swh:1:snp:f50ab94432af916b5fb8b4ad831e8dddded77084
Tip revision: caf56bbb57a18aa036e8431aef1e5c6ec4ceb19e authored by Cheng Tang on 30 March 2017, 17:28:40 UTC
update UT baseline
update UT baseline
Tip revision: caf56bb
ImageTransformers.cpp
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#include "stdafx.h"
#include <algorithm>
#include <unordered_map>
#include <random>
#include <boost/random/bernoulli_distribution.hpp>
#include <boost/random/normal_distribution.hpp>
#include "ImageTransformers.h"
#include "Config.h"
#include "ConcStack.h"
#include "StringUtil.h"
#include "SequenceData.h"
#include "ImageUtil.h"
#include "ImageDeserializerBase.h"
namespace Microsoft { namespace MSR { namespace CNTK
{
// Transforms a single sequence as open cv dense image. Called once per sequence.
SequenceDataPtr ImageTransformerBase::Transform(SequenceDataPtr sequence)
{
auto inputSequence = dynamic_cast<ImageSequenceData*>(sequence.get());
if (inputSequence == nullptr)
RuntimeError("Unexpected sequence provided");
auto result = std::make_shared<ImageSequenceData>();
Apply(inputSequence->m_copyIndex, inputSequence->m_image);
result->m_image = inputSequence->m_image;
result->m_numberOfSamples = inputSequence->m_numberOfSamples;
result->m_elementType = GetElementTypeFromOpenCVType(inputSequence->m_image.depth());
result->m_copyIndex = inputSequence->m_copyIndex;
result->m_key = inputSequence->m_key;
ImageDimensions outputDimensions(inputSequence->m_image.cols, inputSequence->m_image.rows, inputSequence->m_image.channels());
result->m_sampleLayout = std::make_shared<TensorShape>(outputDimensions.AsTensorShape(HWC));
return result;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
CropTransformer::CropTransformer(const ConfigParameters& config) : ImageTransformerBase(config)
{
intargvector cropSize = config(L"cropSize", "0");
m_cropWidth = cropSize[0];
m_cropHeight = cropSize[1];
if (m_cropWidth < 0 || m_cropHeight < 0)
{
RuntimeError("Invalid cropSize value, must be >= 0");
}
m_useSideRatio = true;
floatargvector sideRatio = config(L"sideRatio", "0.0");
m_sideRatioMin = sideRatio[0];
m_sideRatioMax = sideRatio[1];
if (m_sideRatioMin == 0.0 && m_sideRatioMax == 0.0) // taking default value means not specified
{
m_useSideRatio = false;
}
else if (!(m_sideRatioMin > 0 && m_sideRatioMax <= 1.0) ||
m_sideRatioMin > m_sideRatioMax)
{
RuntimeError("Invalid sideRatio value, must be > 0 and <= 1. sideMin must <= sideMax");
}
m_useAreaRatio = true;
floatargvector areaRatio = config(L"areaRatio", "0.0");
m_areaRatioMin = areaRatio[0];
m_areaRatioMax = areaRatio[1];
if (m_areaRatioMin == 0.0 && m_areaRatioMax == 0.0) // taking default value means not specified
{
m_useAreaRatio = false;
}
else if (!(m_areaRatioMin > 0 && m_areaRatioMax <= 1.0) ||
m_areaRatioMin > m_areaRatioMax)
{
RuntimeError("Invalid areaRatio value, must be > 0 and <= 1. areaMin must <= areaMax");
}
if (m_useSideRatio && m_useAreaRatio)
RuntimeError("sideRatio and areaRatio cannot be specified simultaneously");
floatargvector aspectRatio = config(L"aspectRatio", "1.0");
m_aspectRatioMin = aspectRatio[0];
m_aspectRatioMax = aspectRatio[1];
if (!(m_aspectRatioMin > 0 && m_aspectRatioMax <= 1.0) ||
m_aspectRatioMin > m_aspectRatioMax)
{
RuntimeError("Invalid aspectRatio value, must be > 0 and <= 1. aspectMin must <= aspectMax");
}
m_jitterType = ParseJitterType(config(L"jitterType", ""));
m_cropType = ImageConfigHelper::ParseCropType(config(L"cropType", ""));
if (!config.ExistsCurrent(L"hflip"))
{
m_hFlip = (m_cropType == CropType::RandomSide || m_cropType == CropType::RandomArea);
}
else
{
m_hFlip = config(L"hflip");
}
// for MultiView10 we need to set m_hflip = false, otherwise we might not get 5 unflipped image (see CropTransformer::Apply below)
if (m_cropType == CropType::MultiView10)
{
m_hFlip = false;
}
}
void CropTransformer::StartEpoch(const EpochConfiguration &config)
{
ImageTransformerBase::StartEpoch(config);
}
void CropTransformer::Apply(uint8_t copyId, cv::Mat &mat)
{
auto seed = GetSeed();
auto rng = m_rngs.pop_or_create([seed]() { return std::make_unique<std::mt19937>(seed); });
int viewIndex = m_cropType == CropType::MultiView10 ? (int)(copyId % ImageDeserializerBase::NumMultiViewCopies) : 0;
switch (m_cropType)
{
case CropType::Center:
mat = mat(GetCropRectCenter(mat.rows, mat.cols, *rng));
break;
case CropType::RandomSide:
mat = mat(GetCropRectRandomSide(mat.rows, mat.cols, *rng));
break;
case CropType::RandomArea:
mat = mat(GetCropRectRandomArea(mat.rows, mat.cols, *rng));
break;
case CropType::MultiView10:
mat = mat(GetCropRectMultiView10(viewIndex, mat.rows, mat.cols, *rng));
break;
default:
RuntimeError("Invalid crop type.");
break;
}
// for MultiView10 m_hFlip is false, hence the first 5 will be unflipped, the later 5 will be flipped
if ((m_hFlip && boost::random::bernoulli_distribution<>()(*rng)) ||
viewIndex >= 5)
{
cv::flip(mat, mat, 1);
}
m_rngs.push(std::move(rng));
}
CropTransformer::RatioJitterType
CropTransformer::ParseJitterType(const std::string &src)
{
if (src.empty() || AreEqualIgnoreCase(src, "none"))
{
return RatioJitterType::None;
}
if (AreEqualIgnoreCase(src, "uniratio"))
{
return RatioJitterType::UniRatio;
}
RuntimeError("Invalid jitter type: %s.", src.c_str());
}
double CropTransformer::ApplyRatioJitter(const double minVal, const double maxVal, std::mt19937 &rng)
{
assert(minVal > 0 && minVal <= maxVal); // ratio should always be > 0
switch (m_jitterType)
{
case RatioJitterType::None:
return minVal;
case RatioJitterType::UniRatio:
if (minVal == maxVal)
return minVal;
else
return UniRealT(minVal, maxVal)(rng);
default:
RuntimeError("Jitter type currently not implemented.");
}
return -1;
}
cv::Rect CropTransformer::GetCropRectCenter(int crow, int ccol, std::mt19937 &rng)
{
assert(crow > 0);
assert(ccol > 0);
assert(!(m_useSideRatio && m_useAreaRatio)); // cannot be applied simultaneously
int cropSizeX=ccol, cropSizeY=crow;
if (m_cropWidth > 0 && m_cropHeight > 0) // crop sizes are specified with meaningful values
{
cropSizeX = min(ccol, m_cropWidth);
cropSizeY = min(crow, m_cropHeight);
int xOff = (ccol - cropSizeX) / 2;
int yOff = (crow - cropSizeY) / 2;
return cv::Rect(xOff, yOff, cropSizeX, cropSizeY);
}
bool bFound = false;
int nAttempt = 0;
while (!bFound && nAttempt < 10)
{
if (m_useSideRatio)
{
double sideRatio = ApplyRatioJitter(m_sideRatioMin, m_sideRatioMax, rng);
assert(sideRatio >= m_sideRatioMin && sideRatio <= m_sideRatioMax);
cropSizeX = cropSizeY = (int)std::round(std::min(crow, ccol) * sideRatio); // we always crop square shape unless aspectRatio is not 1.0
}
else if (m_useAreaRatio)
{
double areaRatio = ApplyRatioJitter(m_areaRatioMin, m_areaRatioMax, rng);
assert(areaRatio >= m_areaRatioMin && areaRatio <= m_areaRatioMax);
cropSizeX = cropSizeY = (int)std::round(std::sqrt(crow * ccol * areaRatio)); // we always crop square shape unless aspectRatio is not 1.0
}
double aspectRatio = ApplyRatioJitter(m_aspectRatioMin, m_aspectRatioMax, rng);
assert(aspectRatio >= m_aspectRatioMin && aspectRatio <= m_aspectRatioMax);
if (aspectRatio != 1.0)
{
double area = cropSizeX * cropSizeY;
if (boost::random::bernoulli_distribution<>()(rng))
{
cropSizeX = (int)std::sqrt(area * aspectRatio);
cropSizeY = (int)std::sqrt(area / aspectRatio);
}
else
{
cropSizeY = (int)std::sqrt(area * aspectRatio);
cropSizeX = (int)std::sqrt(area / aspectRatio);
}
}
if (cropSizeX <= ccol && cropSizeY <= crow)
{
bFound = true;
break;
}
nAttempt++;
}
if (bFound)
{
int xOff = (ccol - cropSizeX) / 2;
int yOff = (crow - cropSizeY) / 2;
return cv::Rect(xOff, yOff, cropSizeX, cropSizeY);
}
else
{ // fall back to return the whole image
return cv::Rect(0, 0, ccol, crow);
}
}
cv::Rect CropTransformer::GetCropRectRandomSide(int crow, int ccol, std::mt19937 &rng)
{
assert(m_useSideRatio);
cv::Rect rc = GetCropRectCenter(crow, ccol, rng);
int xOff = UniIntT(0, ccol - rc.width)(rng);
int yOff = UniIntT(0, crow - rc.height)(rng);
return cv::Rect(xOff, yOff, rc.width, rc.height);
}
cv::Rect CropTransformer::GetCropRectRandomArea(int crow, int ccol, std::mt19937 &rng)
{
assert(m_useAreaRatio);
cv::Rect rc = GetCropRectCenter(crow, ccol, rng);
int xOff = UniIntT(0, ccol - rc.width)(rng);
int yOff = UniIntT(0, crow - rc.height)(rng);
return cv::Rect(xOff, yOff, rc.width, rc.height);
}
cv::Rect CropTransformer::GetCropRectMultiView10(int viewIndex, int crow, int ccol, std::mt19937 &rng)
{
assert(viewIndex >= 0);
cv::Rect rc = GetCropRectCenter(crow, ccol, rng);
viewIndex = viewIndex % 10;
// 0 - 4: 4 corners + center crop. 5 - 9: same, but with a flip in CropTransformer::Apply().
int isubView = viewIndex % 5;
int xOff=-1, yOff=-1, cropSizeX = rc.width, cropSizeY = rc.height;
switch (isubView)
{
case 0: // top-left
xOff = 0;
yOff = 0;
break;
case 1: // top-right
xOff = ccol - cropSizeX;
yOff = 0;
break;
case 2: // bottom-left
xOff = 0;
yOff = crow - cropSizeY;
break;
case 3: // bottom-right
xOff = ccol - cropSizeX;
yOff = crow - cropSizeY;
break;
case 4: // center
xOff = (ccol - cropSizeX) / 2;
yOff = (crow - cropSizeY) / 2;
break;
default: // should never happen
assert(false);
}
assert(xOff >= 0 && xOff <= ccol - cropSizeX);
assert(yOff >= 0 && yOff <= crow - cropSizeY);
return cv::Rect(xOff, yOff, cropSizeX, cropSizeY);
}
// scaleMode = "fill" (default) - warp the image to the given target size
// scaleMode = "crop" - resize the image's shorter side to the given target size and crops the overlap
// scaleMode = "pad" - resize the image's larger side to the given target size, center it and pad the rest
ScaleTransformer::ScaleTransformer(const ConfigParameters& config) : ImageTransformerBase(config)
{
m_imgWidth = config(L"width");
m_imgHeight = config(L"height");
m_imgChannels = config(L"channels");
size_t cfeat = m_imgWidth * m_imgHeight * m_imgChannels;
if (cfeat == 0 || cfeat > SIZE_MAX / 2)
RuntimeError("Invalid image dimensions.");
string scaleMode = config(L"scaleMode", "fill");
if (scaleMode == "crop") m_scaleMode = ScaleMode::Crop;
else if (scaleMode == "pad") m_scaleMode = ScaleMode::Pad;
else if (scaleMode == "fill") m_scaleMode = ScaleMode::Fill;
else RuntimeError("Invalid scaleMode value, must be fill, crop or pad (all lower case)");
// the pad value used for the 'pad' mode. if set to -1 then the border will be replicated.
m_padValue = config(L"padValue", -1);
if (m_padValue >= 0) m_borderType = cv::BORDER_CONSTANT;
else if (m_padValue == -1) m_borderType = cv::BORDER_REPLICATE;
else RuntimeError("Invalid padValue value, must be -1 (replicates border) or >= 0 (constant)");
// for old config options use case-insensitve comparison ...
string interpolation = config(L"interpolations", "linear");
if (AreEqualIgnoreCase(interpolation, "nearest")) m_interp = cv::INTER_NEAREST;
else if (AreEqualIgnoreCase(interpolation, "cubic")) m_interp = cv::INTER_CUBIC;
else if (AreEqualIgnoreCase(interpolation, "lanczos")) m_interp = cv::INTER_LANCZOS4;
else if (AreEqualIgnoreCase(interpolation, "linear")) m_interp = cv::INTER_LINEAR;
else RuntimeError("Invalid interpolations value, must be nearest, cubic, lanczos or linear");
}
// The method describes how input stream is transformed to the output stream. Called once per applied stream.
// Scale transformer transforms the stream so that all samples are of the same size.
StreamDescription ScaleTransformer::Transform(const StreamDescription& inputStream)
{
TransformBase::Transform(inputStream);
m_outputStream.m_sampleLayout = std::make_shared<TensorShape>(ImageDimensions(m_imgWidth, m_imgHeight, m_imgChannels).AsTensorShape(HWC));
return m_outputStream;
}
void ScaleTransformer::Apply(uint8_t, cv::Mat &mat)
{
if (m_scaleMode == ScaleMode::Fill)
{ // warp the image to the given target size
cv::resize(mat, mat, cv::Size((int)m_imgWidth, (int)m_imgHeight), 0, 0, m_interp);
}
else
{
int height = mat.rows;
int width = mat.cols;
// which dimension is our scaled one?
bool scaleW;
if (m_scaleMode == ScaleMode::Crop)
scaleW = width < height; // in "crop" mode we resize the smaller side
else
scaleW = width > height; // else we resize the larger side
size_t targetW, targetH;
if (scaleW)
{
targetW = (size_t)m_imgWidth;
targetH = (size_t)round(height * m_imgWidth / (double)width);
}
else
{
targetH = (size_t)m_imgHeight;
targetW = (size_t)round(width * m_imgHeight / (double)height);
}
cv::resize(mat, mat, cv::Size((int)targetW, (int)targetH), 0, 0, m_interp);
if (m_scaleMode == ScaleMode::Crop)
{ // crop the overlap
size_t xOff = max((size_t)0, (targetW - m_imgWidth) / 2);
size_t yOff = max((size_t)0, (targetH - m_imgHeight) / 2);
mat = mat(cv::Rect((int)xOff, (int)yOff, (int)m_imgWidth, (int)m_imgHeight));
}
else
{ // ScaleMode::PAD --> center it and pad the rest
size_t hdiff = max((size_t)0, (m_imgHeight - mat.rows) / 2);
size_t wdiff = max((size_t)0, (m_imgWidth - mat.cols) / 2);
size_t top = hdiff;
size_t bottom = m_imgHeight - top - mat.rows;
size_t left = wdiff;
size_t right = m_imgWidth - left - mat.cols;
cv::copyMakeBorder(mat, mat, (int)top, (int)bottom, (int)left, (int)right, m_borderType, m_padValue);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
MeanTransformer::MeanTransformer(const ConfigParameters& config) : ImageTransformerBase(config)
{
std::wstring meanFile = config(L"meanFile", L"");
if (meanFile.empty())
m_meanImg.release();
else
{
cv::FileStorage fs;
fs.open(msra::strfun::utf8(meanFile).c_str(), cv::FileStorage::READ);
if (!fs.isOpened())
RuntimeError("Could not open file: %ls", meanFile.c_str());
fs["MeanImg"] >> m_meanImg;
int cchan;
fs["Channel"] >> cchan;
int crow;
fs["Row"] >> crow;
int ccol;
fs["Col"] >> ccol;
if (cchan * crow * ccol !=
m_meanImg.channels() * m_meanImg.rows * m_meanImg.cols)
RuntimeError("Invalid data in file: %ls", meanFile.c_str());
fs.release();
m_meanImg = m_meanImg.reshape(cchan, crow);
}
}
void MeanTransformer::Apply(uint8_t, cv::Mat &mat)
{
assert(m_meanImg.size() == cv::Size(0, 0) ||
(m_meanImg.size() == mat.size() &&
m_meanImg.channels() == mat.channels()));
if (m_meanImg.size() == mat.size())
{
// If matrix has not been converted to the right type, do it now as maen requires floating point type.
ConvertToFloatingPointIfRequired(mat);
mat = mat - m_meanImg;
}
else
{
fprintf(stderr, "WARNING: Mean file does not match the size of the input image, will be ignored.\n"
"Please remove mean transformation from the config.\n");
}
}
TransposeTransformer::TransposeTransformer(const ConfigParameters& config) : TransformBase(config),
m_floatTransform(this), m_doubleTransform(this)
{}
// The method describes how input stream is transformed to the output stream. Called once per applied stream.
// Transpose transformer expects the dense input stream with samples as HWC and outputs CHW.
StreamDescription TransposeTransformer::Transform(const StreamDescription& inputStream)
{
m_outputStream = TransformBase::Transform(inputStream);
// Changing from NHWC to NCHW
m_outputStream.m_elementType = m_precision;
if (m_inputStream.m_sampleLayout != nullptr)
{
ImageDimensions dimensions(*m_inputStream.m_sampleLayout, HWC);
m_outputStream.m_sampleLayout = std::make_shared<TensorShape>(dimensions.AsTensorShape(CHW));
}
return m_outputStream;
}
// Transformation of the sequence.
SequenceDataPtr TransposeTransformer::Transform(SequenceDataPtr sequence)
{
auto inputSequence = dynamic_cast<ImageSequenceData*>(sequence.get());
if (inputSequence == nullptr)
RuntimeError("Currently Transpose transform only works with images.");
ElementType elementType = m_inputStream.m_elementType != ElementType::tvariant ?
m_inputStream.m_elementType :
sequence->m_elementType;
switch (elementType)
{
case ElementType::tdouble:
if (m_precision == ElementType::tfloat)
return m_floatTransform.Apply<double>(inputSequence);
if (m_precision == ElementType::tdouble)
return m_doubleTransform.Apply<double>(inputSequence);
case ElementType::tfloat:
if (m_precision == ElementType::tdouble)
return m_doubleTransform.Apply<float>(inputSequence);
if (m_precision == ElementType::tfloat)
return m_floatTransform.Apply<float>(inputSequence);
case ElementType::tuchar:
if (m_precision == ElementType::tdouble)
return m_doubleTransform.Apply<unsigned char>(inputSequence);
if (m_precision == ElementType::tfloat)
return m_floatTransform.Apply<unsigned char>(inputSequence);
default:
RuntimeError("Unsupported type. Please apply a cast transform with 'double' or 'float' precision.");
}
return nullptr; // Make compiler happy
}
template <class TElementTo>
template<class TElementFrom>
SequenceDataPtr TransposeTransformer::TypedTranspose<TElementTo>::Apply(ImageSequenceData* inputSequence)
{
TensorShapePtr shape = m_parent->m_inputStream.m_sampleLayout;
if (shape == nullptr) // Taking the shape from the sequence.
shape = inputSequence->m_sampleLayout;
if (!shape)
RuntimeError("Unknown shape of the sample in stream '%ls'.", m_parent->m_inputStream.m_name.c_str());
assert(inputSequence->m_numberOfSamples == 1);
size_t count = shape->GetNumElements();
auto result = std::make_shared<DenseSequenceWithBuffer<TElementTo>>(m_memBuffers, count);
result->m_key = inputSequence->m_key;
ImageDimensions dimensions(*shape, ImageLayoutKind::HWC);
size_t rowCount = dimensions.m_height * dimensions.m_width;
size_t channelCount = dimensions.m_numChannels;
auto dst = result->GetBuffer();
if (channelCount == 3) // Unrolling for BGR, the most common case.
{
size_t nRows = inputSequence->m_image.rows;
size_t nCols = inputSequence->m_image.cols;
TElementTo* b = dst;
TElementTo* g = dst + rowCount;
TElementTo* r = dst + 2 * rowCount;
for (size_t i = 0; i < nRows; ++i)
{
auto* x = inputSequence->m_image.ptr<TElementFrom>((int)i);
for (size_t j = 0; j < nCols; ++j)
{
auto row = j * 3;
*b++ = static_cast<TElementTo>(x[row]);
*g++ = static_cast<TElementTo>(x[row + 1]);
*r++ = static_cast<TElementTo>(x[row + 2]);
}
}
}
else
{
auto src = reinterpret_cast<const TElementFrom*>(inputSequence->GetDataBuffer());
for (size_t irow = 0; irow < rowCount; irow++)
{
for (size_t icol = 0; icol < channelCount; icol++)
{
dst[icol * rowCount + irow] = static_cast<TElementTo>(src[irow * channelCount + icol]);
}
}
}
result->m_sampleLayout = m_parent->m_outputStream.m_sampleLayout != nullptr ?
m_parent->m_outputStream.m_sampleLayout :
std::make_shared<TensorShape>(dimensions.AsTensorShape(CHW));
result->m_numberOfSamples = inputSequence->m_numberOfSamples;
return result;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
IntensityTransformer::IntensityTransformer(const ConfigParameters &config) : ImageTransformerBase(config)
{
m_stdDev = config(L"intensityStdDev", "0.0");
std::wstring intFile = config(L"intensityFile", L"");
if (intFile.empty())
{
m_eigVal.release();
m_eigVec.release();
}
else
{
cv::FileStorage fs;
fs.open(msra::strfun::utf8(intFile).c_str(), cv::FileStorage::READ);
if (!fs.isOpened())
RuntimeError("Could not open file: %ls", intFile.c_str());
fs["EigVal"] >> m_eigVal;
if (m_eigVal.rows != 1 || m_eigVal.cols != 3 || m_eigVal.channels() != 1)
RuntimeError("Invalid EigVal data in file: %ls", intFile.c_str());
fs["EigVec"] >> m_eigVec;
if (m_eigVec.rows != 3 || m_eigVec.cols != 3 || m_eigVec.channels() != 1)
RuntimeError("Invalid EigVec data in file: %ls", intFile.c_str());
fs.release();
}
}
void IntensityTransformer::StartEpoch(const EpochConfiguration &config)
{
ImageTransformerBase::StartEpoch(config);
}
void IntensityTransformer::Apply(uint8_t, cv::Mat &mat)
{
if (m_eigVal.empty() || m_eigVec.empty() || m_stdDev == 0.0)
return;
// Have to convert to float.
int type = m_precision == ElementType::tfloat ? CV_32F : CV_64F;
if (mat.type() != type)
mat.convertTo(mat, type);
if (mat.type() == CV_64FC(mat.channels()))
Apply<double>(mat);
else if (mat.type() == CV_32FC(mat.channels()))
Apply<float>(mat);
else
RuntimeError("Unsupported type");
}
template <typename ElemType>
void IntensityTransformer::Apply(cv::Mat &mat)
{
auto seed = GetSeed();
auto rng = m_rngs.pop_or_create([seed]() { return std::make_unique<std::mt19937>(seed); } );
// Using single precision as EigVal and EigVec matrices are single precision.
boost::random::normal_distribution<float> d(0, (float)m_stdDev);
cv::Mat alphas(1, 3, CV_32FC1);
assert(m_eigVal.rows == 1 && m_eigVec.cols == 3);
alphas.at<float>(0) = d(*rng) * m_eigVal.at<float>(0);
alphas.at<float>(1) = d(*rng) * m_eigVal.at<float>(1);
alphas.at<float>(2) = d(*rng) * m_eigVal.at<float>(2);
m_rngs.push(std::move(rng));
assert(m_eigVec.rows == 3 && m_eigVec.cols == 3);
cv::Mat shifts = m_eigVec * alphas.t();
// For multi-channel images data is in BGR format.
size_t cdst = mat.rows * mat.cols * mat.channels();
ElemType* pdstBase = reinterpret_cast<ElemType*>(mat.data);
for (ElemType* pdst = pdstBase; pdst < pdstBase + cdst;)
{
for (int c = 0; c < mat.channels(); c++)
{
float shift = shifts.at<float>(mat.channels() - c - 1);
*pdst = std::min(std::max(*pdst + shift, (ElemType)0), (ElemType)255);
pdst++;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
ColorTransformer::ColorTransformer(const ConfigParameters &config) : ImageTransformerBase(config)
{
m_brightnessRadius = config(L"brightnessRadius", "0.0");
if (m_brightnessRadius < 0 || m_brightnessRadius > 1.0)
InvalidArgument("brightnessRadius must be >= 0.0 and <= 1.0");
m_contrastRadius = config(L"contrastRadius", "0.0");
if (m_contrastRadius < 0 || m_contrastRadius > 1.0)
InvalidArgument("contrastRadius must be >= 0.0 and <= 1.0");
m_saturationRadius = config(L"saturationRadius", "0.0");
if (m_saturationRadius < 0 || m_saturationRadius > 1.0)
InvalidArgument("saturationRadius must be >= 0.0 and <= 1.0");
}
void ColorTransformer::StartEpoch(const EpochConfiguration &config)
{
ImageTransformerBase::StartEpoch(config);
}
void ColorTransformer::Apply(uint8_t, cv::Mat &mat)
{
if (m_brightnessRadius == 0.0 && m_contrastRadius == 0.0 && m_saturationRadius == 0.0)
return;
// Have to convert to float
ConvertToFloatingPointIfRequired(mat);
if (mat.type() == CV_64FC(mat.channels()))
Apply<double>(mat);
else if (mat.type() == CV_32FC(mat.channels()))
Apply<float>(mat);
else
RuntimeError("Unsupported type");
}
template <typename ElemType>
void ColorTransformer::Apply(cv::Mat &mat)
{
auto seed = GetSeed();
auto rng = m_rngs.pop_or_create([seed]() { return std::make_unique<std::mt19937>(seed); });
if (m_brightnessRadius > 0 || m_contrastRadius > 0)
{
// To change brightness and/or contrast the following standard transformation is used:
// Xij = alpha * Xij + beta, where
// alpha is a contrast adjustment and beta - brightness adjustment.
ElemType beta = 0;
if (m_brightnessRadius > 0)
{
UniRealT d(-m_brightnessRadius, m_brightnessRadius);
// Compute mean value of the image.
cv::Scalar imgMean = cv::sum(cv::sum(mat));
// Compute beta as a fraction of the mean.
beta = (ElemType)(d(*rng) * imgMean[0] / (mat.rows * mat.cols * mat.channels()));
}
ElemType alpha = 1;
if (m_contrastRadius > 0)
{
UniRealT d(-m_contrastRadius, m_contrastRadius);
alpha = (ElemType)(1 + d(*rng));
}
// Could potentially use mat.convertTo(mat, -1, alpha, beta)
// but it does not do range checking for single/double precision matrix. saturate_cast won't work either.
size_t count = mat.rows * mat.cols * mat.channels();
ElemType* pbase = reinterpret_cast<ElemType*>(mat.data);
for (ElemType* p = pbase; p < pbase + count; p++)
{
*p = std::min(std::max(*p * alpha + beta, (ElemType)0), (ElemType)255);
}
}
if (m_saturationRadius > 0 && mat.channels() == 3)
{
UniRealT d(-m_saturationRadius, m_saturationRadius);
double ratio = 1.0 + d(*rng);
assert(0 <= ratio && ratio <= 2);
auto hsv = m_hsvTemp.pop_or_create([]() { return std::make_unique<cv::Mat>(); });
// To change saturation, we need to convert the image to HSV format first,
// the change S channgel and convert the image back to BGR format.
cv::cvtColor(mat, *hsv, CV_BGR2HSV);
assert(hsv->rows == mat.rows && hsv->cols == mat.cols);
size_t count = hsv->rows * hsv->cols * mat.channels();
ElemType* phsvBase = reinterpret_cast<ElemType*>(hsv->data);
for (ElemType* phsv = phsvBase; phsv < phsvBase + count; phsv += 3)
{
const int HsvIndex = 1;
phsv[HsvIndex] = std::min((ElemType)(phsv[HsvIndex] * ratio), (ElemType)1);
}
cv::cvtColor(*hsv, mat, CV_HSV2BGR);
m_hsvTemp.push(std::move(hsv));
}
m_rngs.push(std::move(rng));
}
CastTransformer::CastTransformer(const ConfigParameters& config) : TransformBase(config), m_floatTransform(this), m_doubleTransform(this)
{
}
StreamDescription CastTransformer::Transform(const StreamDescription& inputStream)
{
m_outputStream = TransformBase::Transform(inputStream);
m_outputStream.m_elementType = m_precision;
return m_outputStream;
}
SequenceDataPtr CastTransformer::Transform(SequenceDataPtr sequence)
{
if (m_inputStream.m_elementType == m_precision || sequence->m_elementType == m_precision)
{
// No need to do anything, exit.
return sequence;
}
SequenceDataPtr result;
ElementType inputType = m_inputStream.m_elementType != ElementType::tvariant
? m_inputStream.m_elementType
: sequence->m_elementType;
switch (m_precision)
{
case ElementType::tdouble:
if (inputType == ElementType::tfloat)
result = m_doubleTransform.Apply<float>(sequence);
else if (inputType == ElementType::tuchar)
result = m_doubleTransform.Apply<unsigned char>(sequence);
else
RuntimeError("Unsupported type. Please apply a cast transform with 'double' or 'float' precision.");
break;
case ElementType::tfloat:
if (inputType == ElementType::tdouble)
result = m_floatTransform.Apply<double>(sequence);
if (inputType == ElementType::tuchar)
result = m_floatTransform.Apply<unsigned char>(sequence);
else
RuntimeError("Unsupported type. Please apply a cast transform with 'double' or 'float' precision.");
break;
default:
RuntimeError("Unsupported type. Please apply a cast transform with 'double' or 'float' precision.");
}
result->m_elementType = m_precision;
return result;
}
template <class TElementTo>
template<class TElementFrom>
SequenceDataPtr CastTransformer::TypedCast<TElementTo>::Apply(SequenceDataPtr sequence)
{
TensorShapePtr shape = m_parent->m_inputStream.m_sampleLayout;
if (!shape) // Taking the shape from the sequence.
shape = sequence->m_sampleLayout;
if (!shape)
RuntimeError("Unknown shape of the sample in stream '%ls'.", m_parent->m_inputStream.m_name.c_str());
auto& inputSequence = static_cast<DenseSequenceData&>(*sequence);
size_t count = shape->GetNumElements() * sequence->m_numberOfSamples;
auto result = std::make_shared<DenseSequenceWithBuffer<TElementTo>>(m_memBuffers, count);
result->m_key = sequence->m_key;
auto src = reinterpret_cast<const TElementFrom*>(inputSequence.GetDataBuffer());
auto dst = result->GetBuffer();
for (size_t i = 0; i < count; i++)
{
dst[i] = static_cast<TElementTo>(src[i]);
}
result->m_sampleLayout = shape;
result->m_numberOfSamples = inputSequence.m_numberOfSamples;
return result;
}
}}}