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Dominant Color support (#1569)

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* 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
This commit is contained in:
LordGrey
2023-02-17 16:02:51 +01:00
committed by GitHub
parent 093361d3e4
commit 1ae37d151e
16 changed files with 1048 additions and 155 deletions
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10
include/hyperion/GrabberWrapper.h
View File

@@ -147,10 +147,7 @@ private slots:
void handleSourceRequest(hyperion::Components component, int hyperionInd, bool listen);
///
/// @brief Update Update capture rate
/// @param type interval between frames in milliseconds
///
void updateTimer(int interval);
protected:
@@ -168,6 +165,11 @@ protected:
///
virtual bool close() { return true; }
/// @brief Update Update capture rate
/// @param type interval between frames in milliseconds
///
void updateTimer(int interval);
QString _grabberName;

99
include/hyperion/ImageProcessor.h
View File

@@ -1,6 +1,7 @@
#pragma once
#include <QString>
#include <QSharedPointer>
// Utils includes
#include <utils/Image.h>
@@ -46,7 +47,7 @@ public:
/// @param[in] width The new width of the buffer-image
/// @param[in] height The new height of the buffer-image
///
void setSize(unsigned width, unsigned height);
void setSize(int width, int height);
///
/// @brief Update the led string (eg on settings change)
@@ -56,6 +57,19 @@ public:
/// Returns state of black border detector
bool blackBorderDetectorEnabled() const;
///
/// Factor to reduce the number of pixels evaluated during processing
///
/// @param[in] count Use every "count" pixel
void setReducedPixelSetFactorFactor(int count);
///
/// Set the accuracy used during processing
/// (only for selected types)
///
/// @param[in] level The accuracy level (0-4)
void setAccuracyLevel(int level);
/// Returns the current _userMappingType, this may not be the current applied type!
int getUserLedMappingType() const { return _userMappingType; }
@@ -98,30 +112,45 @@ public:
}
///
/// Processes the image to a list of led colors. This will update the size of the buffer-image
/// if required and call the image-to-leds mapping to determine the mean color per led.
/// Processes the image to a list of LED colors. This will update the size of the buffer-image
/// if required and call the image-to-LEDs mapping to determine the color per LED.
///
/// @param[in] image The image to translate to led values
/// @param[in] image The image to translate to LED values
///
/// @return The color value per led
/// @return The color value per LED
///
template <typename Pixel_T>
std::vector<ColorRgb> process(const Image<Pixel_T>& image)
{
std::vector<ColorRgb> colors;
if (image.width()>0 && image.height()>0)
{
// Ensure that the buffer-image is the proper size
setSize(image);
assert(!_imageToLedColors.isNull());
// Check black border detection
verifyBorder(image);
// Create a result vector and call the 'in place' function
switch (_mappingType)
{
case 1: colors = _imageToLeds->getUniLedColor(image); break;
default: colors = _imageToLeds->getMeanLedColor(image);
case 1:
colors = _imageToLedColors->getUniLedColor(image);
break;
case 2:
colors = _imageToLedColors->getMeanLedColorSqrt(image);
break;
case 3:
colors = _imageToLedColors->getDominantLedColor(image);
break;
case 4:
colors = _imageToLedColors->getDominantLedColorAdv(image);
break;
default:
colors = _imageToLedColors->getMeanLedColor(image);
}
}
else
@@ -136,8 +165,8 @@ public:
///
/// Determines the led colors of the image in the buffer.
///
/// @param[in] image The image to translate to led values
/// @param[out] ledColors The color value per led
/// @param[in] image The image to translate to LED values
/// @param[out] ledColors The color value per LED
///
template <typename Pixel_T>
void process(const Image<Pixel_T>& image, std::vector<ColorRgb>& ledColors)
@@ -153,8 +182,20 @@ public:
// Determine the mean or uni colors of each led (using the existing mapping)
switch (_mappingType)
{
case 1: _imageToLeds->getUniLedColor(image, ledColors); break;
default: _imageToLeds->getMeanLedColor(image, ledColors);
case 1:
_imageToLedColors->getUniLedColor(image, ledColors);
break;
case 2:
_imageToLedColors->getMeanLedColorSqrt(image, ledColors);
break;
case 3:
_imageToLedColors->getDominantLedColor(image, ledColors);
break;
case 4:
_imageToLedColors->getDominantLedColorAdv(image, ledColors);
break;
default:
_imageToLedColors->getMeanLedColor(image, ledColors);
}
}
else
@@ -164,9 +205,9 @@ public:
}
///
/// Get the hscan and vscan parameters for a single led
/// Get the hscan and vscan parameters for a single LED
///
/// @param[in] led Index of the led
/// @param[in] led Index of the LED
/// @param[out] hscanBegin begin of the hscan
/// @param[out] hscanEnd end of the hscan
/// @param[out] vscanBegin begin of the hscan
@@ -175,6 +216,13 @@ public:
bool getScanParameters(size_t led, double & hscanBegin, double & hscanEnd, double & vscanBegin, double & vscanEnd) const;
private:
void registerProcessingUnit(
int width,
int height,
int horizontalBorder,
int verticalBorder);
///
/// Performs black-border detection (if enabled) on the given image
///
@@ -183,34 +231,25 @@ private:
template <typename Pixel_T>
void verifyBorder(const Image<Pixel_T> & image)
{
if (!_borderProcessor->enabled() && ( _imageToLeds->horizontalBorder()!=0 || _imageToLeds->verticalBorder()!=0 ))
if (!_borderProcessor->enabled() && ( _imageToLedColors->horizontalBorder()!=0 || _imageToLedColors->verticalBorder()!=0 ))
{
Debug(_log, "Reset border");
_borderProcessor->process(image);
delete _imageToLeds;
_imageToLeds = new hyperion::ImageToLedsMap(image.width(), image.height(), 0, 0, _ledString.leds());
registerProcessingUnit(image.width(), image.height(), 0, 0);
}
if(_borderProcessor->enabled() && _borderProcessor->process(image))
{
const hyperion::BlackBorder border = _borderProcessor->getCurrentBorder();
// Clean up the old mapping
delete _imageToLeds;
if (border.unknown)
{
// Construct a new buffer and mapping
_imageToLeds = new hyperion::ImageToLedsMap(image.width(), image.height(), 0, 0, _ledString.leds());
registerProcessingUnit(image.width(), image.height(), 0, 0);
}
else
{
// Construct a new buffer and mapping
_imageToLeds = new hyperion::ImageToLedsMap(image.width(), image.height(), border.horizontalSize, border.verticalSize, _ledString.leds());
registerProcessingUnit(image.width(), image.height(), border.horizontalSize, border.verticalSize);
}
//Debug(Logger::getInstance("BLACKBORDER"), "CURRENT BORDER TYPE: unknown=%d hor.size=%d vert.size=%d",
// border.unknown, border.horizontalSize, border.verticalSize );
}
}
@@ -218,6 +257,7 @@ private slots:
void handleSettingsUpdate(settings::type type, const QJsonDocument& config);
private:
Logger * _log;
/// The Led-string specification
LedString _ledString;
@@ -226,15 +266,18 @@ private:
hyperion::BlackBorderProcessor * _borderProcessor;
/// The mapping of image-pixels to LEDs
hyperion::ImageToLedsMap* _imageToLeds;
QSharedPointer<hyperion::ImageToLedsMap> _imageToLedColors;
/// Type of image 2 led mapping
/// Type of image to LED mapping
int _mappingType;
/// Type of last requested user type
int _userMappingType;
/// Type of last requested hard type
int _hardMappingType;
int _accuraryLevel;
int _reducedPixelSetFactorFactor;
/// Hyperion instance pointer
Hyperion* _hyperion;
};

544
include/hyperion/ImageToLedsMap.h
View File

@@ -1,72 +1,90 @@
#pragma once
#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 (or mean) color per led for a specific region.
/// 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
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,
/// 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(
const unsigned width,
const unsigned height,
const unsigned horizontalBorder,
const unsigned verticalBorder,
const std::vector<Led> & leds);
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]
///
unsigned width() const;
int width() const;
///
/// Returns the height of the indexed image
///
/// @return The height of the indexed image [pixels]
///
unsigned height() const;
int height() const;
unsigned horizontalBorder() const { return _horizontalBorder; }
unsigned verticalBorder() const { return _verticalBorder; }
int horizontalBorder() const { return _horizontalBorder; }
int verticalBorder() const { return _verticalBorder; }
///
/// Determines the mean color for each led using the mapping the image given
/// 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 ledColors The vector containing the output
/// @return The vector containing the output
///
template <typename Pixel_T>
std::vector<ColorRgb> getMeanLedColor(const Image<Pixel_T> & image) const
@@ -77,20 +95,18 @@ namespace hyperion
}
///
/// Determines the mean color for each led using the mapping the image given
/// 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[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
{
// Sanity check for the number of leds
//assert(_colorsMap.size() == ledColors.size());
if(_colorsMap.size() != ledColors.size())
{
Debug(Logger::getInstance("HYPERION"), "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
Debug(_log, "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _colorsMap.size(), ledColors.size());
return;
}
@@ -104,12 +120,52 @@ namespace hyperion
}
///
/// Determines the uni color for each led using the mapping the image given
/// 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 ledColors The vector containing the output
/// @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
@@ -120,57 +176,145 @@ namespace hyperion
}
///
/// Determines the uni color for each led using the mapping the image given
/// at construction.
/// 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[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
{
// Sanity check for the number of leds
// assert(_colorsMap.size() == ledColors.size());
if(_colorsMap.size() != ledColors.size())
{
Debug(Logger::getInstance("HYPERION"), "ImageToLedsMap: colorsMap.size != ledColors.size -> %d != %d", _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);
}
private:
/// The width of the indexed image
const unsigned _width;
/// The height of the indexed image
const unsigned _height;
const unsigned _horizontalBorder;
const unsigned _verticalBorder;
/// The absolute indices into the image for each led
std::vector<std::vector<int32_t>> _colorsMap;
///
/// 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;
}
///
/// Calculates the 'mean color' of the given list. This is the mean over each color-channel
/// 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] colors The list with colors
/// @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> & colors) const
ColorRgb calcMeanColor(const Image<Pixel_T> & image, const std::vector<int32_t> & pixels) const
{
const auto colorVecSize = colors.size();
if (colorVecSize == 0)
const auto pixelNum = pixels.size();
if (pixelNum == 0)
{
return ColorRgb::BLACK;
}
@@ -179,20 +323,20 @@ namespace hyperion
uint_fast32_t cummRed = 0;
uint_fast32_t cummGreen = 0;
uint_fast32_t cummBlue = 0;
const auto& imgData = image.memptr();
for (const unsigned colorOffset : colors)
const auto& imgData = image.memptr();
for (const int pixelOffset : pixels)
{
const auto& pixel = imgData[colorOffset];
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/colorVecSize);
const uint8_t avgGreen = uint8_t(cummGreen/colorVecSize);
const uint8_t avgBlue = uint8_t(cummBlue/colorVecSize);
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};
@@ -213,11 +357,11 @@ namespace hyperion
uint_fast32_t cummRed = 0;
uint_fast32_t cummGreen = 0;
uint_fast32_t cummBlue = 0;
const unsigned imageSize = image.width() * image.height();
const unsigned pixelNum = image.width() * image.height();
const auto& imgData = image.memptr();
for (unsigned idx=0; idx<imageSize; idx++)
for (unsigned idx=0; idx<pixelNum; idx++)
{
const auto& pixel = imgData[idx];
cummRed += pixel.red;
@@ -226,13 +370,289 @@ namespace hyperion
}
// Compute the average of each color channel
const uint8_t avgRed = uint8_t(cummRed/imageSize);
const uint8_t avgGreen = uint8_t(cummGreen/imageSize);
const uint8_t avgBlue = uint8_t(cummBlue/imageSize);
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