--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/classify/picofeat.cpp	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,256 @@
+/******************************************************************************
+ ** Filename:    picofeat.c
+ ** Purpose:     Definition of pico-features.
+ ** Author:      Dan Johnson
+ **
+ ** (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 "picofeat.h"
+
+#include "classify.h"
+#include "featdefs.h"
+#include "fpoint.h"
+#include "mfoutline.h"
+#include "ocrfeatures.h"
+#include "params.h"
+#include "trainingsample.h"
+
+#include <cmath>
+#include <cstdio>
+
+namespace tesseract {
+
+/*---------------------------------------------------------------------------
+          Variables
+----------------------------------------------------------------------------*/
+
+double_VAR(classify_pico_feature_length, 0.05, "Pico Feature Length");
+
+/*---------------------------------------------------------------------------
+          Private Function Prototypes
+----------------------------------------------------------------------------*/
+void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet);
+
+void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet);
+
+void NormalizePicoX(FEATURE_SET FeatureSet);
+
+/*----------------------------------------------------------------------------
+              Public Code
+----------------------------------------------------------------------------*/
+/*---------------------------------------------------------------------------*/
+/**
+ * Operation: Dummy for now.
+ *
+ * Globals:
+ * - classify_norm_method normalization method currently specified
+ * @param Blob blob to extract pico-features from
+ * @return Pico-features for Blob.
+ */
+FEATURE_SET Classify::ExtractPicoFeatures(TBLOB *Blob) {
+  auto FeatureSet = new FEATURE_SET_STRUCT(MAX_PICO_FEATURES);
+  auto Outlines = ConvertBlob(Blob);
+  float XScale, YScale;
+  NormalizeOutlines(Outlines, &XScale, &YScale);
+  auto RemainingOutlines = Outlines;
+  iterate(RemainingOutlines) {
+    auto Outline = static_cast<MFOUTLINE>(RemainingOutlines->first_node());
+    ConvertToPicoFeatures2(Outline, FeatureSet);
+  }
+  if (classify_norm_method == baseline) {
+    NormalizePicoX(FeatureSet);
+  }
+  FreeOutlines(Outlines);
+  return (FeatureSet);
+
+} /* ExtractPicoFeatures */
+
+/*----------------------------------------------------------------------------
+              Private Code
+----------------------------------------------------------------------------*/
+/*---------------------------------------------------------------------------*/
+/**
+ * This routine converts an entire segment of an outline
+ * into a set of pico features which are added to
+ * FeatureSet.  The length of the segment is rounded to the
+ * nearest whole number of pico-features.  The pico-features
+ * are spaced evenly over the entire segment.
+ * Results are placed in FeatureSet.
+ * Globals:
+ * - classify_pico_feature_length length of a single pico-feature
+ * @param Start starting point of pico-feature
+ * @param End ending point of pico-feature
+ * @param FeatureSet set to add pico-feature to
+ */
+void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet) {
+  float Angle;
+  float Length;
+  int NumFeatures;
+  FPOINT Center;
+  FPOINT Delta;
+  int i;
+
+  Angle = NormalizedAngleFrom(Start, End, 1.0);
+  Length = DistanceBetween(*Start, *End);
+  NumFeatures = static_cast<int>(floor(Length / classify_pico_feature_length + 0.5));
+  if (NumFeatures < 1) {
+    NumFeatures = 1;
+  }
+
+  /* compute vector for one pico feature */
+  Delta.x = XDelta(*Start, *End) / NumFeatures;
+  Delta.y = YDelta(*Start, *End) / NumFeatures;
+
+  /* compute position of first pico feature */
+  Center.x = Start->x + Delta.x / 2.0;
+  Center.y = Start->y + Delta.y / 2.0;
+
+  /* compute each pico feature in segment and add to feature set */
+  for (i = 0; i < NumFeatures; i++) {
+    auto Feature = new FEATURE_STRUCT(&PicoFeatDesc);
+    Feature->Params[PicoFeatDir] = Angle;
+    Feature->Params[PicoFeatX] = Center.x;
+    Feature->Params[PicoFeatY] = Center.y;
+    AddFeature(FeatureSet, Feature);
+
+    Center.x += Delta.x;
+    Center.y += Delta.y;
+  }
+} /* ConvertSegmentToPicoFeat */
+
+/*---------------------------------------------------------------------------*/
+/**
+ * This routine steps through the specified outline and cuts it
+ * up into pieces of equal length.  These pieces become the
+ * desired pico-features.  Each segment in the outline
+ * is converted into an integral number of pico-features.
+ * Results are returned in FeatureSet.
+ *
+ * Globals:
+ * - classify_pico_feature_length length of features to be extracted
+ * @param Outline outline to extract micro-features from
+ * @param FeatureSet set of features to add pico-features to
+ */
+void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet) {
+  MFOUTLINE Next;
+  MFOUTLINE First;
+  MFOUTLINE Current;
+
+  if (DegenerateOutline(Outline)) {
+    return;
+  }
+
+  First = Outline;
+  Current = First;
+  Next = NextPointAfter(Current);
+  do {
+    /* note that an edge is hidden if the ending point of the edge is
+   marked as hidden.  This situation happens because the order of
+   the outlines is reversed when they are converted from the old
+   format.  In the old format, a hidden edge is marked by the
+   starting point for that edge. */
+    if (!(PointAt(Next)->Hidden)) {
+      ConvertSegmentToPicoFeat(&(PointAt(Current)->Point), &(PointAt(Next)->Point), FeatureSet);
+    }
+
+    Current = Next;
+    Next = NextPointAfter(Current);
+  } while (Current != First);
+
+} /* ConvertToPicoFeatures2 */
+
+/*---------------------------------------------------------------------------*/
+/**
+ * This routine computes the average x position over all
+ * of the pico-features in FeatureSet and then renormalizes
+ * the pico-features to force this average to be the x origin
+ * (i.e. x=0).
+ * FeatureSet is changed.
+ * @param FeatureSet pico-features to be normalized
+ */
+void NormalizePicoX(FEATURE_SET FeatureSet) {
+  int i;
+  FEATURE Feature;
+  float Origin = 0.0;
+
+  for (i = 0; i < FeatureSet->NumFeatures; i++) {
+    Feature = FeatureSet->Features[i];
+    Origin += Feature->Params[PicoFeatX];
+  }
+  Origin /= FeatureSet->NumFeatures;
+
+  for (i = 0; i < FeatureSet->NumFeatures; i++) {
+    Feature = FeatureSet->Features[i];
+    Feature->Params[PicoFeatX] -= Origin;
+  }
+} /* NormalizePicoX */
+
+/*---------------------------------------------------------------------------*/
+/**
+ * @param blob blob to extract features from
+ * @param fx_info
+ * @return Integer character-normalized features for blob.
+ */
+FEATURE_SET Classify::ExtractIntCNFeatures(const TBLOB &blob, const INT_FX_RESULT_STRUCT &fx_info) {
+  INT_FX_RESULT_STRUCT local_fx_info(fx_info);
+  std::vector<INT_FEATURE_STRUCT> bl_features;
+  tesseract::TrainingSample *sample =
+      tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
+  if (sample == nullptr) {
+    return nullptr;
+  }
+
+  uint32_t num_features = sample->num_features();
+  const INT_FEATURE_STRUCT *features = sample->features();
+  auto feature_set = new FEATURE_SET_STRUCT(num_features);
+  for (uint32_t f = 0; f < num_features; ++f) {
+    auto feature = new FEATURE_STRUCT(&IntFeatDesc);
+    feature->Params[IntX] = features[f].X;
+    feature->Params[IntY] = features[f].Y;
+    feature->Params[IntDir] = features[f].Theta;
+    AddFeature(feature_set, feature);
+  }
+  delete sample;
+
+  return feature_set;
+} /* ExtractIntCNFeatures */
+
+/*---------------------------------------------------------------------------*/
+/**
+ * @param blob blob to extract features from
+ * @param fx_info
+ * @return Geometric (top/bottom/width) features for blob.
+ */
+FEATURE_SET Classify::ExtractIntGeoFeatures(const TBLOB &blob,
+                                            const INT_FX_RESULT_STRUCT &fx_info) {
+  INT_FX_RESULT_STRUCT local_fx_info(fx_info);
+  std::vector<INT_FEATURE_STRUCT> bl_features;
+  tesseract::TrainingSample *sample =
+      tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
+  if (sample == nullptr) {
+    return nullptr;
+  }
+
+  auto feature_set = new FEATURE_SET_STRUCT(1);
+  auto feature = new FEATURE_STRUCT(&IntFeatDesc);
+
+  feature->Params[GeoBottom] = sample->geo_feature(GeoBottom);
+  feature->Params[GeoTop] = sample->geo_feature(GeoTop);
+  feature->Params[GeoWidth] = sample->geo_feature(GeoWidth);
+  AddFeature(feature_set, feature);
+  delete sample;
+
+  return feature_set;
+} /* ExtractIntGeoFeatures */
+
+} // namespace tesseract.