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mftraining.cpp
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1 /******************************************************************************
2 ** Filename: mftraining.c
3 ** Purpose: Separates training pages into files for each character.
4 ** Strips from files only the features and there parameters of
5 the feature type mf.
6 ** Author: Dan Johnson
7 ** Revisment: Christy Russon
8 ** Environment: HPUX 6.5
9 ** Library: HPUX 6.5
10 ** History: Fri Aug 18 08:53:50 1989, DSJ, Created.
11 ** 5/25/90, DSJ, Adapted to multiple feature types.
12 ** Tuesday, May 17, 1998 Changes made to make feature specific and
13 ** simplify structures. First step in simplifying training process.
14 **
15 ** (c) Copyright Hewlett-Packard Company, 1988.
16 ** Licensed under the Apache License, Version 2.0 (the "License");
17 ** you may not use this file except in compliance with the License.
18 ** You may obtain a copy of the License at
19 ** http://www.apache.org/licenses/LICENSE-2.0
20 ** Unless required by applicable law or agreed to in writing, software
21 ** distributed under the License is distributed on an "AS IS" BASIS,
22 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
23 ** See the License for the specific language governing permissions and
24 ** limitations under the License.
25 ******************************************************************************/
29 #include <string.h>
30 #include <stdio.h>
31 #define _USE_MATH_DEFINES
32 #include <math.h>
33 #ifdef _WIN32
34 #ifndef M_PI
35 #define M_PI 3.14159265358979323846
36 #endif
37 #endif
38
39 // Include automatically generated configuration file if running autoconf.
40 #ifdef HAVE_CONFIG_H
41 #include "config_auto.h"
42 #endif
43
44 #include "classify.h"
45 #include "cluster.h"
46 #include "clusttool.h"
47 #include "commontraining.h"
48 #include "danerror.h"
49 #include "efio.h"
50 #include "emalloc.h"
51 #include "featdefs.h"
52 #include "fontinfo.h"
53 #include "genericvector.h"
54 #include "indexmapbidi.h"
55 #include "intproto.h"
56 #include "mastertrainer.h"
57 #include "mergenf.h"
58 #include "mf.h"
59 #include "ndminx.h"
60 #include "ocrfeatures.h"
61 #include "oldlist.h"
62 #include "protos.h"
63 #include "shapetable.h"
64 #include "tessopt.h"
65 #include "tprintf.h"
66 #include "unicity_table.h"
67
68 using tesseract::Classify;
69 using tesseract::FontInfo;
70 using tesseract::FontSpacingInfo;
71 using tesseract::IndexMapBiDi;
72 using tesseract::MasterTrainer;
73 using tesseract::Shape;
74 using tesseract::ShapeTable;
75
76 #define PROGRAM_FEATURE_TYPE "mf"
77
78 // Max length of a fake shape label.
79 const int kMaxShapeLabelLength = 10;
80
81 DECLARE_STRING_PARAM_FLAG(test_ch);
82
86 int main (
87 int argc,
88 char **argv);
89
90
91 /*----------------------------------------------------------------------------
92 Public Code
93 -----------------------------------------------------------------------------*/
94 #ifndef GRAPHICS_DISABLED
95 static void DisplayProtoList(const char* ch, LIST protolist) {
96 void* window = c_create_window("Char samples", 50, 200,
97 520, 520, -130.0, 130.0, -130.0, 130.0);
98 LIST proto = protolist;
99 iterate(proto) {
100 PROTOTYPE* prototype = reinterpret_cast< PROTOTYPE *>(first_node(proto));
101 if (prototype->Significant)
102 c_line_color_index(window, Green);
103 else if (prototype->NumSamples == 0)
104 c_line_color_index(window, Blue);
105 else if (prototype->Merged)
106 c_line_color_index(window, Magenta);
107 else
108 c_line_color_index(window, Red);
109 float x = CenterX(prototype->Mean);
110 float y = CenterY(prototype->Mean);
111 double angle = OrientationOf(prototype->Mean) * 2 * M_PI;
112 float dx = static_cast<float>(LengthOf(prototype->Mean) * cos(angle) / 2);
113 float dy = static_cast<float>(LengthOf(prototype->Mean) * sin(angle) / 2);
114 c_move(window, (x - dx) * 256, (y - dy) * 256);
115 c_draw(window, (x + dx) * 256, (y + dy) * 256);
116 if (prototype->Significant)
117 tprintf("Green proto at (%g,%g)+(%g,%g) %d samples\n",
118 x, y, dx, dy, prototype->NumSamples);
119 else if (prototype->NumSamples > 0 && !prototype->Merged)
120 tprintf("Red proto at (%g,%g)+(%g,%g) %d samples\n",
121 x, y, dx, dy, prototype->NumSamples);
122 }
123 c_make_current(window);
124 }
125 #endif // GRAPHICS_DISABLED
126
127 // Helper to run clustering on a single config.
128 // Mostly copied from the old mftraining, but with renamed variables.
129 static LIST ClusterOneConfig(int shape_id, const char* class_label,
130 LIST mf_classes,
131 const ShapeTable& shape_table,
132 MasterTrainer* trainer) {
133 int num_samples;
134 CLUSTERER *clusterer = trainer->SetupForClustering(shape_table,
135 feature_defs,
136 shape_id,
137 &num_samples);
138 Config.MagicSamples = num_samples;
139 LIST proto_list = ClusterSamples(clusterer, &Config);
140 CleanUpUnusedData(proto_list);
141
142 // Merge protos where reasonable to make more of them significant by
143 // representing almost all samples of the class/font.
144 MergeInsignificantProtos(proto_list, class_label, clusterer, &Config);
145 #ifndef GRAPHICS_DISABLED
146 if (strcmp(FLAGS_test_ch.c_str(), class_label) == 0)
147 DisplayProtoList(FLAGS_test_ch.c_str(), proto_list);
148 #endif // GRAPHICS_DISABLED
149 // Delete the protos that will not be used in the inttemp output file.
150 proto_list = RemoveInsignificantProtos(proto_list, true,
151 false,
152 clusterer->SampleSize);
153 FreeClusterer(clusterer);
154 MERGE_CLASS merge_class = FindClass(mf_classes, class_label);
155 if (merge_class == NULL) {
156 merge_class = NewLabeledClass(class_label);
157 mf_classes = push(mf_classes, merge_class);
158 }
159 int config_id = AddConfigToClass(merge_class->Class);
160 merge_class->Class->font_set.push_back(shape_id);
161 LIST proto_it = proto_list;
162 iterate(proto_it) {
163 PROTOTYPE* prototype = reinterpret_cast< PROTOTYPE*>(first_node(proto_it));
164 // See if proto can be approximated by existing proto.
165 int p_id = FindClosestExistingProto(merge_class->Class,
166 merge_class->NumMerged, prototype);
167 if (p_id == NO_PROTO) {
168 // Need to make a new proto, as it doesn't match anything.
169 p_id = AddProtoToClass(merge_class->Class);
170 MakeNewFromOld(ProtoIn(merge_class->Class, p_id), prototype);
171 merge_class->NumMerged[p_id] = 1;
172 } else {
173 PROTO_STRUCT dummy_proto;
174 MakeNewFromOld(&dummy_proto, prototype);
175 // Merge with the similar proto.
176 ComputeMergedProto(ProtoIn(merge_class->Class, p_id), &dummy_proto,
177 static_cast<FLOAT32>(merge_class->NumMerged[p_id]),
178 1.0,
179 ProtoIn(merge_class->Class, p_id));
180 merge_class->NumMerged[p_id]++;
181 }
182 AddProtoToConfig(p_id, merge_class->Class->Configurations[config_id]);
183 }
184 FreeProtoList(&proto_list);
185 return mf_classes;
186 }
187
188 // Helper to setup the config map.
189 // Setup an index mapping from the shapes in the shape table to the classes
190 // that will be trained. In keeping with the original design, each shape
191 // with the same list of unichars becomes a different class and the configs
192 // represent the different combinations of fonts.
193 static void SetupConfigMap(ShapeTable* shape_table, IndexMapBiDi* config_map) {
194 int num_configs = shape_table->NumShapes();
195 config_map->Init(num_configs, true);
196 config_map->Setup();
197 for (int c1 = 0; c1 < num_configs; ++c1) {
198 // Only process ids that are not already merged.
199 if (config_map->SparseToCompact(c1) == c1) {
200 Shape* shape1 = shape_table->MutableShape(c1);
201 // Find all the subsequent shapes that are equal.
202 for (int c2 = c1 + 1; c2 < num_configs; ++c2) {
203 if (shape_table->MutableShape(c2)->IsEqualUnichars(shape1)) {
204 config_map->Merge(c1, c2);
205 }
206 }
207 }
208 }
209 config_map->CompleteMerges();
210 }
211
212 /*---------------------------------------------------------------------------*/
213 int main (int argc, char **argv) {
214 /*
215 ** Parameters:
216 ** argc number of command line arguments
217 ** argv array of command line arguments
218 ** Globals: none
219 ** Operation:
220 ** This program reads in a text file consisting of feature
221 ** samples from a training page in the following format:
222 **
223 ** FontName UTF8-char-str xmin ymin xmax ymax page-number
224 ** NumberOfFeatureTypes(N)
225 ** FeatureTypeName1 NumberOfFeatures(M)
226 ** Feature1
227 ** ...
228 ** FeatureM
229 ** FeatureTypeName2 NumberOfFeatures(M)
230 ** Feature1
231 ** ...
232 ** FeatureM
233 ** ...
234 ** FeatureTypeNameN NumberOfFeatures(M)
235 ** Feature1
236 ** ...
237 ** FeatureM
238 ** FontName CharName ...
239 **
240 ** The result of this program is a binary inttemp file used by
241 ** the OCR engine.
242 ** Return: none
243 ** Exceptions: none
244 ** History: Fri Aug 18 08:56:17 1989, DSJ, Created.
245 ** Mon May 18 1998, Christy Russson, Revistion started.
246 */
247 ParseArguments(&argc, &argv);
248
249 ShapeTable* shape_table = NULL;
250 STRING file_prefix;
251 // Load the training data.
252 MasterTrainer* trainer = tesseract::LoadTrainingData(argc, argv,
253 false,
254 &shape_table,
255 &file_prefix);
256 if (trainer == NULL)
257 return 1; // Failed.
258
259 // Setup an index mapping from the shapes in the shape table to the classes
260 // that will be trained. In keeping with the original design, each shape
261 // with the same list of unichars becomes a different class and the configs
262 // represent the different combinations of fonts.
263 IndexMapBiDi config_map;
264 SetupConfigMap(shape_table, &config_map);
265
266 WriteShapeTable(file_prefix, *shape_table);
267 // If the shape_table is flat, then either we didn't run shape clustering, or
268 // it did nothing, so we just output the trainer's unicharset.
269 // Otherwise shape_set will hold a fake unicharset with an entry for each
270 // shape in the shape table, and we will output that instead.
271 UNICHARSET shape_set;
272 const UNICHARSET* unicharset = &trainer->unicharset();
273 // If we ran shapeclustering (and it worked) then at least one shape will
274 // have multiple unichars, so we have to build a fake unicharset.
275 if (shape_table->AnyMultipleUnichars()) {
276 unicharset = &shape_set;
277 // Now build a fake unicharset for the compact shape space to keep the
278 // output modules happy that we are doing things correctly.
279 int num_shapes = config_map.CompactSize();
280 for (int s = 0; s < num_shapes; ++s) {
281 char shape_label[kMaxShapeLabelLength + 1];
282 snprintf(shape_label, kMaxShapeLabelLength, "sh%04d", s);
283 shape_set.unichar_insert(shape_label);
284 }
285 }
286
287 // Now train each config separately.
288 int num_configs = shape_table->NumShapes();
289 LIST mf_classes = NIL_LIST;
290 for (int s = 0; s < num_configs; ++s) {
291 int unichar_id, font_id;
292 if (unicharset == &shape_set) {
293 // Using fake unichar_ids from the config_map/shape_set.
294 unichar_id = config_map.SparseToCompact(s);
295 } else {
296 // Get the real unichar_id from the shape table/unicharset.
297 shape_table->GetFirstUnicharAndFont(s, &unichar_id, &font_id);
298 }
299 const char* class_label = unicharset->id_to_unichar(unichar_id);
300 mf_classes = ClusterOneConfig(s, class_label, mf_classes, *shape_table,
301 trainer);
302 }
303 STRING inttemp_file = file_prefix;
304 inttemp_file += "inttemp";
305 STRING pffmtable_file = file_prefix;
306 pffmtable_file += "pffmtable";
307 CLASS_STRUCT* float_classes = SetUpForFloat2Int(*unicharset, mf_classes);
308 // Now write the inttemp and pffmtable.
309 trainer->WriteInttempAndPFFMTable(trainer->unicharset(), *unicharset,
310 *shape_table, float_classes,
311 inttemp_file.string(),
312 pffmtable_file.string());
313 delete [] float_classes;
314 FreeLabeledClassList(mf_classes);
315 delete trainer;
316 delete shape_table;
317 printf("Done!\n");
318 if (!FLAGS_test_ch.empty()) {
319 // If we are displaying debug window(s), wait for the user to look at them.
320 printf("Hit return to exit...\n");
321 while (getchar() != '\n');
322 }
323 return 0;
324 } /* main */