public override void Extract(Narray <Floatarray> outarrays, Floatarray inarray)
{
outarrays.Clear();
Floatarray image = outarrays.Push(new Floatarray());
image.Copy(inarray);
}
public override void Arcs(Intarray out_inputs, Intarray out_targets, Intarray out_outputs, Floatarray out_costs, int from)
{
out_inputs.Copy(m_inputs[from]);
out_targets.Copy(m_targets[from]);
out_outputs.Copy(m_outputs[from]);
out_costs.Copy(m_costs[from]);
}
/// <summary>
/// If it's initialized with an array, the result vector
/// is copied into that array when the vector gets destroyed.
/// This allows calls like classifier.Outputs(v,x); with
/// floatarray v.
/// </summary>
public OutputVector(Floatarray v)
: this()
{
_result = new Floatarray();
_result.Copy(v);
v.Clear();
}
public override void Binarize(Bytearray outa, Bytearray ina_)
{
fraction = (float)PGetf("f");
Floatarray ina = new Floatarray();
ina.Copy(ina_);
binarize_by_range(outa, ina, fraction);
}
public override void Arcs(Intarray out_inputs, Intarray out_targets, Intarray out_outputs, Floatarray out_costs, int from)
{
out_inputs.Copy(m_inputs[from]);
out_targets.Copy(m_targets[from]);
out_outputs.Copy(m_outputs[from]);
out_costs.Copy(m_costs[from]);
}
public override float OutputsDense(Floatarray result, Floatarray x_raw)
{
CHECK_ARG(x_raw.Length() == w1.Dim(1), "x_raw.Length() == w1.Dim(1)");
Floatarray z = new Floatarray();
int sparse = PGeti("sparse");
Floatarray y = new Floatarray();
Floatarray x = new Floatarray();
x.Copy(x_raw);
mvmul0(y, w1, x);
y += b1;
for (int i = 0; i < y.Length(); i++)
{
y[i] = sigmoid(y[i]);
}
if (sparse > 0)
{
ClassifierUtil.Sparsify(y, sparse);
}
mvmul0(z, w2, y);
z += b2;
for (int i = 0; i < z.Length(); i++)
{
z[i] = sigmoid(z[i]);
}
result.Copy(z);
//int idx = NarrayUtil.ArgMax(result);
//float val = NarrayUtil.Max(result);
return(Convert.ToSingle(Math.Abs(NarrayUtil.Sum(z) - 1.0)));
}
public override void Binarize(Bytearray outa, Bytearray ina_)
{
fraction = (float)PGetf("f");
Floatarray ina = new Floatarray();
ina.Copy(ina_);
binarize_by_range(outa, ina, fraction);
}
public virtual void Extract(Bytearray outa, Bytearray ina)
{
Floatarray fina = new Floatarray();
Floatarray fouta = new Floatarray();
fina.Copy(ina);
Extract(fouta, fina);
outa.Copy(fouta);
}
public float Outputs(Floatarray p, Floatarray x)
{
OutputVector ov = new OutputVector();
float cost = XOutputs(ov, x);
p.Clear();
p.Copy(ov.AsArray());
return(cost);
}
public static void a_star_backwards(Floatarray costs_for_all_nodes, IGenericFst fst)
{
IGenericFst reverse = FstFactory.MakeOcroFST();
FstUtil.fst_copy_reverse(reverse, fst, true); // creates an extra vertex
AStarSearch a = new AStarSearch(reverse);
a.Loop();
costs_for_all_nodes.Copy(a.g);
costs_for_all_nodes.Pop(); // remove the extra vertex
}
public virtual void Extract(Bytearray outa, Bytearray ina)
{
Floatarray fina = new Floatarray();
Floatarray fouta = new Floatarray();
fina.Copy(ina);
Extract(fouta, fina);
outa.Copy(fouta);
}
public static void a_star_backwards(Floatarray costs_for_all_nodes, IGenericFst fst)
{
IGenericFst reverse = FstFactory.MakeOcroFST();
FstUtil.fst_copy_reverse(reverse, fst, true); // creates an extra vertex
AStarSearch a = new AStarSearch(reverse);
a.Loop();
costs_for_all_nodes.Copy(a.g);
costs_for_all_nodes.Pop(); // remove the extra vertex
}
public static void binsmooth(Bytearray binary, Floatarray input, float sigma)
{
Floatarray smoothed = new Floatarray();
smoothed.Copy(input);
smoothed -= NarrayUtil.Min(smoothed);
smoothed /= NarrayUtil.Max(smoothed);
if (sigma > 0)
{
Gauss.Gauss2d(smoothed, sigma, sigma);
}
binarize_with_threshold(binary, smoothed, 0.5f);
}
private void DoTestRecognize(LenetClassifier classifier)
{
OutputVector ov = new OutputVector();
Floatarray v = new Floatarray();
Bytearray ba = new Bytearray(1, 1);
ImgIo.read_image_gray(ba, testPngFileName);
NarrayUtil.Sub(255, ba);
v.Copy(ba);
v /= 255.0;
classifier.XOutputs(ov, v);
Console.WriteLine("Featured output class '{0}', score '{1}'", (char)ov.Key(ov.BestIndex), ov.Value(ov.BestIndex));
}
public static double Perplexity(Floatarray weights)
{
Floatarray w = new Floatarray();
w.Copy(weights);
w /= NarrayUtil.Sum(w);
double total = 0.0;
for (int i = 0; i < w.Length(); i++)
{
float value = w[i];
total += value * Math.Log(value);
}
return(Math.Exp(-total));
}
public void Copy(MlpClassifier other)
{
w1.Copy(other.w1);
b1.Copy(other.b1);
w2.Copy(other.w2);
b2.Copy(other.b2);
if (c2i.Length() < 1)
{
c2i.Copy(other.c2i);
}
if (i2c.Length() < 1)
{
i2c.Copy(other.i2c);
}
}
/// <summary>
/// Propagate labels across the entire image from a set of non-zero seeds.
/// </summary>
public static void propagate_labels(ref Intarray image)
{
Floatarray dist = new Floatarray();
Narray <Point> source = new Narray <Point>();
dist.Copy(image);
BrushFire.brushfire_2(ref dist, ref source, 1000000);
for (int i = 0; i < dist.Length1d(); i++)
{
Point p = source.At1d(i);
if (image.At1d(i) == 0)
{
image.Put1d(i, image[p.X, p.Y]);
}
}
}
public static void propagate_labels_to(ref Intarray target, Intarray seed)
{
Floatarray dist = new Floatarray();
Narray <Point> source = new Narray <Point>();
dist.Copy(seed);
BrushFire.brushfire_2(ref dist, ref source, 1000000);
for (int i = 0; i < dist.Length1d(); i++)
{
Point p = source.At1d(i);
if (target.At1d(i) > 0)
{
target.Put1d(i, seed[p.X, p.Y]);
}
}
}
public static void weighted_sample(Intarray samples, Floatarray weights, int n)
{
Floatarray cs = new Floatarray();
cs.Copy(weights);
for (int i = 1; i < cs.Length(); i++)
{
cs[i] += cs[i - 1];
}
cs /= NarrayUtil.Max(cs);
samples.Clear();
for (int i = 0; i < n; i++)
{
float value = (float)DRandomizer.Default.drand();
int where = Binsearch(cs, value);
samples.Push(where);
}
}
public void Copy(Floatarray v, float eps = 1e-11f)
{
Clear();
int n = v.Length();
for (int i = 0; i < n; i++)
{
float value = v.At1d(i);
if (Math.Abs(value) >= eps)
{
_keys.Push(i);
_values.Push(value);
}
}
_len = v.Length();
_keys.Resize(_len);
for (int i = 0; i < _len; i++)
{
_keys.Put1d(i, i);
}
_values.Copy(v);
}
public override void Extract(Narray<Floatarray> outarrays, Floatarray inarray)
{
outarrays.Clear();
Floatarray input = new Floatarray();
input.Copy(inarray);
int w = input.Dim(0), h = input.Dim(1);
Floatarray a = new Floatarray(); // working array
int csize = PGeti("csize");
// get rid of small components
SegmRoutine.erase_small_components(input, PGetf("minsize"), PGetf("threshold"));
// compute a thresholded version for morphological operations
Bytearray thresholded = new Bytearray();
OcrRoutine.threshold_frac(thresholded, input, PGetf("threshold"));
// compute a smoothed version of the input for gradient computations
float sigma = PGetf("gradsigma");
Floatarray smoothed = new Floatarray();
smoothed.Copy(input);
Gauss.Gauss2d(smoothed, sigma, sigma);
// x gradient
a.Resize(w, h);
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
float delta;
if (i == 0) delta = 0f;
else delta = smoothed[i, j] - smoothed[i - 1, j];
a[i, j] = delta;
}
}
Floatarray xgrad = outarrays.Push(new Floatarray());
OcrRoutine.scale_to(xgrad, a, csize, PGetf("noupscale"), PGetf("aa"));
for (int j = 0; j < csize; j++)
{
for (int i = 0; i < csize; i++)
{
if (j % 2 == 0) xgrad[i, j] = Math.Max(xgrad[i, j], 0f);
else xgrad[i, j] = Math.Min(xgrad[i, j], 0f);
}
}
// y gradient
a.Resize(w, h);
for (int i = 0; i < w; i++)
{
for (int j = 0; j < h; j++)
{
float delta;
if (j == 0) delta = 0f;
else delta = smoothed[i, j] - smoothed[i, j - 1];
a[i, j] = delta;
}
}
Floatarray ygrad = outarrays.Push(new Floatarray());
OcrRoutine.scale_to(ygrad, a, csize, PGetf("noupscale"), PGetf("aa"));
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
if (i % 2 == 0) ygrad[i, j] = Math.Max(ygrad[i, j], 0f);
else ygrad[i, j] = Math.Min(ygrad[i, j], 0f);
}
}
// junctions, endpoints, and holes
Floatarray junctions = new Floatarray();
Floatarray endpoints = new Floatarray();
Floatarray holes = new Floatarray();
Bytearray junctions1 = new Bytearray();
Bytearray endpoints1 = new Bytearray();
Bytearray holes1 = new Bytearray();
Bytearray dilated = new Bytearray();
Bytearray binary = new Bytearray();
junctions.MakeLike(input, 0f);
endpoints.MakeLike(input, 0f);
holes.MakeLike(input, 0f);
int n = PGeti("n");
float step = PGetf("step");
int bs = PGeti("binsmooth");
for(int i=0; i<n; i++)
{
sigma = step * i;
if(bs > 0)
OcrRoutine.binsmooth(binary, input, sigma);
else
{
binary.Copy(thresholded);
Morph.binary_dilate_circle(binary, (int)(sigma));
}
OcrRoutine.skeletal_features(endpoints1, junctions1, binary, 0.0f, 0.0f);
NarrayUtil.Greater(junctions1, (byte)0, (byte)0, (byte)1);
junctions.Copy(junctions1);
NarrayUtil.Greater(endpoints1, (byte)0, (byte)0, (byte)1);
endpoints.Copy(endpoints1);
SegmRoutine.extract_holes(ref holes1, binary);
NarrayUtil.Greater(holes1, (byte)0, (byte)0, (byte)1);
holes.Copy(holes1);
}
junctions *= 1.0f / (float)n;
endpoints *= 1.0f / (float)n;
holes *= 1.0f / (float)n;
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), junctions, csize, PGetf("noupscale"), PGetf("aa"));
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), endpoints, csize, PGetf("noupscale"), PGetf("aa"));
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), holes, csize, PGetf("noupscale"), PGetf("aa"));
}
/// <summary>
/// If it's initialized with an array, the result vector
/// is copied into that array when the vector gets destroyed.
/// This allows calls like classifier.Outputs(v,x); with
/// floatarray v.
/// </summary>
public OutputVector(Floatarray v) : this()
{
_result = new Floatarray();
_result.Copy(v);
v.Clear();
}
public static void binsmooth(Bytearray binary, Floatarray input, float sigma)
{
Floatarray smoothed = new Floatarray();
smoothed.Copy(input);
smoothed -= NarrayUtil.Min(smoothed);
smoothed /= NarrayUtil.Max(smoothed);
if (sigma > 0)
Gauss.Gauss2d(smoothed, sigma, sigma);
binarize_with_threshold(binary, smoothed, 0.5f);
}
private void DoTestRecognize(LenetClassifier classifier)
{
OutputVector ov = new OutputVector();
Floatarray v = new Floatarray();
Bytearray ba = new Bytearray(1, 1);
ImgIo.read_image_gray(ba, testPngFileName);
NarrayUtil.Sub(255, ba);
v.Copy(ba);
v /= 255.0;
classifier.XOutputs(ov, v);
Console.WriteLine("Featured output class '{0}', score '{1}'", (char)ov.Key(ov.BestIndex), ov.Value(ov.BestIndex));
}
/// <summary>
/// Train on a text line, given a segmentation.
/// <remarks>This is analogous to addTrainingLine(bytearray,nustring) except that
/// it takes the "ground truth" line segmentation.</remarks>
/// </summary>
public override bool AddTrainingLine(Intarray cseg, Bytearray image_grayscale, string tr)
{
Bytearray image = new Bytearray();
image.Copy(image_grayscale);
if (String.IsNullOrEmpty(tr))
{
Global.Debugf("error", "input transcript is empty");
return(false);
}
if (image.Dim(0) < PGeti("minheight"))
{
Global.Debugf("error", "input line too small ({0} x {1})", image.Dim(0), image.Dim(1));
return(false);
}
if (image.Dim(1) > PGeti("maxheight"))
{
Global.Debugf("error", "input line too high ({0} x {1})", image.Dim(0), image.Dim(1));
return(false);
}
if (image.Dim(1) * 1.0 / image.Dim(0) > PGetf("maxaspect"))
{
Global.Debugf("warn", "input line has bad aspect ratio ({0} x {1})", image.Dim(0), image.Dim(1));
return(false);
}
CHECK_ARG(image.Dim(0) == cseg.Dim(0) && image.Dim(1) == cseg.Dim(1),
"image.Dim(0) == cseg.Dim(0) && image.Dim(1) == cseg.Dim(1)");
bool use_reject = PGetb("use_reject") && !DisableJunk;
// check and set the transcript
transcript = tr;
SetLine(image_grayscale);
if (PGeti("invert") > 0)
{
NarrayUtil.Sub(NarrayUtil.Max(image), image);
}
for (int i = 0; i < transcript.Length; i++)
{
CHECK_ARG((int)transcript[i] >= 32, "(int)transcript[i] >= 32");
}
// compute correspondences between actual segmentation and
// ground truth segmentation
Narray <Intarray> segments = new Narray <Intarray>();
GrouperRoutine.segmentation_correspondences(segments, segmentation, cseg);
// now iterate through all the hypothesis segments and
// train the classifier with them
int total = 0;
int junk = 0;
for (int i = 0; i < grouper.Object.Length(); i++)
{
Intarray segs = new Intarray();
grouper.Object.GetSegments(segs, i);
// see whether this is a ground truth segment
int match = -1;
for (int j = 0; j < segments.Length(); j++)
{
if (GrouperRoutine.Equals(segments[j], segs))
{
match = j;
break;
}
}
match -= 1; // segments are numbered starting at 1
int c = reject_class;
if (match >= 0)
{
if (match >= transcript.Length)
{
Global.Debugf("error", "mismatch between transcript and cseg: {0}", transcript);
continue;
}
else
{
c = (int)transcript[match];
Global.Debugf("debugmismatch", "index {0} position {1} char {2} [{3}]", i, match, (char)c, c);
}
}
if (c == reject_class)
{
junk++;
}
// extract the character and add it to the classifier
Rect b;
Bytearray mask = new Bytearray();
grouper.Object.GetMask(out b, ref mask, i, 0);
Bytearray cv = new Bytearray();
grouper.Object.ExtractWithMask(cv, mask, image, i, 0);
Floatarray v = new Floatarray();
v.Copy(cv);
v /= 255.0;
Global.Debugf("cdim", "character dimensions ({0},{1})", v.Dim(0), v.Dim(1));
total++;
if (use_reject)
{
classifier.Object.XAdd(v, c);
}
else
{
if (c != reject_class)
{
classifier.Object.XAdd(v, c);
}
}
if (c != reject_class)
{
IncClass(c);
}
ntrained++;
}
Global.Debugf("detail", "AddTrainingLine trained {0} chars, {1} junk", total - junk, junk);
return(true);
}
/// <summary>
/// This is a weird, optional method that exposes character segmentation
/// for those line recognizers that have it segmentation contains colored pixels,
/// and a transition in the transducer of the form * --- 1/eps --> * --- 2/a --> *
/// means that pixels with color 1 and 2 together form the letter "a"
/// </summary>
public override double RecognizeLine(Intarray segmentation_, IGenericFst result, Bytearray image_)
{
double rate = 0.0;
CHECK_ARG(image_.Dim(1) < PGeti("maxheight"),
String.Format("input line too high ({0} x {1})", image_.Dim(0), image_.Dim(1)));
CHECK_ARG(image_.Dim(1) * 1.0 / image_.Dim(0) < PGetf("maxaspect"),
String.Format("input line has bad aspect ratio ({0} x {1})", image_.Dim(0), image_.Dim(1)));
bool use_reject = PGetb("use_reject") && !DisableJunk;
//Console.WriteLine("IMG: imin:{0} imax:{1}", NarrayUtil.ArgMin(image_), NarrayUtil.ArgMax(image_));
Bytearray image = new Bytearray();
image.Copy(image_);
SetLine(image_);
if (PGeti("invert") > 0)
{
NarrayUtil.Sub(NarrayUtil.Max(image), image);
}
segmentation_.Copy(segmentation);
Bytearray available = new Bytearray();
Floatarray cp = new Floatarray();
Floatarray ccosts = new Floatarray();
Floatarray props = new Floatarray();
OutputVector p = new OutputVector();
int ncomponents = grouper.Object.Length();
int minclass = PGeti("minclass");
float minprob = PGetf("minprob");
float space_yes = PGetf("space_yes");
float space_no = PGetf("space_no");
float maxcost = PGetf("maxcost");
// compute priors if possible; fall back on
// using no priors if no counts are available
Floatarray priors = new Floatarray();
bool use_priors = PGeti("use_priors") > 0;
if (use_priors)
{
if (counts.Length() > 0)
{
priors.Copy(counts);
priors /= NarrayUtil.Sum(priors);
}
else
{
if (!counts_warned)
{
Global.Debugf("warn", "use_priors specified but priors unavailable (old model)");
}
use_priors = false;
counts_warned = true;
}
}
EstimateSpaceSize();
for (int i = 0; i < ncomponents; i++)
{
Rect b;
Bytearray mask = new Bytearray();
grouper.Object.GetMask(out b, ref mask, i, 0);
Bytearray cv = new Bytearray();
grouper.Object.ExtractWithMask(cv, mask, image, i, 0);
//ImgIo.write_image_gray("extrmask_image.png", cv);
Floatarray v = new Floatarray();
v.Copy(cv);
v /= 255.0f;
float ccost = classifier.Object.XOutputs(p, v);
if (use_reject && classifier.Object.HigherOutputIsBetter)
{
ccost = 0;
float total = p.Sum();
if (total > 1e-11f)
{
//p /= total;
}
else
{
p.Values.Fill(0.0f);
}
}
int count = 0;
Global.Debugf("dcost", "output {0}", p.Keys.Length());
for (int index = 0; index < p.Keys.Length(); index++)
{
int j = p.Keys[index];
if (j < minclass)
{
continue;
}
if (j == reject_class)
{
continue;
}
float value = p.Values[index];
if (value <= 0.0f)
{
continue;
}
if (value < minprob)
{
continue;
}
float pcost = classifier.Object.HigherOutputIsBetter ? (float)-Math.Log(value) : value;
Global.Debugf("dcost", "{0} {1} {2}", j, pcost + ccost, (j > 32 ? (char)j : '_'));
float total_cost = pcost + ccost;
if (total_cost < maxcost)
{
if (use_priors)
{
total_cost -= (float)-Math.Log(priors[j]);
}
grouper.Object.SetClass(i, j, total_cost);
count++;
}
}
Global.Debugf("dcost", "");
if (count == 0)
{
float xheight = 10.0f;
if (b.Height() < xheight / 2 && b.Width() < xheight / 2)
{
grouper.Object.SetClass(i, (int)'~', high_cost / 2);
}
else
{
grouper.Object.SetClass(i, (int)'#', (b.Width() / xheight) * high_cost);
}
}
if (grouper.Object.PixelSpace(i) > space_threshold)
{
Global.Debugf("spaces", "space {0}", grouper.Object.PixelSpace(i));
grouper.Object.SetSpaceCost(i, space_yes, space_no);
}
}
grouper.Object.GetLattice(result);
return(rate);
}
public float Outputs(Floatarray p, Floatarray x)
{
OutputVector ov = new OutputVector();
float cost = XOutputs(ov, x);
p.Clear();
p.Copy(ov.AsArray());
return cost;
}
/// <summary>
/// Train on a text line, given a segmentation.
/// <remarks>This is analogous to addTrainingLine(bytearray,nustring) except that
/// it takes the "ground truth" line segmentation.</remarks>
/// </summary>
public override bool AddTrainingLine(Intarray cseg, Bytearray image_grayscale, string tr)
{
Bytearray image = new Bytearray();
image.Copy(image_grayscale);
if (String.IsNullOrEmpty(tr))
{
Global.Debugf("error", "input transcript is empty");
return false;
}
if (image.Dim(0) < PGeti("minheight"))
{
Global.Debugf("error", "input line too small ({0} x {1})", image.Dim(0), image.Dim(1));
return false;
}
if (image.Dim(1) > PGeti("maxheight"))
{
Global.Debugf("error", "input line too high ({0} x {1})", image.Dim(0), image.Dim(1));
return false;
}
if (image.Dim(1) * 1.0 / image.Dim(0) > PGetf("maxaspect"))
{
Global.Debugf("warn", "input line has bad aspect ratio ({0} x {1})", image.Dim(0), image.Dim(1));
return false;
}
CHECK_ARG(image.Dim(0) == cseg.Dim(0) && image.Dim(1) == cseg.Dim(1),
"image.Dim(0) == cseg.Dim(0) && image.Dim(1) == cseg.Dim(1)");
bool use_reject = PGetb("use_reject") && !DisableJunk;
// check and set the transcript
transcript = tr;
SetLine(image_grayscale);
if (PGeti("invert") > 0)
NarrayUtil.Sub(NarrayUtil.Max(image), image);
for (int i = 0; i < transcript.Length; i++)
CHECK_ARG((int)transcript[i] >= 32, "(int)transcript[i] >= 32");
// compute correspondences between actual segmentation and
// ground truth segmentation
Narray<Intarray> segments = new Narray<Intarray>();
GrouperRoutine.segmentation_correspondences(segments, segmentation, cseg);
// now iterate through all the hypothesis segments and
// train the classifier with them
int total = 0;
int junk = 0;
for (int i = 0; i < grouper.Object.Length(); i++)
{
Intarray segs = new Intarray();
grouper.Object.GetSegments(segs, i);
// see whether this is a ground truth segment
int match = -1;
for (int j = 0; j < segments.Length(); j++)
{
if (GrouperRoutine.Equals(segments[j], segs))
{
match = j;
break;
}
}
match -= 1; // segments are numbered starting at 1
int c = reject_class;
if (match >= 0)
{
if (match >= transcript.Length)
{
Global.Debugf("error", "mismatch between transcript and cseg: {0}", transcript);
continue;
}
else
{
c = (int)transcript[match];
Global.Debugf("debugmismatch", "index {0} position {1} char {2} [{3}]", i, match, (char)c, c);
}
}
if (c == reject_class)
junk++;
// extract the character and add it to the classifier
Rect b;
Bytearray mask = new Bytearray();
grouper.Object.GetMask(out b, ref mask, i, 0);
Bytearray cv = new Bytearray();
grouper.Object.ExtractWithMask(cv, mask, image, i, 0);
Floatarray v = new Floatarray();
v.Copy(cv);
v /= 255.0;
Global.Debugf("cdim", "character dimensions ({0},{1})", v.Dim(0), v.Dim(1));
total++;
if (use_reject)
{
classifier.Object.XAdd(v, c);
}
else
{
if (c != reject_class)
classifier.Object.XAdd(v, c);
}
if (c != reject_class)
IncClass(c);
ntrained++;
}
Global.Debugf("detail", "AddTrainingLine trained {0} chars, {1} junk", total - junk, junk);
return true;
}
public override float OutputsDense(Floatarray result, Floatarray x_raw)
{
CHECK_ARG(x_raw.Length() == w1.Dim(1), "x_raw.Length() == w1.Dim(1)");
Floatarray z = new Floatarray();
int sparse = PGeti("sparse");
Floatarray y = new Floatarray();
Floatarray x = new Floatarray();
x.Copy(x_raw);
mvmul0(y, w1, x);
y += b1;
for (int i = 0; i < y.Length(); i++)
y[i] = sigmoid(y[i]);
if (sparse > 0)
ClassifierUtil.Sparsify(y, sparse);
mvmul0(z, w2, y);
z += b2;
for (int i = 0; i < z.Length(); i++)
z[i] = sigmoid(z[i]);
result.Copy(z);
//int idx = NarrayUtil.ArgMax(result);
//float val = NarrayUtil.Max(result);
return Convert.ToSingle(Math.Abs(NarrayUtil.Sum(z) - 1.0));
}
/// <summary>
/// Propagate labels across the entire image from a set of non-zero seeds.
/// </summary>
public static void propagate_labels(ref Intarray image)
{
Floatarray dist = new Floatarray();
Narray<Point> source = new Narray<Point>();
dist.Copy(image);
BrushFire.brushfire_2(ref dist, ref source, 1000000);
for (int i = 0; i < dist.Length1d(); i++)
{
Point p = source.At1d(i);
if (image.At1d(i) == 0) image.Put1d(i, image[p.X, p.Y]);
}
}
public static void propagate_labels_to(ref Intarray target, Intarray seed)
{
Floatarray dist = new Floatarray();
Narray<Point> source = new Narray<Point>();
dist.Copy(seed);
BrushFire.brushfire_2(ref dist, ref source, 1000000);
for (int i = 0; i < dist.Length1d(); i++)
{
Point p = source.At1d(i);
if (target.At1d(i) > 0) target.Put1d(i, seed[p.X, p.Y]);
}
}
/// <summary>
/// This is a weird, optional method that exposes character segmentation
/// for those line recognizers that have it segmentation contains colored pixels,
/// and a transition in the transducer of the form * --- 1/eps --> * --- 2/a --> *
/// means that pixels with color 1 and 2 together form the letter "a"
/// </summary>
public override double RecognizeLine(Intarray segmentation_, IGenericFst result, Bytearray image_)
{
double rate = 0.0;
CHECK_ARG(image_.Dim(1) < PGeti("maxheight"),
String.Format("input line too high ({0} x {1})", image_.Dim(0), image_.Dim(1)));
CHECK_ARG(image_.Dim(1) * 1.0 / image_.Dim(0) < PGetf("maxaspect"),
String.Format("input line has bad aspect ratio ({0} x {1})", image_.Dim(0), image_.Dim(1)));
bool use_reject = PGetb("use_reject") && !DisableJunk;
//Console.WriteLine("IMG: imin:{0} imax:{1}", NarrayUtil.ArgMin(image_), NarrayUtil.ArgMax(image_));
Bytearray image = new Bytearray();
image.Copy(image_);
SetLine(image_);
if (PGeti("invert") > 0)
NarrayUtil.Sub(NarrayUtil.Max(image), image);
segmentation_.Copy(segmentation);
Bytearray available = new Bytearray();
Floatarray cp = new Floatarray();
Floatarray ccosts = new Floatarray();
Floatarray props = new Floatarray();
OutputVector p = new OutputVector();
int ncomponents = grouper.Object.Length();
int minclass = PGeti("minclass");
float minprob = PGetf("minprob");
float space_yes = PGetf("space_yes");
float space_no = PGetf("space_no");
float maxcost = PGetf("maxcost");
// compute priors if possible; fall back on
// using no priors if no counts are available
Floatarray priors = new Floatarray();
bool use_priors = PGeti("use_priors") > 0;
if (use_priors)
{
if (counts.Length() > 0)
{
priors.Copy(counts);
priors /= NarrayUtil.Sum(priors);
}
else
{
if (!counts_warned)
Global.Debugf("warn", "use_priors specified but priors unavailable (old model)");
use_priors = false;
counts_warned = true;
}
}
EstimateSpaceSize();
for (int i = 0; i < ncomponents; i++)
{
Rect b;
Bytearray mask = new Bytearray();
grouper.Object.GetMask(out b, ref mask, i, 0);
Bytearray cv = new Bytearray();
grouper.Object.ExtractWithMask(cv, mask, image, i, 0);
//ImgIo.write_image_gray("extrmask_image.png", cv);
Floatarray v = new Floatarray();
v.Copy(cv);
v /= 255.0f;
float ccost = classifier.Object.XOutputs(p, v);
if (use_reject && classifier.Object.HigherOutputIsBetter)
{
ccost = 0;
float total = p.Sum();
if (total > 1e-11f)
{
//p /= total;
}
else
p.Values.Fill(0.0f);
}
int count = 0;
Global.Debugf("dcost", "output {0}", p.Keys.Length());
for (int index = 0; index < p.Keys.Length(); index++)
{
int j = p.Keys[index];
if (j < minclass) continue;
if (j == reject_class) continue;
float value = p.Values[index];
if (value <= 0.0f) continue;
if (value < minprob) continue;
float pcost = classifier.Object.HigherOutputIsBetter ? (float)-Math.Log(value) : value;
Global.Debugf("dcost", "{0} {1} {2}", j, pcost + ccost, (j > 32 ? (char)j : '_'));
float total_cost = pcost + ccost;
if (total_cost < maxcost)
{
if (use_priors)
{
total_cost -= (float)-Math.Log(priors[j]);
}
grouper.Object.SetClass(i, j, total_cost);
count++;
}
}
Global.Debugf("dcost", "");
if (count == 0)
{
float xheight = 10.0f;
if (b.Height() < xheight / 2 && b.Width() < xheight / 2)
{
grouper.Object.SetClass(i, (int)'~', high_cost / 2);
}
else
{
grouper.Object.SetClass(i, (int)'#', (b.Width() / xheight) * high_cost);
}
}
if (grouper.Object.PixelSpace(i) > space_threshold)
{
Global.Debugf("spaces", "space {0}", grouper.Object.PixelSpace(i));
grouper.Object.SetSpaceCost(i, space_yes, space_no);
}
}
grouper.Object.GetLattice(result);
return rate;
}
public override void Extract(Narray <Floatarray> outarrays, Floatarray inarray)
{
outarrays.Clear();
Floatarray input = new Floatarray();
input.Copy(inarray);
int w = input.Dim(0), h = input.Dim(1);
Floatarray a = new Floatarray(); // working array
int csize = PGeti("csize");
// get rid of small components
SegmRoutine.erase_small_components(input, PGetf("minsize"), PGetf("threshold"));
// compute a thresholded version for morphological operations
Bytearray thresholded = new Bytearray();
OcrRoutine.threshold_frac(thresholded, input, PGetf("threshold"));
// compute a smoothed version of the input for gradient computations
float sigma = PGetf("gradsigma");
Floatarray smoothed = new Floatarray();
smoothed.Copy(input);
Gauss.Gauss2d(smoothed, sigma, sigma);
// x gradient
a.Resize(w, h);
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
float delta;
if (i == 0)
{
delta = 0f;
}
else
{
delta = smoothed[i, j] - smoothed[i - 1, j];
}
a[i, j] = delta;
}
}
Floatarray xgrad = outarrays.Push(new Floatarray());
OcrRoutine.scale_to(xgrad, a, csize, PGetf("noupscale"), PGetf("aa"));
for (int j = 0; j < csize; j++)
{
for (int i = 0; i < csize; i++)
{
if (j % 2 == 0)
{
xgrad[i, j] = Math.Max(xgrad[i, j], 0f);
}
else
{
xgrad[i, j] = Math.Min(xgrad[i, j], 0f);
}
}
}
// y gradient
a.Resize(w, h);
for (int i = 0; i < w; i++)
{
for (int j = 0; j < h; j++)
{
float delta;
if (j == 0)
{
delta = 0f;
}
else
{
delta = smoothed[i, j] - smoothed[i, j - 1];
}
a[i, j] = delta;
}
}
Floatarray ygrad = outarrays.Push(new Floatarray());
OcrRoutine.scale_to(ygrad, a, csize, PGetf("noupscale"), PGetf("aa"));
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
if (i % 2 == 0)
{
ygrad[i, j] = Math.Max(ygrad[i, j], 0f);
}
else
{
ygrad[i, j] = Math.Min(ygrad[i, j], 0f);
}
}
}
// junctions, endpoints, and holes
Floatarray junctions = new Floatarray();
Floatarray endpoints = new Floatarray();
Floatarray holes = new Floatarray();
Bytearray junctions1 = new Bytearray();
Bytearray endpoints1 = new Bytearray();
Bytearray holes1 = new Bytearray();
Bytearray dilated = new Bytearray();
Bytearray binary = new Bytearray();
junctions.MakeLike(input, 0f);
endpoints.MakeLike(input, 0f);
holes.MakeLike(input, 0f);
int n = PGeti("n");
float step = PGetf("step");
int bs = PGeti("binsmooth");
for (int i = 0; i < n; i++)
{
sigma = step * i;
if (bs > 0)
{
OcrRoutine.binsmooth(binary, input, sigma);
}
else
{
binary.Copy(thresholded);
Morph.binary_dilate_circle(binary, (int)(sigma));
}
OcrRoutine.skeletal_features(endpoints1, junctions1, binary, 0.0f, 0.0f);
NarrayUtil.Greater(junctions1, (byte)0, (byte)0, (byte)1);
junctions.Copy(junctions1);
NarrayUtil.Greater(endpoints1, (byte)0, (byte)0, (byte)1);
endpoints.Copy(endpoints1);
SegmRoutine.extract_holes(ref holes1, binary);
NarrayUtil.Greater(holes1, (byte)0, (byte)0, (byte)1);
holes.Copy(holes1);
}
junctions *= 1.0f / (float)n;
endpoints *= 1.0f / (float)n;
holes *= 1.0f / (float)n;
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), junctions, csize, PGetf("noupscale"), PGetf("aa"));
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), endpoints, csize, PGetf("noupscale"), PGetf("aa"));
OcrRoutine.scale_to(outarrays.Push(new Floatarray()), holes, csize, PGetf("noupscale"), PGetf("aa"));
}