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https://github.com/tesseract-ocr/tesseract.git
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98346c2cd4
The code was modernized using clang-tidy with "modernize-use-using". The modified files were then formatted using clang-tidy with "google-readability-braces-around-statements", then clang-format was applied. Signed-off-by: Stefan Weil <sw@weilnetz.de>
129 lines
5.3 KiB
C
129 lines
5.3 KiB
C
/******************************************************************************
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** Filename: cluster.h
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** Purpose: Definition of feature space clustering routines
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** Author: Dan Johnson
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**
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** (c) Copyright Hewlett-Packard Company, 1988.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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*****************************************************************************/
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#ifndef CLUSTER_H
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#define CLUSTER_H
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#include "kdtree.h"
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#include "oldlist.h"
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struct BUCKETS;
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#define MINBUCKETS 5
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#define MAXBUCKETS 39
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/*----------------------------------------------------------------------
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Types
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----------------------------------------------------------------------*/
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typedef struct sample {
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bool Clustered : 1; // true if included in a higher cluster
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bool Prototype : 1; // true if cluster represented by a proto
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unsigned SampleCount : 30; // number of samples in this cluster
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struct sample* Left; // ptr to left sub-cluster
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struct sample* Right; // ptr to right sub-cluster
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int32_t CharID; // identifier of char sample came from
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float Mean[1]; // mean of cluster - SampleSize floats
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} CLUSTER;
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using SAMPLE = CLUSTER; // can refer to as either sample or cluster
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typedef enum { spherical, elliptical, mixed, automatic } PROTOSTYLE;
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typedef struct { // parameters to control clustering
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PROTOSTYLE ProtoStyle; // specifies types of protos to be made
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float MinSamples; // min # of samples per proto - % of total
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float MaxIllegal; // max percentage of samples in a cluster which
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// have more than 1 feature in that cluster
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float Independence; // desired independence between dimensions
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double Confidence; // desired confidence in prototypes created
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int MagicSamples; // Ideal number of samples in a cluster.
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} CLUSTERCONFIG;
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typedef enum { normal, uniform, D_random, DISTRIBUTION_COUNT } DISTRIBUTION;
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typedef union {
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float Spherical;
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float* Elliptical;
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} FLOATUNION;
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typedef struct {
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bool Significant : 1; // true if prototype is significant
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bool Merged : 1; // Merged after clustering so do not output
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// but kept for display purposes. If it has no
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// samples then it was actually merged.
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// Otherwise it matched an already significant
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// cluster.
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unsigned Style : 2; // spherical, elliptical, or mixed
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unsigned NumSamples : 28; // number of samples in the cluster
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CLUSTER* Cluster; // ptr to cluster which made prototype
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DISTRIBUTION* Distrib; // different distribution for each dimension
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float* Mean; // prototype mean
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float TotalMagnitude; // total magnitude over all dimensions
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float LogMagnitude; // log base e of TotalMagnitude
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FLOATUNION Variance; // prototype variance
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FLOATUNION Magnitude; // magnitude of density function
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FLOATUNION Weight; // weight of density function
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} PROTOTYPE;
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typedef struct {
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int16_t SampleSize; // number of parameters per sample
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PARAM_DESC* ParamDesc; // description of each parameter
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int32_t NumberOfSamples; // total number of samples being clustered
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KDTREE* KDTree; // for optimal nearest neighbor searching
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CLUSTER* Root; // ptr to root cluster of cluster tree
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LIST ProtoList; // list of prototypes
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int32_t NumChar; // # of characters represented by samples
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// cache of reusable histograms by distribution type and number of buckets.
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BUCKETS* bucket_cache[DISTRIBUTION_COUNT][MAXBUCKETS + 1 - MINBUCKETS];
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} CLUSTERER;
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typedef struct {
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int32_t NumSamples; // number of samples in list
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int32_t MaxNumSamples; // maximum size of list
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SAMPLE* Sample[1]; // array of ptrs to sample data structures
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} SAMPLELIST;
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// low level cluster tree analysis routines.
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#define InitSampleSearch(S, C) \
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(((C) == nullptr) ? (S = NIL_LIST) : (S = push(NIL_LIST, (C))))
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/*--------------------------------------------------------------------------
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Public Function Prototypes
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--------------------------------------------------------------------------*/
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CLUSTERER* MakeClusterer(int16_t SampleSize, const PARAM_DESC ParamDesc[]);
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SAMPLE* MakeSample(CLUSTERER* Clusterer, const float* Feature, int32_t CharID);
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LIST ClusterSamples(CLUSTERER* Clusterer, CLUSTERCONFIG* Config);
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void FreeClusterer(CLUSTERER* Clusterer);
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void FreeProtoList(LIST* ProtoList);
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void FreePrototype(void* arg); // PROTOTYPE *Prototype);
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CLUSTER* NextSample(LIST* SearchState);
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float Mean(PROTOTYPE* Proto, uint16_t Dimension);
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float StandardDeviation(PROTOTYPE* Proto, uint16_t Dimension);
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int32_t MergeClusters(int16_t N, PARAM_DESC ParamDesc[], int32_t n1, int32_t n2,
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float m[], float m1[], float m2[]);
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#endif
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