1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
|
// Copyright 2011 Google Inc. All Rights Reserved.
// Author: rays@google.com (Ray Smith)
///////////////////////////////////////////////////////////////////////
// File: intfeaturedist.cpp
// Description: Fast set-difference-based feature distance calculator.
// Created: Thu Sep 01 13:07:30 PDT 2011
//
// 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 "intfeaturedist.h"
#include "intfeaturemap.h"
namespace tesseract {
IntFeatureDist::IntFeatureDist()
: size_(0), total_feature_weight_(0.0),
feature_map_(nullptr), features_(nullptr),
features_delta_one_(nullptr), features_delta_two_(nullptr) {
}
IntFeatureDist::~IntFeatureDist() {
Clear();
}
// Initialize the table to the given size of feature space.
void IntFeatureDist::Init(const IntFeatureMap* feature_map) {
size_ = feature_map->sparse_size();
Clear();
feature_map_ = feature_map;
features_ = new bool[size_];
features_delta_one_ = new bool[size_];
features_delta_two_ = new bool[size_];
memset(features_, false, size_ * sizeof(features_[0]));
memset(features_delta_one_, false, size_ * sizeof(features_delta_one_[0]));
memset(features_delta_two_, false, size_ * sizeof(features_delta_two_[0]));
total_feature_weight_ = 0.0;
}
// Setup the map for the given indexed_features that have been indexed by
// feature_map.
void IntFeatureDist::Set(const GenericVector<int>& indexed_features,
int canonical_count, bool value) {
total_feature_weight_ = canonical_count;
for (int i = 0; i < indexed_features.size(); ++i) {
const int f = indexed_features[i];
features_[f] = value;
for (int dir = -kNumOffsetMaps; dir <= kNumOffsetMaps; ++dir) {
if (dir == 0) continue;
const int mapped_f = feature_map_->OffsetFeature(f, dir);
if (mapped_f >= 0) {
features_delta_one_[mapped_f] = value;
for (int dir2 = -kNumOffsetMaps; dir2 <= kNumOffsetMaps; ++dir2) {
if (dir2 == 0) continue;
const int mapped_f2 = feature_map_->OffsetFeature(mapped_f, dir2);
if (mapped_f2 >= 0)
features_delta_two_[mapped_f2] = value;
}
}
}
}
}
// Compute the distance between the given feature vector and the last
// Set feature vector.
double IntFeatureDist::FeatureDistance(
const GenericVector<int>& features) const {
const int num_test_features = features.size();
const double denominator = total_feature_weight_ + num_test_features;
double misses = denominator;
for (int i = 0; i < num_test_features; ++i) {
const int index = features[i];
const double weight = 1.0;
if (features_[index]) {
// A perfect match.
misses -= 2.0 * weight;
} else if (features_delta_one_[index]) {
misses -= 1.5 * weight;
} else if (features_delta_two_[index]) {
// A near miss.
misses -= 1.0 * weight;
}
}
return misses / denominator;
}
// Compute the distance between the given feature vector and the last
// Set feature vector.
double IntFeatureDist::DebugFeatureDistance(
const GenericVector<int>& features) const {
const int num_test_features = features.size();
const double denominator = total_feature_weight_ + num_test_features;
double misses = denominator;
for (int i = 0; i < num_test_features; ++i) {
const int index = features[i];
const double weight = 1.0;
INT_FEATURE_STRUCT f = feature_map_->InverseMapFeature(features[i]);
tprintf("Testing feature weight %g:", weight);
f.print();
if (features_[index]) {
// A perfect match.
misses -= 2.0 * weight;
tprintf("Perfect hit\n");
} else if (features_delta_one_[index]) {
misses -= 1.5 * weight;
tprintf("-1 hit\n");
} else if (features_delta_two_[index]) {
// A near miss.
misses -= 1.0 * weight;
tprintf("-2 hit\n");
} else {
tprintf("Total miss\n");
}
}
tprintf("Features present:");
for (int i = 0; i < size_; ++i) {
if (features_[i]) {
INT_FEATURE_STRUCT f = feature_map_->InverseMapFeature(i);
f.print();
}
}
tprintf("\nMinus one features:");
for (int i = 0; i < size_; ++i) {
if (features_delta_one_[i]) {
INT_FEATURE_STRUCT f = feature_map_->InverseMapFeature(i);
f.print();
}
}
tprintf("\nMinus two features:");
for (int i = 0; i < size_; ++i) {
if (features_delta_two_[i]) {
INT_FEATURE_STRUCT f = feature_map_->InverseMapFeature(i);
f.print();
}
}
tprintf("\n");
return misses / denominator;
}
// Clear all data.
void IntFeatureDist::Clear() {
delete [] features_;
features_ = nullptr;
delete [] features_delta_one_;
features_delta_one_ = nullptr;
delete [] features_delta_two_;
features_delta_two_ = nullptr;
}
} // namespace tesseract
|
GitWeb