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trainingsample.cpp
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1 // Copyright 2010 Google Inc. All Rights Reserved.
2 // Author: rays@google.com (Ray Smith)
3 //
4 // Licensed under the Apache License, Version 2.0 (the "License");
5 // you may not use this file except in compliance with the License.
6 // You may obtain a copy of the License at
7 // http://www.apache.org/licenses/LICENSE-2.0
8 // Unless required by applicable law or agreed to in writing, software
9 // distributed under the License is distributed on an "AS IS" BASIS,
10 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 // See the License for the specific language governing permissions and
12 // limitations under the License.
13 //
15
16 // Include automatically generated configuration file if running autoconf.
17 #ifdef HAVE_CONFIG_H
18 #include "config_auto.h"
19 #endif
20
21 #include "trainingsample.h"
22
23 #include <math.h>
24 #include "allheaders.h"
25 #include "helpers.h"
26 #include "intfeaturemap.h"
27 #include "normfeat.h"
28 #include "shapetable.h"
29
30 namespace tesseract {
31
32 ELISTIZE(TrainingSample)
33
34 // Center of randomizing operations.
35 const int kRandomizingCenter = 128;
36
37 // Randomizing factors.
38 const int TrainingSample::kYShiftValues[kSampleYShiftSize] = {
39 6, 3, -3, -6, 0
40 };
41 const double TrainingSample::kScaleValues[kSampleScaleSize] = {
42 1.0625, 0.9375, 1.0
43 };
44
45 TrainingSample::~TrainingSample() {
46 delete [] features_;
47 delete [] micro_features_;
48 }
49
50 // WARNING! Serialize/DeSerialize do not save/restore the "cache" data
51 // members, which is mostly the mapped features, and the weight.
52 // It is assumed these can all be reconstructed from what is saved.
53 // Writes to the given file. Returns false in case of error.
54 bool TrainingSample::Serialize(FILE* fp) const {
55 if (fwrite(&class_id_, sizeof(class_id_), 1, fp) != 1) return false;
56 if (fwrite(&font_id_, sizeof(font_id_), 1, fp) != 1) return false;
57 if (fwrite(&page_num_, sizeof(page_num_), 1, fp) != 1) return false;
58 if (!bounding_box_.Serialize(fp)) return false;
59 if (fwrite(&num_features_, sizeof(num_features_), 1, fp) != 1) return false;
60 if (fwrite(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1)
61 return false;
62 if (fwrite(features_, sizeof(*features_), num_features_, fp) != num_features_)
63 return false;
64 if (fwrite(micro_features_, sizeof(*micro_features_), num_micro_features_,
65 fp) != num_micro_features_)
66 return false;
67 if (fwrite(cn_feature_, sizeof(*cn_feature_), kNumCNParams, fp) !=
68 kNumCNParams) return false;
69 if (fwrite(geo_feature_, sizeof(*geo_feature_), GeoCount, fp) != GeoCount)
70 return false;
71 return true;
72 }
73
74 // Creates from the given file. Returns NULL in case of error.
75 // If swap is true, assumes a big/little-endian swap is needed.
76 TrainingSample* TrainingSample::DeSerializeCreate(bool swap, FILE* fp) {
77 TrainingSample* sample = new TrainingSample;
78 if (sample->DeSerialize(swap, fp)) return sample;
79 delete sample;
80 return NULL;
81 }
82
83 // Reads from the given file. Returns false in case of error.
84 // If swap is true, assumes a big/little-endian swap is needed.
85 bool TrainingSample::DeSerialize(bool swap, FILE* fp) {
86 if (fread(&class_id_, sizeof(class_id_), 1, fp) != 1) return false;
87 if (fread(&font_id_, sizeof(font_id_), 1, fp) != 1) return false;
88 if (fread(&page_num_, sizeof(page_num_), 1, fp) != 1) return false;
89 if (!bounding_box_.DeSerialize(swap, fp)) return false;
90 if (fread(&num_features_, sizeof(num_features_), 1, fp) != 1) return false;
91 if (fread(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1)
92 return false;
93 if (swap) {
94 ReverseN(&class_id_, sizeof(class_id_));
95 ReverseN(&num_features_, sizeof(num_features_));
96 ReverseN(&num_micro_features_, sizeof(num_micro_features_));
97 }
98 delete [] features_;
99 features_ = new INT_FEATURE_STRUCT[num_features_];
100 if (fread(features_, sizeof(*features_), num_features_, fp) != num_features_)
101 return false;
102 delete [] micro_features_;
103 micro_features_ = new MicroFeature[num_micro_features_];
104 if (fread(micro_features_, sizeof(*micro_features_), num_micro_features_,
105 fp) != num_micro_features_)
106 return false;
107 if (fread(cn_feature_, sizeof(*cn_feature_), kNumCNParams, fp) !=
108 kNumCNParams) return false;
109 if (fread(geo_feature_, sizeof(*geo_feature_), GeoCount, fp) != GeoCount)
110 return false;
111 return true;
112 }
113
114 // Saves the given features into a TrainingSample.
115 TrainingSample* TrainingSample::CopyFromFeatures(
116 const INT_FX_RESULT_STRUCT& fx_info, const INT_FEATURE_STRUCT* features,
117 int num_features) {
118 TrainingSample* sample = new TrainingSample;
119 sample->num_features_ = num_features;
120 sample->features_ = new INT_FEATURE_STRUCT[num_features];
121 memcpy(sample->features_, features, num_features * sizeof(features[0]));
122 sample->geo_feature_[GeoBottom] = fx_info.YBottom;
123 sample->geo_feature_[GeoTop] = fx_info.YTop;
124 sample->geo_feature_[GeoWidth] = fx_info.Width;
125 sample->features_are_indexed_ = false;
126 sample->features_are_mapped_ = false;
127 return sample;
128 }
129
130 // Constructs and returns a copy randomized by the method given by
131 // the randomizer index. If index is out of [0, kSampleRandomSize) then
132 // an exact copy is returned.
133 TrainingSample* TrainingSample::RandomizedCopy(int index) const {
134 TrainingSample* sample = Copy();
135 if (index >= 0 && index < kSampleRandomSize) {
136 ++index; // Remove the first combination.
137 int yshift = kYShiftValues[index / kSampleScaleSize];
138 double scaling = kScaleValues[index % kSampleScaleSize];
139 for (int i = 0; i < num_features_; ++i) {
140 double result = (features_[i].X - kRandomizingCenter) * scaling;
141 result += kRandomizingCenter;
142 sample->features_[i].X = ClipToRange(static_cast<int>(result + 0.5), 0,
143 MAX_UINT8);
144 result = (features_[i].Y - kRandomizingCenter) * scaling;
145 result += kRandomizingCenter + yshift;
146 sample->features_[i].Y = ClipToRange(static_cast<int>(result + 0.5), 0,
147 MAX_UINT8);
148 }
149 }
150 return sample;
151 }
152
153 // Constructs and returns an exact copy.
154 TrainingSample* TrainingSample::Copy() const {
155 TrainingSample* sample = new TrainingSample;
156 sample->class_id_ = class_id_;
157 sample->font_id_ = font_id_;
158 sample->weight_ = weight_;
159 sample->sample_index_ = sample_index_;
160 sample->num_features_ = num_features_;
161 if (num_features_ > 0) {
162 sample->features_ = new INT_FEATURE_STRUCT[num_features_];
163 memcpy(sample->features_, features_, num_features_ * sizeof(features_[0]));
164 }
165 sample->num_micro_features_ = num_micro_features_;
166 if (num_micro_features_ > 0) {
167 sample->micro_features_ = new MicroFeature[num_micro_features_];
168 memcpy(sample->micro_features_, micro_features_,
169 num_micro_features_ * sizeof(micro_features_[0]));
170 }
171 memcpy(sample->cn_feature_, cn_feature_, sizeof(*cn_feature_) * kNumCNParams);
172 memcpy(sample->geo_feature_, geo_feature_, sizeof(*geo_feature_) * GeoCount);
173 return sample;
174 }
175
176 // Extracts the needed information from the CHAR_DESC_STRUCT.
177 void TrainingSample::ExtractCharDesc(int int_feature_type,
178 int micro_type,
179 int cn_type,
180 int geo_type,
181 CHAR_DESC_STRUCT* char_desc) {
182 // Extract the INT features.
183 if (features_ != NULL) delete [] features_;
184 FEATURE_SET_STRUCT* char_features = char_desc->FeatureSets[int_feature_type];
185 if (char_features == NULL) {
186 tprintf("Error: no features to train on of type %s\n",
187 kIntFeatureType);
188 num_features_ = 0;
189 features_ = NULL;
190 } else {
191 num_features_ = char_features->NumFeatures;
192 features_ = new INT_FEATURE_STRUCT[num_features_];
193 for (int f = 0; f < num_features_; ++f) {
194 features_[f].X =
195 static_cast< uinT8 >(char_features->Features[f]->Params[IntX]);
196 features_[f].Y =
197 static_cast< uinT8 >(char_features->Features[f]->Params[IntY]);
198 features_[f].Theta =
199 static_cast< uinT8 >(char_features->Features[f]->Params[IntDir]);
200 features_[f].CP_misses = 0;
201 }
202 }
203 // Extract the Micro features.
204 if (micro_features_ != NULL) delete [] micro_features_;
205 char_features = char_desc->FeatureSets[micro_type];
206 if (char_features == NULL) {
207 tprintf("Error: no features to train on of type %s\n",
208 kMicroFeatureType);
209 num_micro_features_ = 0;
210 micro_features_ = NULL;
211 } else {
212 num_micro_features_ = char_features->NumFeatures;
213 micro_features_ = new MicroFeature[num_micro_features_];
214 for (int f = 0; f < num_micro_features_; ++f) {
215 for (int d = 0; d < MFCount; ++d) {
216 micro_features_[f][d] = char_features->Features[f]->Params[d];
217 }
218 }
219 }
220 // Extract the CN feature.
221 char_features = char_desc->FeatureSets[cn_type];
222 if (char_features == NULL) {
223 tprintf("Error: no CN feature to train on.\n");
224 } else {
225 ASSERT_HOST(char_features->NumFeatures == 1);
226 cn_feature_[CharNormY] = char_features->Features[0]->Params[CharNormY];
227 cn_feature_[CharNormLength] =
228 char_features->Features[0]->Params[CharNormLength];
229 cn_feature_[CharNormRx] = char_features->Features[0]->Params[CharNormRx];
230 cn_feature_[CharNormRy] = char_features->Features[0]->Params[CharNormRy];
231 }
232 // Extract the Geo feature.
233 char_features = char_desc->FeatureSets[geo_type];
234 if (char_features == NULL) {
235 tprintf("Error: no Geo feature to train on.\n");
236 } else {
237 ASSERT_HOST(char_features->NumFeatures == 1);
238 geo_feature_[GeoBottom] = char_features->Features[0]->Params[GeoBottom];
239 geo_feature_[GeoTop] = char_features->Features[0]->Params[GeoTop];
240 geo_feature_[GeoWidth] = char_features->Features[0]->Params[GeoWidth];
241 }
242 features_are_indexed_ = false;
243 features_are_mapped_ = false;
244 }
245
246 // Sets the mapped_features_ from the features_ using the provided
247 // feature_space to the indexed versions of the features.
248 void TrainingSample::IndexFeatures(const IntFeatureSpace& feature_space) {
249 GenericVector<int> indexed_features;
250 feature_space.IndexAndSortFeatures(features_, num_features_,
251 &mapped_features_);
252 features_are_indexed_ = true;
253 features_are_mapped_ = false;
254 }
255
256 // Sets the mapped_features_ from the features using the provided
257 // feature_map.
258 void TrainingSample::MapFeatures(const IntFeatureMap& feature_map) {
259 GenericVector<int> indexed_features;
260 feature_map.feature_space().IndexAndSortFeatures(features_, num_features_,
261 &indexed_features);
262 feature_map.MapIndexedFeatures(indexed_features, &mapped_features_);
263 features_are_indexed_ = false;
264 features_are_mapped_ = true;
265 }
266
267 // Returns a pix representing the sample. (Int features only.)
268 Pix* TrainingSample::RenderToPix(const UNICHARSET* unicharset) const {
269 Pix* pix = pixCreate(kIntFeatureExtent, kIntFeatureExtent, 1);
270 for (int f = 0; f < num_features_; ++f) {
271 int start_x = features_[f].X;
272 int start_y = kIntFeatureExtent - features_[f].Y;
273 double dx = cos((features_[f].Theta / 256.0) * 2.0 * PI - PI);
274 double dy = -sin((features_[f].Theta / 256.0) * 2.0 * PI - PI);
275 for (int i = 0; i <= 5; ++i) {
276 int x = static_cast<int>(start_x + dx * i);
277 int y = static_cast<int>(start_y + dy * i);
278 if (x >= 0 && x < 256 && y >= 0 && y < 256)
279 pixSetPixel(pix, x, y, 1);
280 }
281 }
282 if (unicharset != NULL)
283 pixSetText(pix, unicharset->id_to_unichar(class_id_));
284 return pix;
285 }
286
287 // Displays the features in the given window with the given color.
288 void TrainingSample::DisplayFeatures(ScrollView::Color color,
289 ScrollView* window) const {
290 #ifndef GRAPHICS_DISABLED
291 for (int f = 0; f < num_features_; ++f) {
292 RenderIntFeature(window, &features_[f], color);
293 }
294 #endif // GRAPHICS_DISABLED
295 }
296
297 // Returns a pix of the original sample image. The pix is padded all round
298 // by padding wherever possible.
299 // The returned Pix must be pixDestroyed after use.
300 // If the input page_pix is NULL, NULL is returned.
301 Pix* TrainingSample::GetSamplePix(int padding, Pix* page_pix) const {
302 if (page_pix == NULL)
303 return NULL;
304 int page_width = pixGetWidth(page_pix);
305 int page_height = pixGetHeight(page_pix);
306 TBOX padded_box = bounding_box();
307 padded_box.pad(padding, padding);
308 // Clip the padded_box to the limits of the page
309 TBOX page_box(0, 0, page_width, page_height);
310 padded_box &= page_box;
311 Box* box = boxCreate(page_box.left(), page_height - page_box.top(),
312 page_box.width(), page_box.height());
313 Pix* sample_pix = pixClipRectangle(page_pix, box, NULL);
314 boxDestroy(&box);
315 return sample_pix;
316 }
317
318 } // namespace tesseract