mirror of
https://github.com/tesseract-ocr/tesseract.git
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319 lines
12 KiB
C++
319 lines
12 KiB
C++
/******************************************************************************
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** Filename: mftraining.c
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** Purpose: Separates training pages into files for each character.
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** Strips from files only the features and there parameters of
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the feature type mf.
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** Author: Dan Johnson
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** Revisment: Christy Russon
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** Environment: HPUX 6.5
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** Library: HPUX 6.5
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** History: Fri Aug 18 08:53:50 1989, DSJ, Created.
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** 5/25/90, DSJ, Adapted to multiple feature types.
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** Tuesday, May 17, 1998 Changes made to make feature specific and
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** simplify structures. First step in simplifying training process.
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**
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** (c) Copyright Hewlett-Packard Company, 1988.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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******************************************************************************/
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/*----------------------------------------------------------------------------
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Include Files and Type Defines
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----------------------------------------------------------------------------*/
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#ifdef HAVE_CONFIG_H
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#include "config_auto.h"
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#endif
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#include <string.h>
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#include <stdio.h>
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#define _USE_MATH_DEFINES
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#include <math.h>
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#ifdef _WIN32
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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#endif
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#include "classify.h"
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#include "cluster.h"
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#include "clusttool.h"
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#include "commontraining.h"
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#include "danerror.h"
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#include "efio.h"
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#include "emalloc.h"
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#include "featdefs.h"
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#include "fontinfo.h"
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#include "genericvector.h"
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#include "indexmapbidi.h"
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#include "intproto.h"
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#include "mastertrainer.h"
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#include "mergenf.h"
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#include "mf.h"
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#include "ndminx.h"
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#include "ocrfeatures.h"
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#include "oldlist.h"
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#include "protos.h"
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#include "shapetable.h"
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#include "tessopt.h"
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#include "tprintf.h"
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#include "unicity_table.h"
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using tesseract::IndexMapBiDi;
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using tesseract::MasterTrainer;
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using tesseract::Shape;
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using tesseract::ShapeTable;
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#define PROGRAM_FEATURE_TYPE "mf"
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// Max length of a fake shape label.
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const int kMaxShapeLabelLength = 10;
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DECLARE_STRING_PARAM_FLAG(test_ch);
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/*----------------------------------------------------------------------------
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Public Function Prototypes
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----------------------------------------------------------------------------*/
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int main (
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int argc,
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char **argv);
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/*----------------------------------------------------------------------------
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Public Code
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-----------------------------------------------------------------------------*/
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#ifndef GRAPHICS_DISABLED
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static void DisplayProtoList(const char* ch, LIST protolist) {
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void* window = c_create_window("Char samples", 50, 200,
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520, 520, -130.0, 130.0, -130.0, 130.0);
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LIST proto = protolist;
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iterate(proto) {
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PROTOTYPE* prototype = reinterpret_cast<PROTOTYPE *>(first_node(proto));
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if (prototype->Significant)
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c_line_color_index(window, Green);
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else if (prototype->NumSamples == 0)
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c_line_color_index(window, Blue);
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else if (prototype->Merged)
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c_line_color_index(window, Magenta);
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else
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c_line_color_index(window, Red);
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float x = CenterX(prototype->Mean);
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float y = CenterY(prototype->Mean);
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double angle = OrientationOf(prototype->Mean) * 2 * M_PI;
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float dx = static_cast<float>(LengthOf(prototype->Mean) * cos(angle) / 2);
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float dy = static_cast<float>(LengthOf(prototype->Mean) * sin(angle) / 2);
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c_move(window, (x - dx) * 256, (y - dy) * 256);
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c_draw(window, (x + dx) * 256, (y + dy) * 256);
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if (prototype->Significant)
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tprintf("Green proto at (%g,%g)+(%g,%g) %d samples\n",
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x, y, dx, dy, prototype->NumSamples);
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else if (prototype->NumSamples > 0 && !prototype->Merged)
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tprintf("Red proto at (%g,%g)+(%g,%g) %d samples\n",
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x, y, dx, dy, prototype->NumSamples);
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}
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c_make_current(window);
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}
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#endif // GRAPHICS_DISABLED
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// Helper to run clustering on a single config.
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// Mostly copied from the old mftraining, but with renamed variables.
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static LIST ClusterOneConfig(int shape_id, const char* class_label,
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LIST mf_classes,
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const ShapeTable& shape_table,
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MasterTrainer* trainer) {
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int num_samples;
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CLUSTERER *clusterer = trainer->SetupForClustering(shape_table,
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feature_defs,
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shape_id,
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&num_samples);
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Config.MagicSamples = num_samples;
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LIST proto_list = ClusterSamples(clusterer, &Config);
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CleanUpUnusedData(proto_list);
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// Merge protos where reasonable to make more of them significant by
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// representing almost all samples of the class/font.
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MergeInsignificantProtos(proto_list, class_label, clusterer, &Config);
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#ifndef GRAPHICS_DISABLED
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if (strcmp(FLAGS_test_ch.c_str(), class_label) == 0)
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DisplayProtoList(FLAGS_test_ch.c_str(), proto_list);
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#endif // GRAPHICS_DISABLED
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// Delete the protos that will not be used in the inttemp output file.
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proto_list = RemoveInsignificantProtos(proto_list, true,
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false,
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clusterer->SampleSize);
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FreeClusterer(clusterer);
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MERGE_CLASS merge_class = FindClass(mf_classes, class_label);
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if (merge_class == nullptr) {
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merge_class = NewLabeledClass(class_label);
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mf_classes = push(mf_classes, merge_class);
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}
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int config_id = AddConfigToClass(merge_class->Class);
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merge_class->Class->font_set.push_back(shape_id);
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LIST proto_it = proto_list;
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iterate(proto_it) {
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PROTOTYPE* prototype = reinterpret_cast<PROTOTYPE*>(first_node(proto_it));
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// See if proto can be approximated by existing proto.
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int p_id = FindClosestExistingProto(merge_class->Class,
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merge_class->NumMerged, prototype);
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if (p_id == NO_PROTO) {
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// Need to make a new proto, as it doesn't match anything.
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p_id = AddProtoToClass(merge_class->Class);
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MakeNewFromOld(ProtoIn(merge_class->Class, p_id), prototype);
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merge_class->NumMerged[p_id] = 1;
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} else {
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PROTO_STRUCT dummy_proto;
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MakeNewFromOld(&dummy_proto, prototype);
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// Merge with the similar proto.
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ComputeMergedProto(ProtoIn(merge_class->Class, p_id), &dummy_proto,
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static_cast<FLOAT32>(merge_class->NumMerged[p_id]),
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1.0,
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ProtoIn(merge_class->Class, p_id));
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merge_class->NumMerged[p_id]++;
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}
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AddProtoToConfig(p_id, merge_class->Class->Configurations[config_id]);
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}
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FreeProtoList(&proto_list);
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return mf_classes;
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}
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// Helper to setup the config map.
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// Setup an index mapping from the shapes in the shape table to the classes
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// that will be trained. In keeping with the original design, each shape
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// with the same list of unichars becomes a different class and the configs
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// represent the different combinations of fonts.
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static void SetupConfigMap(ShapeTable* shape_table, IndexMapBiDi* config_map) {
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int num_configs = shape_table->NumShapes();
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config_map->Init(num_configs, true);
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config_map->Setup();
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for (int c1 = 0; c1 < num_configs; ++c1) {
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// Only process ids that are not already merged.
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if (config_map->SparseToCompact(c1) == c1) {
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Shape* shape1 = shape_table->MutableShape(c1);
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// Find all the subsequent shapes that are equal.
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for (int c2 = c1 + 1; c2 < num_configs; ++c2) {
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if (shape_table->MutableShape(c2)->IsEqualUnichars(shape1)) {
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config_map->Merge(c1, c2);
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}
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}
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}
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}
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config_map->CompleteMerges();
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}
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/**
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* This program reads in a text file consisting of feature
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* samples from a training page in the following format:
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* @verbatim
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FontName UTF8-char-str xmin ymin xmax ymax page-number
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NumberOfFeatureTypes(N)
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FeatureTypeName1 NumberOfFeatures(M)
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Feature1
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...
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FeatureM
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FeatureTypeName2 NumberOfFeatures(M)
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Feature1
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...
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FeatureM
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...
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FeatureTypeNameN NumberOfFeatures(M)
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Feature1
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...
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FeatureM
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FontName CharName ...
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@endverbatim
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* The result of this program is a binary inttemp file used by
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* the OCR engine.
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* @param argc number of command line arguments
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* @param argv array of command line arguments
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* @return none
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* @note Exceptions: none
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* @note History: Fri Aug 18 08:56:17 1989, DSJ, Created.
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* @note History: Mon May 18 1998, Christy Russson, Revistion started.
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*/
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int main (int argc, char **argv) {
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ParseArguments(&argc, &argv);
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ShapeTable* shape_table = nullptr;
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STRING file_prefix;
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// Load the training data.
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MasterTrainer* trainer = tesseract::LoadTrainingData(argc, argv,
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false,
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&shape_table,
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&file_prefix);
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if (trainer == nullptr) return 1; // Failed.
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// Setup an index mapping from the shapes in the shape table to the classes
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// that will be trained. In keeping with the original design, each shape
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// with the same list of unichars becomes a different class and the configs
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// represent the different combinations of fonts.
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IndexMapBiDi config_map;
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SetupConfigMap(shape_table, &config_map);
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WriteShapeTable(file_prefix, *shape_table);
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// If the shape_table is flat, then either we didn't run shape clustering, or
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// it did nothing, so we just output the trainer's unicharset.
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// Otherwise shape_set will hold a fake unicharset with an entry for each
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// shape in the shape table, and we will output that instead.
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UNICHARSET shape_set;
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const UNICHARSET* unicharset = &trainer->unicharset();
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// If we ran shapeclustering (and it worked) then at least one shape will
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// have multiple unichars, so we have to build a fake unicharset.
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if (shape_table->AnyMultipleUnichars()) {
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unicharset = &shape_set;
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// Now build a fake unicharset for the compact shape space to keep the
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// output modules happy that we are doing things correctly.
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int num_shapes = config_map.CompactSize();
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for (int s = 0; s < num_shapes; ++s) {
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char shape_label[kMaxShapeLabelLength + 1];
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snprintf(shape_label, kMaxShapeLabelLength, "sh%04d", s);
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shape_set.unichar_insert(shape_label);
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}
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}
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// Now train each config separately.
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int num_configs = shape_table->NumShapes();
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LIST mf_classes = NIL_LIST;
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for (int s = 0; s < num_configs; ++s) {
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int unichar_id, font_id;
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if (unicharset == &shape_set) {
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// Using fake unichar_ids from the config_map/shape_set.
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unichar_id = config_map.SparseToCompact(s);
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} else {
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// Get the real unichar_id from the shape table/unicharset.
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shape_table->GetFirstUnicharAndFont(s, &unichar_id, &font_id);
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}
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const char* class_label = unicharset->id_to_unichar(unichar_id);
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mf_classes = ClusterOneConfig(s, class_label, mf_classes, *shape_table,
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trainer);
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}
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STRING inttemp_file = file_prefix;
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inttemp_file += "inttemp";
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STRING pffmtable_file = file_prefix;
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pffmtable_file += "pffmtable";
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CLASS_STRUCT* float_classes = SetUpForFloat2Int(*unicharset, mf_classes);
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// Now write the inttemp and pffmtable.
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trainer->WriteInttempAndPFFMTable(trainer->unicharset(), *unicharset,
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*shape_table, float_classes,
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inttemp_file.string(),
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pffmtable_file.string());
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for (int c = 0; c < unicharset->size(); ++c) {
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FreeClassFields(&float_classes[c]);
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}
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delete [] float_classes;
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FreeLabeledClassList(mf_classes);
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delete trainer;
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delete shape_table;
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printf("Done!\n");
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if (!FLAGS_test_ch.empty()) {
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// If we are displaying debug window(s), wait for the user to look at them.
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printf("Hit return to exit...\n");
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while (getchar() != '\n');
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}
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return 0;
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} /* main */
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