mirror of
https://github.com/hyperion-project/hyperion.ng.git
synced 2023-10-10 13:36:59 +02:00
1ae37d151e
* Dominant Color and Mean Color Squared * Workaround - Suppress empty LED updates * Add missing text * Dominant Colors advanced * Test with fixed initial colors * Test with fixed initial colors * Support new processing values via API * ImageToLED - Add reduced pixel processing, make dominant color advanced configurable * Updates on Grabber fps setting * ImageToLedMap - Remove maptype and update test * Update dynamic cluster array allocation
659 lines
20 KiB
C++
659 lines
20 KiB
C++
#ifndef IMAGETOLEDSMAP_H
|
|
#define IMAGETOLEDSMAP_H
|
|
|
|
// STL includes
|
|
#include <cassert>
|
|
#include <memory>
|
|
#include <sstream>
|
|
#include <cmath>
|
|
|
|
// hyperion-utils includes
|
|
#include <utils/Image.h>
|
|
#include <utils/Logger.h>
|
|
#include <utils/ColorRgbScalar.h>
|
|
#include <utils/ColorSys.h>
|
|
|
|
// hyperion includes
|
|
#include <hyperion/LedString.h>
|
|
|
|
namespace hyperion
|
|
{
|
|
///
|
|
/// The ImageToLedsMap holds a mapping of indices into an image to LEDs. It can be used to
|
|
/// calculate the average (aka mean) or dominant color per LED for a given region.
|
|
///
|
|
class ImageToLedsMap : public QObject
|
|
{
|
|
Q_OBJECT
|
|
|
|
public:
|
|
|
|
///
|
|
/// Constructs an mapping from the absolute indices in an image to each LED based on the border
|
|
/// definition given in the list of LEDs. The map holds absolute indices to any given image,
|
|
/// provided that it is row-oriented.
|
|
/// The mapping is created purely on size (width and height). The given borders are excluded
|
|
/// from indexing.
|
|
///
|
|
/// @param[in] log Logger
|
|
/// @param[in] width The width of the indexed image
|
|
/// @param[in] height The width of the indexed image
|
|
/// @param[in] horizontalBorder The size of the horizontal border (0=no border)
|
|
/// @param[in] verticalBorder The size of the vertical border (0=no border)
|
|
/// @param[in] leds The list with led specifications
|
|
/// @param[in] reducedProcessingFactor Factor to reduce the number of pixels evaluated during processing
|
|
/// @param[in] accuraryLevel The accuracy used during processing (only for selected types)
|
|
///
|
|
ImageToLedsMap(
|
|
Logger* log,
|
|
int width,
|
|
int height,
|
|
int horizontalBorder,
|
|
int verticalBorder,
|
|
const std::vector<Led> & leds,
|
|
int reducedProcessingFactor = 0,
|
|
int accuraryLevel = 0);
|
|
|
|
///
|
|
/// Returns the width of the indexed image
|
|
///
|
|
/// @return The width of the indexed image [pixels]
|
|
///
|
|
int width() const;
|
|
|
|
///
|
|
/// Returns the height of the indexed image
|
|
///
|
|
/// @return The height of the indexed image [pixels]
|
|
///
|
|
int height() const;
|
|
|
|
int horizontalBorder() const { return _horizontalBorder; }
|
|
int verticalBorder() const { return _verticalBorder; }
|
|
|
|
///
|
|
/// Set the accuracy used during processing
|
|
/// (only for selected types)
|
|
///
|
|
/// @param[in] level The accuracy level (0-4)
|
|
void setAccuracyLevel (int level);
|
|
|
|
///
|
|
/// Determines the mean color for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the led colors
|
|
///
|
|
/// @return The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
std::vector<ColorRgb> getMeanLedColor(const Image<Pixel_T> & image) const
|
|
{
|
|
std::vector<ColorRgb> colors(_colorsMap.size(), ColorRgb{0,0,0});
|
|
getMeanLedColor(image, colors);
|
|
return colors;
|
|
}
|
|
|
|
///
|
|
/// Determines the mean color for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED colors
|
|
/// @param[out] ledColors The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
void getMeanLedColor(const Image<Pixel_T> & image, std::vector<ColorRgb> & ledColors) const
|
|
{
|
|
if(_colorsMap.size() != ledColors.size())
|
|
{
|
|
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
|
|
return;
|
|
}
|
|
|
|
// Iterate each led and compute the mean
|
|
auto led = ledColors.begin();
|
|
for (auto colors = _colorsMap.begin(); colors != _colorsMap.end(); ++colors, ++led)
|
|
{
|
|
const ColorRgb color = calcMeanColor(image, *colors);
|
|
*led = color;
|
|
}
|
|
}
|
|
|
|
///
|
|
/// Determines the mean color squared for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the led colors
|
|
///
|
|
/// @return The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
std::vector<ColorRgb> getMeanLedColorSqrt(const Image<Pixel_T> & image) const
|
|
{
|
|
std::vector<ColorRgb> colors(_colorsMap.size(), ColorRgb{0,0,0});
|
|
getMeanLedColorSqrt(image, colors);
|
|
return colors;
|
|
}
|
|
|
|
///
|
|
/// Determines the mean color squared for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED colors
|
|
/// @param[out] ledColors The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
void getMeanLedColorSqrt(const Image<Pixel_T> & image, std::vector<ColorRgb> & ledColors) const
|
|
{
|
|
if(_colorsMap.size() != ledColors.size())
|
|
{
|
|
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
|
|
return;
|
|
}
|
|
|
|
// Iterate each led and compute the mean
|
|
auto led = ledColors.begin();
|
|
for (auto colors = _colorsMap.begin(); colors != _colorsMap.end(); ++colors, ++led)
|
|
{
|
|
const ColorRgb color = calcMeanColorSqrt(image, *colors);
|
|
*led = color;
|
|
}
|
|
}
|
|
|
|
///
|
|
/// Determines the mean color of the image and assigns it to all LEDs
|
|
///
|
|
/// @param[in] image The image from which to extract the led color
|
|
///
|
|
/// @return The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
std::vector<ColorRgb> getUniLedColor(const Image<Pixel_T> & image) const
|
|
{
|
|
std::vector<ColorRgb> colors(_colorsMap.size(), ColorRgb{0,0,0});
|
|
getUniLedColor(image, colors);
|
|
return colors;
|
|
}
|
|
|
|
///
|
|
/// Determines the mean color of the image and assigns it to all LEDs
|
|
///
|
|
/// @param[in] image The image from which to extract the LED colors
|
|
/// @param[out] ledColors The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
void getUniLedColor(const Image<Pixel_T> & image, std::vector<ColorRgb> & ledColors) const
|
|
{
|
|
if(_colorsMap.size() != ledColors.size())
|
|
{
|
|
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
|
|
return;
|
|
}
|
|
|
|
// calculate uni color
|
|
const ColorRgb color = calcMeanColor(image);
|
|
//Update all LEDs with same color
|
|
std::fill(ledColors.begin(),ledColors.end(), color);
|
|
}
|
|
|
|
///
|
|
/// Determines the dominant color for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED color
|
|
///
|
|
/// @return The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
std::vector<ColorRgb> getDominantLedColor(const Image<Pixel_T> & image) const
|
|
{
|
|
std::vector<ColorRgb> colors(_colorsMap.size(), ColorRgb{0,0,0});
|
|
getDominantLedColor(image, colors);
|
|
return colors;
|
|
}
|
|
|
|
///
|
|
/// Determines the dominant color for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED colors
|
|
/// @param[out] ledColors The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
void getDominantLedColor(const Image<Pixel_T> & image, std::vector<ColorRgb> & ledColors) const
|
|
{
|
|
// Sanity check for the number of LEDs
|
|
if(_colorsMap.size() != ledColors.size())
|
|
{
|
|
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
|
|
return;
|
|
}
|
|
|
|
// Iterate each led and compute the dominant color
|
|
auto led = ledColors.begin();
|
|
for (auto colors = _colorsMap.begin(); colors != _colorsMap.end(); ++colors, ++led)
|
|
{
|
|
const ColorRgb color = calculateDominantColor(image, *colors);
|
|
*led = color;
|
|
}
|
|
}
|
|
|
|
///
|
|
/// Determines the dominant color using a k-means algorithm for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED color
|
|
///
|
|
/// @return The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
std::vector<ColorRgb> getDominantLedColorAdv(const Image<Pixel_T> & image) const
|
|
{
|
|
std::vector<ColorRgb> colors(_colorsMap.size(), ColorRgb{0,0,0});
|
|
getDominantLedColorAdv(image, colors);
|
|
return colors;
|
|
}
|
|
|
|
///
|
|
/// Determines the dominant color using a k-means algorithm for each LED using the LED area mapping given
|
|
/// at construction.
|
|
///
|
|
/// @param[in] image The image from which to extract the LED colors
|
|
/// @param[out] ledColors The vector containing the output
|
|
///
|
|
template <typename Pixel_T>
|
|
void getDominantLedColorAdv(const Image<Pixel_T> & image, std::vector<ColorRgb> & ledColors) const
|
|
{
|
|
// Sanity check for the number of LEDs
|
|
if(_colorsMap.size() != ledColors.size())
|
|
{
|
|
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
|
|
return;
|
|
}
|
|
|
|
// Iterate each led and compute the dominant color
|
|
auto led = ledColors.begin();
|
|
for (auto colors = _colorsMap.begin(); colors != _colorsMap.end(); ++colors, ++led)
|
|
{
|
|
const ColorRgb color = calculateDominantColorAdv(image, *colors);
|
|
*led = color;
|
|
}
|
|
}
|
|
|
|
private:
|
|
|
|
Logger* _log;
|
|
|
|
/// The width of the indexed image
|
|
const int _width;
|
|
/// The height of the indexed image
|
|
const int _height;
|
|
|
|
const int _horizontalBorder;
|
|
const int _verticalBorder;
|
|
|
|
/// Evaluate every "count" pixel
|
|
int _nextPixelCount;
|
|
|
|
/// Number of clusters used during dominant color advanced processing (k-means)
|
|
int _clusterCount;
|
|
|
|
/// The absolute indices into the image for each led
|
|
std::vector<std::vector<int>> _colorsMap;
|
|
|
|
///
|
|
/// Calculates the 'mean color' over the given image. This is the mean over each color-channel
|
|
/// (red, green, blue)
|
|
///
|
|
/// @param[in] image The image a section from which an average color must be computed
|
|
/// @param[in] pixels The list of pixel indices for the given image to be evaluated///
|
|
///
|
|
/// @return The mean of the given list of colors (or black when empty)
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calcMeanColor(const Image<Pixel_T> & image, const std::vector<int32_t> & pixels) const
|
|
{
|
|
const auto pixelNum = pixels.size();
|
|
if (pixelNum == 0)
|
|
{
|
|
return ColorRgb::BLACK;
|
|
}
|
|
|
|
// Accumulate the sum of each separate color channel
|
|
uint_fast32_t cummRed = 0;
|
|
uint_fast32_t cummGreen = 0;
|
|
uint_fast32_t cummBlue = 0;
|
|
|
|
const auto& imgData = image.memptr();
|
|
for (const int pixelOffset : pixels)
|
|
{
|
|
const auto& pixel = imgData[pixelOffset];
|
|
cummRed += pixel.red;
|
|
cummGreen += pixel.green;
|
|
cummBlue += pixel.blue;
|
|
}
|
|
|
|
// Compute the average of each color channel
|
|
const uint8_t avgRed = uint8_t(cummRed/pixelNum);
|
|
const uint8_t avgGreen = uint8_t(cummGreen/pixelNum);
|
|
const uint8_t avgBlue = uint8_t(cummBlue/pixelNum);
|
|
|
|
// Return the computed color
|
|
return {avgRed, avgGreen, avgBlue};
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'mean color' over the given image. This is the mean over each color-channel
|
|
/// (red, green, blue)
|
|
///
|
|
/// @param[in] image The image a section from which an average color must be computed
|
|
///
|
|
/// @return The mean of the given list of colors (or black when empty)
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calcMeanColor(const Image<Pixel_T> & image) const
|
|
{
|
|
// Accumulate the sum of each separate color channel
|
|
uint_fast32_t cummRed = 0;
|
|
uint_fast32_t cummGreen = 0;
|
|
uint_fast32_t cummBlue = 0;
|
|
|
|
const unsigned pixelNum = image.width() * image.height();
|
|
const auto& imgData = image.memptr();
|
|
|
|
for (unsigned idx=0; idx<pixelNum; idx++)
|
|
{
|
|
const auto& pixel = imgData[idx];
|
|
cummRed += pixel.red;
|
|
cummGreen += pixel.green;
|
|
cummBlue += pixel.blue;
|
|
}
|
|
|
|
// Compute the average of each color channel
|
|
const uint8_t avgRed = uint8_t(cummRed/pixelNum);
|
|
const uint8_t avgGreen = uint8_t(cummGreen/pixelNum);
|
|
const uint8_t avgBlue = uint8_t(cummBlue/pixelNum);
|
|
|
|
// Return the computed color
|
|
return {avgRed, avgGreen, avgBlue};
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'mean color' squared over the given image. This is the mean over each color-channel
|
|
/// (red, green, blue)
|
|
///
|
|
/// @param[in] image The image a section from which an average color must be computed
|
|
/// @param[in] pixels The list of pixel indices for the given image to be evaluated
|
|
///
|
|
/// @return The mean of the given list of colors (or black when empty)
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calcMeanColorSqrt(const Image<Pixel_T> & image, const std::vector<int32_t> & pixels) const
|
|
{
|
|
const auto pixelNum = pixels.size();
|
|
if (pixelNum == 0)
|
|
{
|
|
return ColorRgb::BLACK;
|
|
}
|
|
|
|
// Accumulate the squared sum of each separate color channel
|
|
uint_fast32_t cummRed = 0;
|
|
uint_fast32_t cummGreen = 0;
|
|
uint_fast32_t cummBlue = 0;
|
|
|
|
const auto& imgData = image.memptr();
|
|
|
|
for (const int colorOffset : pixels)
|
|
{
|
|
const auto& pixel = imgData[colorOffset];
|
|
|
|
cummRed += pixel.red * pixel.red;
|
|
cummGreen += pixel.green * pixel.green;
|
|
cummBlue += pixel.blue * pixel.blue;
|
|
}
|
|
|
|
// Compute the average of each color channel
|
|
const uint8_t avgRed = uint8_t(std::min(std::lround(sqrt(static_cast<double>(cummRed/pixelNum))), 255L));
|
|
const uint8_t avgGreen = uint8_t(std::min(std::lround(sqrt(static_cast<double>(cummGreen/pixelNum))), 255L));
|
|
const uint8_t avgBlue = uint8_t(std::min(std::lround(sqrt(static_cast<double>(cummBlue/pixelNum))), 255L));
|
|
|
|
// Return the computed color
|
|
return {avgRed, avgGreen, avgBlue};
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'mean color' squared over the given image. This is the mean over each color-channel
|
|
/// (red, green, blue)
|
|
///
|
|
/// @param[in] image The image a section from which an average color must be computed
|
|
///
|
|
/// @return The mean of the given list of colors (or black when empty)
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calcMeanColorSqrt(const Image<Pixel_T> & image) const
|
|
{
|
|
// Accumulate the squared sum of each separate color channel
|
|
uint_fast32_t cummRed = 0;
|
|
uint_fast32_t cummGreen = 0;
|
|
uint_fast32_t cummBlue = 0;
|
|
|
|
const unsigned pixelNum = image.width() * image.height();
|
|
const auto& imgData = image.memptr();
|
|
|
|
for (int idx=0; idx<pixelNum; ++idx)
|
|
{
|
|
const auto& pixel = imgData[idx];
|
|
cummRed += pixel.red * pixel.red;
|
|
cummGreen += pixel.green * pixel.green;
|
|
cummBlue += pixel.blue * pixel.blue;
|
|
}
|
|
|
|
// Compute the average of each color channel
|
|
const uint8_t avgRed = uint8_t(std::lround(sqrt(static_cast<double>(cummRed/pixelNum))));
|
|
const uint8_t avgGreen = uint8_t(std::lround(sqrt(static_cast<double>(cummGreen/pixelNum))));
|
|
const uint8_t avgBlue = uint8_t(std::lround(sqrt(static_cast<double>(cummBlue/pixelNum))));
|
|
|
|
// Return the computed color
|
|
return {avgRed, avgGreen, avgBlue};
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'dominant color' of an image area defined by a list of pixel indices
|
|
///
|
|
/// @param[in] image The image for which a dominant color is to be computed
|
|
/// @param[in] pixels The list of pixel indices for the given image to be evaluated
|
|
///
|
|
/// @return The image area's dominant color or black, if no pixel indices provided
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calculateDominantColor(const Image<Pixel_T> & image, const std::vector<int> & pixels) const
|
|
{
|
|
ColorRgb dominantColor {ColorRgb::BLACK};
|
|
|
|
const auto pixelNum = pixels.size();
|
|
if (pixelNum > 0)
|
|
{
|
|
const auto& imgData = image.memptr();
|
|
|
|
QMap<QRgb,int> colorDistributionMap;
|
|
int count = 0;
|
|
for (const int pixelOffset : pixels)
|
|
{
|
|
QRgb color = imgData[pixelOffset].rgb();
|
|
if (colorDistributionMap.contains(color)) {
|
|
colorDistributionMap[color] = colorDistributionMap[color] + 1;
|
|
}
|
|
else {
|
|
colorDistributionMap[color] = 1;
|
|
}
|
|
|
|
int colorsFound = colorDistributionMap[color];
|
|
if (colorsFound > count) {
|
|
dominantColor.setRgb(color);
|
|
count = colorsFound;
|
|
}
|
|
}
|
|
}
|
|
return dominantColor;
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'dominant color' of an image
|
|
///
|
|
/// @param[in] image The image for which a dominant color is to be computed
|
|
///
|
|
/// @return The image's dominant color
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calculateDominantColor(const Image<Pixel_T> & image) const
|
|
{
|
|
const unsigned pixelNum = image.width() * image.height();
|
|
|
|
std::vector<int> pixels(pixelNum);
|
|
std::iota(pixels.begin(), pixels.end(), 0);
|
|
|
|
return calculateDominantColor(image, pixels);
|
|
}
|
|
|
|
template <typename Pixel_T>
|
|
struct ColorCluster {
|
|
|
|
ColorCluster():count(0) {}
|
|
ColorCluster(Pixel_T color):count(0),color(color) {}
|
|
|
|
Pixel_T color;
|
|
Pixel_T newColor;
|
|
int count;
|
|
};
|
|
|
|
const ColorRgb DEFAULT_CLUSTER_COLORS[5] {
|
|
{ColorRgb::BLACK},
|
|
{ColorRgb::GREEN},
|
|
{ColorRgb::WHITE},
|
|
{ColorRgb::RED},
|
|
{ColorRgb::YELLOW}
|
|
};
|
|
|
|
///
|
|
/// Calculates the 'dominant color' of an image area defined by a list of pixel indices
|
|
/// using a k-means algorithm (https://robocraft.ru/computervision/1063)
|
|
///
|
|
/// @param[in] image The image for which a dominant color is to be computed
|
|
/// @param[in] pixels The list of pixel indices for the given image to be evaluated
|
|
///
|
|
/// @return The image area's dominant color or black, if no pixel indices provided
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calculateDominantColorAdv(const Image<Pixel_T> & image, const std::vector<int> & pixels) const
|
|
{
|
|
ColorRgb dominantColor {ColorRgb::BLACK};
|
|
const auto pixelNum = pixels.size();
|
|
if (pixelNum > 0)
|
|
{
|
|
// initial cluster with different colors
|
|
auto clusters = std::unique_ptr< ColorCluster<ColorRgbScalar> >(new ColorCluster<ColorRgbScalar>[_clusterCount]);
|
|
for(int k = 0; k < _clusterCount; ++k)
|
|
{
|
|
clusters.get()[k].newColor = DEFAULT_CLUSTER_COLORS[k];
|
|
}
|
|
|
|
// k-means
|
|
double min_rgb_euclidean {0};
|
|
double old_rgb_euclidean {0};
|
|
|
|
while(1)
|
|
{
|
|
for(int k = 0; k < _clusterCount; ++k)
|
|
{
|
|
clusters.get()[k].count = 0;
|
|
clusters.get()[k].color = clusters.get()[k].newColor;
|
|
clusters.get()[k].newColor.setRgb(ColorRgb::BLACK);
|
|
}
|
|
|
|
const auto& imgData = image.memptr();
|
|
for (const int pixelOffset : pixels)
|
|
{
|
|
const auto& pixel = imgData[pixelOffset];
|
|
|
|
min_rgb_euclidean = 255 * 255 * 255;
|
|
int clusterIndex = -1;
|
|
for(int k = 0; k < _clusterCount; ++k)
|
|
{
|
|
double euclid = ColorSys::rgb_euclidean(ColorRgbScalar(pixel), clusters.get()[k].color);
|
|
|
|
if( euclid < min_rgb_euclidean ) {
|
|
min_rgb_euclidean = euclid;
|
|
clusterIndex = k;
|
|
}
|
|
}
|
|
|
|
clusters.get()[clusterIndex].count++;
|
|
clusters.get()[clusterIndex].newColor += ColorRgbScalar(pixel);
|
|
}
|
|
|
|
min_rgb_euclidean = 0;
|
|
for(int k = 0; k < _clusterCount; ++k)
|
|
{
|
|
if (clusters.get()[k].count > 0)
|
|
{
|
|
// new color
|
|
clusters.get()[k].newColor /= clusters.get()[k].count;
|
|
double ecli = ColorSys::rgb_euclidean(clusters.get()[k].newColor, clusters.get()[k].color);
|
|
if(ecli > min_rgb_euclidean)
|
|
{
|
|
min_rgb_euclidean = ecli;
|
|
}
|
|
}
|
|
}
|
|
|
|
if( fabs(min_rgb_euclidean - old_rgb_euclidean) < 1)
|
|
{
|
|
break;
|
|
}
|
|
|
|
old_rgb_euclidean = min_rgb_euclidean;
|
|
}
|
|
|
|
int colorsFoundMax = 0;
|
|
int dominantClusterIdx {0};
|
|
|
|
for(int clusterIdx=0; clusterIdx < _clusterCount; ++clusterIdx){
|
|
int colorsFoundinCluster = clusters.get()[clusterIdx].count;
|
|
if (colorsFoundinCluster > colorsFoundMax) {
|
|
colorsFoundMax = colorsFoundinCluster;
|
|
dominantClusterIdx = clusterIdx;
|
|
}
|
|
}
|
|
|
|
dominantColor.red = static_cast<uint8_t>(clusters.get()[dominantClusterIdx].newColor.red);
|
|
dominantColor.green = static_cast<uint8_t>(clusters.get()[dominantClusterIdx].newColor.green);
|
|
dominantColor.blue = static_cast<uint8_t>(clusters.get()[dominantClusterIdx].newColor.blue);
|
|
}
|
|
|
|
return dominantColor;
|
|
}
|
|
|
|
///
|
|
/// Calculates the 'dominant color' of an image area defined by a list of pixel indices
|
|
/// using a k-means algorithm (https://robocraft.ru/computervision/1063)
|
|
///
|
|
/// @param[in] image The image for which a dominant color is to be computed
|
|
///
|
|
/// @return The image's dominant color
|
|
///
|
|
template <typename Pixel_T>
|
|
ColorRgb calculateDominantColorAdv(const Image<Pixel_T> & image) const
|
|
{
|
|
const unsigned pixelNum = image.width() * image.height();
|
|
|
|
std::vector<int> pixels(pixelNum);
|
|
std::iota(pixels.begin(), pixels.end(), 0);
|
|
|
|
return calculateDominantColorAdv(image, pixels);
|
|
}
|
|
};
|
|
|
|
} // end namespace hyperion
|
|
|
|
#endif // IMAGETOLEDSMAP_H
|