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tesseract/classify/mastertrainer.cpp
Stefan Weil 1327551b49 classify/mastertraining: Fix memory leaks 
Coverity report:

CID 1164739 (#1 of 1): Resource leak (RESOURCE_LEAK)
18. leaked_storage: Variable frag going out of scope leaks the storage it points to.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2016-10-24 13:50:42 +02:00

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// Copyright 2010 Google Inc. All Rights Reserved.
// Author: rays@google.com (Ray Smith)
///////////////////////////////////////////////////////////////////////
// File: mastertrainer.cpp
// Description: Trainer to build the MasterClassifier.
// Author: Ray Smith
// Created: Wed Nov 03 18:10:01 PDT 2010
//
// (C) Copyright 2010, Google Inc.
// Licensed 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
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
///////////////////////////////////////////////////////////////////////
// Include automatically generated configuration file if running autoconf.
#ifdef HAVE_CONFIG_H
#include "config_auto.h"
#endif
#include "mastertrainer.h"
#include <math.h>
#include <time.h>
#include "allheaders.h"
#include "boxread.h"
#include "classify.h"
#include "efio.h"
#include "errorcounter.h"
#include "featdefs.h"
#include "sampleiterator.h"
#include "shapeclassifier.h"
#include "shapetable.h"
#include "svmnode.h"
#include "scanutils.h"
namespace tesseract {
// Constants controlling clustering. With a low kMinClusteredShapes and a high
// kMaxUnicharsPerCluster, then kFontMergeDistance is the only limiting factor.
// Min number of shapes in the output.
const int kMinClusteredShapes = 1;
// Max number of unichars in any individual cluster.
const int kMaxUnicharsPerCluster = 2000;
// Mean font distance below which to merge fonts and unichars.
const float kFontMergeDistance = 0.025;
MasterTrainer::MasterTrainer(NormalizationMode norm_mode,
bool shape_analysis,
bool replicate_samples,
int debug_level)
: norm_mode_(norm_mode), samples_(fontinfo_table_),
junk_samples_(fontinfo_table_), verify_samples_(fontinfo_table_),
charsetsize_(0),
enable_shape_anaylsis_(shape_analysis),
enable_replication_(replicate_samples),
fragments_(NULL), prev_unichar_id_(-1), debug_level_(debug_level) {
}
MasterTrainer::~MasterTrainer() {
delete [] fragments_;
for (int p = 0; p < page_images_.size(); ++p)
pixDestroy(&page_images_[p]);
}
// WARNING! Serialize/DeSerialize are only partial, providing
// enough data to get the samples back and display them.
// Writes to the given file. Returns false in case of error.
bool MasterTrainer::Serialize(FILE* fp) const {
if (fwrite(&norm_mode_, sizeof(norm_mode_), 1, fp) != 1) return false;
if (!unicharset_.save_to_file(fp)) return false;
if (!feature_space_.Serialize(fp)) return false;
if (!samples_.Serialize(fp)) return false;
if (!junk_samples_.Serialize(fp)) return false;
if (!verify_samples_.Serialize(fp)) return false;
if (!master_shapes_.Serialize(fp)) return false;
if (!flat_shapes_.Serialize(fp)) return false;
if (!fontinfo_table_.Serialize(fp)) return false;
if (!xheights_.Serialize(fp)) return false;
return true;
}
// Reads from the given file. Returns false in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
bool MasterTrainer::DeSerialize(bool swap, FILE* fp) {
if (fread(&norm_mode_, sizeof(norm_mode_), 1, fp) != 1) return false;
if (swap) {
ReverseN(&norm_mode_, sizeof(norm_mode_));
}
if (!unicharset_.load_from_file(fp)) return false;
charsetsize_ = unicharset_.size();
if (!feature_space_.DeSerialize(swap, fp)) return false;
feature_map_.Init(feature_space_);
if (!samples_.DeSerialize(swap, fp)) return false;
if (!junk_samples_.DeSerialize(swap, fp)) return false;
if (!verify_samples_.DeSerialize(swap, fp)) return false;
if (!master_shapes_.DeSerialize(swap, fp)) return false;
if (!flat_shapes_.DeSerialize(swap, fp)) return false;
if (!fontinfo_table_.DeSerialize(swap, fp)) return false;
if (!xheights_.DeSerialize(swap, fp)) return false;
return true;
}
// Load an initial unicharset, or set one up if the file cannot be read.
void MasterTrainer::LoadUnicharset(const char* filename) {
if (!unicharset_.load_from_file(filename)) {
tprintf("Failed to load unicharset from file %s\n"
"Building unicharset for training from scratch...\n",
filename);
unicharset_.clear();
UNICHARSET initialized;
// Add special characters, as they were removed by the clear, but the
// default constructor puts them in.
unicharset_.AppendOtherUnicharset(initialized);
}
charsetsize_ = unicharset_.size();
delete [] fragments_;
fragments_ = new int[charsetsize_];
memset(fragments_, 0, sizeof(*fragments_) * charsetsize_);
samples_.LoadUnicharset(filename);
junk_samples_.LoadUnicharset(filename);
verify_samples_.LoadUnicharset(filename);
}
// Reads the samples and their features from the given .tr format file,
// adding them to the trainer with the font_id from the content of the file.
// See mftraining.cpp for a description of the file format.
// If verification, then these are verification samples, not training.
void MasterTrainer::ReadTrainingSamples(const char* page_name,
const FEATURE_DEFS_STRUCT& feature_defs,
bool verification) {
char buffer[2048];
int int_feature_type = ShortNameToFeatureType(feature_defs, kIntFeatureType);
int micro_feature_type = ShortNameToFeatureType(feature_defs,
kMicroFeatureType);
int cn_feature_type = ShortNameToFeatureType(feature_defs, kCNFeatureType);
int geo_feature_type = ShortNameToFeatureType(feature_defs, kGeoFeatureType);
FILE* fp = Efopen(page_name, "rb");
if (fp == NULL) {
tprintf("Failed to open tr file: %s\n", page_name);
return;
}
tr_filenames_.push_back(STRING(page_name));
while (fgets(buffer, sizeof(buffer), fp) != NULL) {
if (buffer[0] == '\n')
continue;
char* space = strchr(buffer, ' ');
if (space == NULL) {
tprintf("Bad format in tr file, reading fontname, unichar\n");
continue;
}
*space++ = '\0';
int font_id = GetFontInfoId(buffer);
if (font_id < 0) font_id = 0;
int page_number;
STRING unichar;
TBOX bounding_box;
if (!ParseBoxFileStr(space, &page_number, &unichar, &bounding_box)) {
tprintf("Bad format in tr file, reading box coords\n");
continue;
}
CHAR_DESC char_desc = ReadCharDescription(feature_defs, fp);
TrainingSample* sample = new TrainingSample;
sample->set_font_id(font_id);
sample->set_page_num(page_number + page_images_.size());
sample->set_bounding_box(bounding_box);
sample->ExtractCharDesc(int_feature_type, micro_feature_type,
cn_feature_type, geo_feature_type, char_desc);
AddSample(verification, unichar.string(), sample);
FreeCharDescription(char_desc);
}
charsetsize_ = unicharset_.size();
fclose(fp);
}
// Adds the given single sample to the trainer, setting the classid
// appropriately from the given unichar_str.
void MasterTrainer::AddSample(bool verification, const char* unichar,
TrainingSample* sample) {
if (verification) {
verify_samples_.AddSample(unichar, sample);
prev_unichar_id_ = -1;
} else if (unicharset_.contains_unichar(unichar)) {
if (prev_unichar_id_ >= 0)
fragments_[prev_unichar_id_] = -1;
prev_unichar_id_ = samples_.AddSample(unichar, sample);
if (flat_shapes_.FindShape(prev_unichar_id_, sample->font_id()) < 0)
flat_shapes_.AddShape(prev_unichar_id_, sample->font_id());
} else {
int junk_id = junk_samples_.AddSample(unichar, sample);
if (prev_unichar_id_ >= 0) {
CHAR_FRAGMENT* frag = CHAR_FRAGMENT::parse_from_string(unichar);
if (frag != NULL && frag->is_natural()) {
if (fragments_[prev_unichar_id_] == 0)
fragments_[prev_unichar_id_] = junk_id;
else if (fragments_[prev_unichar_id_] != junk_id)
fragments_[prev_unichar_id_] = -1;
}
delete frag;
}
prev_unichar_id_ = -1;
}
}
// Loads all pages from the given tif filename and append to page_images_.
// Must be called after ReadTrainingSamples, as the current number of images
// is used as an offset for page numbers in the samples.
void MasterTrainer::LoadPageImages(const char* filename) {
int page;
Pix* pix;
for (page = 0; (pix = pixReadTiff(filename, page)) != NULL; ++page) {
page_images_.push_back(pix);
}
tprintf("Loaded %d page images from %s\n", page, filename);
}
// Cleans up the samples after initial load from the tr files, and prior to
// saving the MasterTrainer:
// Remaps fragmented chars if running shape anaylsis.
// Sets up the samples appropriately for class/fontwise access.
// Deletes outlier samples.
void MasterTrainer::PostLoadCleanup() {
if (debug_level_ > 0)
tprintf("PostLoadCleanup...\n");
if (enable_shape_anaylsis_)
ReplaceFragmentedSamples();
SampleIterator sample_it;
sample_it.Init(NULL, NULL, true, &verify_samples_);
sample_it.NormalizeSamples();
verify_samples_.OrganizeByFontAndClass();
samples_.IndexFeatures(feature_space_);
// TODO(rays) DeleteOutliers is currently turned off to prove NOP-ness
// against current training.
// samples_.DeleteOutliers(feature_space_, debug_level_ > 0);
samples_.OrganizeByFontAndClass();
if (debug_level_ > 0)
tprintf("ComputeCanonicalSamples...\n");
samples_.ComputeCanonicalSamples(feature_map_, debug_level_ > 0);
}
// Gets the samples ready for training. Use after both
// ReadTrainingSamples+PostLoadCleanup or DeSerialize.
// Re-indexes the features and computes canonical and cloud features.
void MasterTrainer::PreTrainingSetup() {
if (debug_level_ > 0)
tprintf("PreTrainingSetup...\n");
samples_.IndexFeatures(feature_space_);
samples_.ComputeCanonicalFeatures();
if (debug_level_ > 0)
tprintf("ComputeCloudFeatures...\n");
samples_.ComputeCloudFeatures(feature_space_.Size());
}
// Sets up the master_shapes_ table, which tells which fonts should stay
// together until they get to a leaf node classifier.
void MasterTrainer::SetupMasterShapes() {
tprintf("Building master shape table\n");
int num_fonts = samples_.NumFonts();
ShapeTable char_shapes_begin_fragment(samples_.unicharset());
ShapeTable char_shapes_end_fragment(samples_.unicharset());
ShapeTable char_shapes(samples_.unicharset());
for (int c = 0; c < samples_.charsetsize(); ++c) {
ShapeTable shapes(samples_.unicharset());
for (int f = 0; f < num_fonts; ++f) {
if (samples_.NumClassSamples(f, c, true) > 0)
shapes.AddShape(c, f);
}
ClusterShapes(kMinClusteredShapes, 1, kFontMergeDistance, &shapes);
const CHAR_FRAGMENT *fragment = samples_.unicharset().get_fragment(c);
if (fragment == NULL)
char_shapes.AppendMasterShapes(shapes, NULL);
else if (fragment->is_beginning())
char_shapes_begin_fragment.AppendMasterShapes(shapes, NULL);
else if (fragment->is_ending())
char_shapes_end_fragment.AppendMasterShapes(shapes, NULL);
else
char_shapes.AppendMasterShapes(shapes, NULL);
}
ClusterShapes(kMinClusteredShapes, kMaxUnicharsPerCluster,
kFontMergeDistance, &char_shapes_begin_fragment);
char_shapes.AppendMasterShapes(char_shapes_begin_fragment, NULL);
ClusterShapes(kMinClusteredShapes, kMaxUnicharsPerCluster,
kFontMergeDistance, &char_shapes_end_fragment);
char_shapes.AppendMasterShapes(char_shapes_end_fragment, NULL);
ClusterShapes(kMinClusteredShapes, kMaxUnicharsPerCluster,
kFontMergeDistance, &char_shapes);
master_shapes_.AppendMasterShapes(char_shapes, NULL);
tprintf("Master shape_table:%s\n", master_shapes_.SummaryStr().string());
}
// Adds the junk_samples_ to the main samples_ set. Junk samples are initially
// fragments and n-grams (all incorrectly segmented characters).
// Various training functions may result in incorrectly segmented characters
// being added to the unicharset of the main samples, perhaps because they
// form a "radical" decomposition of some (Indic) grapheme, or because they
// just look the same as a real character (like rn/m)
// This function moves all the junk samples, to the main samples_ set, but
// desirable junk, being any sample for which the unichar already exists in
// the samples_ unicharset gets the unichar-ids re-indexed to match, but
// anything else gets re-marked as unichar_id 0 (space character) to identify
// it as junk to the error counter.
void MasterTrainer::IncludeJunk() {
// Get ids of fragments in junk_samples_ that replace the dead chars.
const UNICHARSET& junk_set = junk_samples_.unicharset();
const UNICHARSET& sample_set = samples_.unicharset();
int num_junks = junk_samples_.num_samples();
tprintf("Moving %d junk samples to master sample set.\n", num_junks);
for (int s = 0; s < num_junks; ++s) {
TrainingSample* sample = junk_samples_.mutable_sample(s);
int junk_id = sample->class_id();
const char* junk_utf8 = junk_set.id_to_unichar(junk_id);
int sample_id = sample_set.unichar_to_id(junk_utf8);
if (sample_id == INVALID_UNICHAR_ID)
sample_id = 0;
sample->set_class_id(sample_id);
junk_samples_.extract_sample(s);
samples_.AddSample(sample_id, sample);
}
junk_samples_.DeleteDeadSamples();
samples_.OrganizeByFontAndClass();
}
// Replicates the samples and perturbs them if the enable_replication_ flag
// is set. MUST be used after the last call to OrganizeByFontAndClass on
// the training samples, ie after IncludeJunk if it is going to be used, as
// OrganizeByFontAndClass will eat the replicated samples into the regular
// samples.
void MasterTrainer::ReplicateAndRandomizeSamplesIfRequired() {
if (enable_replication_) {
if (debug_level_ > 0)
tprintf("ReplicateAndRandomize...\n");
verify_samples_.ReplicateAndRandomizeSamples();
samples_.ReplicateAndRandomizeSamples();
samples_.IndexFeatures(feature_space_);
}
}
// Loads the basic font properties file into fontinfo_table_.
// Returns false on failure.
bool MasterTrainer::LoadFontInfo(const char* filename) {
FILE* fp = fopen(filename, "rb");
if (fp == NULL) {
fprintf(stderr, "Failed to load font_properties from %s\n", filename);
return false;
}
int italic, bold, fixed, serif, fraktur;
while (!feof(fp)) {
FontInfo fontinfo;
char* font_name = new char[1024];
fontinfo.name = font_name;
fontinfo.properties = 0;
fontinfo.universal_id = 0;
if (tfscanf(fp, "%1024s %i %i %i %i %i\n", font_name,
&italic, &bold, &fixed, &serif, &fraktur) != 6)
continue;
fontinfo.properties =
(italic << 0) +
(bold << 1) +
(fixed << 2) +
(serif << 3) +
(fraktur << 4);
if (!fontinfo_table_.contains(fontinfo)) {
fontinfo_table_.push_back(fontinfo);
}
}
fclose(fp);
return true;
}
// Loads the xheight font properties file into xheights_.
// Returns false on failure.
bool MasterTrainer::LoadXHeights(const char* filename) {
tprintf("fontinfo table is of size %d\n", fontinfo_table_.size());
xheights_.init_to_size(fontinfo_table_.size(), -1);
if (filename == NULL) return true;
FILE *f = fopen(filename, "rb");
if (f == NULL) {
fprintf(stderr, "Failed to load font xheights from %s\n", filename);
return false;
}
tprintf("Reading x-heights from %s ...\n", filename);
FontInfo fontinfo;
fontinfo.properties = 0; // Not used to lookup in the table.
fontinfo.universal_id = 0;
char buffer[1024];
int xht;
int total_xheight = 0;
int xheight_count = 0;
while (!feof(f)) {
if (tfscanf(f, "%1023s %d\n", buffer, &xht) != 2)
continue;
buffer[1023] = '\0';
fontinfo.name = buffer;
if (!fontinfo_table_.contains(fontinfo)) continue;
int fontinfo_id = fontinfo_table_.get_index(fontinfo);
xheights_[fontinfo_id] = xht;
total_xheight += xht;
++xheight_count;
}
if (xheight_count == 0) {
fprintf(stderr, "No valid xheights in %s!\n", filename);
fclose(f);
return false;
}
int mean_xheight = DivRounded(total_xheight, xheight_count);
for (int i = 0; i < fontinfo_table_.size(); ++i) {
if (xheights_[i] < 0)
xheights_[i] = mean_xheight;
}
fclose(f);
return true;
} // LoadXHeights
// Reads spacing stats from filename and adds them to fontinfo_table.
bool MasterTrainer::AddSpacingInfo(const char *filename) {
FILE* fontinfo_file = fopen(filename, "rb");
if (fontinfo_file == NULL)
return true; // We silently ignore missing files!
// Find the fontinfo_id.
int fontinfo_id = GetBestMatchingFontInfoId(filename);
if (fontinfo_id < 0) {
tprintf("No font found matching fontinfo filename %s\n", filename);
fclose(fontinfo_file);
return false;
}
tprintf("Reading spacing from %s for font %d...\n", filename, fontinfo_id);
// TODO(rays) scale should probably be a double, but keep as an int for now
// to duplicate current behavior.
int scale = kBlnXHeight / xheights_[fontinfo_id];
int num_unichars;
char uch[UNICHAR_LEN];
char kerned_uch[UNICHAR_LEN];
int x_gap, x_gap_before, x_gap_after, num_kerned;
ASSERT_HOST(tfscanf(fontinfo_file, "%d\n", &num_unichars) == 1);
FontInfo *fi = &fontinfo_table_.get(fontinfo_id);
fi->init_spacing(unicharset_.size());
FontSpacingInfo *spacing = NULL;
for (int l = 0; l < num_unichars; ++l) {
if (tfscanf(fontinfo_file, "%s %d %d %d",
uch, &x_gap_before, &x_gap_after, &num_kerned) != 4) {
tprintf("Bad format of font spacing file %s\n", filename);
fclose(fontinfo_file);
return false;
}
bool valid = unicharset_.contains_unichar(uch);
if (valid) {
spacing = new FontSpacingInfo();
spacing->x_gap_before = static_cast<inT16>(x_gap_before * scale);
spacing->x_gap_after = static_cast<inT16>(x_gap_after * scale);
}
for (int k = 0; k < num_kerned; ++k) {
if (tfscanf(fontinfo_file, "%s %d", kerned_uch, &x_gap) != 2) {
tprintf("Bad format of font spacing file %s\n", filename);
fclose(fontinfo_file);
delete spacing;
return false;
}
if (!valid || !unicharset_.contains_unichar(kerned_uch)) continue;
spacing->kerned_unichar_ids.push_back(
unicharset_.unichar_to_id(kerned_uch));
spacing->kerned_x_gaps.push_back(static_cast<inT16>(x_gap * scale));
}
if (valid) fi->add_spacing(unicharset_.unichar_to_id(uch), spacing);
}
fclose(fontinfo_file);
return true;
}
// Returns the font id corresponding to the given font name.
// Returns -1 if the font cannot be found.
int MasterTrainer::GetFontInfoId(const char* font_name) {
FontInfo fontinfo;
// We are only borrowing the string, so it is OK to const cast it.
fontinfo.name = const_cast<char*>(font_name);
fontinfo.properties = 0; // Not used to lookup in the table
fontinfo.universal_id = 0;
return fontinfo_table_.get_index(fontinfo);
}
// Returns the font_id of the closest matching font name to the given
// filename. It is assumed that a substring of the filename will match
// one of the fonts. If more than one is matched, the longest is returned.
int MasterTrainer::GetBestMatchingFontInfoId(const char* filename) {
int fontinfo_id = -1;
int best_len = 0;
for (int f = 0; f < fontinfo_table_.size(); ++f) {
if (strstr(filename, fontinfo_table_.get(f).name) != NULL) {
int len = strlen(fontinfo_table_.get(f).name);
// Use the longest matching length in case a substring of a font matched.
if (len > best_len) {
best_len = len;
fontinfo_id = f;
}
}
}
return fontinfo_id;
}
// Sets up a flat shapetable with one shape per class/font combination.
void MasterTrainer::SetupFlatShapeTable(ShapeTable* shape_table) {
// To exactly mimic the results of the previous implementation, the shapes
// must be clustered in order the fonts arrived, and reverse order of the
// characters within each font.
// Get a list of the fonts in the order they appeared.
GenericVector<int> active_fonts;
int num_shapes = flat_shapes_.NumShapes();
for (int s = 0; s < num_shapes; ++s) {
int font = flat_shapes_.GetShape(s)[0].font_ids[0];
int f = 0;
for (f = 0; f < active_fonts.size(); ++f) {
if (active_fonts[f] == font)
break;
}
if (f == active_fonts.size())
active_fonts.push_back(font);
}
// For each font in order, add all the shapes with that font in reverse order.
int num_fonts = active_fonts.size();
for (int f = 0; f < num_fonts; ++f) {
for (int s = num_shapes - 1; s >= 0; --s) {
int font = flat_shapes_.GetShape(s)[0].font_ids[0];
if (font == active_fonts[f]) {
shape_table->AddShape(flat_shapes_.GetShape(s));
}
}
}
}
// Sets up a Clusterer for mftraining on a single shape_id.
// Call FreeClusterer on the return value after use.
CLUSTERER* MasterTrainer::SetupForClustering(
const ShapeTable& shape_table,
const FEATURE_DEFS_STRUCT& feature_defs,
int shape_id,
int* num_samples) {
int desc_index = ShortNameToFeatureType(feature_defs, kMicroFeatureType);
int num_params = feature_defs.FeatureDesc[desc_index]->NumParams;
ASSERT_HOST(num_params == MFCount);
CLUSTERER* clusterer = MakeClusterer(
num_params, feature_defs.FeatureDesc[desc_index]->ParamDesc);
// We want to iterate over the samples of just the one shape.
IndexMapBiDi shape_map;
shape_map.Init(shape_table.NumShapes(), false);
shape_map.SetMap(shape_id, true);
shape_map.Setup();
// Reverse the order of the samples to match the previous behavior.
GenericVector<const TrainingSample*> sample_ptrs;
SampleIterator it;
it.Init(&shape_map, &shape_table, false, &samples_);
for (it.Begin(); !it.AtEnd(); it.Next()) {
sample_ptrs.push_back(&it.GetSample());
}
int sample_id = 0;
for (int i = sample_ptrs.size() - 1; i >= 0; --i) {
const TrainingSample* sample = sample_ptrs[i];
int num_features = sample->num_micro_features();
for (int f = 0; f < num_features; ++f)
MakeSample(clusterer, sample->micro_features()[f], sample_id);
++sample_id;
}
*num_samples = sample_id;
return clusterer;
}
// Writes the given float_classes (produced by SetupForFloat2Int) as inttemp
// to the given inttemp_file, and the corresponding pffmtable.
// The unicharset is the original encoding of graphemes, and shape_set should
// match the size of the shape_table, and may possibly be totally fake.
void MasterTrainer::WriteInttempAndPFFMTable(const UNICHARSET& unicharset,
const UNICHARSET& shape_set,
const ShapeTable& shape_table,
CLASS_STRUCT* float_classes,
const char* inttemp_file,
const char* pffmtable_file) {
tesseract::Classify *classify = new tesseract::Classify();
// Move the fontinfo table to classify.
fontinfo_table_.MoveTo(&classify->get_fontinfo_table());
INT_TEMPLATES int_templates = classify->CreateIntTemplates(float_classes,
shape_set);
FILE* fp = fopen(inttemp_file, "wb");
classify->WriteIntTemplates(fp, int_templates, shape_set);
fclose(fp);
// Now write pffmtable. This is complicated by the fact that the adaptive
// classifier still wants one indexed by unichar-id, but the static
// classifier needs one indexed by its shape class id.
// We put the shapetable_cutoffs in a GenericVector, and compute the
// unicharset cutoffs along the way.
GenericVector<uinT16> shapetable_cutoffs;
GenericVector<uinT16> unichar_cutoffs;
for (int c = 0; c < unicharset.size(); ++c)
unichar_cutoffs.push_back(0);
/* then write out each class */
for (int i = 0; i < int_templates->NumClasses; ++i) {
INT_CLASS Class = ClassForClassId(int_templates, i);
// Todo: Test with min instead of max
// int MaxLength = LengthForConfigId(Class, 0);
uinT16 max_length = 0;
for (int config_id = 0; config_id < Class->NumConfigs; config_id++) {
// Todo: Test with min instead of max
// if (LengthForConfigId (Class, config_id) < MaxLength)
uinT16 length = Class->ConfigLengths[config_id];
if (length > max_length)
max_length = Class->ConfigLengths[config_id];
int shape_id = float_classes[i].font_set.get(config_id);
const Shape& shape = shape_table.GetShape(shape_id);
for (int c = 0; c < shape.size(); ++c) {
int unichar_id = shape[c].unichar_id;
if (length > unichar_cutoffs[unichar_id])
unichar_cutoffs[unichar_id] = length;
}
}
shapetable_cutoffs.push_back(max_length);
}
fp = fopen(pffmtable_file, "wb");
shapetable_cutoffs.Serialize(fp);
for (int c = 0; c < unicharset.size(); ++c) {
const char *unichar = unicharset.id_to_unichar(c);
if (strcmp(unichar, " ") == 0) {
unichar = "NULL";
}
fprintf(fp, "%s %d\n", unichar, unichar_cutoffs[c]);
}
fclose(fp);
free_int_templates(int_templates);
delete classify;
}
// Generate debug output relating to the canonical distance between the
// two given UTF8 grapheme strings.
void MasterTrainer::DebugCanonical(const char* unichar_str1,
const char* unichar_str2) {
int class_id1 = unicharset_.unichar_to_id(unichar_str1);
int class_id2 = unicharset_.unichar_to_id(unichar_str2);
if (class_id2 == INVALID_UNICHAR_ID)
class_id2 = class_id1;
if (class_id1 == INVALID_UNICHAR_ID) {
tprintf("No unicharset entry found for %s\n", unichar_str1);
return;
} else {
tprintf("Font ambiguities for unichar %d = %s and %d = %s\n",
class_id1, unichar_str1, class_id2, unichar_str2);
}
int num_fonts = samples_.NumFonts();
const IntFeatureMap& feature_map = feature_map_;
// Iterate the fonts to get the similarity with other fonst of the same
// class.
tprintf(" ");
for (int f = 0; f < num_fonts; ++f) {
if (samples_.NumClassSamples(f, class_id2, false) == 0)
continue;
tprintf("%6d", f);
}
tprintf("\n");
for (int f1 = 0; f1 < num_fonts; ++f1) {
// Map the features of the canonical_sample.
if (samples_.NumClassSamples(f1, class_id1, false) == 0)
continue;
tprintf("%4d ", f1);
for (int f2 = 0; f2 < num_fonts; ++f2) {
if (samples_.NumClassSamples(f2, class_id2, false) == 0)
continue;
float dist = samples_.ClusterDistance(f1, class_id1, f2, class_id2,
feature_map);
tprintf(" %5.3f", dist);
}
tprintf("\n");
}
// Build a fake ShapeTable containing all the sample types.
ShapeTable shapes(unicharset_);
for (int f = 0; f < num_fonts; ++f) {
if (samples_.NumClassSamples(f, class_id1, true) > 0)
shapes.AddShape(class_id1, f);
if (class_id1 != class_id2 &&
samples_.NumClassSamples(f, class_id2, true) > 0)
shapes.AddShape(class_id2, f);
}
}
#ifndef GRAPHICS_DISABLED
// Debugging for cloud/canonical features.
// Displays a Features window containing:
// If unichar_str2 is in the unicharset, and canonical_font is non-negative,
// displays the canonical features of the char/font combination in red.
// If unichar_str1 is in the unicharset, and cloud_font is non-negative,
// displays the cloud feature of the char/font combination in green.
// The canonical features are drawn first to show which ones have no
// matches in the cloud features.
// Until the features window is destroyed, each click in the features window
// will display the samples that have that feature in a separate window.
void MasterTrainer::DisplaySamples(const char* unichar_str1, int cloud_font,
const char* unichar_str2,
int canonical_font) {
const IntFeatureMap& feature_map = feature_map_;
const IntFeatureSpace& feature_space = feature_map.feature_space();
ScrollView* f_window = CreateFeatureSpaceWindow("Features", 100, 500);
ClearFeatureSpaceWindow(norm_mode_ == NM_BASELINE ? baseline : character,
f_window);
int class_id2 = samples_.unicharset().unichar_to_id(unichar_str2);
if (class_id2 != INVALID_UNICHAR_ID && canonical_font >= 0) {
const TrainingSample* sample = samples_.GetCanonicalSample(canonical_font,
class_id2);
for (int f = 0; f < sample->num_features(); ++f) {
RenderIntFeature(f_window, &sample->features()[f], ScrollView::RED);
}
}
int class_id1 = samples_.unicharset().unichar_to_id(unichar_str1);
if (class_id1 != INVALID_UNICHAR_ID && cloud_font >= 0) {
const BitVector& cloud = samples_.GetCloudFeatures(cloud_font, class_id1);
for (int f = 0; f < cloud.size(); ++f) {
if (cloud[f]) {
INT_FEATURE_STRUCT feature =
feature_map.InverseIndexFeature(f);
RenderIntFeature(f_window, &feature, ScrollView::GREEN);
}
}
}
f_window->Update();
ScrollView* s_window = CreateFeatureSpaceWindow("Samples", 100, 500);
SVEventType ev_type;
do {
SVEvent* ev;
// Wait until a click or popup event.
ev = f_window->AwaitEvent(SVET_ANY);
ev_type = ev->type;
if (ev_type == SVET_CLICK) {
int feature_index = feature_space.XYToFeatureIndex(ev->x, ev->y);
if (feature_index >= 0) {
// Iterate samples and display those with the feature.
Shape shape;
shape.AddToShape(class_id1, cloud_font);
s_window->Clear();
samples_.DisplaySamplesWithFeature(feature_index, shape,
feature_space, ScrollView::GREEN,
s_window);
s_window->Update();
}
}
delete ev;
} while (ev_type != SVET_DESTROY);
}
#endif // GRAPHICS_DISABLED
void MasterTrainer::TestClassifierVOld(bool replicate_samples,
ShapeClassifier* test_classifier,
ShapeClassifier* old_classifier) {
SampleIterator sample_it;
sample_it.Init(NULL, NULL, replicate_samples, &samples_);
ErrorCounter::DebugNewErrors(test_classifier, old_classifier,
CT_UNICHAR_TOPN_ERR, fontinfo_table_,
page_images_, &sample_it);
}
// Tests the given test_classifier on the internal samples.
// See TestClassifier for details.
void MasterTrainer::TestClassifierOnSamples(CountTypes error_mode,
int report_level,
bool replicate_samples,
ShapeClassifier* test_classifier,
STRING* report_string) {
TestClassifier(error_mode, report_level, replicate_samples, &samples_,
test_classifier, report_string);
}
// Tests the given test_classifier on the given samples.
// error_mode indicates what counts as an error.
// report_levels:
// 0 = no output.
// 1 = bottom-line error rate.
// 2 = bottom-line error rate + time.
// 3 = font-level error rate + time.
// 4 = list of all errors + short classifier debug output on 16 errors.
// 5 = list of all errors + short classifier debug output on 25 errors.
// If replicate_samples is true, then the test is run on an extended test
// sample including replicated and systematically perturbed samples.
// If report_string is non-NULL, a summary of the results for each font
// is appended to the report_string.
double MasterTrainer::TestClassifier(CountTypes error_mode,
int report_level,
bool replicate_samples,
TrainingSampleSet* samples,
ShapeClassifier* test_classifier,
STRING* report_string) {
SampleIterator sample_it;
sample_it.Init(NULL, NULL, replicate_samples, samples);
if (report_level > 0) {
int num_samples = 0;
for (sample_it.Begin(); !sample_it.AtEnd(); sample_it.Next())
++num_samples;
tprintf("Iterator has charset size of %d/%d, %d shapes, %d samples\n",
sample_it.SparseCharsetSize(), sample_it.CompactCharsetSize(),
test_classifier->GetShapeTable()->NumShapes(), num_samples);
tprintf("Testing %sREPLICATED:\n", replicate_samples ? "" : "NON-");
}
double unichar_error = 0.0;
ErrorCounter::ComputeErrorRate(test_classifier, report_level,
error_mode, fontinfo_table_,
page_images_, &sample_it, &unichar_error,
NULL, report_string);
return unichar_error;
}
// Returns the average (in some sense) distance between the two given
// shapes, which may contain multiple fonts and/or unichars.
float MasterTrainer::ShapeDistance(const ShapeTable& shapes, int s1, int s2) {
const IntFeatureMap& feature_map = feature_map_;
const Shape& shape1 = shapes.GetShape(s1);
const Shape& shape2 = shapes.GetShape(s2);
int num_chars1 = shape1.size();
int num_chars2 = shape2.size();
float dist_sum = 0.0f;
int dist_count = 0;
if (num_chars1 > 1 || num_chars2 > 1) {
// In the multi-char case try to optimize the calculation by computing
// distances between characters of matching font where possible.
for (int c1 = 0; c1 < num_chars1; ++c1) {
for (int c2 = 0; c2 < num_chars2; ++c2) {
dist_sum += samples_.UnicharDistance(shape1[c1], shape2[c2],
true, feature_map);
++dist_count;
}
}
} else {
// In the single unichar case, there is little alternative, but to compute
// the squared-order distance between pairs of fonts.
dist_sum = samples_.UnicharDistance(shape1[0], shape2[0],
false, feature_map);
++dist_count;
}
return dist_sum / dist_count;
}
// Replaces samples that are always fragmented with the corresponding
// fragment samples.
void MasterTrainer::ReplaceFragmentedSamples() {
if (fragments_ == NULL) return;
// Remove samples that are replaced by fragments. Each class that was
// always naturally fragmented should be replaced by its fragments.
int num_samples = samples_.num_samples();
for (int s = 0; s < num_samples; ++s) {
TrainingSample* sample = samples_.mutable_sample(s);
if (fragments_[sample->class_id()] > 0)
samples_.KillSample(sample);
}
samples_.DeleteDeadSamples();
// Get ids of fragments in junk_samples_ that replace the dead chars.
const UNICHARSET& frag_set = junk_samples_.unicharset();
#if 0
// TODO(rays) The original idea was to replace only graphemes that were
// always naturally fragmented, but that left a lot of the Indic graphemes
// out. Determine whether we can go back to that idea now that spacing
// is fixed in the training images, or whether this code is obsolete.
bool* good_junk = new bool[frag_set.size()];
memset(good_junk, 0, sizeof(*good_junk) * frag_set.size());
for (int dead_ch = 1; dead_ch < unicharset_.size(); ++dead_ch) {
int frag_ch = fragments_[dead_ch];
if (frag_ch <= 0) continue;
const char* frag_utf8 = frag_set.id_to_unichar(frag_ch);
CHAR_FRAGMENT* frag = CHAR_FRAGMENT::parse_from_string(frag_utf8);
// Mark the chars for all parts of the fragment as good in good_junk.
for (int part = 0; part < frag->get_total(); ++part) {
frag->set_pos(part);
int good_ch = frag_set.unichar_to_id(frag->to_string().string());
if (good_ch != INVALID_UNICHAR_ID)
good_junk[good_ch] = true; // We want this one.
}
delete frag;
}
#endif
// For now just use all the junk that was from natural fragments.
// Get samples of fragments in junk_samples_ that replace the dead chars.
int num_junks = junk_samples_.num_samples();
for (int s = 0; s < num_junks; ++s) {
TrainingSample* sample = junk_samples_.mutable_sample(s);
int junk_id = sample->class_id();
const char* frag_utf8 = frag_set.id_to_unichar(junk_id);
CHAR_FRAGMENT* frag = CHAR_FRAGMENT::parse_from_string(frag_utf8);
if (frag != NULL && frag->is_natural()) {
junk_samples_.extract_sample(s);
samples_.AddSample(frag_set.id_to_unichar(junk_id), sample);
}
delete frag;
}
junk_samples_.DeleteDeadSamples();
junk_samples_.OrganizeByFontAndClass();
samples_.OrganizeByFontAndClass();
unicharset_.clear();
unicharset_.AppendOtherUnicharset(samples_.unicharset());
// delete [] good_junk;
// Fragments_ no longer needed?
delete [] fragments_;
fragments_ = NULL;
}
// Runs a hierarchical agglomerative clustering to merge shapes in the given
// shape_table, while satisfying the given constraints:
// * End with at least min_shapes left in shape_table,
// * No shape shall have more than max_shape_unichars in it,
// * Don't merge shapes where the distance between them exceeds max_dist.
const float kInfiniteDist = 999.0f;
void MasterTrainer::ClusterShapes(int min_shapes, int max_shape_unichars,
float max_dist, ShapeTable* shapes) {
int num_shapes = shapes->NumShapes();
int max_merges = num_shapes - min_shapes;
GenericVector<ShapeDist>* shape_dists =
new GenericVector<ShapeDist>[num_shapes];
float min_dist = kInfiniteDist;
int min_s1 = 0;
int min_s2 = 0;
tprintf("Computing shape distances...");
for (int s1 = 0; s1 < num_shapes; ++s1) {
for (int s2 = s1 + 1; s2 < num_shapes; ++s2) {
ShapeDist dist(s1, s2, ShapeDistance(*shapes, s1, s2));
shape_dists[s1].push_back(dist);
if (dist.distance < min_dist) {
min_dist = dist.distance;
min_s1 = s1;
min_s2 = s2;
}
}
tprintf(" %d", s1);
}
tprintf("\n");
int num_merged = 0;
while (num_merged < max_merges && min_dist < max_dist) {
tprintf("Distance = %f: ", min_dist);
int num_unichars = shapes->MergedUnicharCount(min_s1, min_s2);
shape_dists[min_s1][min_s2 - min_s1 - 1].distance = kInfiniteDist;
if (num_unichars > max_shape_unichars) {
tprintf("Merge of %d and %d with %d would exceed max of %d unichars\n",
min_s1, min_s2, num_unichars, max_shape_unichars);
} else {
shapes->MergeShapes(min_s1, min_s2);
shape_dists[min_s2].clear();
++num_merged;
for (int s = 0; s < min_s1; ++s) {
if (!shape_dists[s].empty()) {
shape_dists[s][min_s1 - s - 1].distance =
ShapeDistance(*shapes, s, min_s1);
shape_dists[s][min_s2 - s -1].distance = kInfiniteDist;
}
}
for (int s2 = min_s1 + 1; s2 < num_shapes; ++s2) {
if (shape_dists[min_s1][s2 - min_s1 - 1].distance < kInfiniteDist)
shape_dists[min_s1][s2 - min_s1 - 1].distance =
ShapeDistance(*shapes, min_s1, s2);
}
for (int s = min_s1 + 1; s < min_s2; ++s) {
if (!shape_dists[s].empty()) {
shape_dists[s][min_s2 - s - 1].distance = kInfiniteDist;
}
}
}
min_dist = kInfiniteDist;
for (int s1 = 0; s1 < num_shapes; ++s1) {
for (int i = 0; i < shape_dists[s1].size(); ++i) {
if (shape_dists[s1][i].distance < min_dist) {
min_dist = shape_dists[s1][i].distance;
min_s1 = s1;
min_s2 = s1 + 1 + i;
}
}
}
}
tprintf("Stopped with %d merged, min dist %f\n", num_merged, min_dist);
delete [] shape_dists;
if (debug_level_ > 1) {
for (int s1 = 0; s1 < num_shapes; ++s1) {
if (shapes->MasterDestinationIndex(s1) == s1) {
tprintf("Master shape:%s\n", shapes->DebugStr(s1).string());
}
}
}
}
} // namespace tesseract.
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