/// <summary>
/// Merge segments from start to end.
/// </summary>
/// <param name="cseg">Output</param>
/// <param name="rseg">Input</param>
/// <param name="start">start merge position</param>
/// <param name="end">end merge position</param>
public static void rseg_to_cseg(Intarray cseg, Intarray rseg, int start, int end)
{
int maxSegNum = NarrayUtil.Max(rseg);
if (start > end)
{
throw new Exception("segmentation encoded in IDs looks seriously broken!");
}
if (start > maxSegNum || end > maxSegNum)
{
throw new Exception("segmentation encoded in IDs doesn't fit!");
}
Intarray map = new Intarray(maxSegNum + 1);
map.Fill(0);
int color = 1;
for (int i = 1; i <= maxSegNum; i++)
{
map[i] = color;
if (i < start || i >= end)
{
color++;
}
}
cseg.MakeLike(rseg);
for (int i = 0; i < cseg.Length1d(); i++)
{
cseg.Put1d(i, map[rseg.At1d(i)]);
}
}
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 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);
}
}
/// <summary>
/// Remove segments from start to end.
/// </summary>
/// <param name="cseg">Output</param>
/// <param name="rseg">Input</param>
/// <param name="start">start remove position</param>
/// <param name="end">end remove position</param>
public static void rseg_to_cseg_remove(Intarray cseg, Intarray rseg,
Bytearray outimg, Bytearray img, int start, int end)
{
int maxSegNum = NarrayUtil.Max(rseg);
if (start > end)
{
throw new Exception("segmentation encoded in IDs looks seriously broken!");
}
if (start > maxSegNum || end > maxSegNum)
{
throw new Exception("segmentation encoded in IDs doesn't fit!");
}
if (rseg.Length1d() != img.Length1d())
{
throw new Exception("rseg and img must have same a dimension!");
}
Intarray map = new Intarray(maxSegNum + 1);
map.Fill(0);
int color = 1;
for (int i = 1; i <= maxSegNum; i++)
{
map[i] = color;
if (i < start || i > end)
{
color++;
}
else
{
map[i] = 0;
}
}
cseg.MakeLike(rseg);
outimg.Copy(img);
for (int i = 0; i < cseg.Length1d(); i++)
{
int val = rseg.At1d(i);
cseg.Put1d(i, map[val]);
if (val > 0 && map[val] == 0)
{
outimg.Put1d(i, 255);
}
}
}
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 Charseg(ref Intarray segmentation, Bytearray inraw)
{
setParams();
//Logger.Default.Image("segmenting", inraw);
int PADDING = 3;
OcrRoutine.optional_check_background_is_lighter(inraw);
Bytearray image = new Bytearray();
image.Copy(inraw);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
SetImage(image);
FindAllCuts();
FindBestCuts();
Intarray seg = new Intarray();
seg.MakeLike(image);
seg.Fill(255);
for (int r = 0; r < bestcuts.Length(); r++)
{
int w = seg.Dim(0);
int c = bestcuts[r];
Narray <Point> cut = cuts[c];
for (int y = 0; y < image.Dim(1); y++)
{
for (int i = -1; i <= 1; i++)
{
int x = cut[y].X;
if (x < 1 || x >= w - 1)
{
continue;
}
seg[x + i, y] = 0;
}
}
}
ImgLabels.label_components(ref seg);
// dshowr(seg,"YY"); dwait();
segmentation.Copy(image);
for (int i = 0; i < seg.Length1d(); i++)
{
if (segmentation.At1d(i) == 0)
{
seg.Put1d(i, 0);
}
}
ImgLabels.propagate_labels_to(ref segmentation, seg);
if (PGeti("component_segmentation") > 0)
{
Intarray ccseg = new Intarray();
ccseg.Copy(image);
ImgLabels.label_components(ref ccseg);
SegmRoutine.combine_segmentations(ref segmentation, ccseg);
if (PGeti("fix_diacritics") > 0)
{
SegmRoutine.fix_diacritics(segmentation);
}
}
#if false
SegmRoutine.line_segmentation_merge_small_components(ref segmentation, small_merge_threshold);
SegmRoutine.line_segmentation_sort_x(segmentation);
#endif
SegmRoutine.make_line_segmentation_white(segmentation);
// set_line_number(segmentation, 1);
//Logger.Default.Image("resulting segmentation", segmentation);
}
public override void Charseg(ref Intarray segmentation, Bytearray inraw)
{
setParams();
//Logger.Default.Image("segmenting", inraw);
int PADDING = 3;
OcrRoutine.optional_check_background_is_lighter(inraw);
Bytearray image = new Bytearray();
image.Copy(inraw);
OcrRoutine.binarize_simple(image);
OcrRoutine.Invert(image);
SetImage(image);
FindAllCuts();
FindBestCuts();
Intarray seg = new Intarray();
seg.MakeLike(image);
seg.Fill(255);
for (int r = 0; r < bestcuts.Length(); r++)
{
int w = seg.Dim(0);
int c = bestcuts[r];
Narray<Point> cut = cuts[c];
for (int y = 0; y < image.Dim(1); y++)
{
for (int i = -1; i <= 1; i++)
{
int x = cut[y].X;
if (x < 1 || x >= w - 1) continue;
seg[x + i, y] = 0;
}
}
}
ImgLabels.label_components(ref seg);
// dshowr(seg,"YY"); dwait();
segmentation.Copy(image);
for (int i = 0; i < seg.Length1d(); i++)
if (segmentation.At1d(i) == 0) seg.Put1d(i, 0);
ImgLabels.propagate_labels_to(ref segmentation, seg);
if (PGeti("component_segmentation") > 0)
{
Intarray ccseg = new Intarray();
ccseg.Copy(image);
ImgLabels.label_components(ref ccseg);
SegmRoutine.combine_segmentations(ref segmentation, ccseg);
if (PGeti("fix_diacritics") > 0)
{
SegmRoutine.fix_diacritics(segmentation);
}
}
#if false
SegmRoutine.line_segmentation_merge_small_components(ref segmentation, small_merge_threshold);
SegmRoutine.line_segmentation_sort_x(segmentation);
#endif
SegmRoutine.make_line_segmentation_white(segmentation);
// set_line_number(segmentation, 1);
//Logger.Default.Image("resulting segmentation", segmentation);
}
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));*/
}
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);
}
}
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));*/
}