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283 lines
9.9 KiB
C++
283 lines
9.9 KiB
C++
/******************************************************************************
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** Filename: picofeat.c
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** Purpose: Definition of pico-features.
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** Author: Dan Johnson
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** History: 9/4/90, DSJ, Created.
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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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/*----------------------------------------------------------------------------
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Include Files and Type Defines
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----------------------------------------------------------------------------*/
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#include "picofeat.h"
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#include "classify.h"
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#include "efio.h"
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#include "featdefs.h"
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#include "fpoint.h"
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#include "mfoutline.h"
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#include "ocrfeatures.h"
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#include "params.h"
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#include "trainingsample.h"
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#include <math.h>
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#include <stdio.h>
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/*---------------------------------------------------------------------------
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Variables
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----------------------------------------------------------------------------*/
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double_VAR(classify_pico_feature_length, 0.05, "Pico Feature Length");
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/*---------------------------------------------------------------------------
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Private Function Prototypes
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----------------------------------------------------------------------------*/
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void ConvertSegmentToPicoFeat(FPOINT *Start,
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FPOINT *End,
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FEATURE_SET FeatureSet);
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void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet);
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void NormalizePicoX(FEATURE_SET FeatureSet);
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/*----------------------------------------------------------------------------
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Public Code
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----------------------------------------------------------------------------*/
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/*---------------------------------------------------------------------------*/
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namespace tesseract {
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/**
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* Operation: Dummy for now.
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*
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* Globals:
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* - classify_norm_method normalization method currently specified
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* @param Blob blob to extract pico-features from
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* @return Pico-features for Blob.
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* @note Exceptions: none
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* @note History: 9/4/90, DSJ, Created.
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*/
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FEATURE_SET Classify::ExtractPicoFeatures(TBLOB *Blob) {
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LIST Outlines;
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LIST RemainingOutlines;
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MFOUTLINE Outline;
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FEATURE_SET FeatureSet;
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FLOAT32 XScale, YScale;
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FeatureSet = NewFeatureSet(MAX_PICO_FEATURES);
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Outlines = ConvertBlob(Blob);
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NormalizeOutlines(Outlines, &XScale, &YScale);
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RemainingOutlines = Outlines;
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iterate(RemainingOutlines) {
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Outline = (MFOUTLINE) first_node (RemainingOutlines);
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ConvertToPicoFeatures2(Outline, FeatureSet);
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}
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if (classify_norm_method == baseline)
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NormalizePicoX(FeatureSet);
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FreeOutlines(Outlines);
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return (FeatureSet);
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} /* ExtractPicoFeatures */
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} // namespace tesseract
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/*----------------------------------------------------------------------------
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Private Code
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----------------------------------------------------------------------------*/
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/*---------------------------------------------------------------------------*/
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/**
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* This routine converts an entire segment of an outline
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* into a set of pico features which are added to
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* FeatureSet. The length of the segment is rounded to the
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* nearest whole number of pico-features. The pico-features
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* are spaced evenly over the entire segment.
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* Globals:
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* - classify_pico_feature_length length of a single pico-feature
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* @param Start starting point of pico-feature
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* @param End ending point of pico-feature
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* @param FeatureSet set to add pico-feature to
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* @return none (results are placed in FeatureSet)
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* @note Exceptions: none
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* @note History: Tue Apr 30 15:44:34 1991, DSJ, Created.
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*/
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void ConvertSegmentToPicoFeat(FPOINT *Start,
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FPOINT *End,
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FEATURE_SET FeatureSet) {
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FEATURE Feature;
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FLOAT32 Angle;
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FLOAT32 Length;
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int NumFeatures;
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FPOINT Center;
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FPOINT Delta;
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int i;
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Angle = NormalizedAngleFrom (Start, End, 1.0);
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Length = DistanceBetween (*Start, *End);
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NumFeatures = (int) floor (Length / classify_pico_feature_length + 0.5);
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if (NumFeatures < 1)
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NumFeatures = 1;
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/* compute vector for one pico feature */
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Delta.x = XDelta (*Start, *End) / NumFeatures;
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Delta.y = YDelta (*Start, *End) / NumFeatures;
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/* compute position of first pico feature */
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Center.x = Start->x + Delta.x / 2.0;
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Center.y = Start->y + Delta.y / 2.0;
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/* compute each pico feature in segment and add to feature set */
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for (i = 0; i < NumFeatures; i++) {
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Feature = NewFeature (&PicoFeatDesc);
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Feature->Params[PicoFeatDir] = Angle;
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Feature->Params[PicoFeatX] = Center.x;
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Feature->Params[PicoFeatY] = Center.y;
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AddFeature(FeatureSet, Feature);
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Center.x += Delta.x;
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Center.y += Delta.y;
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}
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} /* ConvertSegmentToPicoFeat */
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/*---------------------------------------------------------------------------*/
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/**
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* This routine steps through the specified outline and cuts it
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* up into pieces of equal length. These pieces become the
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* desired pico-features. Each segment in the outline
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* is converted into an integral number of pico-features.
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*
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* Globals:
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* - classify_pico_feature_length length of features to be extracted
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* @param Outline outline to extract micro-features from
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* @param FeatureSet set of features to add pico-features to
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* @return none (results are returned in FeatureSet)
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* @note Exceptions: none
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* @note History: 4/30/91, DSJ, Adapted from ConvertToPicoFeatures().
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*/
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void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet) {
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MFOUTLINE Next;
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MFOUTLINE First;
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MFOUTLINE Current;
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if (DegenerateOutline(Outline))
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return;
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First = Outline;
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Current = First;
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Next = NextPointAfter(Current);
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do {
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/* note that an edge is hidden if the ending point of the edge is
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marked as hidden. This situation happens because the order of
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the outlines is reversed when they are converted from the old
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format. In the old format, a hidden edge is marked by the
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starting point for that edge. */
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if (!(PointAt(Next)->Hidden))
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ConvertSegmentToPicoFeat (&(PointAt(Current)->Point),
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&(PointAt(Next)->Point), FeatureSet);
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Current = Next;
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Next = NextPointAfter(Current);
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}
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while (Current != First);
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} /* ConvertToPicoFeatures2 */
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/*---------------------------------------------------------------------------*/
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/**
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* This routine computes the average x position over all
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* of the pico-features in FeatureSet and then renormalizes
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* the pico-features to force this average to be the x origin
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* (i.e. x=0).
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* @param FeatureSet pico-features to be normalized
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* @return none (FeatureSet is changed)
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* @note Globals: none
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* @note Exceptions: none
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* @note History: Tue Sep 4 16:50:08 1990, DSJ, Created.
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*/
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void NormalizePicoX(FEATURE_SET FeatureSet) {
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int i;
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FEATURE Feature;
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FLOAT32 Origin = 0.0;
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for (i = 0; i < FeatureSet->NumFeatures; i++) {
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Feature = FeatureSet->Features[i];
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Origin += Feature->Params[PicoFeatX];
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}
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Origin /= FeatureSet->NumFeatures;
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for (i = 0; i < FeatureSet->NumFeatures; i++) {
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Feature = FeatureSet->Features[i];
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Feature->Params[PicoFeatX] -= Origin;
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}
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} /* NormalizePicoX */
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namespace tesseract {
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/*---------------------------------------------------------------------------*/
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/**
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* @param blob blob to extract features from
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* @param fx_info
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* @return Integer character-normalized features for blob.
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* @note Exceptions: none
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* @note History: 8/8/2011, rays, Created.
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*/
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FEATURE_SET Classify::ExtractIntCNFeatures(
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const TBLOB& blob, const INT_FX_RESULT_STRUCT& fx_info) {
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INT_FX_RESULT_STRUCT local_fx_info(fx_info);
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GenericVector<INT_FEATURE_STRUCT> bl_features;
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tesseract::TrainingSample* sample = tesseract::BlobToTrainingSample(
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blob, false, &local_fx_info, &bl_features);
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if (sample == NULL) return NULL;
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int num_features = sample->num_features();
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const INT_FEATURE_STRUCT* features = sample->features();
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FEATURE_SET feature_set = NewFeatureSet(num_features);
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for (int f = 0; f < num_features; ++f) {
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FEATURE feature = NewFeature(&IntFeatDesc);
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feature->Params[IntX] = features[f].X;
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feature->Params[IntY] = features[f].Y;
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feature->Params[IntDir] = features[f].Theta;
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AddFeature(feature_set, feature);
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}
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delete sample;
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return feature_set;
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} /* ExtractIntCNFeatures */
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/*---------------------------------------------------------------------------*/
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/**
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* @param blob blob to extract features from
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* @param fx_info
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* @return Geometric (top/bottom/width) features for blob.
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* @note Exceptions: none
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* @note History: 8/8/2011, rays, Created.
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*/
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FEATURE_SET Classify::ExtractIntGeoFeatures(
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const TBLOB& blob, const INT_FX_RESULT_STRUCT& fx_info) {
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INT_FX_RESULT_STRUCT local_fx_info(fx_info);
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GenericVector<INT_FEATURE_STRUCT> bl_features;
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tesseract::TrainingSample* sample = tesseract::BlobToTrainingSample(
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blob, false, &local_fx_info, &bl_features);
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if (sample == NULL) return NULL;
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FEATURE_SET feature_set = NewFeatureSet(1);
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FEATURE feature = NewFeature(&IntFeatDesc);
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feature->Params[GeoBottom] = sample->geo_feature(GeoBottom);
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feature->Params[GeoTop] = sample->geo_feature(GeoTop);
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feature->Params[GeoWidth] = sample->geo_feature(GeoWidth);
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AddFeature(feature_set, feature);
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delete sample;
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return feature_set;
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} /* ExtractIntGeoFeatures */
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} // namespace tesseract.
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