns k
unsigned char value(size_t k) const;
};
/// typedef for the unsigned byte histogram feeder specialization
typedef THistogramFeeder<unsigned char> CUBHistogramFeeder;
/**
\ingroup helpers
\brief a simple histogram that uses an instance of THistogramFeeder
as input converter
This class implements a simple histogram that uses the nearest neighbor
approach implemeneted in THistogramFeeder to fill the histogram and provides
some funcionallity to work with the histogram.
\tparam the input feeder
*/
template <typename Feeder>
class THistogram {
public:
/// STL iterator
typedef std::vector<size_t>::const_iterator const_iterator;
/// A type for the value-index pair \todo change to meaningful name
typedef std::pair<typename Feeder::value_type, size_t> value_type;
typedef std::pair<typename Feeder::value_type, typename Feeder::value_type> range_type;
/**
Constructor to create the histogram with the given input feeder.
*/
THistogram(const Feeder& f);
/**
Constructor to create a histogram by copying another histogram and
cutting of part ot the upper tail.
\param org original histogram to copy from
\param perc percentage of the bins to cut off
\todo this should actually cut of a percentage of the data and not of the
bins
*/
THistogram(const THistogram<Feeder>& org, double perc);
/**
Add a value x to the histogram
\param x
*/
void push(typename Feeder::value_type x);
/**
Add a value x to the histogram count times
\param x
\param count
*/
void push(typename Feeder::value_type x, size_t count);
/**
Add a range of data to the histogram
\tparam Iterator forward iterator
\param begin start of input range
\param end end of input range (STL convention)
*/
template <typename Iterator>
void push_range(Iterator begin, Iterator end);
/// \returns size of histogram
size_t size() const;
/// \returns start of histogram
const_iterator begin() const;
/// \returns end of histogram
const_iterator end() const;
/// \returns value of histogram bin at idx
size_t operator [] (size_t idx) const;
/** Return the count and input range value corresponding to
the bin at idx
\param idx
\returns <value,count> pair
*/
const value_type at(size_t idx) const;
/// \returns median of the histogram
typename Feeder::value_type median() const;
/// \returns Median Average Distance of the histogram
typename Feeder::value_type MAD() const;
/// \returns mean of the histogram
double average() const;
/// \returns deviation of the histogram
double deviation() const;
/**
return the histogram range that cuts off the \a remove percent of pixels
from the lower ane upper end of the histogram
\param remove the amout of pixels to remove from the upper and lower end of the historam [0,40]
\returns the pair <low,high> of the resulting histogram range
*/
range_type get_reduced_range(double remove) const;
private:
Feeder m_feeder;
std::vector<size_t> m_histogram;
size_t m_n;
};
// inline inplementation
template <typename T>
THistogramFeeder<T>::THistogramFeeder(T min, T max, size_t bins):
m_min(min),
m_max(max),
m_bins(bins),
m_step(( double(max) - double(min) ) / double(bins - 1)),
m_inv_step(double(bins - 1) / (double(max) - double(min)))
{
}
template <typename T>
size_t THistogramFeeder<T>::size() const
{
return m_bins;
}
template <typename T>
inline size_t THistogramFeeder<T>::index(T x) const
{
double val = floor(m_inv_step * (x - m_min) + 0.5);
if (val < 0)
return 0;
if (val < m_bins)
return val;
return m_bins - 1;
}
template <typename T>
T THistogramFeeder<T>::value(size_t k) const
{
return k * m_step + m_min;
}
inline THistogramFeeder<unsigned char>::THistogramFeeder(unsigned char /*min*/, unsigned char /*max*/, size_t /*bins*/)
{
}
inline size_t THistogramFeeder<unsigned char>::size() const
{
return 256;
}
inline
size_t THistogramFeeder<unsigned char>::index(unsigned char x) const
{
return x;
}
inline
unsigned char THistogramFeeder<unsigned char>::value(size_t k) const
{
return k;
}
template <typename Feeder>
THistogram<Feeder>::THistogram(const Feeder& f):
m_feeder(f),
m_histogram(f.size()),
m_n(0)
{
}
template <typename Feeder>
THistogram<Feeder>::THistogram(const THistogram<Feeder>& org, double perc):
m_feeder(org.m_feeder),
m_histogram(m_feeder.size()),
m_n(0)
{
size_t n = (size_t)(org.m_n * (1.0 - perc));
size_t i = 0;
while (n > m_n && i < m_histogram.size()) {
m_n += org.m_histogram[i];
m_histogram[i] = org.m_histogram[i];
++i;
}
}
template <typename Feeder>
size_t THistogram<Feeder>::size() const
{
return m_histogram.size();
}
template <typename Feeder>
void THistogram<Feeder>::push(typename Feeder::value_type x)
{
++m_n;
++m_histogram[m_feeder.index(x)];
}
template <typename Feeder>
template <typename Iterator>
void THistogram<Feeder>::push_range(Iterator begin, Iterator end)
{
while (begin != end)
push(*begin++);
}
template <typename Feeder>
void THistogram<Feeder>::push(typename Feeder::value_type x, size_t count)
{
m_n += count;
m_histogram[m_feeder.index(x)] += count;
}
template <typename Feeder>
typename THistogram<Feeder>::const_iterator THistogram<Feeder>::begin() const
{
return m_histogram.begin();
}
template <typename Feeder>
typename THistogram<Feeder>::const_iterator THistogram<Feeder>::end() const
{
return m_histogram.end();
}
template <typename Feeder>
size_t THistogram<Feeder>::operator [] (size_t idx) const
{
assert(idx < m_histogram.size());
return m_histogram[idx];
}
template <typename Feeder>
typename Feeder::value_type THistogram<Feeder>::median() const
{
float n_2 = m_n / 2.0f;
float sum = 0;
size_t k = 0;
while ( sum < n_2 )
sum += m_histogram[k++];
return m_feeder.value(k > 0 ? k-1 : k);
}
template <typename Feeder>
typename Feeder::value_type THistogram<Feeder>::MAD() const
{
typedef typename Feeder::value_type T;
T m = median();
THistogram<Feeder> help(m_feeder);
;
for (size_t k = 0; k < size(); ++k) {
T v = m_feeder.value(k);
help.push(v > m ? v - m : m -v, m_histogram[k]);
}
return help.median();
}
template <typename Feeder>
const typename THistogram<Feeder>::value_type THistogram<Feeder>::at(size_t idx) const
{
if (idx < m_histogram.size())
return value_type(m_feeder.value(idx), m_histogram[idx]);
else
return value_type(m_feeder.value(idx), 0);
}
template <typename Feeder>
double THistogram<Feeder>::average() const
{
if (m_n < 1)
return 0.0;
double sum = 0.0;
for (size_t i = 0; i < size(); ++i) {
const typename THistogram<Feeder>::value_type value = at(i);
sum += value.first * value.second;
}
return sum / m_n;
}
template <typename Feeder>
double THistogram<Feeder>::deviation() const
{
if (m_n < 2)
return 0.0;
double sum = 0.0;
double sum2 = 0.0;
for (size_t i = 0; i < size(); ++i) {
const typename THistogram<Feeder>::value_type value = at(i);
sum += value.first * value.second;
sum2 += value.first * value.first * value.second;
}
return sqrt((sum2 - sum * sum / m_n) / (m_n - 1));
}
template <typename Feeder>
typename THistogram<Feeder>::range_type
THistogram<Feeder>::get_reduced_range(double remove) const
{
assert(remove >= 0.0 && remove < 49.0);
long remove_count = static_cast<long>(remove * m_n / 100.0);
range_type result(m_feeder.value(0), m_feeder.value(m_histogram.size() - 1));
if (remove_count > 0) {
long low_end = -1;
long counted_pixels_low = 0;
while (counted_pixels_low < remove_count && low_end < (long)m_histogram.size())
counted_pixels_low += m_histogram[++low_end];
result.first = m_feeder.value(low_end);
long high_end = m_histogram.size();
long counted_pixels_high = 0;
while (counted_pixels_high <= remove_count && high_end > 0)
counted_pixels_high += m_histogram[--high_end];
cvdebug() << " int range = " << low_end << ", " << high_end << " removing " << remove_count << " pixels at each end\n";
result.second = m_feeder.value(high_end);
}
return result;
}
NS_MIA_END
#endif
|