[Libreoffice-commits] core.git: Branch 'private/bansan/chardraw' - 6 commits - config_host.mk.in configure.ac cui/Library_cui.mk cui/source download.lst RepositoryExternal.mk
Vincent Le Garrec
legarrec.vincent at gmail.com
Tue Jan 16 21:18:13 UTC 2018
RepositoryExternal.mk | 15 +
config_host.mk.in | 2
configure.ac | 6
cui/Library_cui.mk | 4
cui/source/dialogs/cuicharmap.cxx | 386 ++++++++++++++++++++++++++-
cui/source/factory/neuralnetwork.cxx | 37 ++
cui/source/factory/neuralnetworkfann.cxx | 133 +++++++++
cui/source/factory/neuralnetworkfann.hxx | 58 ++++
cui/source/factory/neuralnetworkinternal.cxx | 360 +++++++++++++++++++++++++
cui/source/inc/cuicharmap.hxx | 23 +
cui/source/inc/neuralnetwork.hxx | 55 +++
download.lst | 2
12 files changed, 1080 insertions(+), 1 deletion(-)
New commits:
commit 56bfe8ecdc5f3e41ea8ce84ef4e9b54c86f1e2ec
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:15:44 2018 +0100
First try to make a NN implementation
It doesn't work but a good work has been done.
Change-Id: I09d9ed910ef8a905fa0f3e0a689b741006f6fd18
diff --git a/cui/source/factory/neuralnetwork.cxx b/cui/source/factory/neuralnetwork.cxx
index 5651b80b3ab8..cd08d084b7f4 100644
--- a/cui/source/factory/neuralnetwork.cxx
+++ b/cui/source/factory/neuralnetwork.cxx
@@ -20,15 +20,18 @@
#include <neuralnetwork.hxx>
#include "neuralnetworkfann.hxx"
+#include "neuralnetworkinternal.hxx"
AbstractNeuralNetwork * AbstractNeuralNetwork::CreateFactory(sal_uInt32 nLayers, const sal_uInt32* nLayer)
{
- return new NeuralNetworkFann(nLayers, nLayer);
+// return new NeuralNetworkFann(nLayers, nLayer);
+ return new NeuralNetworkInternal(nLayers, nLayer);
}
AbstractNeuralNetwork * AbstractNeuralNetwork::CreateFactory(const OUString& file)
{
- return new NeuralNetworkFann(file);
+// return new NeuralNetworkFann(file);
+ return new NeuralNetworkInternal(file);
}
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
diff --git a/cui/source/factory/neuralnetworkfann.cxx b/cui/source/factory/neuralnetworkfann.cxx
index e622916c1ccf..2366989e7d8b 100644
--- a/cui/source/factory/neuralnetworkfann.cxx
+++ b/cui/source/factory/neuralnetworkfann.cxx
@@ -39,6 +39,11 @@ void NeuralNetworkFann::SetActivationFunction(ActivationFunction function)
{
case ActivationFunction::SIGMOID:
{
+ func = FANN_SIGMOID;
+ break;
+ }
+ case ActivationFunction::SIGMOID_SYMMETRIC:
+ {
func = FANN_SIGMOID_SYMMETRIC;
break;
}
@@ -75,8 +80,6 @@ void NeuralNetworkFann::SetLearningRate(float rate)
fann_set_learning_rate(ann.get(), rate);
}
-#include <iostream>
-
void NeuralNetworkFann::InitTraining(sal_uInt32 nExamples)
{
data.reset(fann_create_train(nExamples, fann_get_num_input(ann.get()), fann_get_num_output(ann.get())));
@@ -108,9 +111,18 @@ float* NeuralNetworkFann::GetOutput(sal_uInt32 nIeme)
return &data->output[nIeme][0];
}
-float* NeuralNetworkFann::Run(float *data_input)
+void NeuralNetworkFann::Run(float *data_input, float *result)
{
- return fann_run(ann.get(), data_input);
+ float * fann_result = fann_run(ann.get(), data_input);
+
+ if (result == nullptr)
+ {
+ return;
+ }
+ for (sal_uInt32 i = 0; i < GetNumOutput(); i++)
+ {
+ result[i] = fann_result[i];
+ }
}
void NeuralNetworkFann::Train(sal_uInt32 nEpochs, float error)
diff --git a/cui/source/factory/neuralnetworkfann.hxx b/cui/source/factory/neuralnetworkfann.hxx
index 472f4640dde3..993d78c0f3d2 100644
--- a/cui/source/factory/neuralnetworkfann.hxx
+++ b/cui/source/factory/neuralnetworkfann.hxx
@@ -41,11 +41,13 @@ public:
float* GetOutput(sal_uInt32 nIeme) override;
void Train(sal_uInt32 nEpochs, float error) override;
- float* Run(float *data_input) override;
+ void Run(float *data_input, float* result) override;
void Save(const OUString& file) override;
virtual ~NeuralNetworkFann(){}
+ virtual void * GetTrain(){return data.get();}
+
private:
std::unique_ptr<struct fann, void(*)(struct fann *)> ann;
std::unique_ptr<struct fann_train_data, void(*)(struct fann_train_data *)> data;
diff --git a/cui/source/factory/neuralnetworkinternal.cxx b/cui/source/factory/neuralnetworkinternal.cxx
index 52597c57ca92..087f9c1c6d55 100644
--- a/cui/source/factory/neuralnetworkinternal.cxx
+++ b/cui/source/factory/neuralnetworkinternal.cxx
@@ -17,4 +17,344 @@
* the License at http://www.apache.org/licenses/LICENSE-2.0 .
*/
+#include "neuralnetworkinternal.hxx"
+
+#include <iostream>
+#include <fstream>
+
+NeuralNetworkInternal::NeuralNetworkInternal(sal_uInt32 nLayers, const sal_uInt32* nLayer) :
+ n(nLayers, std::vector<Neuron>()),
+ learning_rate(0.2),
+ learning_rate_alpha(0.4),
+ learning_input(),
+ learning_output()
+{
+ for (sal_uInt32 i = 0; i < nLayers; i++)
+ {
+ n[i].resize(nLayer[i]+1); // One neuron is biais.
+
+ if (i != nLayers-1)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size() /*with biais*/; j++)
+ {
+ n[i][j].w.resize(nLayer[i+1]); /*no biais*/
+ n[i][j].dw.resize(nLayer[i+1]);
+ for (sal_uInt32 k = 0; k < nLayer[i+1]; k++)
+ {
+ // Between [0;.1]
+ n[i][j].w[k] = (float)std::rand() / (float)RAND_MAX *0.2 - 0.1;
+ }
+ }
+ }
+ }
+}
+
+NeuralNetworkInternal::NeuralNetworkInternal(const OUString& file) :
+ n(), learning_rate(0.), learning_rate_alpha(0.), learning_input(),
+ learning_output()
+{
+ OString o = OUStringToOString(file, RTL_TEXTENCODING_ASCII_US);
+ const char * o_str = o.pData->buffer;
+
+ std::ifstream infile (o_str, std::ifstream::binary);
+ sal_uInt32 tmpInt;
+
+ infile.read(reinterpret_cast<char *>(&learning_rate), sizeof(learning_rate));
+ infile.read(reinterpret_cast<char *>(&learning_rate_alpha), sizeof(learning_rate_alpha));
+ infile.read(reinterpret_cast<char *>(&tmpInt), sizeof(tmpInt));
+ n.resize(tmpInt);
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ {
+ infile.read(reinterpret_cast<char *>(&tmpInt), sizeof(tmpInt));
+ n[i].resize(tmpInt);
+ }
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ {
+ if (i != n.size()-1)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ n[i][j].w.resize(n[i+1].size());
+ n[i][j].dw.resize(n[i+1].size());
+ }
+ }
+ }
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ infile.read(reinterpret_cast<char *>(&n[i][j].w[0]), n[i][j].w.size()*sizeof(n[i][j].w[0]));
+ infile.read(reinterpret_cast<char *>(&n[i][j].dw[0]), n[i][j].dw.size()*sizeof(n[i][j].dw[0]));
+ }
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ infile.read(reinterpret_cast<char *>(&n[i][j].f), sizeof(n[i][j].f));
+ infile.read(reinterpret_cast<char *>(&n[i][j].stp), sizeof(n[i][j].stp));
+ }
+}
+
+void NeuralNetworkInternal::SetActivationFunction(ActivationFunction function)
+{
+ FunctionTrans func;
+
+ switch (function)
+ {
+ case ActivationFunction::SIGMOID:
+ {
+ func = FunctionTrans::Sigmoid;
+ break;
+ }
+ case ActivationFunction::SIGMOID_SYMMETRIC:
+ {
+ func = FunctionTrans::SigmoidSymmetric;
+ break;
+ }
+ default:
+ {
+ return;
+ }
+ }
+
+ for (sal_uInt32 i = 1; i < n.size(); i++)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ n[i][j].f = func;
+ }
+ }
+}
+
+void NeuralNetworkInternal::SetTrainingAlgorithm(TrainingAlgorithm algorithm)
+{
+ return;
+}
+
+void NeuralNetworkInternal::SetLearningRate(float rate)
+{
+ learning_rate = rate;
+}
+
+void NeuralNetworkInternal::InitTraining(sal_uInt32 nExamples)
+{
+ learning_input.resize(nExamples, std::vector<float>(n[0].size(), 0));
+ learning_output.resize(nExamples, std::vector<float>(n.back().size(), 0));
+}
+
+sal_uInt32 NeuralNetworkInternal::GetNumInput()
+{
+ return n[0].size();
+}
+
+float* NeuralNetworkInternal::GetInput(sal_uInt32 nIeme)
+{
+ if (nIeme >= learning_input.size())
+ return nullptr;
+ return &learning_input[nIeme][0];
+}
+
+sal_uInt32 NeuralNetworkInternal::GetNumOutput()
+{
+ return n.back().size();
+}
+
+float* NeuralNetworkInternal::GetOutput(sal_uInt32 nIeme)
+{
+ if (nIeme >= learning_output.size())
+ return nullptr;
+ return &learning_output[nIeme][0];
+}
+
+void NeuralNetworkInternal::Train(sal_uInt32 nEpochs, float error)
+{
+ for (sal_uInt32 l = 0; l < nEpochs; l++)
+ {
+ for (sal_uInt32 m = 0; m < learning_input.size(); m++)
+ {
+ Run(&learning_input[m][0], nullptr);
+
+ if (l == nEpochs-1)
+ {
+ for (sal_uInt32 i = 0; i < n.back().size(); i++)
+ std::cout << n.back()[i].a << " ";
+ std::cout << std::endl;
+ }
+
+ // error
+ float errori = 0.f;
+ for (sal_uInt32 i = 0; i < n.back().size()-1 /*no biais*/; i++)
+ {
+ errori += (learning_input[m][i] - n.back()[i].a)*(learning_input[m][i] - n.back()[i].a);
+ }
+ errori = errori / (n.back().size()-1);
+ errori = sqrt(errori);
+ std::cout << "error " << l << "," << m << ":" << errori << std::endl;
+
+ // Calcul de sM
+ // s: ann->train_errors
+ for (sal_uInt32 i = 0; i < n.back().size()-1 /*no biais*/; i++)
+ {
+ float diff = learning_output[m][i] - n.back()[i].a;
+ switch (n.back()[i].f)
+ {
+ case FunctionTrans::Sigmoid:
+ {
+ n.back()[i].s = 2.*n.back()[i].stp*n.back()[i].a*(1.-n.back()[i].a)*diff;
+ break;
+ }
+ case FunctionTrans::SigmoidSymmetric:
+ {
+ // If Symmetric: diff/2.
+ diff = diff / 2.;
+ n.back()[i].s = n.back()[i].stp*(1.-n.back()[i].a*n.back()[i].a)*diff;
+ break;
+ }
+
+ /*
+ftLineaire : RNA.s[High(RNA.s)][I] := -2*(SortiesTest[I] - RNA.a[High(RNA.a)][I]);
+
+ftTanHyperb : RNA.s[High(RNA.s)][I] := -2*(1-RNA.a[High(RNA.a)][I]*RNA.a[High(RNA.a)][I])*
+
+(SortiesTest[I] - RNA.a[High(RNA.a)][I])
+*/
+ }
+ }
+
+ // Calcul des sk
+ // fann_activation_derived
+ for (sal_uInt32 i = n.size()-2 /*No Output layer*/; i > 0; i--)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size() /*with biais*/; j++)
+ {
+ float sum = 0.f;
+ //connection_rate >= 1
+ for (sal_uInt32 k = 0; k < n[i+1].size()-1 /*no biais*/; k++)
+ {
+ sum += n[i][j].w[k] * n[i+1][k].s;
+ }
+ switch (n[i][j].f)
+ {
+ case FunctionTrans::Sigmoid:
+ {
+ n[i][j].s = sum * 2. * n[i][j].stp * n[i][j].a * (1. - n[i][j].a);
+ break;
+ }
+ case FunctionTrans::SigmoidSymmetric:
+ {
+ n[i][j].s = sum * n[i][j].stp * (1. - n[i][j].a*n[i][j].a);
+ break;
+ }
+ default:
+ {
+ return;
+ }
+
+ // Cette ligne est volontairement commentée car pour une fonction linéaire,
+ // il aurait fallu mettre RNA.s[I][J] := RNA.s[I][J] * 1, ce qui ne sert à rien
+ // ftLineaire : RNA.s[I][J] := RNA.s[I][J] * 1;
+
+ // ftTanHyperb : RNA.s[I][J] := RNA.s[I][J] * (1 - RNA.a[I][J]*RNA.a[I][J]);
+ }
+ }
+ }
+
+ // Modifications des poids et des biais
+ for (sal_uInt32 i = n.size()-1 /*No input layer*/; i > 0; --i)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size() - 1 /*no biais*/; j++)
+ {
+ for (sal_uInt32 k = 0; k < n[i-1].size() /*with biais*/; k++)
+ {
+ float delta = learning_rate * n[i][j].s * n[i-1][k].a + learning_rate_alpha * n[i-1][k].dw[j];
+ n[i-1][k].dw[j] = delta;
+ n[i-1][k].w[j] += delta;
+ }
+ }
+ }
+ }
+ }
+}
+
+void NeuralNetworkInternal::Run(float *data_input, float *result)
+{
+ // Calcul de a0
+ for (sal_uInt32 i = 0; i < n[0].size(); i++)
+ n[0][i].a = data_input[i];
+
+ // Calcul des ak
+
+ for (sal_uInt32 i = 1; i < n.size(); i++)
+ {
+ for (sal_uInt32 j = 0; j < n[i].size()-1 /*no biais*/; j++)
+ {
+ n[i][j].sum = 0;
+ for (sal_uInt32 k = 0; k < n[i-1].size() /*with biais*/; k++)
+ n[i][j].sum += n[i-1][k].a * n[i-1][k].w[j];
+ n[i][j].sum *= n[i][j].stp;
+ switch (n[i][j].f)
+ {
+ case FunctionTrans::Sigmoid:
+ {
+ n[i][j].a = 1.f / (1.f + exp(-2.f*n[i][j].sum));
+ break;
+ }
+ case FunctionTrans::SigmoidSymmetric:
+ {
+ n[i][j].a = 2.f/(1.f + exp(-2.f/n[i][j].sum)) - 1.f;
+ break;
+ }
+ default:
+ {
+ return;
+ }
+ }
+ }
+ }
+
+
+ if (result == nullptr)
+ {
+ return;
+ }
+ for (sal_uInt32 i = 0; i < n.back().size(); i++)
+ result[i] = n.back()[i].a;
+}
+
+void NeuralNetworkInternal::Save(const OUString& file)
+{
+ OString o = OUStringToOString(file, RTL_TEXTENCODING_ASCII_US);
+ const char * o_str = o.pData->buffer;
+
+ std::ofstream outfile (o_str, std::ofstream::binary);
+
+ outfile.write(reinterpret_cast<const char *>(&learning_rate), sizeof(learning_rate));
+ outfile.write(reinterpret_cast<const char *>(&learning_rate_alpha), sizeof(learning_rate_alpha));
+
+ sal_uInt32 tmpInt = n.size();
+ outfile.write(reinterpret_cast<const char *>(&tmpInt), sizeof(tmpInt));
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ {
+ tmpInt = n[i].size();
+ outfile.write(reinterpret_cast<const char *>(&tmpInt), sizeof(tmpInt));
+ }
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ outfile.write(reinterpret_cast<const char *>(&n[i][j].w[0]), n[i][j].w.size()*sizeof(n[i][j].w[0]));
+ outfile.write(reinterpret_cast<const char *>(&n[i][j].dw[0]), n[i][j].dw.size()*sizeof(n[i][j].dw[0]));
+ }
+
+ for (sal_uInt32 i = 0; i < n.size(); i++)
+ for (sal_uInt32 j = 0; j < n[i].size(); j++)
+ {
+ outfile.write(reinterpret_cast<const char *>(&n[i][j].f), sizeof(n[i][j].f));
+ outfile.write(reinterpret_cast<const char *>(&n[i][j].stp), sizeof(n[i][j].stp));
+ }
+}
+
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
commit 6bdd077f569a629965a4f1544d83606bf410f4fe
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:13:21 2018 +0100
Use libfann through an abstract factory
With this, libfann can easily replaced by internal implementation or
other lib.
Use memory instead of using a temporary file to train the neural
network.
Change-Id: I1ec207ec5674ebba112d50425d445e5f38ada666
diff --git a/cui/Library_cui.mk b/cui/Library_cui.mk
index 449a44ea22d3..5933cae387f5 100644
--- a/cui/Library_cui.mk
+++ b/cui/Library_cui.mk
@@ -134,6 +134,9 @@ $(eval $(call gb_Library_add_exception_objects,cui,\
cui/source/factory/cuiresmgr \
cui/source/factory/dlgfact \
cui/source/factory/init \
+ cui/source/factory/neuralnetwork \
+ cui/source/factory/neuralnetworkfann \
+ cui/source/factory/neuralnetworkinternal \
cui/source/options/certpath \
cui/source/options/cfgchart \
cui/source/options/connpoolconfig \
diff --git a/cui/source/dialogs/cuicharmap.cxx b/cui/source/dialogs/cuicharmap.cxx
index ecff74b6a6e0..a1fd1a8ee3c9 100644
--- a/cui/source/dialogs/cuicharmap.cxx
+++ b/cui/source/dialogs/cuicharmap.cxx
@@ -42,6 +42,7 @@
#include <dialmgr.hxx>
#include <cuicharmap.hxx>
+#include <neuralnetwork.hxx>
#include <sfx2/request.hxx>
#include <sfx2/sfxsids.hrc>
#include <sfx2/app.hxx>
@@ -215,46 +216,6 @@ void Ocr::ScaleBitmap(long width, long height)
h_ = height;
}
-void Ocr::ToFile(std::ofstream &file)
-{
- BitmapReadAccess * r = m_bitmap.AcquireReadAccess ();
-
- std::cout << "2r->Width () != w_" << r->Width () << " vs " << w_ << std::endl;
- std::cout << "2r->Height() != h_" << r->Height () << " vs " << h_ << std::endl;
- for (long j = 0; j < h_; j++)
- {
- for (long i = 0; i < w_; i++)
- {
- if (i >= r->Width () || j >= r->Height())
- {
- file << "0 ";
- std::cout << "-1 ";
- }
- else
- {
- Color c = r->GetPixel(j, i);
- if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
- {
- file << "0 ";
- std::cout << "-1 ";
- }
- else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
- {
- file << " 1 ";
- std::cout << " 1 ";
- }
- else
- {
- file << " 1 ";
- std::cout << " 1 ";
- }
- }
- }
- std::cout << std::endl;
- }
- file << std::endl;
-}
-
void Ocr::ToFann(fann_type *out_data)
{
BitmapReadAccess * r = m_bitmap.AcquireReadAccess ();
@@ -269,7 +230,7 @@ void Ocr::ToFann(fann_type *out_data)
{
if (i >= r->Width () || j >= r->Height())
{
- out_data[idata] = 0;
+ out_data[idata] = 0.;
std::cout << "-1 ";
}
else
@@ -277,17 +238,17 @@ void Ocr::ToFann(fann_type *out_data)
Color c = r->GetPixel(j, i);
if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
{
- out_data[idata] = 0;
+ out_data[idata] = 0.;
std::cout << "-1 ";
}
else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
{
- out_data[idata] = 1;
+ out_data[idata] = 1.;
std::cout << " 1 ";
}
else
{
- out_data[idata] = 1;
+ out_data[idata] = 1.;
std::cout << " 1 ";
}
}
@@ -1224,8 +1185,6 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
// Deep learning. Take about 30-60 minutes
if (access( "/tmp/fann.net", F_OK ) == -1)
{
-
-
// One input for each pixel.
const unsigned int num_input = Ocr::SIZE*Ocr::SIZE;
// One ouput for each possible char.
@@ -1234,21 +1193,18 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
const unsigned int num_layers = 6;
// Number of neurons for each layer is usually between number of input and number on output.
const unsigned int num_neurons_hidden = std::max(num_input, num_output);
- const float desired_error = (const float) 0.001;
+ const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 500;
// To see progression. It's take time...
const unsigned int epochs_between_reports = 1;
- std::ofstream file;
- // Write in /tmp/xor.data all database of example.
- // TODO: MUST be replace by the memory stream.
- file.open("/tmp/xor.data");
-
- // First line of the file.
- file << num_output << " " << num_input << " " << num_output << std::endl;
- // Then, the current char :
- // - For each pixel : -1 if blank, 1 if not blank.
- // - For each possible char : -1 if not the current char, 1 if it is the current char.
+// First possibility: fixed internal layer.
+ sal_uInt32 layers[num_layers] = {num_input, /*num_neurons_hidden, num_neurons_hidden, */num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_output};
+ AbstractNeuralNetwork * ann = AbstractNeuralNetwork::CreateFactory(num_layers, layers);
+ ann->SetActivationFunction(AbstractNeuralNetwork::ActivationFunction::SIGMOID);
+ ann->SetTrainingAlgorithm(AbstractNeuralNetwork::TrainingAlgorithm::INCREMENTAL);
+ ann->SetLearningRate(0.07);
+ ann->InitTraining(num_output);
long i = 0;
sal_UCS4 sChar = xFontCharMap->GetFirstChar();
@@ -1299,21 +1255,21 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
o2.CropBitmap();
o2.ScaleBitmap(Ocr::SIZE, Ocr::SIZE);
// Write the draw of the current char to the file.
- o2.ToFile(file);
+ o2.ToFann(ann->GetInput(i));
// Write that the current char is the only one good.
+ fann_type *output = ann->GetOutput(i);
for (long j = 0; j < i; j++)
{
- file << "0 ";
+ output[j] = 0;
}
- file << "1 ";
+ output[i] = 1;
for (long j = i + 1; j < num_output; j++)
{
- file << "0 ";
+ output[j] = 0;
}
- file << std::endl;
sChar = xFontCharMap->GetNextChar(sChar);
i++;
@@ -1322,19 +1278,10 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
break;
}
- file.close();
// Creating the database of example done.
std::cout << "DONE" << std::endl;
-
-// First possibility: fixed internal layer.
- struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_output); // TODO
- // Default function. Usually works well.
- fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
- fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
- fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
- fann_set_learning_rate(ann, 0.2);
- fann_train_on_file(ann, "/tmp/xor.data", max_epochs, epochs_between_reports, desired_error);
+ ann->Train(max_epochs, desired_error);
// Second possibility: none predictive internal layer.
// Can't find convergence for Total error:900,0000. Start at iteration around 370.
@@ -1345,41 +1292,41 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
// fann_set_cascade_max_cand_epochs(ann, std::max(2U*num_input, 150U));
// fann_set_cascade_min_out_epochs(ann, std::max(num_input/5U, 150U/2));
// fann_set_cascade_min_cand_epochs(ann, std::max(num_input/5U, 150U/2));
- // fann_set_cascade_output_change_fraction(ann, 0.15);
- // fann_set_cascade_candidate_change_fraction(ann, 0.15);
- // fann_set_cascade_output_stagnation_epochs(ann, 5);
- // fann_set_cascade_candidate_stagnation_epochs(ann, 5);
- // fann_set_cascade_weight_multiplier(ann, 0.2);
+ fann_set_cascade_output_change_fraction(ann, 0.001);
+ fann_set_cascade_candidate_change_fraction(ann, 0.001);
+ fann_set_cascade_output_stagnation_epochs(ann, 3);
+ fann_set_cascade_candidate_stagnation_epochs(ann, 3);
+ fann_set_cascade_weight_multiplier(ann, 0.01);
+ fann_set_cascade_candidate_limit(ann, 1000000000.);
fann_cascadetrain_on_file(ann, "/tmp/xor.data", 100000, epochs_between_reports, desired_error);
*/
// Save the result. Huge !!!
- fann_save(ann, "/tmp/fann.net");
-
- fann_destroy(ann);
+ ann->Save("/tmp/fann.net");
+ delete ann;
}
fann_type *calc_out;
fann_type input[Ocr::SIZE*Ocr::SIZE];
std::cout << "Starting loading fann" << std::endl;
- struct fann *ann = fann_create_from_file("/tmp/fann.net");
+ AbstractNeuralNetwork * ann = AbstractNeuralNetwork::CreateFactory("/tmp/fann.net");
o.ToFann(&input[0]);
std::cout << "Starting finding best result" << std::endl;
- calc_out = fann_run(ann, input);
+ calc_out = ann->Run(input);
std::cout << "End of fann" << std::endl;
std::multimap<float, sal_UCS4> sorted_results;
- std::cout << fann_get_num_output(ann) << std::endl;
- for (long i = 0; i < fann_get_num_output(ann); i++)
+ std::cout << ann->GetNumOutput() << std::endl;
+ for (long i = 0; i < ann->GetNumOutput(); i++)
{
// -1.-x: the minimum will be at end.
sorted_results.insert(std::pair<float, sal_UCS4>(-1.-calc_out[i], i));
std::cout << static_cast<char>(xFontCharMap->GetCharFromIndex(i)) << " : " << calc_out[i] << " " << std::endl;
}
- fann_destroy(ann);
+ delete ann;
// TODO : sort the output and write the better char at first.
for (auto it = sorted_results.begin(); it != sorted_results.end(); ++it)
diff --git a/cui/source/factory/neuralnetwork.cxx b/cui/source/factory/neuralnetwork.cxx
new file mode 100644
index 000000000000..5651b80b3ab8
--- /dev/null
+++ b/cui/source/factory/neuralnetwork.cxx
@@ -0,0 +1,34 @@
+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you 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 .
+ */
+
+#include <neuralnetwork.hxx>
+
+#include "neuralnetworkfann.hxx"
+
+AbstractNeuralNetwork * AbstractNeuralNetwork::CreateFactory(sal_uInt32 nLayers, const sal_uInt32* nLayer)
+{
+ return new NeuralNetworkFann(nLayers, nLayer);
+}
+
+AbstractNeuralNetwork * AbstractNeuralNetwork::CreateFactory(const OUString& file)
+{
+ return new NeuralNetworkFann(file);
+}
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
diff --git a/cui/source/factory/neuralnetworkfann.cxx b/cui/source/factory/neuralnetworkfann.cxx
new file mode 100644
index 000000000000..e622916c1ccf
--- /dev/null
+++ b/cui/source/factory/neuralnetworkfann.cxx
@@ -0,0 +1,121 @@
+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you 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 .
+ */
+
+#include "neuralnetworkfann.hxx"
+
+NeuralNetworkFann::NeuralNetworkFann(sal_uInt32 nLayers, const sal_uInt32* nLayer) :
+ ann(fann_create_standard_array(nLayers, nLayer), fann_destroy),
+ data(nullptr, fann_destroy_train)
+{
+}
+
+NeuralNetworkFann::NeuralNetworkFann(const OUString& file) :
+ ann(fann_create_from_file(OUStringToOString(file, RTL_TEXTENCODING_ASCII_US).pData->buffer), fann_destroy),
+ data(nullptr, fann_destroy_train)
+{
+}
+
+void NeuralNetworkFann::SetActivationFunction(ActivationFunction function)
+{
+ fann_activationfunc_enum func;
+
+ switch (function)
+ {
+ case ActivationFunction::SIGMOID:
+ {
+ func = FANN_SIGMOID_SYMMETRIC;
+ break;
+ }
+ default:
+ {
+ return;
+ }
+ }
+ fann_set_activation_function_hidden(ann.get(), func);
+ fann_set_activation_function_output(ann.get(), func);
+}
+
+void NeuralNetworkFann::SetTrainingAlgorithm(TrainingAlgorithm algorithm)
+{
+ fann_train_enum algo;
+
+ switch (algorithm)
+ {
+ case TrainingAlgorithm::INCREMENTAL:
+ {
+ algo = FANN_TRAIN_INCREMENTAL;
+ break;
+ }
+ default:
+ {
+ return;
+ }
+ }
+ fann_set_training_algorithm(ann.get(), algo);
+}
+
+void NeuralNetworkFann::SetLearningRate(float rate)
+{
+ fann_set_learning_rate(ann.get(), rate);
+}
+
+#include <iostream>
+
+void NeuralNetworkFann::InitTraining(sal_uInt32 nExamples)
+{
+ data.reset(fann_create_train(nExamples, fann_get_num_input(ann.get()), fann_get_num_output(ann.get())));
+}
+
+void NeuralNetworkFann::Save(const OUString& file)
+{
+ OString o = OUStringToOString(file, RTL_TEXTENCODING_ASCII_US);
+ fann_save(ann.get(), o.pData->buffer);
+}
+
+sal_uInt32 NeuralNetworkFann::GetNumInput()
+{
+ return fann_get_num_input(ann.get());
+}
+
+float* NeuralNetworkFann::GetInput(sal_uInt32 nIeme)
+{
+ return &data->input[nIeme][0];
+}
+
+sal_uInt32 NeuralNetworkFann::GetNumOutput()
+{
+ return fann_get_num_output(ann.get());
+}
+
+float* NeuralNetworkFann::GetOutput(sal_uInt32 nIeme)
+{
+ return &data->output[nIeme][0];
+}
+
+float* NeuralNetworkFann::Run(float *data_input)
+{
+ return fann_run(ann.get(), data_input);
+}
+
+void NeuralNetworkFann::Train(sal_uInt32 nEpochs, float error)
+{
+ fann_train_on_data(ann.get(), data.get(), nEpochs, 1, error);
+}
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
diff --git a/cui/source/factory/neuralnetworkfann.hxx b/cui/source/factory/neuralnetworkfann.hxx
new file mode 100644
index 000000000000..472f4640dde3
--- /dev/null
+++ b/cui/source/factory/neuralnetworkfann.hxx
@@ -0,0 +1,56 @@
+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you 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 .
+ */
+#ifndef INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORKFANN_HXX
+#define INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORKFANN_HXX
+
+#include "neuralnetwork.hxx"
+
+#include <fann.h>
+#include <memory>
+
+class NeuralNetworkFann : public AbstractNeuralNetwork
+{
+public:
+ NeuralNetworkFann(sal_uInt32 nLayers, const sal_uInt32* nLayer);
+ NeuralNetworkFann(const OUString& file);
+
+ void SetActivationFunction(ActivationFunction function) override;
+ void SetTrainingAlgorithm(TrainingAlgorithm algorithm) override;
+ void SetLearningRate(float rate) override;
+
+ void InitTraining(sal_uInt32 nExamples) override;
+ virtual sal_uInt32 GetNumInput() override;
+ float* GetInput(sal_uInt32 nIeme) override;
+ virtual sal_uInt32 GetNumOutput() override;
+ float* GetOutput(sal_uInt32 nIeme) override;
+
+ void Train(sal_uInt32 nEpochs, float error) override;
+ float* Run(float *data_input) override;
+ void Save(const OUString& file) override;
+
+ virtual ~NeuralNetworkFann(){}
+
+private:
+ std::unique_ptr<struct fann, void(*)(struct fann *)> ann;
+ std::unique_ptr<struct fann_train_data, void(*)(struct fann_train_data *)> data;
+};
+
+#endif
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
diff --git a/cui/source/factory/neuralnetworkinternal.cxx b/cui/source/factory/neuralnetworkinternal.cxx
new file mode 100644
index 000000000000..52597c57ca92
--- /dev/null
+++ b/cui/source/factory/neuralnetworkinternal.cxx
@@ -0,0 +1,20 @@
+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you 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 .
+ */
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
diff --git a/cui/source/inc/cuicharmap.hxx b/cui/source/inc/cuicharmap.hxx
index d999d2f316a8..1c30cd404ec5 100644
--- a/cui/source/inc/cuicharmap.hxx
+++ b/cui/source/inc/cuicharmap.hxx
@@ -51,7 +51,6 @@ public:
void ReadBitmap();
void CropBitmap();
void ScaleBitmap(long width, long height);
- void ToFile(std::ofstream &file);
void ToFann(fann_type *out_data);
static const int SIZE = 20;
diff --git a/cui/source/inc/neuralnetwork.hxx b/cui/source/inc/neuralnetwork.hxx
new file mode 100644
index 000000000000..045909214de9
--- /dev/null
+++ b/cui/source/inc/neuralnetwork.hxx
@@ -0,0 +1,55 @@
+/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
+/*
+ * This file is part of the LibreOffice project.
+ *
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at http://mozilla.org/MPL/2.0/.
+ *
+ * This file incorporates work covered by the following license notice:
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed
+ * with this work for additional information regarding copyright
+ * ownership. The ASF licenses this file to you 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 .
+ */
+#ifndef INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORK_HXX
+#define INCLUDED_CUI_SOURCE_FACTORY_NEURALNETWORK_HXX
+
+#include <sal/types.h>
+#include <rtl/ustring.hxx>
+
+class AbstractNeuralNetwork
+{
+public:
+ enum class ActivationFunction {
+ SIGMOID
+ };
+ enum class TrainingAlgorithm {
+ INCREMENTAL
+ };
+ static AbstractNeuralNetwork * CreateFactory(sal_uInt32 nLayers, const sal_uInt32* nLayer);
+ static AbstractNeuralNetwork * CreateFactory(const OUString& file);
+ virtual void SetActivationFunction(ActivationFunction function) = 0;
+ virtual void SetTrainingAlgorithm(TrainingAlgorithm algorithm) = 0;
+ virtual void SetLearningRate(float rate) = 0;
+
+ virtual void InitTraining(sal_uInt32 nExamples) = 0;
+ virtual sal_uInt32 GetNumInput() = 0;
+ virtual float* GetInput(sal_uInt32 nIeme) = 0;
+ virtual sal_uInt32 GetNumOutput() = 0;
+ virtual float* GetOutput(sal_uInt32 nIeme) = 0;
+
+ virtual void Train(sal_uInt32 nEpochs, float error) = 0;
+ virtual float* Run(float *data_input) = 0;
+ virtual void Save(const OUString& file) = 0;
+
+ virtual ~AbstractNeuralNetwork(){}
+};
+
+#endif
+
+/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
commit b421a9e3183fd6ee8901f0f42787039499c3e201
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:09:00 2018 +0100
Show the result of the neural network in the UI.
And reduce the number of char that can be recognized to start with a
small subset.
Change-Id: I41d0861f39ff4ba16c1fbebd892ce7f0e6f8043f
diff --git a/cui/source/dialogs/cuicharmap.cxx b/cui/source/dialogs/cuicharmap.cxx
index b571fdeaf073..ecff74b6a6e0 100644
--- a/cui/source/dialogs/cuicharmap.cxx
+++ b/cui/source/dialogs/cuicharmap.cxx
@@ -184,8 +184,8 @@ void Ocr::CropBitmap()
long right = j;
- w_ = right-left+1;
- h_ = bottom-top+1;
+ w_ = right - left + 1;
+ h_ = bottom - top + 1;
tools::Rectangle r_src(left, top, right, bottom);
tools::Rectangle r_dst(0, 0, w_, h_);
Size s(w_, h_);
@@ -223,31 +223,34 @@ void Ocr::ToFile(std::ofstream &file)
std::cout << "2r->Height() != h_" << r->Height () << " vs " << h_ << std::endl;
for (long j = 0; j < h_; j++)
{
- std::cout << j << std::endl;
for (long i = 0; i < w_; i++)
{
- std::cout << i << std::endl;
if (i >= r->Width () || j >= r->Height())
{
- file << "-1 ";
+ file << "0 ";
+ std::cout << "-1 ";
}
else
{
Color c = r->GetPixel(j, i);
if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
{
- file << "-1 ";
+ file << "0 ";
+ std::cout << "-1 ";
}
else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
{
- file << "1 ";
+ file << " 1 ";
+ std::cout << " 1 ";
}
else
{
- file << "1 ";
+ file << " 1 ";
+ std::cout << " 1 ";
}
}
}
+ std::cout << std::endl;
}
file << std::endl;
}
@@ -266,7 +269,7 @@ void Ocr::ToFann(fann_type *out_data)
{
if (i >= r->Width () || j >= r->Height())
{
- out_data[idata] = -1;
+ out_data[idata] = 0;
std::cout << "-1 ";
}
else
@@ -274,22 +277,23 @@ void Ocr::ToFann(fann_type *out_data)
Color c = r->GetPixel(j, i);
if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
{
- out_data[idata] = -1;
+ out_data[idata] = 0;
std::cout << "-1 ";
}
else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
{
out_data[idata] = 1;
- std::cout << "1 ";
+ std::cout << " 1 ";
}
else
{
out_data[idata] = 1;
- std::cout << "1 ";
+ std::cout << " 1 ";
}
}
idata++;
}
+ std::cout << std::endl;
}
std::cout << std::endl;
}
@@ -1213,33 +1217,28 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
o.ScaleBitmap(Ocr::SIZE, Ocr::SIZE);
m_pSearchSet->ClearPreviousData();
+ toggleSearchView(true);
+ FontCharMapRef xFontCharMap(new FontCharMap());
+ m_pSearchSet->GetFontCharMap(xFontCharMap);
// Deep learning. Take about 30-60 minutes
if (access( "/tmp/fann.net", F_OK ) == -1)
{
- FontCharMapRef xFontCharMap(new FontCharMap());
- m_pSearchSet->GetFontCharMap(xFontCharMap);
// One input for each pixel.
const unsigned int num_input = Ocr::SIZE*Ocr::SIZE;
// One ouput for each possible char.
- const unsigned int num_output = xFontCharMap->GetCharCount()-1;
- // 4 layers (one input, one output and two is recommended for OCR).
- const unsigned int num_layers = 4;
+ const unsigned int num_output = 2*16/*xFontCharMap->GetCharCount()-1*/;
+ // 6 layers (one input, one output and the others).
+ const unsigned int num_layers = 6;
// Number of neurons for each layer is usually between number of input and number on output.
const unsigned int num_neurons_hidden = std::max(num_input, num_output);
const float desired_error = (const float) 0.001;
- const unsigned int max_epochs = 500000;
+ const unsigned int max_epochs = 500;
// To see progression. It's take time...
const unsigned int epochs_between_reports = 1;
- struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_output);
-
- // Default function. Usually works well.
- fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
- fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
-
std::ofstream file;
// Write in /tmp/xor.data all database of example.
// TODO: MUST be replace by the memory stream.
@@ -1305,20 +1304,22 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
// Write that the current char is the only one good.
for (long j = 0; j < i; j++)
{
- file << "-1 ";
+ file << "0 ";
}
file << "1 ";
for (long j = i + 1; j < num_output; j++)
{
- file << "-1 ";
+ file << "0 ";
}
file << std::endl;
sChar = xFontCharMap->GetNextChar(sChar);
- m_pSearchSet->AppendCharToList(sChar);
i++;
+
+ if (i == num_output)
+ break;
}
file.close();
@@ -1326,9 +1327,32 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
std::cout << "DONE" << std::endl;
- // Training the network. Take age !!!
+// First possibility: fixed internal layer.
+ struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_neurons_hidden, num_output); // TODO
+ // Default function. Usually works well.
+ fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
+ fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
+ fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
+ fann_set_learning_rate(ann, 0.2);
fann_train_on_file(ann, "/tmp/xor.data", max_epochs, epochs_between_reports, desired_error);
+// Second possibility: none predictive internal layer.
+// Can't find convergence for Total error:900,0000. Start at iteration around 370.
+ /*
+ struct fann *ann = fann_create_shortcut(2, num_input, num_output);
+ // Useful???
+ // fann_set_cascade_max_out_epochs(ann, std::max(2U*num_input, 150U));
+ // fann_set_cascade_max_cand_epochs(ann, std::max(2U*num_input, 150U));
+ // fann_set_cascade_min_out_epochs(ann, std::max(num_input/5U, 150U/2));
+ // fann_set_cascade_min_cand_epochs(ann, std::max(num_input/5U, 150U/2));
+ // fann_set_cascade_output_change_fraction(ann, 0.15);
+ // fann_set_cascade_candidate_change_fraction(ann, 0.15);
+ // fann_set_cascade_output_stagnation_epochs(ann, 5);
+ // fann_set_cascade_candidate_stagnation_epochs(ann, 5);
+ // fann_set_cascade_weight_multiplier(ann, 0.2);
+ fann_cascadetrain_on_file(ann, "/tmp/xor.data", 100000, epochs_between_reports, desired_error);
+ */
+
// Save the result. Huge !!!
fann_save(ann, "/tmp/fann.net");
@@ -1346,13 +1370,28 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
calc_out = fann_run(ann, input);
std::cout << "End of fann" << std::endl;
- for (long i = 0; i < 2294; i++)
- std::cout << calc_out[i] << " ";
+ std::multimap<float, sal_UCS4> sorted_results;
+ std::cout << fann_get_num_output(ann) << std::endl;
+ for (long i = 0; i < fann_get_num_output(ann); i++)
+ {
+ // -1.-x: the minimum will be at end.
+ sorted_results.insert(std::pair<float, sal_UCS4>(-1.-calc_out[i], i));
+ std::cout << static_cast<char>(xFontCharMap->GetCharFromIndex(i)) << " : " << calc_out[i] << " " << std::endl;
+ }
fann_destroy(ann);
// TODO : sort the output and write the better char at first.
-// m_pSearchSet->AppendCharToList(sChar);
+ for (auto it = sorted_results.begin(); it != sorted_results.end(); ++it)
+ {
+ /*
+ if ((*it).first == -1)
+ break;
+ */
+ m_pSearchSet->AppendCharToList(xFontCharMap->GetCharFromIndex((*it).second));
+ std::cout << (*it).second << " ";
+ }
+ std::cout << std::endl;
m_pSearchSet->Resize();
}
diff --git a/cui/source/inc/cuicharmap.hxx b/cui/source/inc/cuicharmap.hxx
index 0e80d4002c1a..d999d2f316a8 100644
--- a/cui/source/inc/cuicharmap.hxx
+++ b/cui/source/inc/cuicharmap.hxx
@@ -54,7 +54,7 @@ public:
void ToFile(std::ofstream &file);
void ToFann(fann_type *out_data);
- static const int SIZE = 15;
+ static const int SIZE = 20;
private:
Bitmap m_bitmap;
commit 85188af850f4e7ea252df6202215c328b63bb115
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:05:50 2018 +0100
Recognize char by using lib fann network
Read all char, train the neural network with these char then apply
the draw to get recognition.
Change-Id: Ie48a173703f599b054f0c6edfa38acfdfb25ec71
diff --git a/cui/source/dialogs/cuicharmap.cxx b/cui/source/dialogs/cuicharmap.cxx
index 8bf4d7166801..b571fdeaf073 100644
--- a/cui/source/dialogs/cuicharmap.cxx
+++ b/cui/source/dialogs/cuicharmap.cxx
@@ -52,6 +52,9 @@
#include <unicode/uchar.h>
#include <unicode/utypes.h>
+#include <fann.h>
+#include <floatfann.h>
+
using namespace css;
Ocr::Ocr(const Bitmap& b, long w, long h) : m_bitmap(b), w_(w), h_(h), data()
@@ -67,18 +70,25 @@ void Ocr::ReadBitmap()
{
for (long i = 0; i < w_; i++)
{
- Color c = r->GetPixel (j, i);
- if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
- {
- data[j*w_+i] = 1;
- }
- else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ if (i >= r->Width () || j >= r->Height())
{
data[j*w_+i] = 0;
}
else
{
- data[j*w_+i] = 2;
+ Color c = r->GetPixel (j, i);
+ if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ data[j*w_+i] = 1;
+ }
+ else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ data[j*w_+i] = 0;
+ }
+ else
+ {
+ data[j*w_+i] = 2;
+ }
}
}
}
@@ -200,6 +210,88 @@ void Ocr::ScaleBitmap(long width, long height)
}
m_bitmap.Scale(new_size, new_size, BmpScaleFlag::Fast);
+
+ w_ = width;
+ h_ = height;
+}
+
+void Ocr::ToFile(std::ofstream &file)
+{
+ BitmapReadAccess * r = m_bitmap.AcquireReadAccess ();
+
+ std::cout << "2r->Width () != w_" << r->Width () << " vs " << w_ << std::endl;
+ std::cout << "2r->Height() != h_" << r->Height () << " vs " << h_ << std::endl;
+ for (long j = 0; j < h_; j++)
+ {
+ std::cout << j << std::endl;
+ for (long i = 0; i < w_; i++)
+ {
+ std::cout << i << std::endl;
+ if (i >= r->Width () || j >= r->Height())
+ {
+ file << "-1 ";
+ }
+ else
+ {
+ Color c = r->GetPixel(j, i);
+ if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ file << "-1 ";
+ }
+ else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ file << "1 ";
+ }
+ else
+ {
+ file << "1 ";
+ }
+ }
+ }
+ }
+ file << std::endl;
+}
+
+void Ocr::ToFann(fann_type *out_data)
+{
+ BitmapReadAccess * r = m_bitmap.AcquireReadAccess ();
+ long idata = 0;
+
+ std::cout << "3r->Width () != w_" << r->Width () << " vs " << w_ << std::endl;
+ std::cout << "3r->Height() != h_" << r->Height () << " vs " << h_ << std::endl;
+
+ for (long j = 0; j < h_; j++)
+ {
+ for (long i = 0; i < w_; i++)
+ {
+ if (i >= r->Width () || j >= r->Height())
+ {
+ out_data[idata] = -1;
+ std::cout << "-1 ";
+ }
+ else
+ {
+ Color c = r->GetPixel(j, i);
+ if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ out_data[idata] = -1;
+ std::cout << "-1 ";
+ }
+ else if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ out_data[idata] = 1;
+ std::cout << "1 ";
+ }
+ else
+ {
+ out_data[idata] = 1;
+ std::cout << "1 ";
+ }
+ }
+ idata++;
+ }
+ }
+ std::cout << std::endl;
}
// class SvxCharacterMap =================================================
@@ -1095,6 +1187,8 @@ IMPL_LINK_NOARG(SvxCharacterMap, InsertClickHdl, Button*, void)
EndDialog(RET_OK);
}
+#include <iostream>
+
IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
{
if (m_pDrawChk->IsChecked())
@@ -1120,57 +1214,145 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
m_pSearchSet->ClearPreviousData();
- sal_UCS4 *range = new sal_UCS4[2];
- range[0] = 0;
- range[1] = 1114111;
- CmapResult rCR (true, range, 1);
- FontCharMapRef xFontCharMap(new FontCharMap(rCR));
- m_pSearchSet->GetFontCharMap(xFontCharMap);
-
- sal_UCS4 sChar = xFontCharMap->GetFirstChar();
- while(sChar != xFontCharMap->GetLastChar())
+ // Deep learning. Take about 30-60 minutes
+ if (access( "/tmp/fann.net", F_OK ) == -1)
{
- VirtualDevice od;
- OUString aOUStr( &sChar, 1 );
- Size s50(Ocr::SIZE, Ocr::SIZE);
- od.SetOutputSize( s50 );
- vcl::Font aFontBis(aFont);
- aFontBis.SetFontHeight(Ocr::SIZE);
- od.SetFont( aFontBis );
- od.DrawText( p, aOUStr );
- w = od.GetOutputWidthPixel ();
- h = od.GetOutputHeightPixel ();
-
- Size s2(w, h);
- b = od.GetBitmap (p, s2);
-
- BitmapReadAccess* r = b.AcquireReadAccess ();
-
- for (long j = 0; j < h; j++)
+ FontCharMapRef xFontCharMap(new FontCharMap());
+ m_pSearchSet->GetFontCharMap(xFontCharMap);
+
+
+ // One input for each pixel.
+ const unsigned int num_input = Ocr::SIZE*Ocr::SIZE;
+ // One ouput for each possible char.
+ const unsigned int num_output = xFontCharMap->GetCharCount()-1;
+ // 4 layers (one input, one output and two is recommended for OCR).
+ const unsigned int num_layers = 4;
+ // Number of neurons for each layer is usually between number of input and number on output.
+ const unsigned int num_neurons_hidden = std::max(num_input, num_output);
+ const float desired_error = (const float) 0.001;
+ const unsigned int max_epochs = 500000;
+ // To see progression. It's take time...
+ const unsigned int epochs_between_reports = 1;
+
+ struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_output);
+
+ // Default function. Usually works well.
+ fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
+ fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
+
+ std::ofstream file;
+ // Write in /tmp/xor.data all database of example.
+ // TODO: MUST be replace by the memory stream.
+ file.open("/tmp/xor.data");
+
+ // First line of the file.
+ file << num_output << " " << num_input << " " << num_output << std::endl;
+ // Then, the current char :
+ // - For each pixel : -1 if blank, 1 if not blank.
+ // - For each possible char : -1 if not the current char, 1 if it is the current char.
+
+ long i = 0;
+ sal_UCS4 sChar = xFontCharMap->GetFirstChar();
+ while(sChar != xFontCharMap->GetLastChar())
{
- for (long i = 0; i < w; i++)
+ // Draw the current char.
+ VirtualDevice od;
+ OUString aOUStr( &sChar, 1 );
+ Size s50(Ocr::SIZE, Ocr::SIZE);
+ od.SetOutputSize( s50 );
+ vcl::Font aFontBis(aFont);
+ aFontBis.SetFontHeight(Ocr::SIZE);
+ od.SetFont( aFontBis );
+ od.DrawText( p, aOUStr );
+
+ w = od.GetOutputWidthPixel ();
+ h = od.GetOutputHeightPixel ();
+ Size s2(w, h);
+ Bitmap b2 = od.GetBitmap (p, s2);
+
+ BitmapReadAccess* r = b2.AcquireReadAccess ();
+
+ // Show every pixel of the current char.
+ for (long j = 0; j < h; j++)
{
- Color c = r->GetPixel(j, i);
- if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
- {
- std::cout << "1";
- }
- else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
- {
- std::cout << "0";
- }
- else
+ for (long k = 0; k < w; k++)
{
- std::cout << "2";
+ Color c = r->GetPixel(j, k);
+ if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ std::cout << "1";
+ }
+ else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ std::cout << "0";
+ }
+ else
+ {
+ std::cout << "2";
+ }
}
+ std::cout << std::endl;
}
- std::cout << std::endl;
+
+ // Resize the bitmap of the current char.
+ Ocr o2(b2, w, h);
+ o2.ReadBitmap();
+ o2.CropBitmap();
+ o2.ScaleBitmap(Ocr::SIZE, Ocr::SIZE);
+ // Write the draw of the current char to the file.
+ o2.ToFile(file);
+
+ // Write that the current char is the only one good.
+ for (long j = 0; j < i; j++)
+ {
+ file << "-1 ";
+ }
+
+ file << "1 ";
+
+ for (long j = i + 1; j < num_output; j++)
+ {
+ file << "-1 ";
+ }
+ file << std::endl;
+
+ sChar = xFontCharMap->GetNextChar(sChar);
+ m_pSearchSet->AppendCharToList(sChar);
+ i++;
}
- sChar = xFontCharMap->GetNextChar(sChar);
- m_pSearchSet->AppendCharToList(sChar);
+ file.close();
+ // Creating the database of example done.
+ std::cout << "DONE" << std::endl;
+
+
+ // Training the network. Take age !!!
+ fann_train_on_file(ann, "/tmp/xor.data", max_epochs, epochs_between_reports, desired_error);
+
+ // Save the result. Huge !!!
+ fann_save(ann, "/tmp/fann.net");
+
+ fann_destroy(ann);
}
- m_pSearchSet->AppendCharToList(sChar);
+
+ fann_type *calc_out;
+ fann_type input[Ocr::SIZE*Ocr::SIZE];
+
+ std::cout << "Starting loading fann" << std::endl;
+ struct fann *ann = fann_create_from_file("/tmp/fann.net");
+
+ o.ToFann(&input[0]);
+ std::cout << "Starting finding best result" << std::endl;
+ calc_out = fann_run(ann, input);
+ std::cout << "End of fann" << std::endl;
+
+ for (long i = 0; i < 2294; i++)
+ std::cout << calc_out[i] << " ";
+
+ fann_destroy(ann);
+
+ // TODO : sort the output and write the better char at first.
+// m_pSearchSet->AppendCharToList(sChar);
m_pSearchSet->Resize();
}
@@ -1530,7 +1712,6 @@ void DrawingAreaOcr::MouseMove( const MouseEvent &rMEvt )
Invalidate();
Update();
- std::cout << static_cast<int>(state) << std::endl;
}
void DrawingAreaOcr::MouseButtonUp (const MouseEvent &rMEvt)
diff --git a/cui/source/inc/cuicharmap.hxx b/cui/source/inc/cuicharmap.hxx
index 10b7dc956c03..0e80d4002c1a 100644
--- a/cui/source/inc/cuicharmap.hxx
+++ b/cui/source/inc/cuicharmap.hxx
@@ -28,6 +28,10 @@
#include <svx/charmap.hxx>
#include <svx/searchcharmap.hxx>
#include <sfx2/charwin.hxx>
+#include <iostream>
+#include <fstream>
+
+#include <floatfann.h>
using namespace ::com::sun::star;
class SubsetMap;
@@ -47,8 +51,10 @@ public:
void ReadBitmap();
void CropBitmap();
void ScaleBitmap(long width, long height);
+ void ToFile(std::ofstream &file);
+ void ToFann(fann_type *out_data);
- static const int SIZE = 25;
+ static const int SIZE = 15;
private:
Bitmap m_bitmap;
commit 63e2b57bb5ea4286e196b224fecc157c248d7c04
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:03:04 2018 +0100
Add fann dependency
Change-Id: I508ecd58abcd779219c4275d804ec8a268d0ccd8
diff --git a/RepositoryExternal.mk b/RepositoryExternal.mk
index 30709792c010..9539ce05f31d 100644
--- a/RepositoryExternal.mk
+++ b/RepositoryExternal.mk
@@ -2548,6 +2548,21 @@ endef
endif # ENABLE_GIO
+define gb_LinkTarget__use_fann
+$(call gb_LinkTarget_set_include,$(1),\
+ $$(INCLUDE) \
+ $(FANN_CFLAGS) \
+)
+
+$(call gb_LinkTarget_add_libs,$(1),$(FANN_LIBS))
+
+endef
+
+define gb_ExternalProject__use_fann
+$(call gb_ExternalProject_use_package,$(1),fann)
+endef
+
+
ifeq ($(ENABLE_AVAHI),TRUE)
define gb_LinkTarget__use_avahi
diff --git a/config_host.mk.in b/config_host.mk.in
index 0cc343cd351d..a46d6da90e24 100644
--- a/config_host.mk.in
+++ b/config_host.mk.in
@@ -197,6 +197,8 @@ export GIT_LINK_SRC=@GIT_LINK_SRC@
export GIT_NEEDED_SUBMODULES=@GIT_NEEDED_SUBMODULES@
export EPOXY_CFLAGS=$(gb_SPACE)@EPOXY_CFLAGS@
export EPOXY_LIBS=$(gb_SPACE)@EPOXY_LIBS@
+export FANN_CFLAGS=$(gb_SPACE)@FANN_CFLAGS@
+export FANN_LIBS=$(gb_SPACE)@FANN_LIBS@
export GLM_CFLAGS=$(gb_SPACE)@GLM_CFLAGS@
export GPG_ERROR_CFLAGS=$(gb_SPACE)@GPG_ERROR_CFLAGS@
export GPG_ERROR_LIBS=$(gb_SPACE)@GPG_ERROR_LIBS@
diff --git a/configure.ac b/configure.ac
index 7ef4a6c6a334..11ecf94d6658 100644
--- a/configure.ac
+++ b/configure.ac
@@ -7736,6 +7736,12 @@ libo_CHECK_SYSTEM_MODULE([libstaroffice],[STAROFFICE],[libstaroffice-0.0])
libo_PKG_VERSION([STAROFFICE], [libstaroffice-0.0], [0.0.4])
dnl ===================================================================
+dnl Neuronal network fann
+dnl ===================================================================
+libo_CHECK_SYSTEM_MODULE([fann], [FANN], [fann >= 2.0])
+
+
+dnl ===================================================================
dnl Check for system lcms2
dnl ===================================================================
if test "$with_system_lcms2" != "yes"; then
diff --git a/cui/Library_cui.mk b/cui/Library_cui.mk
index c6dc353f59b4..449a44ea22d3 100644
--- a/cui/Library_cui.mk
+++ b/cui/Library_cui.mk
@@ -66,6 +66,7 @@ $(eval $(call gb_Library_use_externals,cui,\
boost_headers \
$(call gb_Helper_optional,OPENCL,\
clew) \
+ fann \
icuuc \
icu_headers \
))
diff --git a/download.lst b/download.lst
index f574617147c9..ca2bf915d38b 100644
--- a/download.lst
+++ b/download.lst
@@ -44,6 +44,8 @@ export ETONYEK_VERSION_MICRO := 7
export ETONYEK_TARBALL := libetonyek-0.1.$(ETONYEK_VERSION_MICRO).tar.xz
export EXPAT_SHA256SUM := d9dc32efba7e74f788fcc4f212a43216fc37cf5f23f4c2339664d473353aedf6
export EXPAT_TARBALL := expat-2.2.5.tar.bz2
+export FANN_SHA256SUM := 434b85fce60701c4e0066c442d60110d8e649f278e4edb814f0c0e7a1e0929fd
+export FANN_TARBALL := FANN-2.2.0-Source.zip
export FIREBIRD_SHA256SUM := 6994be3555e23226630c587444be19d309b25b0fcf1f87df3b4e3f88943e5860
export FIREBIRD_TARBALL := Firebird-3.0.0.32483-0.tar.bz2
export FONTCONFIG_SHA256SUM := cf0c30807d08f6a28ab46c61b8dbd55c97d2f292cf88f3a07d3384687f31f017
commit 99d95dd1bfbe248086850660f000d5b32c545722
Author: Vincent Le Garrec <legarrec.vincent at gmail.com>
Date: Tue Jan 16 22:00:29 2018 +0100
Read the draw and resize it
Convert a char to an image.
VirtualDevice od must be convert to VclPtr<VirtualDevice>.
Change-Id: I2b53c6f2bde12109c56ce1b4d139af6ddc901718
diff --git a/cui/source/dialogs/cuicharmap.cxx b/cui/source/dialogs/cuicharmap.cxx
index 7ca01ffba5b1..8bf4d7166801 100644
--- a/cui/source/dialogs/cuicharmap.cxx
+++ b/cui/source/dialogs/cuicharmap.cxx
@@ -32,6 +32,8 @@
#include <vcl/settings.hxx>
#include <vcl/builderfactory.hxx>
#include <vcl/fontcharmap.hxx>
+#include <vcl/virdev.hxx>
+#include <vcl/bitmapaccess.hxx>
#include <svl/stritem.hxx>
#include <officecfg/Office/Common.hxx>
#include <comphelper/processfactory.hxx>
@@ -52,6 +54,154 @@
using namespace css;
+Ocr::Ocr(const Bitmap& b, long w, long h) : m_bitmap(b), w_(w), h_(h), data()
+{
+}
+
+void Ocr::ReadBitmap()
+{
+ BitmapReadAccess * r = m_bitmap.AcquireReadAccess ();
+ data.resize(h_*w_);
+
+ for (long j = 0; j < h_; j++)
+ {
+ for (long i = 0; i < w_; i++)
+ {
+ Color c = r->GetPixel (j, i);
+ if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ data[j*w_+i] = 1;
+ }
+ else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ data[j*w_+i] = 0;
+ }
+ else
+ {
+ data[j*w_+i] = 2;
+ }
+ }
+ }
+}
+
+void Ocr::CropBitmap()
+{
+ // Remove useless bottom row.
+ long j;
+ for (j = h_ - 1; j >= 0; j--)
+ {
+ long i;
+ for (i = 0; i < w_; i++)
+ {
+ if (data[j*w_+i] != 0)
+ {
+ break;
+ }
+ }
+ if (i != w_)
+ {
+ break;
+ }
+ }
+
+ // All image is empty.
+ if (j == -1)
+ {
+ w_ = 1;
+ h_ = 1;
+ tools::Rectangle r(0, 0, 1, 1);
+ m_bitmap.Crop(r);
+ data.resize(1);
+ return;
+ }
+
+ long bottom = j;
+
+ // Remove useless top row.
+ for (j = 0; j < h_; j++)
+ {
+ long i;
+ for (i = 0; i < w_; i++)
+ {
+ if (data[j*w_+i] != 0)
+ {
+ break;
+ }
+ }
+ if (i != w_)
+ {
+ break;
+ }
+ }
+
+ long top = j;
+
+ // Remove useless left col.
+ for (j = 0; j < w_; j++)
+ {
+ long i;
+ for (i = 0; i < h_; i++)
+ {
+ if (data[i*w_+j] != 0)
+ {
+ break;
+ }
+ }
+ if (i != h_)
+ {
+ break;
+ }
+ }
+
+ long left = j;
+
+ // Remove useless right col.
+ for (j = w_-1; j >= 0; j--)
+ {
+ long i;
+ for (i = 0; i < h_; i++)
+ {
+ if (data[i*w_+j] != 0)
+ {
+ break;
+ }
+ }
+ if (i != h_)
+ {
+ break;
+ }
+ }
+
+ long right = j;
+
+ w_ = right-left+1;
+ h_ = bottom-top+1;
+ tools::Rectangle r_src(left, top, right, bottom);
+ tools::Rectangle r_dst(0, 0, w_, h_);
+ Size s(w_, h_);
+ Bitmap b_dst(s, 1);
+ b_dst.CopyPixel(r_dst, r_src, &m_bitmap);
+ m_bitmap = b_dst;
+}
+
+void Ocr::ScaleBitmap(long width, long height)
+{
+ double new_ratio = (double)width / height;
+ double ratio = (double)w_ / h_;
+
+ double new_size;
+ if (ratio > new_ratio)
+ {
+ new_size = (double)width / w_;
+ }
+ else
+ {
+ new_size = (double)height / h_;
+ }
+
+ m_bitmap.Scale(new_size, new_size, BmpScaleFlag::Fast);
+}
+
// class SvxCharacterMap =================================================
SvxCharacterMap::SvxCharacterMap( vcl::Window* pParent, const SfxItemSet* pSet, bool bInsert )
@@ -956,6 +1106,73 @@ IMPL_LINK_NOARG(SvxCharacterMap, DrawToggleHdl, Button*, void)
{
m_pDrawingArea->Hide();
m_pShowChar->Show();
+
+ Point p(0, 0);
+ long w = m_pDrawingArea->GetOutputWidthPixel ();
+ long h = m_pDrawingArea->GetOutputHeightPixel ();
+ Size s(w, h);
+ Bitmap b = m_pDrawingArea->GetBitmap (p, s);
+
+ Ocr o(b, w, h);
+ o.ReadBitmap();
+ o.CropBitmap();
+ o.ScaleBitmap(Ocr::SIZE, Ocr::SIZE);
+
+ m_pSearchSet->ClearPreviousData();
+
+ sal_UCS4 *range = new sal_UCS4[2];
+ range[0] = 0;
+ range[1] = 1114111;
+ CmapResult rCR (true, range, 1);
+ FontCharMapRef xFontCharMap(new FontCharMap(rCR));
+ m_pSearchSet->GetFontCharMap(xFontCharMap);
+
+ sal_UCS4 sChar = xFontCharMap->GetFirstChar();
+ while(sChar != xFontCharMap->GetLastChar())
+ {
+ VirtualDevice od;
+ OUString aOUStr( &sChar, 1 );
+ Size s50(Ocr::SIZE, Ocr::SIZE);
+ od.SetOutputSize( s50 );
+ vcl::Font aFontBis(aFont);
+ aFontBis.SetFontHeight(Ocr::SIZE);
+ od.SetFont( aFontBis );
+ od.DrawText( p, aOUStr );
+ w = od.GetOutputWidthPixel ();
+ h = od.GetOutputHeightPixel ();
+
+ Size s2(w, h);
+ b = od.GetBitmap (p, s2);
+
+ BitmapReadAccess* r = b.AcquireReadAccess ();
+
+ for (long j = 0; j < h; j++)
+ {
+ for (long i = 0; i < w; i++)
+ {
+ Color c = r->GetPixel(j, i);
+ if (c.GetRed () == 0 && c.GetGreen () == 0 && c.GetBlue () == 0)
+ {
+ std::cout << "1";
+ }
+ else if (c.GetRed () == 255 && c.GetGreen () == 255 && c.GetBlue () == 255)
+ {
+ std::cout << "0";
+ }
+ else
+ {
+ std::cout << "2";
+ }
+ }
+ std::cout << std::endl;
+ }
+
+ sChar = xFontCharMap->GetNextChar(sChar);
+ m_pSearchSet->AppendCharToList(sChar);
+ }
+ m_pSearchSet->AppendCharToList(sChar);
+ m_pSearchSet->Resize();
+
}
}
diff --git a/cui/source/inc/cuicharmap.hxx b/cui/source/inc/cuicharmap.hxx
index 752678e1ad91..10b7dc956c03 100644
--- a/cui/source/inc/cuicharmap.hxx
+++ b/cui/source/inc/cuicharmap.hxx
@@ -39,6 +39,24 @@ namespace svx
struct SvxShowCharSetItem;
}
+class Ocr
+{
+public:
+ Ocr(const Bitmap& b, long w, long h);
+
+ void ReadBitmap();
+ void CropBitmap();
+ void ScaleBitmap(long width, long height);
+
+ static const int SIZE = 25;
+
+private:
+ Bitmap m_bitmap;
+ long w_;
+ long h_;
+ std::vector<char> data;
+};
+
class DrawingAreaOcr : public Control
{
public:
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