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tesseract/classify/classify.cpp
Stefan Weil 83588bc7a1 Classify: Avoid unneeded new / delete operations 
Both class variables BaselineCutoffs and CharNormCutoffs were pointers
to fixed size arrays which were allocated in the constructor and
deallocated in the destructor. These two extra allocations and two
extra deallocations can be avoided.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2017-04-30 19:45:50 +02:00

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///////////////////////////////////////////////////////////////////////
// File: classify.cpp
// Description: classify class.
// Author: Samuel Charron
//
// (C) Copyright 2006, 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 "classify.h"
#include "fontinfo.h"
#include "intproto.h"
#include "mfoutline.h"
#include "scrollview.h"
#include "shapeclassifier.h"
#include "shapetable.h"
#include "unicity_table.h"
#include <string.h>
namespace tesseract {
Classify::Classify()
: BOOL_MEMBER(allow_blob_division, true, "Use divisible blobs chopping",
this->params()),
BOOL_MEMBER(prioritize_division, FALSE,
"Prioritize blob division over chopping", this->params()),
INT_MEMBER(tessedit_single_match, FALSE, "Top choice only from CP",
this->params()),
BOOL_MEMBER(classify_enable_learning, true, "Enable adaptive classifier",
this->params()),
INT_MEMBER(classify_debug_level, 0, "Classify debug level",
this->params()),
INT_MEMBER(classify_norm_method, character, "Normalization Method ...",
this->params()),
double_MEMBER(classify_char_norm_range, 0.2,
"Character Normalization Range ...", this->params()),
double_MEMBER(classify_min_norm_scale_x, 0.0, "Min char x-norm scale ...",
this->params()), /* PREV DEFAULT 0.1 */
double_MEMBER(classify_max_norm_scale_x, 0.325,
"Max char x-norm scale ...",
this->params()), /* PREV DEFAULT 0.3 */
double_MEMBER(classify_min_norm_scale_y, 0.0, "Min char y-norm scale ...",
this->params()), /* PREV DEFAULT 0.1 */
double_MEMBER(classify_max_norm_scale_y, 0.325,
"Max char y-norm scale ...",
this->params()), /* PREV DEFAULT 0.3 */
double_MEMBER(classify_max_rating_ratio, 1.5,
"Veto ratio between classifier ratings", this->params()),
double_MEMBER(classify_max_certainty_margin, 5.5,
"Veto difference between classifier certainties",
this->params()),
BOOL_MEMBER(tess_cn_matching, 0, "Character Normalized Matching",
this->params()),
BOOL_MEMBER(tess_bn_matching, 0, "Baseline Normalized Matching",
this->params()),
BOOL_MEMBER(classify_enable_adaptive_matcher, 1,
"Enable adaptive classifier", this->params()),
BOOL_MEMBER(classify_use_pre_adapted_templates, 0,
"Use pre-adapted classifier templates", this->params()),
BOOL_MEMBER(classify_save_adapted_templates, 0,
"Save adapted templates to a file", this->params()),
BOOL_MEMBER(classify_enable_adaptive_debugger, 0, "Enable match debugger",
this->params()),
BOOL_MEMBER(classify_nonlinear_norm, 0,
"Non-linear stroke-density normalization", this->params()),
INT_MEMBER(matcher_debug_level, 0, "Matcher Debug Level", this->params()),
INT_MEMBER(matcher_debug_flags, 0, "Matcher Debug Flags", this->params()),
INT_MEMBER(classify_learning_debug_level, 0, "Learning Debug Level: ",
this->params()),
double_MEMBER(matcher_good_threshold, 0.125, "Good Match (0-1)",
this->params()),
double_MEMBER(matcher_reliable_adaptive_result, 0.0, "Great Match (0-1)",
this->params()),
double_MEMBER(matcher_perfect_threshold, 0.02, "Perfect Match (0-1)",
this->params()),
double_MEMBER(matcher_bad_match_pad, 0.15, "Bad Match Pad (0-1)",
this->params()),
double_MEMBER(matcher_rating_margin, 0.1, "New template margin (0-1)",
this->params()),
double_MEMBER(matcher_avg_noise_size, 12.0, "Avg. noise blob length",
this->params()),
INT_MEMBER(matcher_permanent_classes_min, 1, "Min # of permanent classes",
this->params()),
INT_MEMBER(matcher_min_examples_for_prototyping, 3,
"Reliable Config Threshold", this->params()),
INT_MEMBER(matcher_sufficient_examples_for_prototyping, 5,
"Enable adaption even if the ambiguities have not been seen",
this->params()),
double_MEMBER(matcher_clustering_max_angle_delta, 0.015,
"Maximum angle delta for prototype clustering",
this->params()),
double_MEMBER(classify_misfit_junk_penalty, 0.0,
"Penalty to apply when a non-alnum is vertically out of "
"its expected textline position",
this->params()),
double_MEMBER(rating_scale, 1.5, "Rating scaling factor", this->params()),
double_MEMBER(certainty_scale, 20.0, "Certainty scaling factor",
this->params()),
double_MEMBER(tessedit_class_miss_scale, 0.00390625,
"Scale factor for features not used", this->params()),
double_MEMBER(
classify_adapted_pruning_factor, 2.5,
"Prune poor adapted results this much worse than best result",
this->params()),
double_MEMBER(classify_adapted_pruning_threshold, -1.0,
"Threshold at which classify_adapted_pruning_factor starts",
this->params()),
INT_MEMBER(classify_adapt_proto_threshold, 230,
"Threshold for good protos during adaptive 0-255",
this->params()),
INT_MEMBER(classify_adapt_feature_threshold, 230,
"Threshold for good features during adaptive 0-255",
this->params()),
BOOL_MEMBER(disable_character_fragments, TRUE,
"Do not include character fragments in the"
" results of the classifier",
this->params()),
double_MEMBER(classify_character_fragments_garbage_certainty_threshold,
-3.0,
"Exclude fragments that do not look like whole"
" characters from training and adaption",
this->params()),
BOOL_MEMBER(classify_debug_character_fragments, FALSE,
"Bring up graphical debugging windows for fragments training",
this->params()),
BOOL_MEMBER(matcher_debug_separate_windows, FALSE,
"Use two different windows for debugging the matching: "
"One for the protos and one for the features.",
this->params()),
STRING_MEMBER(classify_learn_debug_str, "", "Class str to debug learning",
this->params()),
INT_MEMBER(classify_class_pruner_threshold, 229,
"Class Pruner Threshold 0-255", this->params()),
INT_MEMBER(classify_class_pruner_multiplier, 15,
"Class Pruner Multiplier 0-255: ", this->params()),
INT_MEMBER(classify_cp_cutoff_strength, 7,
"Class Pruner CutoffStrength: ", this->params()),
INT_MEMBER(classify_integer_matcher_multiplier, 10,
"Integer Matcher Multiplier 0-255: ", this->params()),
EnableLearning(true),
INT_MEMBER(il1_adaption_test, 0,
"Don't adapt to i/I at beginning of word", this->params()),
BOOL_MEMBER(classify_bln_numeric_mode, 0,
"Assume the input is numbers [0-9].", this->params()),
double_MEMBER(speckle_large_max_size, 0.30, "Max large speckle size",
this->params()),
double_MEMBER(speckle_rating_penalty, 10.0,
"Penalty to add to worst rating for noise", this->params()),
shape_table_(NULL),
dict_(this),
static_classifier_(NULL) {
fontinfo_table_.set_compare_callback(
NewPermanentTessCallback(CompareFontInfo));
fontinfo_table_.set_clear_callback(
NewPermanentTessCallback(FontInfoDeleteCallback));
fontset_table_.set_compare_callback(
NewPermanentTessCallback(CompareFontSet));
fontset_table_.set_clear_callback(
NewPermanentTessCallback(FontSetDeleteCallback));
AdaptedTemplates = NULL;
BackupAdaptedTemplates = NULL;
PreTrainedTemplates = NULL;
AllProtosOn = NULL;
AllConfigsOn = NULL;
AllConfigsOff = NULL;
TempProtoMask = NULL;
NormProtos = NULL;
NumAdaptationsFailed = 0;
learn_debug_win_ = NULL;
learn_fragmented_word_debug_win_ = NULL;
learn_fragments_debug_win_ = NULL;
}
Classify::~Classify() {
EndAdaptiveClassifier();
delete learn_debug_win_;
delete learn_fragmented_word_debug_win_;
delete learn_fragments_debug_win_;
}
// Takes ownership of the given classifier, and uses it for future calls
// to CharNormClassifier.
void Classify::SetStaticClassifier(ShapeClassifier* static_classifier) {
delete static_classifier_;
static_classifier_ = static_classifier;
}
// Moved from speckle.cpp
// Adds a noise classification result that is a bit worse than the worst
// current result, or the worst possible result if no current results.
void Classify::AddLargeSpeckleTo(int blob_length, BLOB_CHOICE_LIST *choices) {
BLOB_CHOICE_IT bc_it(choices);
// If there is no classifier result, we will use the worst possible certainty
// and corresponding rating.
float certainty = -getDict().certainty_scale;
float rating = rating_scale * blob_length;
if (!choices->empty() && blob_length > 0) {
bc_it.move_to_last();
BLOB_CHOICE* worst_choice = bc_it.data();
// Add speckle_rating_penalty to worst rating, matching old value.
rating = worst_choice->rating() + speckle_rating_penalty;
// Compute the rating to correspond to the certainty. (Used to be kept
// the same, but that messes up the language model search.)
certainty = -rating * getDict().certainty_scale /
(rating_scale * blob_length);
}
BLOB_CHOICE* blob_choice = new BLOB_CHOICE(UNICHAR_SPACE, rating, certainty,
-1, 0.0f, MAX_FLOAT32, 0,
BCC_SPECKLE_CLASSIFIER);
bc_it.add_to_end(blob_choice);
}
// Returns true if the blob is small enough to be a large speckle.
bool Classify::LargeSpeckle(const TBLOB &blob) {
double speckle_size = kBlnXHeight * speckle_large_max_size;
TBOX bbox = blob.bounding_box();
return bbox.width() < speckle_size && bbox.height() < speckle_size;
}
} // namespace tesseract
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