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tesseract/src/classify/intmatcher.cpp
Stefan Weil bf74471113 Fix Doxygen comments for void functions 
Void functions should not use @return. It causes compiler warnings
like this one:

    src/classify/intproto.cpp:326:5: warning:
      '@return' command used in a comment that is attached to a function
      returning void [-Wdocumentation]

Some non-void functions also were documented with @return none.
Fix those comments, too.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2019-05-16 20:19:01 +02:00

1231 lines
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/******************************************************************************
** Filename: intmatcher.cpp
** Purpose: Generic high level classification routines.
** Author: Robert Moss
** (c) Copyright Hewlett-Packard Company, 1988.
** 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 Files and Type Defines
----------------------------------------------------------------------------*/
#include "intmatcher.h"
#include <cassert>
#include <cmath>
#include "fontinfo.h"
#include "intproto.h"
#include "callcpp.h"
#include "scrollview.h"
#include "float2int.h"
#include "helpers.h"
#include "classify.h"
#include "shapetable.h"
using tesseract::ScoredFont;
using tesseract::UnicharRating;
/*----------------------------------------------------------------------------
Global Data Definitions and Declarations
----------------------------------------------------------------------------*/
// Parameters of the sigmoid used to convert similarity to evidence in the
// similarity_evidence_table_ that is used to convert distance metric to an
// 8 bit evidence value in the secondary matcher. (See IntMatcher::Init).
const float IntegerMatcher::kSEExponentialMultiplier = 0.0;
const float IntegerMatcher::kSimilarityCenter = 0.0075;
static const uint8_t offset_table[] = {
255, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3,
0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4,
0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 6, 0, 1, 0, 2, 0, 1, 0, 3,
0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5,
0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3,
0, 1, 0, 2, 0, 1, 0, 7, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4,
0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3,
0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 6,
0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3,
0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4,
0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0
};
static const uint8_t next_table[] = {
0, 0, 0, 0x2, 0, 0x4, 0x4, 0x6, 0, 0x8, 0x8, 0x0a, 0x08, 0x0c, 0x0c, 0x0e,
0, 0x10, 0x10, 0x12, 0x10, 0x14, 0x14, 0x16, 0x10, 0x18, 0x18, 0x1a,
0x18, 0x1c, 0x1c, 0x1e, 0, 0x20, 0x20, 0x22, 0x20, 0x24, 0x24, 0x26,
0x20, 0x28, 0x28, 0x2a, 0x28, 0x2c, 0x2c, 0x2e, 0x20, 0x30, 0x30, 0x32,
0x30, 0x34, 0x34, 0x36, 0x30, 0x38, 0x38, 0x3a, 0x38, 0x3c, 0x3c, 0x3e,
0, 0x40, 0x40, 0x42, 0x40, 0x44, 0x44, 0x46, 0x40, 0x48, 0x48, 0x4a,
0x48, 0x4c, 0x4c, 0x4e, 0x40, 0x50, 0x50, 0x52, 0x50, 0x54, 0x54, 0x56,
0x50, 0x58, 0x58, 0x5a, 0x58, 0x5c, 0x5c, 0x5e, 0x40, 0x60, 0x60, 0x62,
0x60, 0x64, 0x64, 0x66, 0x60, 0x68, 0x68, 0x6a, 0x68, 0x6c, 0x6c, 0x6e,
0x60, 0x70, 0x70, 0x72, 0x70, 0x74, 0x74, 0x76, 0x70, 0x78, 0x78, 0x7a,
0x78, 0x7c, 0x7c, 0x7e, 0, 0x80, 0x80, 0x82, 0x80, 0x84, 0x84, 0x86,
0x80, 0x88, 0x88, 0x8a, 0x88, 0x8c, 0x8c, 0x8e, 0x80, 0x90, 0x90, 0x92,
0x90, 0x94, 0x94, 0x96, 0x90, 0x98, 0x98, 0x9a, 0x98, 0x9c, 0x9c, 0x9e,
0x80, 0xa0, 0xa0, 0xa2, 0xa0, 0xa4, 0xa4, 0xa6, 0xa0, 0xa8, 0xa8, 0xaa,
0xa8, 0xac, 0xac, 0xae, 0xa0, 0xb0, 0xb0, 0xb2, 0xb0, 0xb4, 0xb4, 0xb6,
0xb0, 0xb8, 0xb8, 0xba, 0xb8, 0xbc, 0xbc, 0xbe, 0x80, 0xc0, 0xc0, 0xc2,
0xc0, 0xc4, 0xc4, 0xc6, 0xc0, 0xc8, 0xc8, 0xca, 0xc8, 0xcc, 0xcc, 0xce,
0xc0, 0xd0, 0xd0, 0xd2, 0xd0, 0xd4, 0xd4, 0xd6, 0xd0, 0xd8, 0xd8, 0xda,
0xd8, 0xdc, 0xdc, 0xde, 0xc0, 0xe0, 0xe0, 0xe2, 0xe0, 0xe4, 0xe4, 0xe6,
0xe0, 0xe8, 0xe8, 0xea, 0xe8, 0xec, 0xec, 0xee, 0xe0, 0xf0, 0xf0, 0xf2,
0xf0, 0xf4, 0xf4, 0xf6, 0xf0, 0xf8, 0xf8, 0xfa, 0xf8, 0xfc, 0xfc, 0xfe
};
// See http://b/19318793 (#6) for a complete discussion.
namespace tesseract {
/**
* Sort Key array in ascending order using heap sort
* algorithm. Also sort Index array that is tied to
* the key array.
* @param n Number of elements to sort
* @param ra Key array [1..n]
* @param rb Index array [1..n]
*/
static void
HeapSort (int n, int ra[], int rb[]) {
int i, rra, rrb;
int l, j, ir;
l = (n >> 1) + 1;
ir = n;
for (;;) {
if (l > 1) {
rra = ra[--l];
rrb = rb[l];
}
else {
rra = ra[ir];
rrb = rb[ir];
ra[ir] = ra[1];
rb[ir] = rb[1];
if (--ir == 1) {
ra[1] = rra;
rb[1] = rrb;
return;
}
}
i = l;
j = l << 1;
while (j <= ir) {
if (j < ir && ra[j] < ra[j + 1])
++j;
if (rra < ra[j]) {
ra[i] = ra[j];
rb[i] = rb[j];
j += (i = j);
}
else
j = ir + 1;
}
ra[i] = rra;
rb[i] = rrb;
}
}
// Encapsulation of the intermediate data and computations made by the class
// pruner. The class pruner implements a simple linear classifier on binary
// features by heavily quantizing the feature space, and applying
// NUM_BITS_PER_CLASS (2)-bit weights to the features. Lack of resolution in
// weights is compensated by a non-constant bias that is dependent on the
// number of features present.
class ClassPruner {
public:
ClassPruner(int max_classes) {
// The unrolled loop in ComputeScores means that the array sizes need to
// be rounded up so that the array is big enough to accommodate the extra
// entries accessed by the unrolling. Each pruner word is of sized
// BITS_PER_WERD and each entry is NUM_BITS_PER_CLASS, so there are
// BITS_PER_WERD / NUM_BITS_PER_CLASS entries.
// See ComputeScores.
max_classes_ = max_classes;
rounded_classes_ = RoundUp(
max_classes, WERDS_PER_CP_VECTOR * BITS_PER_WERD / NUM_BITS_PER_CLASS);
class_count_ = new int[rounded_classes_];
norm_count_ = new int[rounded_classes_];
sort_key_ = new int[rounded_classes_ + 1];
sort_index_ = new int[rounded_classes_ + 1];
for (int i = 0; i < rounded_classes_; i++) {
class_count_[i] = 0;
}
pruning_threshold_ = 0;
num_features_ = 0;
num_classes_ = 0;
}
~ClassPruner() {
delete []class_count_;
delete []norm_count_;
delete []sort_key_;
delete []sort_index_;
}
/// Computes the scores for every class in the character set, by summing the
/// weights for each feature and stores the sums internally in class_count_.
void ComputeScores(const INT_TEMPLATES_STRUCT* int_templates,
int num_features, const INT_FEATURE_STRUCT* features) {
num_features_ = num_features;
int num_pruners = int_templates->NumClassPruners;
for (int f = 0; f < num_features; ++f) {
const INT_FEATURE_STRUCT* feature = &features[f];
// Quantize the feature to NUM_CP_BUCKETS*NUM_CP_BUCKETS*NUM_CP_BUCKETS.
int x = feature->X * NUM_CP_BUCKETS >> 8;
int y = feature->Y * NUM_CP_BUCKETS >> 8;
int theta = feature->Theta * NUM_CP_BUCKETS >> 8;
int class_id = 0;
// Each CLASS_PRUNER_STRUCT only covers CLASSES_PER_CP(32) classes, so
// we need a collection of them, indexed by pruner_set.
for (int pruner_set = 0; pruner_set < num_pruners; ++pruner_set) {
// Look up quantized feature in a 3-D array, an array of weights for
// each class.
const uint32_t* pruner_word_ptr =
int_templates->ClassPruners[pruner_set]->p[x][y][theta];
for (int word = 0; word < WERDS_PER_CP_VECTOR; ++word) {
uint32_t pruner_word = *pruner_word_ptr++;
// This inner loop is unrolled to speed up the ClassPruner.
// Currently gcc would not unroll it unless it is set to O3
// level of optimization or -funroll-loops is specified.
/*
uint32_t class_mask = (1 << NUM_BITS_PER_CLASS) - 1;
for (int bit = 0; bit < BITS_PER_WERD/NUM_BITS_PER_CLASS; bit++) {
class_count_[class_id++] += pruner_word & class_mask;
pruner_word >>= NUM_BITS_PER_CLASS;
}
*/
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
pruner_word >>= NUM_BITS_PER_CLASS;
class_count_[class_id++] += pruner_word & CLASS_PRUNER_CLASS_MASK;
}
}
}
}
/// Adjusts the scores according to the number of expected features. Used
/// in lieu of a constant bias, this penalizes classes that expect more
/// features than there are present. Thus an actual c will score higher for c
/// than e, even though almost all the features match e as well as c, because
/// e expects more features to be present.
void AdjustForExpectedNumFeatures(const uint16_t* expected_num_features,
int cutoff_strength) {
for (int class_id = 0; class_id < max_classes_; ++class_id) {
if (num_features_ < expected_num_features[class_id]) {
int deficit = expected_num_features[class_id] - num_features_;
class_count_[class_id] -= class_count_[class_id] * deficit /
(num_features_ * cutoff_strength + deficit);
}
}
}
/// Zeros the scores for classes disabled in the unicharset.
/// Implements the black-list to recognize a subset of the character set.
void DisableDisabledClasses(const UNICHARSET& unicharset) {
for (int class_id = 0; class_id < max_classes_; ++class_id) {
if (!unicharset.get_enabled(class_id))
class_count_[class_id] = 0; // This char is disabled!
}
}
/** Zeros the scores of fragments. */
void DisableFragments(const UNICHARSET& unicharset) {
for (int class_id = 0; class_id < max_classes_; ++class_id) {
// Do not include character fragments in the class pruner
// results if disable_character_fragments is true.
if (unicharset.get_fragment(class_id)) {
class_count_[class_id] = 0;
}
}
}
/// Normalizes the counts for xheight, putting the normalized result in
/// norm_count_. Applies a simple subtractive penalty for incorrect vertical
/// position provided by the normalization_factors array, indexed by
/// character class, and scaled by the norm_multiplier.
void NormalizeForXheight(int norm_multiplier,
const uint8_t* normalization_factors) {
for (int class_id = 0; class_id < max_classes_; class_id++) {
norm_count_[class_id] = class_count_[class_id] -
((norm_multiplier * normalization_factors[class_id]) >> 8);
}
}
/** The nop normalization copies the class_count_ array to norm_count_. */
void NoNormalization() {
for (int class_id = 0; class_id < max_classes_; class_id++) {
norm_count_[class_id] = class_count_[class_id];
}
}
/// Prunes the classes using &lt;the maximum count> * pruning_factor/256 as a
/// threshold for keeping classes. If max_of_non_fragments, then ignore
/// fragments in computing the maximum count.
void PruneAndSort(int pruning_factor, int keep_this,
bool max_of_non_fragments, const UNICHARSET& unicharset) {
int max_count = 0;
for (int c = 0; c < max_classes_; ++c) {
if (norm_count_[c] > max_count &&
// This additional check is added in order to ensure that
// the classifier will return at least one non-fragmented
// character match.
// TODO(daria): verify that this helps accuracy and does not
// hurt performance.
(!max_of_non_fragments || !unicharset.get_fragment(c))) {
max_count = norm_count_[c];
}
}
// Prune Classes.
pruning_threshold_ = (max_count * pruning_factor) >> 8;
// Select Classes.
if (pruning_threshold_ < 1)
pruning_threshold_ = 1;
num_classes_ = 0;
for (int class_id = 0; class_id < max_classes_; class_id++) {
if (norm_count_[class_id] >= pruning_threshold_ ||
class_id == keep_this) {
++num_classes_;
sort_index_[num_classes_] = class_id;
sort_key_[num_classes_] = norm_count_[class_id];
}
}
// Sort Classes using Heapsort Algorithm.
if (num_classes_ > 1)
HeapSort(num_classes_, sort_key_, sort_index_);
}
/** Prints debug info on the class pruner matches for the pruned classes only.
*/
void DebugMatch(const Classify& classify,
const INT_TEMPLATES_STRUCT* int_templates,
const INT_FEATURE_STRUCT* features) const {
int num_pruners = int_templates->NumClassPruners;
int max_num_classes = int_templates->NumClasses;
for (int f = 0; f < num_features_; ++f) {
const INT_FEATURE_STRUCT* feature = &features[f];
tprintf("F=%3d(%d,%d,%d),", f, feature->X, feature->Y, feature->Theta);
// Quantize the feature to NUM_CP_BUCKETS*NUM_CP_BUCKETS*NUM_CP_BUCKETS.
int x = feature->X * NUM_CP_BUCKETS >> 8;
int y = feature->Y * NUM_CP_BUCKETS >> 8;
int theta = feature->Theta * NUM_CP_BUCKETS >> 8;
int class_id = 0;
for (int pruner_set = 0; pruner_set < num_pruners; ++pruner_set) {
// Look up quantized feature in a 3-D array, an array of weights for
// each class.
const uint32_t* pruner_word_ptr =
int_templates->ClassPruners[pruner_set]->p[x][y][theta];
for (int word = 0; word < WERDS_PER_CP_VECTOR; ++word) {
uint32_t pruner_word = *pruner_word_ptr++;
for (int word_class = 0; word_class < 16 &&
class_id < max_num_classes; ++word_class, ++class_id) {
if (norm_count_[class_id] >= pruning_threshold_) {
tprintf(" %s=%d,",
classify.ClassIDToDebugStr(int_templates,
class_id, 0).string(),
pruner_word & CLASS_PRUNER_CLASS_MASK);
}
pruner_word >>= NUM_BITS_PER_CLASS;
}
}
tprintf("\n");
}
}
}
/** Prints a summary of the pruner result. */
void SummarizeResult(const Classify& classify,
const INT_TEMPLATES_STRUCT* int_templates,
const uint16_t* expected_num_features,
int norm_multiplier,
const uint8_t* normalization_factors) const {
tprintf("CP:%d classes, %d features:\n", num_classes_, num_features_);
for (int i = 0; i < num_classes_; ++i) {
int class_id = sort_index_[num_classes_ - i];
STRING class_string = classify.ClassIDToDebugStr(int_templates,
class_id, 0);
tprintf("%s:Initial=%d, E=%d, Xht-adj=%d, N=%d, Rat=%.2f\n",
class_string.string(),
class_count_[class_id],
expected_num_features[class_id],
(norm_multiplier * normalization_factors[class_id]) >> 8,
sort_key_[num_classes_ - i],
100.0 - 100.0 * sort_key_[num_classes_ - i] /
(CLASS_PRUNER_CLASS_MASK * num_features_));
}
}
/// Copies the pruned, sorted classes into the output results and returns
/// the number of classes.
int SetupResults(GenericVector<CP_RESULT_STRUCT>* results) const {
CP_RESULT_STRUCT empty;
results->init_to_size(num_classes_, empty);
for (int c = 0; c < num_classes_; ++c) {
(*results)[c].Class = sort_index_[num_classes_ - c];
(*results)[c].Rating = 1.0 - sort_key_[num_classes_ - c] /
(static_cast<float>(CLASS_PRUNER_CLASS_MASK) * num_features_);
}
return num_classes_;
}
private:
/** Array[rounded_classes_] of initial counts for each class. */
int *class_count_;
/// Array[rounded_classes_] of modified counts for each class after
/// normalizing for expected number of features, disabled classes, fragments,
/// and xheights.
int *norm_count_;
/** Array[rounded_classes_ +1] of pruned counts that gets sorted */
int *sort_key_;
/** Array[rounded_classes_ +1] of classes corresponding to sort_key_. */
int *sort_index_;
/** Number of classes in this class pruner. */
int max_classes_;
/** Rounded up number of classes used for array sizes. */
int rounded_classes_;
/** Threshold count applied to prune classes. */
int pruning_threshold_;
/** The number of features used to compute the scores. */
int num_features_;
/** Final number of pruned classes. */
int num_classes_;
};
/*----------------------------------------------------------------------------
Public Code
----------------------------------------------------------------------------*/
/**
* Runs the class pruner from int_templates on the given features, returning
* the number of classes output in results.
* @param int_templates Class pruner tables
* @param num_features Number of features in blob
* @param features Array of features
* @param normalization_factors Array of fudge factors from blob
* normalization process (by CLASS_INDEX)
* @param expected_num_features Array of expected number of features
* for each class (by CLASS_INDEX)
* @param results Sorted Array of pruned classes. Must be an
* array of size at least
* int_templates->NumClasses.
* @param keep_this
*/
int Classify::PruneClasses(const INT_TEMPLATES_STRUCT* int_templates,
int num_features, int keep_this,
const INT_FEATURE_STRUCT* features,
const uint8_t* normalization_factors,
const uint16_t* expected_num_features,
GenericVector<CP_RESULT_STRUCT>* results) {
ClassPruner pruner(int_templates->NumClasses);
// Compute initial match scores for all classes.
pruner.ComputeScores(int_templates, num_features, features);
// Adjust match scores for number of expected features.
pruner.AdjustForExpectedNumFeatures(expected_num_features,
classify_cp_cutoff_strength);
// Apply disabled classes in unicharset - only works without a shape_table.
if (shape_table_ == nullptr)
pruner.DisableDisabledClasses(unicharset);
// If fragments are disabled, remove them, also only without a shape table.
if (disable_character_fragments && shape_table_ == nullptr)
pruner.DisableFragments(unicharset);
// If we have good x-heights, apply the given normalization factors.
if (normalization_factors != nullptr) {
pruner.NormalizeForXheight(classify_class_pruner_multiplier,
normalization_factors);
} else {
pruner.NoNormalization();
}
// Do the actual pruning and sort the short-list.
pruner.PruneAndSort(classify_class_pruner_threshold, keep_this,
shape_table_ == nullptr, unicharset);
if (classify_debug_level > 2) {
pruner.DebugMatch(*this, int_templates, features);
}
if (classify_debug_level > 1) {
pruner.SummarizeResult(*this, int_templates, expected_num_features,
classify_class_pruner_multiplier,
normalization_factors);
}
// Convert to the expected output format.
return pruner.SetupResults(results);
}
} // namespace tesseract
/**
* IntegerMatcher returns the best configuration and rating
* for a single class. The class matched against is determined
* by the uniqueness of the ClassTemplate parameter. The
* best rating and its associated configuration are returned.
*
* Globals:
* - local_matcher_multiplier_ Normalization factor multiplier
* param ClassTemplate Prototypes & tables for a class
* param NumFeatures Number of features in blob
* param Features Array of features
* param NormalizationFactor Fudge factor from blob normalization process
* param Result Class rating & configuration: (0.0 -> 1.0), 0=bad, 1=good
* param Debug Debugger flag: 1=debugger on
*/
void IntegerMatcher::Match(INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
int16_t NumFeatures,
const INT_FEATURE_STRUCT* Features,
UnicharRating* Result,
int AdaptFeatureThreshold,
int Debug,
bool SeparateDebugWindows) {
auto *tables = new ScratchEvidence();
int Feature;
if (MatchDebuggingOn (Debug))
cprintf ("Integer Matcher -------------------------------------------\n");
tables->Clear(ClassTemplate);
Result->feature_misses = 0;
for (Feature = 0; Feature < NumFeatures; Feature++) {
int csum = UpdateTablesForFeature(ClassTemplate, ProtoMask, ConfigMask,
Feature, &Features[Feature],
tables, Debug);
// Count features that were missed over all configs.
if (csum == 0)
++Result->feature_misses;
}
#ifndef GRAPHICS_DISABLED
if (PrintProtoMatchesOn(Debug) || PrintMatchSummaryOn(Debug)) {
DebugFeatureProtoError(ClassTemplate, ProtoMask, ConfigMask, *tables,
NumFeatures, Debug);
}
if (DisplayProtoMatchesOn(Debug)) {
DisplayProtoDebugInfo(ClassTemplate, ConfigMask,
*tables, SeparateDebugWindows);
}
if (DisplayFeatureMatchesOn(Debug)) {
DisplayFeatureDebugInfo(ClassTemplate, ProtoMask, ConfigMask, NumFeatures,
Features, AdaptFeatureThreshold, Debug,
SeparateDebugWindows);
}
#endif
tables->UpdateSumOfProtoEvidences(ClassTemplate, ConfigMask);
tables->NormalizeSums(ClassTemplate, NumFeatures);
FindBestMatch(ClassTemplate, *tables, Result);
#ifndef GRAPHICS_DISABLED
if (PrintMatchSummaryOn(Debug))
Result->Print();
if (MatchDebuggingOn(Debug))
cprintf("Match Complete --------------------------------------------\n");
#endif
delete tables;
}
/**
* FindGoodProtos finds all protos whose normalized proto-evidence
* exceed classify_adapt_proto_thresh. The list is ordered by increasing
* proto id number.
*
* Globals:
* - local_matcher_multiplier_ Normalization factor multiplier
* param ClassTemplate Prototypes & tables for a class
* param ProtoMask AND Mask for proto word
* param ConfigMask AND Mask for config word
* param NumFeatures Number of features in blob
* param Features Array of features
* param ProtoArray Array of good protos
* param AdaptProtoThreshold Threshold for good protos
* param Debug Debugger flag: 1=debugger on
* @return Number of good protos in ProtoArray.
*/
int IntegerMatcher::FindGoodProtos(
INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
int16_t NumFeatures,
INT_FEATURE_ARRAY Features,
PROTO_ID *ProtoArray,
int AdaptProtoThreshold,
int Debug) {
auto *tables = new ScratchEvidence();
int NumGoodProtos = 0;
/* DEBUG opening heading */
if (MatchDebuggingOn (Debug))
cprintf
("Find Good Protos -------------------------------------------\n");
tables->Clear(ClassTemplate);
for (int Feature = 0; Feature < NumFeatures; Feature++)
UpdateTablesForFeature(
ClassTemplate, ProtoMask, ConfigMask, Feature, &(Features[Feature]),
tables, Debug);
#ifndef GRAPHICS_DISABLED
if (PrintProtoMatchesOn (Debug) || PrintMatchSummaryOn (Debug))
DebugFeatureProtoError(ClassTemplate, ProtoMask, ConfigMask, *tables,
NumFeatures, Debug);
#endif
/* Average Proto Evidences & Find Good Protos */
for (int proto = 0; proto < ClassTemplate->NumProtos; proto++) {
/* Compute Average for Actual Proto */
int Temp = 0;
for (uint8_t i = 0;
i < MAX_PROTO_INDEX && i < ClassTemplate->ProtoLengths[proto]; i++)
Temp += tables->proto_evidence_[proto][i];
Temp /= ClassTemplate->ProtoLengths[proto];
/* Find Good Protos */
if (Temp >= AdaptProtoThreshold) {
*ProtoArray = proto;
ProtoArray++;
NumGoodProtos++;
}
}
if (MatchDebuggingOn (Debug))
cprintf ("Match Complete --------------------------------------------\n");
delete tables;
return NumGoodProtos;
}
/**
* FindBadFeatures finds all features with maximum feature-evidence <
* AdaptFeatureThresh. The list is ordered by increasing feature number.
* @param ClassTemplate Prototypes & tables for a class
* @param ProtoMask AND Mask for proto word
* @param ConfigMask AND Mask for config word
* @param NumFeatures Number of features in blob
* @param Features Array of features
* @param FeatureArray Array of bad features
* @param AdaptFeatureThreshold Threshold for bad features
* @param Debug Debugger flag: 1=debugger on
* @return Number of bad features in FeatureArray.
*/
int IntegerMatcher::FindBadFeatures(
INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
int16_t NumFeatures,
INT_FEATURE_ARRAY Features,
FEATURE_ID *FeatureArray,
int AdaptFeatureThreshold,
int Debug) {
auto *tables = new ScratchEvidence();
int NumBadFeatures = 0;
/* DEBUG opening heading */
if (MatchDebuggingOn(Debug))
cprintf("Find Bad Features -------------------------------------------\n");
tables->Clear(ClassTemplate);
for (int Feature = 0; Feature < NumFeatures; Feature++) {
UpdateTablesForFeature(
ClassTemplate, ProtoMask, ConfigMask, Feature, &Features[Feature],
tables, Debug);
/* Find Best Evidence for Current Feature */
int best = 0;
assert(ClassTemplate->NumConfigs < MAX_NUM_CONFIGS);
for (int i = 0; i < MAX_NUM_CONFIGS && i < ClassTemplate->NumConfigs; i++)
if (tables->feature_evidence_[i] > best)
best = tables->feature_evidence_[i];
/* Find Bad Features */
if (best < AdaptFeatureThreshold) {
*FeatureArray = Feature;
FeatureArray++;
NumBadFeatures++;
}
}
#ifndef GRAPHICS_DISABLED
if (PrintProtoMatchesOn(Debug) || PrintMatchSummaryOn(Debug))
DebugFeatureProtoError(ClassTemplate, ProtoMask, ConfigMask, *tables,
NumFeatures, Debug);
#endif
if (MatchDebuggingOn(Debug))
cprintf("Match Complete --------------------------------------------\n");
delete tables;
return NumBadFeatures;
}
IntegerMatcher::IntegerMatcher(tesseract::IntParam *classify_debug_level)
: classify_debug_level_(classify_debug_level)
{
/* Initialize table for evidence to similarity lookup */
for (int i = 0; i < SE_TABLE_SIZE; i++) {
uint32_t IntSimilarity = i << (27 - SE_TABLE_BITS);
double Similarity = (static_cast<double>(IntSimilarity)) / 65536.0 / 65536.0;
double evidence = Similarity / kSimilarityCenter;
evidence = 255.0 / (evidence * evidence + 1.0);
if (kSEExponentialMultiplier > 0.0) {
double scale = 1.0 - exp(-kSEExponentialMultiplier) *
exp(kSEExponentialMultiplier * (static_cast<double>(i) / SE_TABLE_SIZE));
evidence *= ClipToRange(scale, 0.0, 1.0);
}
similarity_evidence_table_[i] = static_cast<uint8_t>(evidence + 0.5);
}
/* Initialize evidence computation variables */
evidence_table_mask_ =
((1 << kEvidenceTableBits) - 1) << (9 - kEvidenceTableBits);
mult_trunc_shift_bits_ = (14 - kIntEvidenceTruncBits);
table_trunc_shift_bits_ = (27 - SE_TABLE_BITS - (mult_trunc_shift_bits_ << 1));
evidence_mult_mask_ = ((1 << kIntEvidenceTruncBits) - 1);
}
/*----------------------------------------------------------------------------
Private Code
----------------------------------------------------------------------------*/
void ScratchEvidence::Clear(const INT_CLASS class_template) {
memset(sum_feature_evidence_, 0,
class_template->NumConfigs * sizeof(sum_feature_evidence_[0]));
memset(proto_evidence_, 0,
class_template->NumProtos * sizeof(proto_evidence_[0]));
}
void ScratchEvidence::ClearFeatureEvidence(const INT_CLASS class_template) {
memset(feature_evidence_, 0,
class_template->NumConfigs * sizeof(feature_evidence_[0]));
}
/**
* Print debugging information for Configurations
*/
static void IMDebugConfiguration(int FeatureNum, uint16_t ActualProtoNum,
uint8_t Evidence, uint32_t ConfigWord) {
cprintf ("F = %3d, P = %3d, E = %3d, Configs = ",
FeatureNum, static_cast<int>(ActualProtoNum), static_cast<int>(Evidence));
while (ConfigWord) {
if (ConfigWord & 1)
cprintf ("1");
else
cprintf ("0");
ConfigWord >>= 1;
}
cprintf ("\n");
}
/**
* Print debugging information for Configurations
*/
static void IMDebugConfigurationSum(int FeatureNum, uint8_t *FeatureEvidence,
int32_t ConfigCount) {
cprintf("F=%3d, C=", FeatureNum);
for (int ConfigNum = 0; ConfigNum < ConfigCount; ConfigNum++) {
cprintf("%4d", FeatureEvidence[ConfigNum]);
}
cprintf("\n");
}
/**
* For the given feature: prune protos, compute evidence,
* update Feature Evidence, Proto Evidence, and Sum of Feature
* Evidence tables.
* @param ClassTemplate Prototypes & tables for a class
* @param FeatureNum Current feature number (for DEBUG only)
* @param Feature Pointer to a feature struct
* @param tables Evidence tables
* @param Debug Debugger flag: 1=debugger on
* @return sum of feature evidence tables
*/
int IntegerMatcher::UpdateTablesForFeature(
INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
int FeatureNum,
const INT_FEATURE_STRUCT* Feature,
ScratchEvidence *tables,
int Debug) {
uint32_t ConfigWord;
uint32_t ProtoWord;
uint32_t ProtoNum;
uint32_t ActualProtoNum;
uint8_t proto_byte;
int32_t proto_word_offset;
int32_t proto_offset;
PROTO_SET ProtoSet;
uint32_t *ProtoPrunerPtr;
INT_PROTO Proto;
int ProtoSetIndex;
uint8_t Evidence;
uint32_t XFeatureAddress;
uint32_t YFeatureAddress;
uint32_t ThetaFeatureAddress;
tables->ClearFeatureEvidence(ClassTemplate);
/* Precompute Feature Address offset for Proto Pruning */
XFeatureAddress = ((Feature->X >> 2) << 1);
YFeatureAddress = (NUM_PP_BUCKETS << 1) + ((Feature->Y >> 2) << 1);
ThetaFeatureAddress = (NUM_PP_BUCKETS << 2) + ((Feature->Theta >> 2) << 1);
for (ProtoSetIndex = 0, ActualProtoNum = 0;
ProtoSetIndex < ClassTemplate->NumProtoSets; ProtoSetIndex++) {
ProtoSet = ClassTemplate->ProtoSets[ProtoSetIndex];
ProtoPrunerPtr = reinterpret_cast<uint32_t *>((*ProtoSet).ProtoPruner);
for (ProtoNum = 0; ProtoNum < PROTOS_PER_PROTO_SET;
ProtoNum += (PROTOS_PER_PROTO_SET >> 1), ActualProtoNum +=
(PROTOS_PER_PROTO_SET >> 1), ProtoMask++, ProtoPrunerPtr++) {
/* Prune Protos of current Proto Set */
ProtoWord = *(ProtoPrunerPtr + XFeatureAddress);
ProtoWord &= *(ProtoPrunerPtr + YFeatureAddress);
ProtoWord &= *(ProtoPrunerPtr + ThetaFeatureAddress);
ProtoWord &= *ProtoMask;
if (ProtoWord != 0) {
proto_byte = ProtoWord & 0xff;
ProtoWord >>= 8;
proto_word_offset = 0;
while (ProtoWord != 0 || proto_byte != 0) {
while (proto_byte == 0) {
proto_byte = ProtoWord & 0xff;
ProtoWord >>= 8;
proto_word_offset += 8;
}
proto_offset = offset_table[proto_byte] + proto_word_offset;
proto_byte = next_table[proto_byte];
Proto = &(ProtoSet->Protos[ProtoNum + proto_offset]);
ConfigWord = Proto->Configs[0];
int32_t A3 = (((Proto->A * (Feature->X - 128)) * 2)
- (Proto->B * (Feature->Y - 128)) + (Proto->C * 512));
int32_t M3 = ((static_cast<int8_t>(Feature->Theta - Proto->Angle)) *
kIntThetaFudge) * 2;
if (A3 < 0)
A3 = ~A3;
if (M3 < 0)
M3 = ~M3;
A3 >>= mult_trunc_shift_bits_;
M3 >>= mult_trunc_shift_bits_;
if (static_cast<uint32_t>(A3) > evidence_mult_mask_)
A3 = evidence_mult_mask_;
if (static_cast<uint32_t>(M3) > evidence_mult_mask_)
M3 = evidence_mult_mask_;
uint32_t A4 = (A3 * A3) + (M3 * M3);
A4 >>= table_trunc_shift_bits_;
if (A4 > evidence_table_mask_)
Evidence = 0;
else
Evidence = similarity_evidence_table_[A4];
if (PrintFeatureMatchesOn (Debug))
IMDebugConfiguration (FeatureNum,
ActualProtoNum + proto_offset,
Evidence, ConfigWord);
ConfigWord &= *ConfigMask;
uint8_t feature_evidence_index = 0;
uint8_t config_byte = 0;
while (ConfigWord != 0 || config_byte != 0) {
while (config_byte == 0) {
config_byte = ConfigWord & 0xff;
ConfigWord >>= 8;
feature_evidence_index += 8;
}
const uint8_t config_offset =
offset_table[config_byte] + feature_evidence_index - 8;
config_byte = next_table[config_byte];
if (Evidence > tables->feature_evidence_[config_offset])
tables->feature_evidence_[config_offset] = Evidence;
}
uint8_t* UINT8Pointer =
&(tables->proto_evidence_[ActualProtoNum + proto_offset][0]);
for (uint8_t ProtoIndex =
ClassTemplate->ProtoLengths[ActualProtoNum + proto_offset];
ProtoIndex > 0; ProtoIndex--, UINT8Pointer++) {
if (Evidence > *UINT8Pointer) {
uint8_t Temp = *UINT8Pointer;
*UINT8Pointer = Evidence;
Evidence = Temp;
}
else if (Evidence == 0)
break;
}
}
}
}
}
if (PrintFeatureMatchesOn(Debug)) {
IMDebugConfigurationSum(FeatureNum, tables->feature_evidence_,
ClassTemplate->NumConfigs);
}
int* IntPointer = tables->sum_feature_evidence_;
uint8_t* UINT8Pointer = tables->feature_evidence_;
int SumOverConfigs = 0;
for (int ConfigNum = ClassTemplate->NumConfigs; ConfigNum > 0; ConfigNum--) {
int evidence = *UINT8Pointer++;
SumOverConfigs += evidence;
*IntPointer++ += evidence;
}
return SumOverConfigs;
}
/**
* Print debugging information for Configurations
*/
#ifndef GRAPHICS_DISABLED
void IntegerMatcher::DebugFeatureProtoError(
INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
const ScratchEvidence& tables,
int16_t NumFeatures,
int Debug) {
float ProtoConfigs[MAX_NUM_CONFIGS];
int ConfigNum;
uint32_t ConfigWord;
int ProtoSetIndex;
uint16_t ProtoNum;
uint8_t ProtoWordNum;
PROTO_SET ProtoSet;
uint16_t ActualProtoNum;
if (PrintMatchSummaryOn(Debug)) {
cprintf("Configuration Mask:\n");
for (ConfigNum = 0; ConfigNum < ClassTemplate->NumConfigs; ConfigNum++)
cprintf("%1d", (((*ConfigMask) >> ConfigNum) & 1));
cprintf("\n");
cprintf("Feature Error for Configurations:\n");
for (ConfigNum = 0; ConfigNum < ClassTemplate->NumConfigs; ConfigNum++) {
cprintf(
" %5.1f",
100.0 * (1.0 - static_cast<float>(tables.sum_feature_evidence_[ConfigNum])
/ NumFeatures / 256.0));
}
cprintf("\n\n\n");
}
if (PrintMatchSummaryOn (Debug)) {
cprintf ("Proto Mask:\n");
for (ProtoSetIndex = 0; ProtoSetIndex < ClassTemplate->NumProtoSets;
ProtoSetIndex++) {
ActualProtoNum = (ProtoSetIndex * PROTOS_PER_PROTO_SET);
for (ProtoWordNum = 0; ProtoWordNum < 2;
ProtoWordNum++, ProtoMask++) {
ActualProtoNum = (ProtoSetIndex * PROTOS_PER_PROTO_SET);
for (ProtoNum = 0;
((ProtoNum < (PROTOS_PER_PROTO_SET >> 1))
&& (ActualProtoNum < ClassTemplate->NumProtos));
ProtoNum++, ActualProtoNum++)
cprintf ("%1d", (((*ProtoMask) >> ProtoNum) & 1));
cprintf ("\n");
}
}
cprintf ("\n");
}
for (int i = 0; i < ClassTemplate->NumConfigs; i++)
ProtoConfigs[i] = 0;
if (PrintProtoMatchesOn (Debug)) {
cprintf ("Proto Evidence:\n");
for (ProtoSetIndex = 0; ProtoSetIndex < ClassTemplate->NumProtoSets;
ProtoSetIndex++) {
ProtoSet = ClassTemplate->ProtoSets[ProtoSetIndex];
ActualProtoNum = (ProtoSetIndex * PROTOS_PER_PROTO_SET);
for (ProtoNum = 0;
((ProtoNum < PROTOS_PER_PROTO_SET) &&
(ActualProtoNum < ClassTemplate->NumProtos));
ProtoNum++, ActualProtoNum++) {
cprintf ("P %3d =", ActualProtoNum);
int temp = 0;
for (uint8_t j = 0; j < ClassTemplate->ProtoLengths[ActualProtoNum]; j++) {
uint8_t data = tables.proto_evidence_[ActualProtoNum][j];
cprintf(" %d", data);
temp += data;
}
cprintf(" = %6.4f%%\n",
temp / 256.0 / ClassTemplate->ProtoLengths[ActualProtoNum]);
ConfigWord = ProtoSet->Protos[ProtoNum].Configs[0];
ConfigNum = 0;
while (ConfigWord) {
cprintf ("%5d", ConfigWord & 1 ? temp : 0);
if (ConfigWord & 1)
ProtoConfigs[ConfigNum] += temp;
ConfigNum++;
ConfigWord >>= 1;
}
cprintf("\n");
}
}
}
if (PrintMatchSummaryOn (Debug)) {
cprintf ("Proto Error for Configurations:\n");
for (ConfigNum = 0; ConfigNum < ClassTemplate->NumConfigs; ConfigNum++)
cprintf (" %5.1f",
100.0 * (1.0 -
ProtoConfigs[ConfigNum] /
ClassTemplate->ConfigLengths[ConfigNum] / 256.0));
cprintf ("\n\n");
}
if (PrintProtoMatchesOn (Debug)) {
cprintf ("Proto Sum for Configurations:\n");
for (ConfigNum = 0; ConfigNum < ClassTemplate->NumConfigs; ConfigNum++)
cprintf (" %4.1f", ProtoConfigs[ConfigNum] / 256.0);
cprintf ("\n\n");
cprintf ("Proto Length for Configurations:\n");
for (ConfigNum = 0; ConfigNum < ClassTemplate->NumConfigs; ConfigNum++)
cprintf (" %4.1f",
static_cast<float>(ClassTemplate->ConfigLengths[ConfigNum]));
cprintf ("\n\n");
}
}
void IntegerMatcher::DisplayProtoDebugInfo(
INT_CLASS ClassTemplate,
BIT_VECTOR ConfigMask,
const ScratchEvidence& tables,
bool SeparateDebugWindows) {
uint16_t ProtoNum;
uint16_t ActualProtoNum;
PROTO_SET ProtoSet;
int ProtoSetIndex;
InitIntMatchWindowIfReqd();
if (SeparateDebugWindows) {
InitFeatureDisplayWindowIfReqd();
InitProtoDisplayWindowIfReqd();
}
for (ProtoSetIndex = 0; ProtoSetIndex < ClassTemplate->NumProtoSets;
ProtoSetIndex++) {
ProtoSet = ClassTemplate->ProtoSets[ProtoSetIndex];
ActualProtoNum = ProtoSetIndex * PROTOS_PER_PROTO_SET;
for (ProtoNum = 0;
((ProtoNum < PROTOS_PER_PROTO_SET) &&
(ActualProtoNum < ClassTemplate->NumProtos));
ProtoNum++, ActualProtoNum++) {
/* Compute Average for Actual Proto */
int temp = 0;
for (uint8_t i = 0; i < ClassTemplate->ProtoLengths[ActualProtoNum]; i++)
temp += tables.proto_evidence_[ActualProtoNum][i];
temp /= ClassTemplate->ProtoLengths[ActualProtoNum];
if ((ProtoSet->Protos[ProtoNum]).Configs[0] & (*ConfigMask)) {
DisplayIntProto(ClassTemplate, ActualProtoNum, temp / 255.0);
}
}
}
}
void IntegerMatcher::DisplayFeatureDebugInfo(
INT_CLASS ClassTemplate,
BIT_VECTOR ProtoMask,
BIT_VECTOR ConfigMask,
int16_t NumFeatures,
const INT_FEATURE_STRUCT* Features,
int AdaptFeatureThreshold,
int Debug,
bool SeparateDebugWindows) {
auto *tables = new ScratchEvidence();
tables->Clear(ClassTemplate);
InitIntMatchWindowIfReqd();
if (SeparateDebugWindows) {
InitFeatureDisplayWindowIfReqd();
InitProtoDisplayWindowIfReqd();
}
for (int Feature = 0; Feature < NumFeatures; Feature++) {
UpdateTablesForFeature(
ClassTemplate, ProtoMask, ConfigMask, Feature, &Features[Feature],
tables, 0);
/* Find Best Evidence for Current Feature */
int best = 0;
assert(ClassTemplate->NumConfigs < MAX_NUM_CONFIGS);
for (int i = 0; i < MAX_NUM_CONFIGS && i < ClassTemplate->NumConfigs; i++)
if (tables->feature_evidence_[i] > best)
best = tables->feature_evidence_[i];
/* Update display for current feature */
if (ClipMatchEvidenceOn(Debug)) {
if (best < AdaptFeatureThreshold)
DisplayIntFeature(&Features[Feature], 0.0);
else
DisplayIntFeature(&Features[Feature], 1.0);
} else {
DisplayIntFeature(&Features[Feature], best / 255.0);
}
}
delete tables;
}
#endif
/**
* Add sum of Proto Evidences into Sum Of Feature Evidence Array
*/
void ScratchEvidence::UpdateSumOfProtoEvidences(
INT_CLASS ClassTemplate, BIT_VECTOR ConfigMask) {
int *IntPointer;
uint32_t ConfigWord;
int ProtoSetIndex;
uint16_t ProtoNum;
PROTO_SET ProtoSet;
int NumProtos;
uint16_t ActualProtoNum;
NumProtos = ClassTemplate->NumProtos;
for (ProtoSetIndex = 0; ProtoSetIndex < ClassTemplate->NumProtoSets;
ProtoSetIndex++) {
ProtoSet = ClassTemplate->ProtoSets[ProtoSetIndex];
ActualProtoNum = (ProtoSetIndex * PROTOS_PER_PROTO_SET);
for (ProtoNum = 0;
((ProtoNum < PROTOS_PER_PROTO_SET) && (ActualProtoNum < NumProtos));
ProtoNum++, ActualProtoNum++) {
int temp = 0;
assert(ClassTemplate->ProtoLengths[ActualProtoNum] < MAX_PROTO_INDEX);
for (uint8_t i = 0; i < MAX_PROTO_INDEX &&
i < ClassTemplate->ProtoLengths[ActualProtoNum]; i++)
temp += proto_evidence_[ActualProtoNum] [i];
ConfigWord = ProtoSet->Protos[ProtoNum].Configs[0];
ConfigWord &= *ConfigMask;
IntPointer = sum_feature_evidence_;
while (ConfigWord) {
if (ConfigWord & 1)
*IntPointer += temp;
IntPointer++;
ConfigWord >>= 1;
}
}
}
}
/**
* Normalize Sum of Proto and Feature Evidence by dividing by the sum of
* the Feature Lengths and the Proto Lengths for each configuration.
*/
void ScratchEvidence::NormalizeSums(
INT_CLASS ClassTemplate, int16_t NumFeatures) {
assert(ClassTemplate->NumConfigs < MAX_NUM_CONFIGS);
for (int i = 0; i < MAX_NUM_CONFIGS && i < ClassTemplate->NumConfigs; i++) {
sum_feature_evidence_[i] = (sum_feature_evidence_[i] << 8) /
(NumFeatures + ClassTemplate->ConfigLengths[i]);
}
}
/**
* Find the best match for the current class and update the Result
* with the configuration and match rating.
* @return The best normalized sum of evidences
*/
int IntegerMatcher::FindBestMatch(
INT_CLASS class_template,
const ScratchEvidence &tables,
UnicharRating* result) {
int best_match = 0;
result->config = 0;
result->fonts.truncate(0);
result->fonts.reserve(class_template->NumConfigs);
/* Find best match */
assert(class_template->NumConfigs < MAX_NUM_CONFIGS);
for (int c = 0; c < MAX_NUM_CONFIGS && c < class_template->NumConfigs; ++c) {
int rating = tables.sum_feature_evidence_[c];
if (*classify_debug_level_ > 2)
tprintf("Config %d, rating=%d\n", c, rating);
if (rating > best_match) {
result->config = c;
best_match = rating;
}
result->fonts.push_back(ScoredFont(c, rating));
}
// Compute confidence on a Probability scale.
result->rating = best_match / 65536.0f;
return best_match;
}
/**
* Applies the CN normalization factor to the given rating and returns
* the modified rating.
*/
float IntegerMatcher::ApplyCNCorrection(float rating, int blob_length,
int normalization_factor,
int matcher_multiplier) {
int divisor = blob_length + matcher_multiplier;
return divisor == 0 ? 1.0f : (rating * blob_length +
matcher_multiplier * normalization_factor / 256.0f) / divisor;
}
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