public void Init(int n = 0)
{
_keys.Resize(n);
for (int i = 0; i < n; i++)
{
_keys.Put1d(i, i);
}
_values.Resize(n);
_values.Fill <float>(0.0f);
}
public void BestPath(Intarray v1, Intarray v2, Intarray inputs,
Intarray outputs, Floatarray costs)
{
stree.Clear();
beam.Resize(1);
beamcost.Resize(1);
beam[0] = stree.Add(-1, fst1.GetStart(), fst2.GetStart(), 0, 0, 0);
beamcost[0] = 0;
best_so_far = 0;
best_cost_so_far = fst1.GetAcceptCost(fst1.GetStart()) +
fst2.GetAcceptCost(fst1.GetStart());
while (beam.Length() > 0)
{
Radiate();
}
stree.Get(v1, v2, inputs, outputs, costs, best_so_far);
costs.Push(fst1.GetAcceptCost(stree.v1[best_so_far]) +
fst2.GetAcceptCost(stree.v2[best_so_far]));
//logger("costs", costs);
}
public override void Charseg(ref Intarray result_segmentation, Bytearray orig_image)
{
Logger.Default.Image("segmenting", orig_image);
int PADDING = 3;
OcrRoutine.optional_check_background_is_lighter(orig_image);
Bytearray image = new Bytearray();
Narray <byte> bimage = image;
image.Copy(orig_image);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
ImgOps.pad_by(ref bimage, PADDING, PADDING);
// pass image to segmenter
segmenter.SetImage(image);
// find all cuts in the image
segmenter.FindAllCuts();
// choose the best of all cuts
segmenter.FindBestCuts();
Intarray segmentation = new Intarray();
segmentation.Resize(image.Dim(0), image.Dim(1));
for (int i = 0; i < image.Dim(0); i++)
{
for (int j = 0; j < image.Dim(1); j++)
{
segmentation[i, j] = image[i, j] > 0 ? 1 : 0;
}
}
for (int r = 0; r < segmenter.bestcuts.Length(); r++)
{
int c = segmenter.bestcuts[r];
Narray <Point> cut = segmenter.cuts[c];
for (int y = 0; y < image.Dim(1); y++)
{
for (int x = cut[y].X; x < image.Dim(0); x++)
{
if (segmentation[x, y] > 0)
{
segmentation[x, y]++;
}
}
}
}
ImgOps.extract_subimage(result_segmentation, segmentation, PADDING, PADDING,
segmentation.Dim(0) - PADDING, segmentation.Dim(1) - PADDING);
if (small_merge_threshold > 0)
{
SegmRoutine.line_segmentation_merge_small_components(ref result_segmentation, small_merge_threshold);
SegmRoutine.line_segmentation_sort_x(result_segmentation);
}
SegmRoutine.make_line_segmentation_white(result_segmentation);
// set_line_number(segmentation, 1);
Logger.Default.Image("resulting segmentation", result_segmentation);
}
/// <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 Bitmap read_image_packed(Intarray image, string path)
{
Bitmap bitmap = LoadBitmapFromFile(path);
image.Resize(bitmap.Width, bitmap.Height);
ImgRoutine.NarrayFromBitmap(image, bitmap);
return(bitmap);
}
/// <summary>
/// constructor for a NBest data structure of size n
/// </summary>
public PriorityQueue(int n)
{
this.n = n;
ids = new Intarray();
tags = new Intarray();
values = new Floatarray();
ids.Resize(n + 1);
tags.Resize(n + 1);
values.Resize(n + 1);
Clear();
}
/// <summary>
/// constructor for a NBest data structure of size n
/// </summary>
public PriorityQueue(int n)
{
this.n = n;
ids = new Intarray();
tags = new Intarray();
values = new Floatarray();
ids.Resize(n + 1);
tags.Resize(n + 1);
values.Resize(n + 1);
Clear();
}
public override void Charseg(ref Intarray result_segmentation, Bytearray orig_image)
{
Logger.Default.Image("segmenting", orig_image);
int PADDING = 3;
OcrRoutine.optional_check_background_is_lighter(orig_image);
Bytearray image = new Bytearray();
Narray<byte> bimage = image;
image.Copy(orig_image);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
ImgOps.pad_by(ref bimage, PADDING, PADDING);
// pass image to segmenter
segmenter.SetImage(image);
// find all cuts in the image
segmenter.FindAllCuts();
// choose the best of all cuts
segmenter.FindBestCuts();
Intarray segmentation = new Intarray();
segmentation.Resize(image.Dim(0), image.Dim(1));
for (int i = 0; i < image.Dim(0); i++)
for (int j = 0; j < image.Dim(1); j++)
segmentation[i, j] = image[i, j] > 0 ? 1 : 0;
for (int r = 0; r < segmenter.bestcuts.Length(); r++)
{
int c = segmenter.bestcuts[r];
Narray<Point> cut = segmenter.cuts[c];
for (int y = 0; y < image.Dim(1); y++)
{
for (int x = cut[y].X; x < image.Dim(0); x++)
{
if (segmentation[x, y] > 0) segmentation[x, y]++;
}
}
}
ImgOps.extract_subimage(result_segmentation, segmentation, PADDING, PADDING,
segmentation.Dim(0) - PADDING, segmentation.Dim(1) - PADDING);
if (small_merge_threshold > 0)
{
SegmRoutine.line_segmentation_merge_small_components(ref result_segmentation, small_merge_threshold);
SegmRoutine.line_segmentation_sort_x(result_segmentation);
}
SegmRoutine.make_line_segmentation_white(result_segmentation);
// set_line_number(segmentation, 1);
Logger.Default.Image("resulting segmentation", result_segmentation);
}
/// <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 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>
/// 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 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 SetImage(Bytearray image)
{
dimage.Copy(image);
int w = image.Dim(0), h = image.Dim(1);
wimage.Resize(w, h);
wimage.Fill(0);
float s1 = 0.0f, sy = 0.0f;
for (int i = 1; i < w; i++)
{
for (int j = 0; j < h; j++)
{
if (image[i, j] > 0)
{
s1++; sy += j;
}
if (image[i - 1, j] == 0 && image[i, j] > 0)
{
wimage[i, j] = boundary_weight;
}
else if (image[i, j] > 0)
{
wimage[i, j] = inside_weight;
}
else
{
wimage[i, j] = outside_weight;
}
}
}
where = (int)(sy / s1);
for (int i = 0; i < dimage.Dim(0); i++)
{
dimage[i, where] = 0x008000;
}
}
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 FindAllCuts()
{
int w = wimage.Dim(0), h = wimage.Dim(1);
// initialize dimensions of cuts, costs etc
cuts.Resize(w);
cutcosts.Resize(w);
costs.Resize(w, h);
sources.Resize(w, h);
costs.Fill(1000000000);
for (int i = 0; i < w; i++)
{
costs[i, 0] = 0;
}
sources.Fill(-1);
limit = where;
direction = 1;
Step(0, w, 0);
for (int x = 0; x < w; x++)
{
cutcosts[x] = costs[x, where];
cuts[x] = new Narray <Point>();
cuts[x].Clear();
// bottom should probably be initialized with 2*where instead of
// h, because where cannot be assumed to be h/2. In the most extreme
// case, the cut could go through 2 pixels in each row
Narray <Point> bottom = new Narray <Point>();
int i = x, j = where;
while (j >= 0)
{
bottom.Push(new Point(i, j));
i = sources[i, j];
j--;
}
//cuts(x).resize(h);
for (i = bottom.Length() - 1; i >= 0; i--)
{
cuts[x].Push(bottom[i]);
}
}
costs.Fill(1000000000);
for (int i = 0; i < w; i++)
{
costs[i, h - 1] = 0;
}
sources.Fill(-1);
limit = where;
direction = -1;
Step(0, w, h - 1);
for (int x = 0; x < w; x++)
{
cutcosts[x] += costs[x, where];
// top should probably be initialized with 2*(h-where) instead of
// h, because where cannot be assumed to be h/2. In the most extreme
// case, the cut could go through 2 pixels in each row
Narray <Point> top = new Narray <Point>();
int i = x, j = where;
while (j < h)
{
if (j > where)
{
top.Push(new Point(i, j));
}
i = sources[i, j];
j++;
}
for (i = 0; i < top.Length(); i++)
{
cuts[x].Push(top[i]);
}
}
// add costs for line "where"
for (int x = 0; x < w; x++)
{
cutcosts[x] += wimage[x, where];
}
}
public static Bitmap read_image_packed(Intarray image, string path)
{
Bitmap bitmap = LoadBitmapFromFile(path);
image.Resize(bitmap.Width, bitmap.Height);
ImgRoutine.NarrayFromBitmap(image, bitmap);
return bitmap;
}
/// <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];
}
}