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mergenf.cpp
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mergenf.cpp
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/******************************************************************************
** Filename: MergeNF.c
** Purpose: Program for merging similar nano-feature protos
** Author: Dan Johnson
** History: Wed Nov 21 09:55:23 1990, DSJ, Created.
**
** (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 "mergenf.h"
#include "host.h"
#include "efio.h"
#include "clusttool.h"
#include "cluster.h"
#include "oldlist.h"
#include "protos.h"
#include "ndminx.h"
#include "ocrfeatures.h"
#include "const.h"
#include "featdefs.h"
#include "intproto.h"
#include "params.h"
#include <algorithm>
#include <cstdio>
#include <cstring>
#include <cmath>
/*-------------------once in subfeat---------------------------------*/
double_VAR(training_angle_match_scale, 1.0, "Angle Match Scale ...");
double_VAR(training_similarity_midpoint, 0.0075, "Similarity Midpoint ...");
double_VAR(training_similarity_curl, 2.0, "Similarity Curl ...");
/*-----------------------------once in fasttrain----------------------------------*/
double_VAR(training_tangent_bbox_pad, 0.5, "Tangent bounding box pad ...");
double_VAR(training_orthogonal_bbox_pad, 2.5, "Orthogonal bounding box pad ...");
double_VAR(training_angle_pad, 45.0, "Angle pad ...");
/**
* Compare protos p1 and p2 and return an estimate of the
* worst evidence rating that will result for any part of p1
* that is compared to p2. In other words, if p1 were broken
* into pico-features and each pico-feature was matched to p2,
* what is the worst evidence rating that will be achieved for
* any pico-feature.
*
* @param p1, p2 protos to be compared
*
* Globals: none
*
* @return Worst possible result when matching p1 to p2.
* @note Exceptions: none
* @note History: Mon Nov 26 08:27:53 1990, DSJ, Created.
*/
FLOAT32 CompareProtos(PROTO p1, PROTO p2) {
FEATURE Feature;
FLOAT32 WorstEvidence = WORST_EVIDENCE;
FLOAT32 Evidence;
FLOAT32 Angle, Length;
/* if p1 and p2 are not close in length, don't let them match */
Length = fabs (p1->Length - p2->Length);
if (Length > MAX_LENGTH_MISMATCH)
return (0.0);
/* create a dummy pico-feature to be used for comparisons */
Feature = NewFeature (&PicoFeatDesc);
Feature->Params[PicoFeatDir] = p1->Angle;
/* convert angle to radians */
Angle = p1->Angle * 2.0 * PI;
/* find distance from center of p1 to 1/2 picofeat from end */
Length = p1->Length / 2.0 - GetPicoFeatureLength () / 2.0;
if (Length < 0) Length = 0;
/* set the dummy pico-feature at one end of p1 and match it to p2 */
Feature->Params[PicoFeatX] = p1->X + cos (Angle) * Length;
Feature->Params[PicoFeatY] = p1->Y + sin (Angle) * Length;
if (DummyFastMatch (Feature, p2)) {
Evidence = SubfeatureEvidence (Feature, p2);
if (Evidence < WorstEvidence)
WorstEvidence = Evidence;
} else {
FreeFeature(Feature);
return 0.0;
}
/* set the dummy pico-feature at the other end of p1 and match it to p2 */
Feature->Params[PicoFeatX] = p1->X - cos (Angle) * Length;
Feature->Params[PicoFeatY] = p1->Y - sin (Angle) * Length;
if (DummyFastMatch (Feature, p2)) {
Evidence = SubfeatureEvidence (Feature, p2);
if (Evidence < WorstEvidence)
WorstEvidence = Evidence;
} else {
FreeFeature(Feature);
return 0.0;
}
FreeFeature (Feature);
return (WorstEvidence);
} /* CompareProtos */
/**
* This routine computes a proto which is the weighted
* average of protos p1 and p2. The new proto is returned
* in MergedProto.
*
* @param p1, p2 protos to be merged
* @param w1, w2 weight of each proto
* @param MergedProto place to put resulting merged proto
*
* Globals: none
*
* @return none (results are returned in MergedProto)
* @note Exceptions: none
* @note History: Mon Nov 26 08:15:08 1990, DSJ, Created.
*/
void ComputeMergedProto (PROTO p1,
PROTO p2,
FLOAT32 w1,
FLOAT32 w2,
PROTO MergedProto) {
FLOAT32 TotalWeight;
TotalWeight = w1 + w2;
w1 /= TotalWeight;
w2 /= TotalWeight;
MergedProto->X = p1->X * w1 + p2->X * w2;
MergedProto->Y = p1->Y * w1 + p2->Y * w2;
MergedProto->Length = p1->Length * w1 + p2->Length * w2;
MergedProto->Angle = p1->Angle * w1 + p2->Angle * w2;
FillABC(MergedProto);
} /* ComputeMergedProto */
/**
* This routine searches through all of the prototypes in
* Class and returns the id of the proto which would provide
* the best approximation of Prototype. If no close
* approximation can be found, NO_PROTO is returned.
*
* @param Class class to search for matching old proto in
* @param NumMerged # of protos merged into each proto of Class
* @param Prototype new proto to find match for
*
* Globals: none
*
* @return Id of closest proto in Class or NO_PROTO.
* @note Exceptions: none
* @note History: Sat Nov 24 11:42:58 1990, DSJ, Created.
*/
int FindClosestExistingProto(CLASS_TYPE Class, int NumMerged[],
PROTOTYPE *Prototype) {
PROTO_STRUCT NewProto;
PROTO_STRUCT MergedProto;
int Pid;
PROTO Proto;
int BestProto;
FLOAT32 BestMatch;
FLOAT32 Match, OldMatch, NewMatch;
MakeNewFromOld (&NewProto, Prototype);
BestProto = NO_PROTO;
BestMatch = WORST_MATCH_ALLOWED;
for (Pid = 0; Pid < Class->NumProtos; Pid++) {
Proto = ProtoIn(Class, Pid);
ComputeMergedProto(Proto, &NewProto,
(FLOAT32) NumMerged[Pid], 1.0, &MergedProto);
OldMatch = CompareProtos(Proto, &MergedProto);
NewMatch = CompareProtos(&NewProto, &MergedProto);
Match = std::min(OldMatch, NewMatch);
if (Match > BestMatch) {
BestProto = Pid;
BestMatch = Match;
}
}
return BestProto;
} /* FindClosestExistingProto */
/**
* This fills in the fields of the New proto based on the
* fields of the Old proto.
*
* @param New new proto to be filled in
* @param Old old proto to be converted
*
* Globals: none
*
* Exceptions: none
* History: Mon Nov 26 09:45:39 1990, DSJ, Created.
*/
void MakeNewFromOld(PROTO New, PROTOTYPE *Old) {
New->X = CenterX(Old->Mean);
New->Y = CenterY(Old->Mean);
New->Length = LengthOf(Old->Mean);
New->Angle = OrientationOf(Old->Mean);
FillABC(New);
} /* MakeNewFromOld */
/*-------------------once in subfeat---------------------------------*/
/**
* @name SubfeatureEvidence
*
* Compare a feature to a prototype. Print the result.
*/
FLOAT32 SubfeatureEvidence(FEATURE Feature, PROTO Proto) {
float Distance;
float Dangle;
Dangle = Proto->Angle - Feature->Params[PicoFeatDir];
if (Dangle < -0.5) Dangle += 1.0;
if (Dangle > 0.5) Dangle -= 1.0;
Dangle *= training_angle_match_scale;
Distance = Proto->A * Feature->Params[PicoFeatX] +
Proto->B * Feature->Params[PicoFeatY] +
Proto->C;
return (EvidenceOf (Distance * Distance + Dangle * Dangle));
}
/**
* @name EvidenceOf
*
* Return the new type of evidence number corresponding to this
* distance value. This number is no longer based on the chi squared
* approximation. The equation that represents the transform is:
* 1 / (1 + (sim / midpoint) ^ curl)
*/
double EvidenceOf (double Similarity) {
Similarity /= training_similarity_midpoint;
if (training_similarity_curl == 3)
Similarity = Similarity * Similarity * Similarity;
else if (training_similarity_curl == 2)
Similarity = Similarity * Similarity;
else
Similarity = pow (Similarity, training_similarity_curl);
return (1.0 / (1.0 + Similarity));
}
/**
* This routine returns true if Feature would be matched
* by a fast match table built from Proto.
*
* @param Feature feature to be "fast matched" to proto
* @param Proto proto being "fast matched" against
*
* Globals:
* - training_tangent_bbox_pad bounding box pad tangent to proto
* - training_orthogonal_bbox_pad bounding box pad orthogonal to proto
*
* @return true if feature could match Proto.
* @note Exceptions: none
*/
bool DummyFastMatch(FEATURE Feature, PROTO Proto)
{
FRECT BoundingBox;
FLOAT32 MaxAngleError;
FLOAT32 AngleError;
MaxAngleError = training_angle_pad / 360.0;
AngleError = fabs (Proto->Angle - Feature->Params[PicoFeatDir]);
if (AngleError > 0.5)
AngleError = 1.0 - AngleError;
if (AngleError > MaxAngleError)
return false;
ComputePaddedBoundingBox (Proto,
training_tangent_bbox_pad * GetPicoFeatureLength (),
training_orthogonal_bbox_pad * GetPicoFeatureLength (),
&BoundingBox);
return PointInside(&BoundingBox, Feature->Params[PicoFeatX],
Feature->Params[PicoFeatY]);
} /* DummyFastMatch */
/**
* This routine computes a bounding box that encloses the
* specified proto along with some padding. The
* amount of padding is specified as separate distances
* in the tangential and orthogonal directions.
*
* @param Proto proto to compute bounding box for
* @param TangentPad amount of pad to add in direction of segment
* @param OrthogonalPad amount of pad to add orthogonal to segment
* @param[out] BoundingBox place to put results
*
* Globals: none
*
* @return none (results are returned in BoundingBox)
* @note Exceptions: none
* @note History: Wed Nov 14 14:55:30 1990, DSJ, Created.
*/
void ComputePaddedBoundingBox (PROTO Proto, FLOAT32 TangentPad,
FLOAT32 OrthogonalPad, FRECT *BoundingBox) {
FLOAT32 Pad, Length, Angle;
FLOAT32 CosOfAngle, SinOfAngle;
Length = Proto->Length / 2.0 + TangentPad;
Angle = Proto->Angle * 2.0 * PI;
CosOfAngle = fabs(cos(Angle));
SinOfAngle = fabs(sin(Angle));
Pad = std::max(CosOfAngle * Length, SinOfAngle * OrthogonalPad);
BoundingBox->MinX = Proto->X - Pad;
BoundingBox->MaxX = Proto->X + Pad;
Pad = std::max(SinOfAngle * Length, CosOfAngle * OrthogonalPad);
BoundingBox->MinY = Proto->Y - Pad;
BoundingBox->MaxY = Proto->Y + Pad;
} /* ComputePaddedBoundingBox */
/**
* Return true if point (X,Y) is inside of Rectangle.
*
* Globals: none
*
* @return true if point (X,Y) is inside of Rectangle.
* @note Exceptions: none
*/
bool PointInside(FRECT *Rectangle, FLOAT32 X, FLOAT32 Y) {
return (X >= Rectangle->MinX) &&
(X <= Rectangle->MaxX) &&
(Y >= Rectangle->MinY) &&
(Y <= Rectangle->MaxY);
} /* PointInside */