public static void segmentation_correspondences(Narray<Intarray> outsegments, Intarray seg, Intarray cseg)
{
if (NarrayUtil.Max(seg) >= 10000)
throw new Exception("CHECK_ARG: (max(seg)<10000)");
if (NarrayUtil.Max(cseg) >= 10000)
throw new Exception("CHECK_ARG: (max(cseg)<10000)");
int nseg = NarrayUtil.Max(seg) + 1;
int ncseg = NarrayUtil.Max(cseg) + 1;
Intarray overlaps = new Intarray(nseg, ncseg);
overlaps.Fill(0);
if (seg.Length() != cseg.Length())
throw new Exception("CHECK_ARG: (seg.Length()==cseg.Length())");
for (int i = 0; i < seg.Length(); i++)
overlaps[seg.At1d(i), cseg.At1d(i)]++;
outsegments.Clear();
outsegments.Resize(ncseg);
for (int i = 0; i < nseg; i++)
{
int j = NarrayRowUtil.RowArgMax(overlaps, i);
if (!(j >= 0 && j < ncseg))
throw new Exception("ASSERT: (j>=0 && j<ncseg)");
if (outsegments[j] == null)
outsegments[j] = new Intarray();
outsegments[j].Push(i);
}
}
public static bool Equals(Intarray a, Intarray b)
{
if (a.Length() != b.Length()) return false;
for (int i = 0; i < a.Length(); i++)
if (a.UnsafeAt1d(i) != b.UnsafeAt1d(i)) return false;
return true;
}
/// <summary>
/// Compute a classmap that maps a set of possibly sparse classes onto a dense
/// list of new classes and vice versa
/// </summary>
public static void ClassMap(Intarray out_class_to_index, Intarray out_index_to_class, Intarray classes)
{
int nclasses = NarrayUtil.Max(classes) + 1;
Intarray hist = new Intarray(nclasses);
hist.Fill(0);
for (int i = 0; i < classes.Length(); i++)
{
if (classes[i] == -1) continue;
hist[classes[i]]++;
}
int count = 0;
for (int i = 0; i < hist.Length(); i++)
if (hist[i] > 0) count++;
out_class_to_index.Resize(nclasses);
out_class_to_index.Fill(-1);
out_index_to_class.Resize(count);
out_index_to_class.Fill(-1);
int index = 0;
for (int i = 0; i < hist.Length(); i++)
{
if (hist[i] > 0)
{
out_class_to_index[i] = index;
out_index_to_class[index] = i;
index++;
}
}
CHECK_ARG(out_class_to_index.Length() == nclasses, "class_to_index.Length() == nclasses");
CHECK_ARG(out_index_to_class.Length() == NarrayUtil.Max(out_class_to_index) + 1,
"index_to_class.Length() == Max(class_to_index)+1");
CHECK_ARG(out_index_to_class.Length() <= out_class_to_index.Length(),
"index_to_class.Length() <= class_to_index.Length()");
}
/// <summary>
/// Copy one FST to another.
/// </summary>
/// <param name="dst">The destination. Will be cleared before copying.</param>
/// <param name="src">The FST to copy.</param>
public static void fst_copy(IGenericFst dst, IGenericFst src)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i < n; i++)
{
dst.NewState();
}
dst.SetStart(src.GetStart());
for (int i = 0; i < n; i++)
{
dst.SetAccept(i, src.GetAcceptCost(i));
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
int inlen = inputs.Length();
if (inlen != targets.Length())
{
throw new Exception("ASSERT: inputs.length() == targets.length()");
}
if (inlen != outputs.Length())
{
throw new Exception("ASSERT: inputs.length() == outputs.length()");
}
if (inlen != costs.Length())
{
throw new Exception("ASSERT: inputs.length() == costs.length()");
}
for (int j = 0; j < inputs.Length(); j++)
{
dst.AddTransition(i, targets.At1d(j), outputs.At1d(j), costs.At1d(j), inputs.At1d(j));
}
}
}
public static void rseg_to_cseg(Intarray cseg, Intarray rseg, Intarray ids)
{
Intarray map = new Intarray(NarrayUtil.Max(rseg) + 1);
map.Fill(0);
int color = 0;
for (int i = 0; i < ids.Length(); i++)
{
if (ids[i] == 0)
{
continue;
}
color++;
int start = ids[i] >> 16;
int end = ids[i] & 0xFFFF;
if (start > end)
{
throw new Exception("segmentation encoded in IDs looks seriously broken!");
}
if (start >= map.Length() || end >= map.Length())
{
throw new Exception("segmentation encoded in IDs doesn't fit!");
}
for (int j = start; j <= end; j++)
{
map[j] = color;
}
}
cseg.MakeLike(rseg);
for (int i = 0; i < cseg.Length1d(); i++)
{
cseg.Put1d(i, map[rseg.At1d(i)]);
}
}
public override void TrainDense(IDataset ds)
{
//PSet("%nsamples", ds.nSamples());
float split = PGetf("cv_split");
int mlp_cv_max = PGeti("cv_max");
if (crossvalidate)
{
// perform a split for cross-validation, making sure
// that we don't have the same sample in both the
// test and the training set (even if the data set
// is the result of resampling)
Intarray test_ids = new Intarray();
Intarray ids = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
ids.Push(ds.Id(i));
}
NarrayUtil.Uniq(ids);
Global.Debugf("cvdetail", "reduced {0} ids to {1} ids", ds.nSamples(), ids.Length());
NarrayUtil.Shuffle(ids);
int nids = (int)((1.0 - split) * ids.Length());
nids = Math.Min(nids, mlp_cv_max);
for (int i = 0; i < nids; i++)
{
test_ids.Push(ids[i]);
}
NarrayUtil.Quicksort(test_ids);
Intarray training = new Intarray();
Intarray testing = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
int id = ds.Id(i);
if (ClassifierUtil.Bincontains(test_ids, id))
{
testing.Push(i);
}
else
{
training.Push(i);
}
}
Global.Debugf("cvdetail", "#training {0} #testing {1}",
training.Length(), testing.Length());
PSet("%ntraining", training.Length());
PSet("%ntesting", testing.Length());
Datasubset trs = new Datasubset(ds, training);
Datasubset tss = new Datasubset(ds, testing);
TrainBatch(trs, tss);
}
else
{
TrainBatch(ds, ds);
}
}
/// <summary>
/// The main loop iteration.
/// </summary>
public void Radiate()
{
Clear();
//logger("beam", beam);
//logger("beamcost", beamcost);
int control_beam_start = beam.Length();
for (int i = 0; i < control_beam_start; i++)
{
TryAccept(i);
}
// in this loop, traversal may add "control nodes" to the beam
for (int i = 0; i < beam.Length(); i++)
{
Traverse(stree.v1[beam[i]], stree.v2[beam[i]],
beamcost[i], i);
}
// try accepts from control beam nodes
// (they're not going to the next beam)
for (int i = control_beam_start; i < beam.Length(); i++)
{
TryAccept(i);
}
Intarray new_beam = new Intarray();
Floatarray new_beamcost = new Floatarray();
for (int i = 0; i < nbest.Length(); i++)
{
int k = nbest.Tag(i);
if (parent_trails[k] < 0) // skip the control beam nodes
{
continue;
}
new_beam.Push(stree.Add(beam[parent_trails[k]],
all_targets1[k], all_targets2[k],
all_inputs[k], all_outputs[k],
all_costs[k]));
new_beamcost.Push(beamcost[parent_trails[k]] + all_costs[k]);
//logger.format("to new beam: trail index %d, stree %d, target %d,%d",
//k, new_beam[new_beam.length() - 1], all_targets1[k], all_targets2[k]);
}
//move(beam, new_beam);
beam.Move(new_beam);
//move(beamcost, new_beamcost);
beamcost.Move(new_beamcost);
}
public override void Info()
{
bool bak = Logger.Default.verbose;
Logger.Default.verbose = true;
Logger.Default.WriteLine("Linerec");
PPrint();
Logger.Default.WriteLine(String.Format("segmenter: {0}", segmenter.IsEmpty ? "null" : segmenter.Object.Description));
Logger.Default.WriteLine(String.Format("grouper: {0}", grouper.IsEmpty ? "null" : grouper.Object.Description));
Logger.Default.WriteLine(String.Format("counts: {0} {1}", counts.Length(), NarrayUtil.Sum(counts)));
//classifier.Object.Info();
Logger.Default.verbose = bak;
}
public static bool Equals(Intarray a, Intarray b)
{
if (a.Length() != b.Length())
{
return(false);
}
for (int i = 0; i < a.Length(); i++)
{
if (a.UnsafeAt1d(i) != b.UnsafeAt1d(i))
{
return(false);
}
}
return(true);
}
public bool GetCharSegmentation(Intarray image, int book, int page, int line)
{
image.Clear();
bookstores[book].GetCharSegmentation(image, page, line, "gt");
if (image.Length() > 0)
{
return(true);
}
bookstores[book].GetCharSegmentation(image, page, line, "");
if (image.Length() > 0)
{
return(true);
}
return(false);
}
/// <summary>
/// Translate classes using a translation map
/// </summary>
private void ctranslate(Intarray result, Intarray values, Intarray translation)
{
result.Resize(values.Length());
for (int i = 0; i < values.Length(); i++)
{
int v = values[i];
if (v < 0)
{
result[i] = v;
}
else
{
result[i] = translation[v];
}
}
}
public override void FindBestCuts()
{
unchecked
{
for (int i = 0; i < cutcosts.Length(); i++)
{
NarrayUtil.ExtPut(dimage, i, (int)(cutcosts[i] + 10), 0xff0000);
}
for (int i = 0; i < cutcosts.Length(); i++)
{
NarrayUtil.ExtPut(dimage, i, (int)(min_thresh + 10), 0x800000);
}
}
Floatarray temp = new Floatarray();
Gauss.Gauss1d(temp, cutcosts, 3.0f);
cutcosts.Move(temp);
SegmRoutine.local_minima(ref bestcuts, cutcosts, min_range, min_thresh);
for (int i = 0; i < bestcuts.Length(); i++)
{
Narray <Point> cut = cuts[bestcuts[i]];
for (int j = 0; j < cut.Length(); j++)
{
Point p = cut[j];
NarrayUtil.ExtPut(dimage, p.X, p.Y, 0x00ff00);
}
}
///-if(debug.Length > 0) write_image_packed(debug, dimage);
// dshow1d(cutcosts,"Y");
//dshow(dimage,"Y");
}
protected override void Train(IDataset ds)
{
if (!(ds.nSamples() > 0))
{
throw new Exception("nSamples of IDataset must be > 0");
}
if (!(ds.nFeatures() > 0))
{
throw new Exception("nFeatures of IDataset must be > 0");
}
if (c2i.Length() < 1)
{
Intarray raw_classes = new Intarray();
raw_classes.ReserveTo(ds.nSamples());
for (int i = 0; i < ds.nSamples(); i++)
{
raw_classes.Push(ds.Cls(i));
}
ClassMap(c2i, i2c, raw_classes);
/*Intarray classes = new Intarray();
* ctranslate(classes, raw_classes, c2i);*/
//debugf("info","[mapped %d to %d classes]\n",c2i.length(),i2c.length());
}
TranslatedDataset mds = new TranslatedDataset(ds, c2i);
TrainDense(mds);
}
public static void line_segmentation_sort_x(Intarray segmentation)
{
if (NarrayUtil.Max(segmentation) > 100000)
{
throw new Exception("line_segmentation_merge_small_components: to many segments");
}
Narray <Rect> bboxes = new Narray <Rect>();
ImgLabels.bounding_boxes(ref bboxes, segmentation);
Floatarray x0s = new Floatarray();
unchecked
{
x0s.Push((float)-999999);
}
for (int i = 1; i < bboxes.Length(); i++)
{
if (bboxes[i].Empty())
{
x0s.Push(999999);
}
else
{
x0s.Push(bboxes[i].x0);
}
}
// dprint(x0s,1000); printf("\n");
Narray <int> permutation = new Intarray();
Narray <int> rpermutation = new Intarray();
NarrayUtil.Quicksort(permutation, x0s);
rpermutation.Resize(permutation.Length());
for (int i = 0; i < permutation.Length(); i++)
{
rpermutation[permutation[i]] = i;
}
// dprint(rpermutation,1000); printf("\n");
for (int i = 0; i < segmentation.Length1d(); i++)
{
if (segmentation.At1d(i) == 0)
{
continue;
}
segmentation.Put1d(i, rpermutation[segmentation.At1d(i)]);
}
}
/// <summary>
/// Copy one FST to another, preserving only lowest-cost arcs.
/// This is useful for visualization.
/// </summary>
/// <param name="dst">The destination. Will be cleared before copying.</param>
/// <param name="src">The FST to copy.</param>
public static void fst_copy_best_arcs_only(IGenericFst dst, IGenericFst src)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i < n; i++)
{
dst.NewState();
}
dst.SetStart(src.GetStart());
for (int i = 0; i < n; i++)
{
dst.SetAccept(i, src.GetAcceptCost(i));
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
int inlen = inputs.Length();
if (inlen != targets.Length())
{
throw new Exception("ASSERT: inputs.length() == targets.length()");
}
if (inlen != outputs.Length())
{
throw new Exception("ASSERT: inputs.length() == outputs.length()");
}
if (inlen != costs.Length())
{
throw new Exception("ASSERT: inputs.length() == costs.length()");
}
Dictionary <int, int> hash = new Dictionary <int, int>();
for (int j = 0; j < n; j++)
{
int t = targets[j];
int best_so_far = -1;
if (hash.ContainsKey(t))
{
best_so_far = hash[t];
}
if (best_so_far == -1 || costs[j] < costs[best_so_far])
{
hash[t] = j;
}
}
Intarray keys = new Intarray();
//hash.keys(keys);
keys.Clear();
foreach (int key in hash.Keys)
{
keys.Push(key);
}
for (int k = 0; k < keys.Length(); k++)
{
int j = hash[keys[k]];
dst.AddTransition(i, targets[j], outputs[j], costs[j], inputs[j]);
}
}
}
public override void Charseg(ref Intarray outimage, Bytearray inarray)
{
Bytearray image = new Bytearray();
image.Copy(inarray);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
outimage.Copy(image);
Intarray labels = new Intarray();
labels.Copy(image);
ImgLabels.label_components(ref labels);
Narray <Rect> boxes = new Narray <Rect>();
ImgLabels.bounding_boxes(ref boxes, labels);
Intarray equiv = new Intarray(boxes.Length());
for (int i = 0; i < boxes.Length(); i++)
{
equiv[i] = i;
}
for (int i = 1; i < boxes.Length(); i++)
{
Rect p = boxes[i];
for (int j = 1; j < boxes.Length(); j++)
{
if (i == j)
{
continue;
}
Rect q = boxes[j];
int x0 = Math.Max(p.x0, q.x0);
int x1 = Math.Min(p.x1, q.x1);
int iw = x1 - x0;
if (iw <= 0)
{
continue; // no overlap
}
int ow = Math.Min(p.Width(), q.Width());
float frac = iw / (float)(ow);
if (frac < 0.5f)
{
continue; // insufficient overlap
}
// printf("%d %d : %d %d : %g\n",i,j,iw,ow,frac);
equiv.Put1d(Math.Max(i, j), Math.Min(i, j));
}
}
for (int i = 0; i < labels.Length(); i++)
{
labels.Put1d(i, equiv.At1d(labels.At1d(i)));
}
ImgLabels.renumber_labels(labels, 1);
outimage.Move(labels);
SegmRoutine.make_line_segmentation_white(outimage);
SegmRoutine.check_line_segmentation(outimage);
}
/// <summary>
/// Compute a classmap that maps a set of possibly sparse classes onto a dense
/// list of new classes and vice versa
/// </summary>
public static void ClassMap(Intarray out_class_to_index, Intarray out_index_to_class, Intarray classes)
{
int nclasses = NarrayUtil.Max(classes) + 1;
Intarray hist = new Intarray(nclasses);
hist.Fill(0);
for (int i = 0; i < classes.Length(); i++)
{
if (classes[i] == -1)
{
continue;
}
hist[classes[i]]++;
}
int count = 0;
for (int i = 0; i < hist.Length(); i++)
{
if (hist[i] > 0)
{
count++;
}
}
out_class_to_index.Resize(nclasses);
out_class_to_index.Fill(-1);
out_index_to_class.Resize(count);
out_index_to_class.Fill(-1);
int index = 0;
for (int i = 0; i < hist.Length(); i++)
{
if (hist[i] > 0)
{
out_class_to_index[i] = index;
out_index_to_class[index] = i;
index++;
}
}
CHECK_ARG(out_class_to_index.Length() == nclasses, "class_to_index.Length() == nclasses");
CHECK_ARG(out_index_to_class.Length() == NarrayUtil.Max(out_class_to_index) + 1,
"index_to_class.Length() == Max(class_to_index)+1");
CHECK_ARG(out_index_to_class.Length() <= out_class_to_index.Length(),
"index_to_class.Length() <= class_to_index.Length()");
}
public static void erase_small_components(Floatarray input, float mins = 0.2f, float thresh = 0.25f)
{
// compute a thresholded image for component labeling
float threshold = thresh * NarrayUtil.Max(input);
Intarray components = new Intarray();
components.MakeLike(input);
components.Fill(0);
for (int i = 0; i < components.Length(); i++)
{
components[i] = (input[i] > threshold ? 1 : 0);
}
// compute the number of pixels in each component
int n = ImgLabels.label_components(ref components);
Intarray totals = new Intarray(n + 1);
totals.Fill(0);
for (int i = 0; i < components.Length(); i++)
{
totals[components[i]] = totals[components[i]] + 1;
}
totals[0] = 0;
int biggest = NarrayUtil.ArgMax(totals);
// erase small components
float minsize = mins * totals[biggest];
Bytearray keep = new Bytearray(n + 1);
float background = NarrayUtil.Min(input);
for (int i = 0; i < keep.Length(); i++)
{
keep[i] = (byte)(totals[i] > minsize ? 1 : 0);
}
for (int i = 0; i < input.Length(); i++)
{
if (keep[components[i]] == 0)
{
input[i] = background;
}
}
}
public override void SplitIndices(Intarray result1, Intarray result2, Intarray indices)
{
result1.MakeLike(indices);
result2.MakeLike(indices);
int k = l2.nStates();
for (int i = 0; i < indices.Length(); i++)
{
result1.Put1d(i, indices.At1d(i) / k);
result2.Put1d(i, indices.At1d(i) % k);
}
}
public static void segmentation_correspondences(Narray <Intarray> outsegments, Intarray seg, Intarray cseg)
{
if (NarrayUtil.Max(seg) >= 10000)
{
throw new Exception("CHECK_ARG: (max(seg)<10000)");
}
if (NarrayUtil.Max(cseg) >= 10000)
{
throw new Exception("CHECK_ARG: (max(cseg)<10000)");
}
int nseg = NarrayUtil.Max(seg) + 1;
int ncseg = NarrayUtil.Max(cseg) + 1;
Intarray overlaps = new Intarray(nseg, ncseg);
overlaps.Fill(0);
if (seg.Length() != cseg.Length())
{
throw new Exception("CHECK_ARG: (seg.Length()==cseg.Length())");
}
for (int i = 0; i < seg.Length(); i++)
{
overlaps[seg.At1d(i), cseg.At1d(i)]++;
}
outsegments.Clear();
outsegments.Resize(ncseg);
for (int i = 0; i < nseg; i++)
{
int j = NarrayRowUtil.RowArgMax(overlaps, i);
if (!(j >= 0 && j < ncseg))
{
throw new Exception("ASSERT: (j>=0 && j<ncseg)");
}
if (outsegments[j] == null)
{
outsegments[j] = new Intarray();
}
outsegments[j].Push(i);
}
}
public static int count_samples(Intarray classes)
{
int count = 0;
for (int i = 0; i < classes.Length(); i++)
{
if (classes[i] >= 0)
{
count++;
}
}
return(count);
}
/// <summary>
/// Remove epsilons (zeros) and converts integers to string.
/// </summary>
public static void remove_epsilons(out string outs, Intarray a)
{
StringBuilder sb = new StringBuilder();
for (int i = 0; i < a.Length(); i++)
{
if (a[i] > 0)
{
sb.Append((char)a[i]);
}
}
outs = sb.ToString();
}
/// <summary>
/// Reverse the FST's arcs, adding a new start vertex (former accept).
/// </summary>
public static void fst_copy_reverse(IGenericFst dst, IGenericFst src, bool no_accept = false)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i <= n; i++)
{
dst.NewState();
}
if (!no_accept)
{
dst.SetAccept(src.GetStart());
}
dst.SetStart(n);
for (int i = 0; i < n; i++)
{
dst.AddTransition(n, i, 0, src.GetAcceptCost(i), 0);
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
if (inputs.Length() != targets.Length())
{
throw new Exception("ASSERT: inputs.length() == targets.length()");
}
if (inputs.Length() != outputs.Length())
{
throw new Exception("ASSERT: inputs.length() == outputs.length()");
}
if (inputs.Length() != costs.Length())
{
throw new Exception("ASSERT: inputs.length() == costs.length()");
}
for (int j = 0; j < inputs.Length(); j++)
{
dst.AddTransition(targets.At1d(j), i, outputs.At1d(j), costs.At1d(j), inputs.At1d(j));
}
}
}
public void FindBestCuts()
{
/*Intarray segm = new Intarray();
* segm.Copy(dimage);
* ImgLabels.simple_recolor(segm);
* ImgIo.write_image_packed("debug1.png", segm);*/
unchecked
{
for (int i = 0; i < cutcosts.Length(); i++)
{
NarrayUtil.ExtPut(dimage, i, (int)(cutcosts[i] + 10), 0xff0000);
}
for (int i = 0; i < cutcosts.Length(); i++)
{
NarrayUtil.ExtPut(dimage, i, (int)(min_thresh + 10), 0x800000);
}
}
Floatarray temp = new Floatarray();
Gauss.Gauss1d(temp, cutcosts, cost_smooth);
cutcosts.Move(temp);
SegmRoutine.local_minima(ref bestcuts, cutcosts, min_range, min_thresh);
for (int i = 0; i < bestcuts.Length(); i++)
{
Narray <Point> cut = cuts[bestcuts[i]];
for (int j = 0; j < cut.Length(); j++)
{
Point p = cut[j];
NarrayUtil.ExtPut(dimage, p.X, p.Y, 0x00ff00);
}
}
/*segm.Copy(dimage);
* ImgLabels.simple_recolor(segm);
* ImgIo.write_image_packed("debug2.png", segm);*/
}
public int Add(int parent, int vertex1, int vertex2,
int input, int output, float cost)
{
int n = parents.Length();
//logger.format("stree: [%d]: parent %d, v1 %d, v2 %d, cost %f",
// n, parent, vertex1, vertex2, cost);
parents.Push(parent);
v1.Push(vertex1);
v2.Push(vertex2);
inputs.Push(input);
outputs.Push(output);
costs.Push(cost);
return(n);
}
/// <summary>
/// Copy one FST to another.
/// </summary>
/// <param name="dst">The destination. Will be cleared before copying.</param>
/// <param name="src">The FST to copy.</param>
public static void fst_copy(IGenericFst dst, IGenericFst src)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i < n; i++)
dst.NewState();
dst.SetStart(src.GetStart());
for (int i = 0; i < n; i++)
{
dst.SetAccept(i, src.GetAcceptCost(i));
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
int inlen = inputs.Length();
if (inlen != targets.Length())
throw new Exception("ASSERT: inputs.length() == targets.length()");
if (inlen != outputs.Length())
throw new Exception("ASSERT: inputs.length() == outputs.length()");
if (inlen != costs.Length())
throw new Exception("ASSERT: inputs.length() == costs.length()");
for (int j = 0; j < inputs.Length(); j++)
dst.AddTransition(i, targets.At1d(j), outputs.At1d(j), costs.At1d(j), inputs.At1d(j));
}
}
public override void Rescore(int from, int to, int output, float cost, int input)
{
Intarray t = m_targets[from];
Intarray i = m_inputs[from];
Intarray o = m_outputs[from];
for (int j = 0; j < t.Length(); j++)
{
if (t[j] == to &&
i[j] == input &&
o[j] == output)
{
m_costs[from][j] = cost;
break;
}
}
}
public Dataset8(Narray<sbyte> data, Intarray classes)
: this()
{
data.Copy(data);
classes.Copy(classes);
if (classes.Length() > 0)
{
nc = NarrayUtil.Max(classes) + 1;
nf = data.Dim(1);
//CHECK_ARG(NarrayUtil.Min(data) > -100 && NarrayUtil.Max(data) < 100, "min(data)>-100 && max(data)<100");
CHECK_ARG(NarrayUtil.Min(classes) >= -1 && NarrayUtil.Max(classes) < 10000, "min(classes)>=-1 && max(classes)<10000");
}
else
{
nc = 0;
nf = -1;
}
}
public Dataset8(Narray <sbyte> data, Intarray classes)
: this()
{
data.Copy(data);
classes.Copy(classes);
if (classes.Length() > 0)
{
nc = NarrayUtil.Max(classes) + 1;
nf = data.Dim(1);
//CHECK_ARG(NarrayUtil.Min(data) > -100 && NarrayUtil.Max(data) < 100, "min(data)>-100 && max(data)<100");
CHECK_ARG(NarrayUtil.Min(classes) >= -1 && NarrayUtil.Max(classes) < 10000, "min(classes)>=-1 && max(classes)<10000");
}
else
{
nc = 0;
nf = -1;
}
}
/// <summary>
/// Copy one FST to another, preserving only lowest-cost arcs.
/// This is useful for visualization.
/// </summary>
/// <param name="dst">The destination. Will be cleared before copying.</param>
/// <param name="src">The FST to copy.</param>
public static void fst_copy_best_arcs_only(IGenericFst dst, IGenericFst src)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i < n; i++)
dst.NewState();
dst.SetStart(src.GetStart());
for(int i = 0; i < n; i++)
{
dst.SetAccept(i, src.GetAcceptCost(i));
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
int inlen = inputs.Length();
if (inlen != targets.Length())
throw new Exception("ASSERT: inputs.length() == targets.length()");
if (inlen != outputs.Length())
throw new Exception("ASSERT: inputs.length() == outputs.length()");
if (inlen != costs.Length())
throw new Exception("ASSERT: inputs.length() == costs.length()");
Dictionary< int, int > hash = new Dictionary<int,int>();
for(int j = 0; j < n; j++) {
int t = targets[j];
int best_so_far = -1;
if (hash.ContainsKey(t))
best_so_far = hash[t];
if(best_so_far == -1 || costs[j] < costs[best_so_far])
hash[t] = j;
}
Intarray keys = new Intarray();
//hash.keys(keys);
keys.Clear();
foreach (int key in hash.Keys)
{
keys.Push(key);
}
for(int k = 0; k < keys.Length(); k++) {
int j = hash[keys[k]];
dst.AddTransition(i, targets[j], outputs[j], costs[j], inputs[j]);
}
}
}
protected static void write_node(BinaryWriter writer, IGenericFst fst, int index)
{
Intarray inputs = new Intarray();
Intarray targets = new Intarray();
Intarray outputs = new Intarray();
Floatarray costs = new Floatarray();
fst.Arcs(inputs, targets, outputs, costs, index);
int narcs = targets.Length();
write_float(writer, fst.GetAcceptCost(index));
write_int64_LE(writer, narcs);
for (int i = 0; i < narcs; i++)
{
write_int32_LE(writer, inputs[i]);
write_int32_LE(writer, outputs[i]);
write_float(writer, costs[i]);
write_int32_LE(writer, targets[i]);
}
}
public void reconstruct_edges(Intarray inputs,
Intarray outputs,
Floatarray costs,
Intarray vertices)
{
int n = vertices.Length();
inputs.Resize(n);
outputs.Resize(n);
costs.Resize(n);
for (int i = 0; i < n - 1; i++)
{
int source = vertices[i];
int target = vertices[i + 1];
Intarray out_ins = new Intarray();
Intarray out_targets = new Intarray();
Intarray out_outs = new Intarray();
Floatarray out_costs = new Floatarray();
fst.Arcs(out_ins, out_targets, out_outs, out_costs, source);
costs[i] = 1e38f;
// find the best arc
for (int j = 0; j < out_targets.Length(); j++)
{
if (out_targets[j] != target)
{
continue;
}
if (out_costs[j] < costs[i])
{
inputs[i] = out_ins[j];
outputs[i] = out_outs[j];
costs[i] = out_costs[j];
}
}
}
inputs[n - 1] = 0;
outputs[n - 1] = 0;
costs[n - 1] = fst.GetAcceptCost(vertices[n - 1]);
}
public override void Charseg(ref Intarray outimage, Bytearray inarray)
{
Bytearray image = new Bytearray();
image.Copy(inarray);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
outimage.Copy(image);
Intarray labels = new Intarray();
labels.Copy(image);
ImgLabels.label_components(ref labels);
Narray<Rect> boxes = new Narray<Rect>();
ImgLabels.bounding_boxes(ref boxes, labels);
Intarray equiv = new Intarray(boxes.Length());
for(int i=0; i<boxes.Length(); i++)
equiv[i] = i;
for(int i=1; i<boxes.Length(); i++) {
Rect p = boxes[i];
for(int j=1;j<boxes.Length();j++) {
if(i==j) continue;
Rect q = boxes[j];
int x0 = Math.Max(p.x0, q.x0);
int x1 = Math.Min(p.x1, q.x1);
int iw = x1-x0;
if(iw <= 0) continue; // no overlap
int ow = Math.Min(p.Width(), q.Width());
float frac = iw/(float)(ow);
if(frac < 0.5f) continue; // insufficient overlap
// printf("%d %d : %d %d : %g\n",i,j,iw,ow,frac);
equiv.Put1d(Math.Max(i, j), Math.Min(i, j));
}
}
for(int i=0; i<labels.Length(); i++)
labels.Put1d(i, equiv.At1d(labels.At1d(i)));
ImgLabels.renumber_labels(labels, 1);
outimage.Move(labels);
SegmRoutine.make_line_segmentation_white(outimage);
SegmRoutine.check_line_segmentation(outimage);
}
public void reconstruct_edges(Intarray inputs,
Intarray outputs,
Floatarray costs,
Intarray vertices)
{
int n = vertices.Length();
inputs.Resize(n);
outputs.Resize(n);
costs.Resize(n);
for (int i = 0; i < n - 1; i++)
{
int source = vertices[i];
int target = vertices[i + 1];
Intarray out_ins = new Intarray();
Intarray out_targets = new Intarray();
Intarray out_outs = new Intarray();
Floatarray out_costs = new Floatarray();
fst.Arcs(out_ins, out_targets, out_outs, out_costs, source);
costs[i] = 1e38f;
// find the best arc
for (int j = 0; j < out_targets.Length(); j++)
{
if (out_targets[j] != target) continue;
if (out_costs[j] < costs[i])
{
inputs[i] = out_ins[j];
outputs[i] = out_outs[j];
costs[i] = out_costs[j];
}
}
}
inputs[n - 1] = 0;
outputs[n - 1] = 0;
costs[n - 1] = fst.GetAcceptCost(vertices[n - 1]);
}
public bool Step()
{
int node = heap.Pop();
if (node == n)
{
return(true); // accept has popped up
}
// get outbound arcs
Intarray inputs = new Intarray();
Intarray targets = new Intarray();
Intarray outputs = new Intarray();
Floatarray costs = new Floatarray();
fst.Arcs(inputs, targets, outputs, costs, node);
for (int i = 0; i < targets.Length(); i++)
{
int t = targets[i];
if (came_from[t] == -1 || g[node] + costs[i] < g[t])
{
// relax the edge
came_from[t] = node;
g[t] = g[node] + costs[i];
heap.Push(t, g[t] + Convert.ToSingle(Heuristic(t)));
}
}
if (accepted_from == -1 ||
g[node] + fst.GetAcceptCost(node) < g_accept)
{
// relax the accept edge
accepted_from = node;
g_accept = g[node] + fst.GetAcceptCost(node);
heap.Push(n, g_accept);
}
return(false);
}
/// <summary>
/// Create char segmentation (cseg) files if missing
/// </summary>
/// <param name="bookPath">path to bookstore</param>
/// <param name="modelFilename">Linerec model file</param>
/// <param name="langModel">language model file</param>
/// <param name="suffix">e.g., 'gt'</param>
public void ComputeMissingCsegForBookStore(string bookPath, string model = "default.model",
string suffix = "", bool saveRseg = false, string langModel = "default.fst")
{
// create line recognizer
Linerec linerec = Linerec.LoadLinerec(model);
// create IBookStore
IBookStore bookstore = new SmartBookStore();
bookstore.SetPrefix(bookPath);
bookstore.Info();
// language model
OcroFST lmodel = OcroFST.MakeOcroFst();
lmodel.Load(langModel);
// iterate lines of pages
for (int page = 0; page < bookstore.NumberOfPages(); page++)
{
int nlines = bookstore.LinesOnPage(page);
Console.WriteLine("Page {0} has {1} lines", page, nlines);
for (int j = 0; j < nlines; j++)
{
int line = bookstore.GetLineId(page, j);
Bytearray image = new Bytearray();
bookstore.GetLine(image, page, line);
Intarray cseg = new Intarray();
bookstore.GetCharSegmentation(cseg, page, line, suffix);
// check missing cseg file
if (cseg.Length() <= 0 && image.Length() > 0)
{
// recognize line
OcroFST fst = OcroFST.MakeOcroFst();
Intarray rseg = new Intarray();
linerec.RecognizeLine(rseg, fst, image);
// find best results
string resText;
Intarray inp = new Intarray();
Floatarray costs = new Floatarray();
double totalCost = BeamSearch.beam_search(out resText, inp, costs, fst, lmodel, 100);
Console.WriteLine(bookstore.PathFile(page, line, suffix));
Console.Write(" beam_search score: {0}", totalCost);
/*string resText2;
* fst.BestPath(out resText2);*/
// write cseg to bookstore
string trans;
bookstore.GetLine(out trans, page, line, suffix);
resText = resText.Replace(" ", "");
if (String.IsNullOrEmpty(trans))
{
bookstore.PutLine(resText, page, line, suffix);
Console.Write("; transcript saved");
}
else if (trans == resText)
{
// convert inputs and rseg to cseg
SegmRoutine.rseg_to_cseg(cseg, rseg, inp);
bookstore.PutCharSegmentation(cseg, page, line, suffix);
Console.Write("; cseg saved");
}
else if (saveRseg)
{
// convert inputs and rseg to cseg
SegmRoutine.rseg_to_cseg(cseg, rseg, inp);
//SegmRoutine.remove_small_components(cseg, 4);
/*bookstore.PutCharSegmentation(cseg, page, line, suffix);
* Console.Write("; cseg saved");*/
SegmRoutine.make_line_segmentation_white(cseg);
ImgLabels.simple_recolor(cseg);
string v = "rseg";
if (!String.IsNullOrEmpty(suffix))
{
v += "."; v += suffix;
}
string rsegpath = bookstore.PathFile(page, line, v, "png");
ImgIo.write_image_packed(rsegpath, cseg);
Console.Write("; rseg saved");
}
Console.WriteLine();
}
}
}
}
protected void rescale(Floatarray v, Floatarray input)
{
if (input.Rank() != 2)
throw new Exception("CHECK_ARG: sub.Rank()==2");
Floatarray sub = new Floatarray();
// find the largest connected component
// and crop to its bounding box
// (use a binary version of the character
// to compute the bounding box)
Intarray components = new Intarray();
float threshold = PGetf("threshold") * NarrayUtil.Max(input);
Global.Debugf("biggestcc", "threshold {0}", threshold);
components.MakeLike(input);
components.Fill(0);
for (int i = 0; i < components.Length(); i++)
components[i] = (input[i] > threshold ? 1 : 0);
int n = ImgLabels.label_components(ref components);
Intarray totals = new Intarray(n + 1);
totals.Fill(0);
for (int i = 0; i < components.Length(); i++)
totals[components[i]]++;
totals[0] = 0;
Narray<Rect> boxes = new Narray<Rect>();
ImgLabels.bounding_boxes(ref boxes, components);
int biggest = NarrayUtil.ArgMax(totals);
Rect r = boxes[biggest];
int pad = (int)(PGetf("pad") + 0.5f);
r.PadBy(pad, pad);
Global.Debugf("biggestcc", "({0}) {1}[{2}] :: {3} {4} {5} {6}",
n, biggest, totals[biggest],
r.x0, r.y0, r.x1, r.y1);
// now perform normal feature extraction
// (use the original grayscale input)
sub = input;
ImgMisc.Crop(sub, r);
int csize = PGeti("csize");
float s = Math.Max(sub.Dim(0), sub.Dim(1))/(float)csize;
if(PGetf("noupscale") > 0 && s < 1.0f)
s = 1.0f;
float sig = s * PGetf("aa");
float dx = (csize*s-sub.Dim(0))/2f;
float dy = (csize*s-sub.Dim(1))/2f;
if(sig > 1e-3f)
Gauss.Gauss2d(sub, sig, sig);
v.Resize(csize, csize);
v.Fill(0f);
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
float x = i * s - dx;
float y = j * s - dy;
if (x < 0 || x >= sub.Dim(0)) continue;
if (y < 0 || y >= sub.Dim(1)) continue;
float value = ImgOps.bilin(sub, x, y);
v[i, j] = value;
}
}
/*Global.Debugf("biggestcc", "{0} {1} ({2}) -> {3} {4} ({5})",
sub.Dim(0), sub.Dim(1), NarrayUtil.Max(sub),
v.Dim(0), v.Dim(1), NarrayUtil.Max(v));*/
}
/// <summary>
/// Create char segmentation (cseg) files if missing
/// </summary>
/// <param name="bookPath">path to bookstore</param>
/// <param name="modelFilename">Linerec model file</param>
/// <param name="langModel">language model file</param>
/// <param name="suffix">e.g., 'gt'</param>
public void ComputeMissingCsegForBookStore(string bookPath, string model = "default.model",
string suffix = "", bool saveRseg = false, string langModel = "default.fst")
{
// create line recognizer
Linerec linerec = Linerec.LoadLinerec(model);
// create IBookStore
IBookStore bookstore = new SmartBookStore();
bookstore.SetPrefix(bookPath);
bookstore.Info();
// language model
OcroFST lmodel = OcroFST.MakeOcroFst();
lmodel.Load(langModel);
// iterate lines of pages
for (int page = 0; page < bookstore.NumberOfPages(); page++)
{
int nlines = bookstore.LinesOnPage(page);
Console.WriteLine("Page {0} has {1} lines", page, nlines);
for (int j = 0; j < nlines; j++)
{
int line = bookstore.GetLineId(page, j);
Bytearray image = new Bytearray();
bookstore.GetLine(image, page, line);
Intarray cseg = new Intarray();
bookstore.GetCharSegmentation(cseg, page, line, suffix);
// check missing cseg file
if (cseg.Length() <= 0 && image.Length() > 0)
{
// recognize line
OcroFST fst = OcroFST.MakeOcroFst();
Intarray rseg = new Intarray();
linerec.RecognizeLine(rseg, fst, image);
// find best results
string resText;
Intarray inp = new Intarray();
Floatarray costs = new Floatarray();
double totalCost = BeamSearch.beam_search(out resText, inp, costs, fst, lmodel, 100);
Console.WriteLine(bookstore.PathFile(page, line, suffix));
Console.Write(" beam_search score: {0}", totalCost);
/*string resText2;
fst.BestPath(out resText2);*/
// write cseg to bookstore
string trans;
bookstore.GetLine(out trans, page, line, suffix);
resText = resText.Replace(" ", "");
if (String.IsNullOrEmpty(trans))
{
bookstore.PutLine(resText, page, line, suffix);
Console.Write("; transcript saved");
}
else if (trans == resText)
{
// convert inputs and rseg to cseg
SegmRoutine.rseg_to_cseg(cseg, rseg, inp);
bookstore.PutCharSegmentation(cseg, page, line, suffix);
Console.Write("; cseg saved");
}
else if (saveRseg)
{
// convert inputs and rseg to cseg
SegmRoutine.rseg_to_cseg(cseg, rseg, inp);
//SegmRoutine.remove_small_components(cseg, 4);
/*bookstore.PutCharSegmentation(cseg, page, line, suffix);
Console.Write("; cseg saved");*/
SegmRoutine.make_line_segmentation_white(cseg);
ImgLabels.simple_recolor(cseg);
string v = "rseg";
if (!String.IsNullOrEmpty(suffix)) { v += "."; v += suffix; }
string rsegpath = bookstore.PathFile(page, line, v, "png");
ImgIo.write_image_packed(rsegpath, cseg);
Console.Write("; rseg saved");
}
Console.WriteLine();
}
}
}
}
public void TestTrainLenetCseg()
{
string bookPath = "data\\0000\\";
string netFileName = "latin-lenet.model";
Linerec.GDef("linerec", "use_reject", 1);
Linerec.GDef("lenet", "junk", 1);
Linerec.GDef("lenet", "epochs", 4);
// create Linerec
Linerec linerec;
if (File.Exists(netFileName))
linerec = Linerec.LoadLinerec(netFileName);
else
{
linerec = new Linerec("lenet");
LenetClassifier classifier = linerec.GetClassifier() as LenetClassifier;
if (classifier != null)
classifier.InitNumSymbLatinAlphabet();
}
// temporary disable junk
//linerec.DisableJunk = true;
linerec.StartTraining();
int nepochs = 10;
LineSource lines = new LineSource();
lines.Init(new string[] { "data2" });
//linerec.GetClassifier().Set("epochs", 1);
for (int epoch = 1; epoch <= nepochs; epoch++)
{
linerec.Epoch(epoch);
// load cseg samples
while (!lines.Done())
{
lines.MoveNext();
Intarray cseg = new Intarray();
//Bytearray image = new Bytearray();
string transcript = lines.GetTranscript();
//lines.GetImage(image);
if (!lines.GetCharSegmentation(cseg) && cseg.Length() == 0)
{
Global.Debugf("warn", "skipping book {0} page {1} line {2} (no or bad cseg)",
lines.CurrentBook, lines.CurrentPage, lines.Current);
continue;
}
SegmRoutine.make_line_segmentation_black(cseg);
linerec.AddTrainingLine(cseg, transcript);
}
lines.Reset();
lines.Shuffle();
// do Train and clear Dataset
linerec.FinishTraining();
// do save
if (epoch % 1 == 0)
linerec.Save(netFileName);
// recognize test line
bool bakDisJunk = linerec.DisableJunk;
linerec.DisableJunk = false;
DoTestLinerecRecognize(linerec, "data2\\", "test1.png");
linerec.DisableJunk = bakDisJunk;
}
// finnaly save
linerec.Save(netFileName);
}
public override void Arcs(Intarray ids, Intarray targets, Intarray outputs, Floatarray costs, int node)
{
int n1 = node / l2.nStates();
int n2 = node % l2.nStates();
Intarray ids1 = new Intarray();
Intarray ids2 = new Intarray();
Intarray t1 = new Intarray();
Intarray t2 = new Intarray();
Intarray o1 = new Intarray();
Intarray o2 = new Intarray();
Floatarray c1 = new Floatarray();
Floatarray c2 = new Floatarray();
l1.Arcs(ids1, t1, o1, c1, n1);
l2.Arcs(ids2, t2, o2, c2, n2);
// sort & permute
Intarray p1 = new Intarray();
Intarray p2 = new Intarray();
NarrayUtil.Quicksort(p1, o1);
NarrayUtil.Permute(ids1, p1);
NarrayUtil.Permute(t1, p1);
NarrayUtil.Permute(o1, p1);
NarrayUtil.Permute(c1, p1);
NarrayUtil.Quicksort(p2, ids2);
NarrayUtil.Permute(ids2, p2);
NarrayUtil.Permute(t2, p2);
NarrayUtil.Permute(o2, p2);
NarrayUtil.Permute(c2, p2);
int k1, k2;
// l1 epsilon moves
for (k1 = 0; k1 < o1.Length() && o1.At1d(k1) == 0; k1++)
{
ids.Push(ids1.At1d(k1));
targets.Push(Combine(t1.At1d(k1), n2));
outputs.Push(0);
costs.Push(c1.At1d(k1));
}
// l2 epsilon moves
for (k2 = 0; k2 < o2.Length() && ids2.At1d(k2) == 0; k2++)
{
ids.Push(0);
targets.Push(Combine(n1, t2.At1d(k2)));
outputs.Push(o2.At1d(k2));
costs.Push(c2.At1d(k2));
}
// non-epsilon moves
while (k1 < o1.Length() && k2 < ids2.Length())
{
while (k1 < o1.Length() && o1.At1d(k1) < ids2.At1d(k2)) k1++;
if (k1 >= o1.Length()) break;
while (k2 < ids2.Length() && o1.At1d(k1) > ids2.At1d(k2)) k2++;
while (k1 < o1.Length() && k2 < ids2.Length() && o1.At1d(k1) == ids2.At1d(k2))
{
for (int j = k2; j < ids2.Length() && o1.At1d(k1) == ids2.At1d(j); j++)
{
ids.Push(ids1.At1d(k1));
targets.Push(Combine(t1.At1d(k1), t2.At1d(j)));
outputs.Push(o2.At1d(j));
costs.Push(c1.At1d(k1) + c2.At1d(j));
}
k1++;
}
}
}
public override void SplitIndices(Intarray result1, Intarray result2, Intarray indices)
{
result1.MakeLike(indices);
result2.MakeLike(indices);
int k = l2.nStates();
for (int i = 0; i < indices.Length(); i++)
{
result1.Put1d(i, indices.At1d(i) / k);
result2.Put1d(i, indices.At1d(i) % k);
}
}
public override void TrainDense(IDataset ds)
{
//PSet("%nsamples", ds.nSamples());
float split = PGetf("cv_split");
int mlp_cv_max = PGeti("cv_max");
if (crossvalidate)
{
// perform a split for cross-validation, making sure
// that we don't have the same sample in both the
// test and the training set (even if the data set
// is the result of resampling)
Intarray test_ids = new Intarray();
Intarray ids = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
ids.Push(ds.Id(i));
NarrayUtil.Uniq(ids);
Global.Debugf("cvdetail", "reduced {0} ids to {1} ids", ds.nSamples(), ids.Length());
NarrayUtil.Shuffle(ids);
int nids = (int)((1.0 - split) * ids.Length());
nids = Math.Min(nids, mlp_cv_max);
for (int i = 0; i < nids; i++)
test_ids.Push(ids[i]);
NarrayUtil.Quicksort(test_ids);
Intarray training = new Intarray();
Intarray testing = new Intarray();
for (int i = 0; i < ds.nSamples(); i++)
{
int id = ds.Id(i);
if (ClassifierUtil.Bincontains(test_ids, id))
testing.Push(i);
else
training.Push(i);
}
Global.Debugf("cvdetail", "#training {0} #testing {1}",
training.Length(), testing.Length());
PSet("%ntraining", training.Length());
PSet("%ntesting", testing.Length());
Datasubset trs = new Datasubset(ds, training);
Datasubset tss = new Datasubset(ds, testing);
TrainBatch(trs, tss);
}
else
{
TrainBatch(ds, ds);
}
}
protected static void write_node(BinaryWriter writer, IGenericFst fst, int index)
{
Intarray inputs = new Intarray();
Intarray targets = new Intarray();
Intarray outputs = new Intarray();
Floatarray costs = new Floatarray();
fst.Arcs(inputs, targets, outputs, costs, index);
int narcs = targets.Length();
write_float(writer, fst.GetAcceptCost(index));
write_int64_LE(writer, narcs);
for (int i = 0; i < narcs; i++)
{
write_int32_LE(writer, inputs[i]);
write_int32_LE(writer, outputs[i]);
write_float(writer, costs[i]);
write_int32_LE(writer, targets[i]);
}
}
/// <summary>
/// Translate classes using a translation map
/// </summary>
private void ctranslate(Intarray result, Intarray values, Intarray translation)
{
result.Resize(values.Length());
for (int i = 0; i < values.Length(); i++)
{
int v = values[i];
if (v < 0) result[i] = v;
else result[i] = translation[v];
}
}
public void TestTrainLenetCseg()
{
string bookPath = "data\\0000\\";
string netFileName = "latin-lenet.model";
Linerec.GDef("linerec", "use_reject", 1);
Linerec.GDef("lenet", "junk", 1);
Linerec.GDef("lenet", "epochs", 4);
// create Linerec
Linerec linerec;
if (File.Exists(netFileName))
{
linerec = Linerec.LoadLinerec(netFileName);
}
else
{
linerec = new Linerec("lenet");
LenetClassifier classifier = linerec.GetClassifier() as LenetClassifier;
if (classifier != null)
{
classifier.InitNumSymbLatinAlphabet();
}
}
// temporary disable junk
//linerec.DisableJunk = true;
linerec.StartTraining();
int nepochs = 10;
LineSource lines = new LineSource();
lines.Init(new string[] { "data2" });
//linerec.GetClassifier().Set("epochs", 1);
for (int epoch = 1; epoch <= nepochs; epoch++)
{
linerec.Epoch(epoch);
// load cseg samples
while (!lines.Done())
{
lines.MoveNext();
Intarray cseg = new Intarray();
//Bytearray image = new Bytearray();
string transcript = lines.GetTranscript();
//lines.GetImage(image);
if (!lines.GetCharSegmentation(cseg) && cseg.Length() == 0)
{
Global.Debugf("warn", "skipping book {0} page {1} line {2} (no or bad cseg)",
lines.CurrentBook, lines.CurrentPage, lines.Current);
continue;
}
SegmRoutine.make_line_segmentation_black(cseg);
linerec.AddTrainingLine(cseg, transcript);
}
lines.Reset();
lines.Shuffle();
// do Train and clear Dataset
linerec.FinishTraining();
// do save
if (epoch % 1 == 0)
{
linerec.Save(netFileName);
}
// recognize test line
bool bakDisJunk = linerec.DisableJunk;
linerec.DisableJunk = false;
DoTestLinerecRecognize(linerec, "data2\\", "test1.png");
linerec.DisableJunk = bakDisJunk;
}
// finnaly save
linerec.Save(netFileName);
}
/// <summary>
/// Reverse the FST's arcs, adding a new start vertex (former accept).
/// </summary>
public static void fst_copy_reverse(IGenericFst dst, IGenericFst src, bool no_accept = false)
{
dst.Clear();
int n = src.nStates();
for (int i = 0; i <= n; i++)
dst.NewState();
if (!no_accept)
dst.SetAccept(src.GetStart());
dst.SetStart(n);
for (int i = 0; i < n; i++)
{
dst.AddTransition(n, i, 0, src.GetAcceptCost(i), 0);
Intarray targets = new Intarray(), outputs = new Intarray(), inputs = new Intarray();
Floatarray costs = new Floatarray();
src.Arcs(inputs, targets, outputs, costs, i);
if (inputs.Length() != targets.Length())
throw new Exception("ASSERT: inputs.length() == targets.length()");
if (inputs.Length() != outputs.Length())
throw new Exception("ASSERT: inputs.length() == outputs.length()");
if (inputs.Length() != costs.Length())
throw new Exception("ASSERT: inputs.length() == costs.length()");
for (int j = 0; j < inputs.Length(); j++)
dst.AddTransition(targets.At1d(j), i, outputs.At1d(j), costs.At1d(j), inputs.At1d(j));
}
}
/// <summary>
/// Remove epsilons (zeros) and converts integers to string.
/// </summary>
public static void remove_epsilons(out string outs, Intarray a)
{
StringBuilder sb = new StringBuilder();
for (int i = 0; i < a.Length(); i++)
{
if (a[i] > 0)
sb.Append((char)a[i]);
}
outs = sb.ToString();
}
public override int nSamples()
{
return(_samples.Length());
}
public bool Step()
{
int node = heap.Pop();
if (node == n)
return true; // accept has popped up
// get outbound arcs
Intarray inputs = new Intarray();
Intarray targets = new Intarray();
Intarray outputs = new Intarray();
Floatarray costs = new Floatarray();
fst.Arcs(inputs, targets, outputs, costs, node);
for (int i = 0; i < targets.Length(); i++)
{
int t = targets[i];
if (came_from[t] == -1 || g[node] + costs[i] < g[t])
{
// relax the edge
came_from[t] = node;
g[t] = g[node] + costs[i];
heap.Push(t, g[t] + Convert.ToSingle(Heuristic(t)));
}
}
if (accepted_from == -1
|| g[node] + fst.GetAcceptCost(node) < g_accept)
{
// relax the accept edge
accepted_from = node;
g_accept = g[node] + fst.GetAcceptCost(node);
heap.Push(n, g_accept);
}
return false;
}
