/**
* 共通初期化関数。
* @param i_param
* @param i_drv_factory
* ラスタドライバのファクトリ。
* @param i_gs_type
* @param i_rgb_type
* @return
* @
*/
private void initInstance(NyARIntSize i_size)
{
//リソースの生成
this.initResource(i_size);
this._gs_hist = new NyARHistogram(256);
this._src_ts = 0;
this._gs_id_ts = 0;
this._gs_hist_ts = 0;
}
public NyARSensor(NyARIntSize i_size)
{
this._gs_raster = NyARGrayscaleRaster.createInstance(i_size.w, i_size.h, NyARBufferType.INT1D_GRAY_8, true);
this._gs_hist = new NyARHistogram(256);
this._src_ts = 0;
this._gs_id_ts = 0;
this._gs_hist_ts = 0;
this._hist_drv = (INyARHistogramFromRaster)this._gs_raster.createInterface(typeof(INyARHistogramFromRaster));
}
/**
* この関数は、判別法を用いて敷居値を1個求めます。敷居値の範囲は、i_histogram引数の範囲と同じです。
* 関数は、thisのプロパティを更新します。
*/
public int getThreshold(NyARHistogram i_histogram)
{
int[] hist = i_histogram.data;
int n = i_histogram.length;
int da, sa, db, sb, dt, pt, st;
int i;
int th = 0;
//後で使う
dt = pt = 0;
for (i = 0; i < n; i++)
{
int h = hist[i];
dt += h * i;
pt += h * i * i;//正規化の時に使う定数
}
st = i_histogram.total_of_data;
//Low側(0<=i<=n-2)
da = dt;
sa = st;
//High側(i=n-1)
db = sb = 0;
double max = -1;
double max_mt = 0;
//各ヒストグラムの分離度を計算する(1<=i<=n-1の範囲で評価)
for (i = n - 1; i > 0; i--)
{
//次のヒストグラムを計算
int hist_count = hist[i];
int hist_val = hist_count * i;
da -= hist_val;
sa -= hist_count;
db += hist_val;
sb += hist_count;
//クラス間分散を計算
double dv = (sa + sb);
double mt = (double)(da + db) / dv;
double ma = (sa != 0 ? ((double)da / (double)sa) : 0) - mt;
double mb = (sb != 0 ? ((double)db / (double)sb) : 0) - mt;
double kai = ((double)(sa * (ma * ma) + sb * (mb * mb))) / dv;
if (max < kai)
{
max_mt = mt;
max = kai;
th = i;
}
//System.out.println(kai);
}
//max_mtを元に正規化
this._score = max / ((double)(pt + max_mt * max_mt * st - 2 * max_mt * dt) / st);//129,0.8888888888888887
return(th);
}
/**
* この関数は、判別法を用いて敷居値を1個求めます。敷居値の範囲は、i_histogram引数の範囲と同じです。
* 関数は、thisのプロパティを更新します。
*/
public int getThreshold(NyARHistogram i_histogram)
{
int[] hist = i_histogram.data;
int n = i_histogram.length;
int da, sa, db, sb, dt, pt, st;
int i;
int th = 0;
//後で使う
dt = pt = 0;
for (i = 0; i < n; i++)
{
int h = hist[i];
dt += h * i;
pt += h * i * i;//正規化の時に使う定数
}
st = i_histogram.total_of_data;
//Low側(0<=i<=n-2)
da = dt;
sa = st;
//High側(i=n-1)
db = sb = 0;
double max = -1;
double max_mt = 0;
//各ヒストグラムの分離度を計算する(1<=i<=n-1の範囲で評価)
for (i = n - 1; i > 0; i--)
{
//次のヒストグラムを計算
int hist_count = hist[i];
int hist_val = hist_count * i;
da -= hist_val;
sa -= hist_count;
db += hist_val;
sb += hist_count;
//クラス間分散を計算
double dv = (sa + sb);
double mt = (double)(da + db) / dv;
double ma = (sa != 0 ? ((double)da / (double)sa) : 0) - mt;
double mb = (sb != 0 ? ((double)db / (double)sb) : 0) - mt;
double kai = ((double)(sa * (ma * ma) + sb * (mb * mb))) / dv;
if (max < kai)
{
max_mt = mt;
max = kai;
th = i;
}
//System.out.println(kai);
}
//max_mtを元に正規化
this._score = max / ((double)(pt + max_mt * max_mt * st - 2 * max_mt * dt) / st);//129,0.8888888888888887
return th;
}
/**
* デバック用関数
* @param args
* main関数引数
*/
public static void main(string[] args)
{
NyARHistogram data = new NyARHistogram(256);
for (int i = 0; i < 256; i++)
{
data.data[i] = 128 - i > 0 ? 128 - i : i - 128;
}
data.total_of_data = data.getTotal(0, 255);
NyARHistogramAnalyzer_DiscriminantThreshold an = new NyARHistogramAnalyzer_DiscriminantThreshold();
int th = an.getThreshold(data);
//System.out.print(th);
return;
}
/**
* デバック用関数
* @param args
* main関数引数
*/
public static void main(string[] args)
{
NyARHistogram data = new NyARHistogram(256);
for (int i = 0; i < 256; i++)
{
data.data[i] = 128 - i > 0 ? 128 - i : i - 128;
}
data.total_of_data = data.getTotal(0, 255);
NyARHistogramAnalyzer_DiscriminantThreshold an = new NyARHistogramAnalyzer_DiscriminantThreshold();
//int th = an.getThreshold(data);
//System.out.print(th);
return;
}
public void createHistogram(int i_l, int i_t, int i_w, int i_h, int i_skip, NyARHistogram o_histogram)
{
o_histogram.reset();
int[] data_ptr = o_histogram.data;
INyARGsPixelDriver drv = this._gsr.getGsPixelDriver();
int pix_count = i_w;
int pix_mod_part = pix_count - (pix_count % 8);
//左上から1行づつ走査していく
for (int y = i_h - 1; y >= 0; y -= i_skip)
{
for (int x = pix_count - 1; x >= pix_mod_part; x--)
{
data_ptr[drv.getPixel(x, y)]++;
}
}
o_histogram.total_of_data = i_w * i_h / i_skip;
return;
}
public void createHistogram(int i_l, int i_t, int i_w, int i_h, int i_skip, NyARHistogram o_histogram)
{
o_histogram.reset();
int[] data_ptr = o_histogram.data;
INyARGsPixelDriver drv = this._gsr.getGsPixelDriver();
int pix_count = i_w;
int pix_mod_part = pix_count - (pix_count % 8);
//左上から1行づつ走査していく
for (int y = i_h - 1; y >= 0; y -= i_skip)
{
for (int x = pix_count - 1; x >= pix_mod_part; x--)
{
data_ptr[drv.getPixel(x, y)]++;
}
}
o_histogram.total_of_data = i_w * i_h / i_skip;
return;
}
public void createHistogram(int i_l, int i_t, int i_w, int i_h, int i_skip, NyARHistogram o_histogram)
{
o_histogram.reset();
int[] data_ptr = o_histogram.data;
INyARRgbRaster drv = this._gsr;
int pix_count = i_w;
int pix_mod_part = pix_count - (pix_count % 8);
//左上から1行づつ走査していく
for (int y = i_h - 1; y >= 0; y -= i_skip)
{
for (int x = pix_count - 1; x >= pix_mod_part; x--)
{
drv.getPixel(x, y, tmp);
data_ptr[(tmp[0] + tmp[1] + tmp[2]) / 3]++;
}
}
o_histogram.total_of_data = i_w * i_h / i_skip;
return;
}
public void doFilter(int i_hist_interval, INyARGrayscaleRaster i_output)
{
//ヒストグラムを得る
NyARHistogram hist = this._histogram;
this._histdrv.createHistogram(i_hist_interval, hist);
//変換テーブルを作成
int hist_total = this._histogram.total_of_data;
int min = hist.getMinData();
int hist_size = this._histogram.length;
int sum = 0;
for (int i = 0; i < hist_size; i++)
{
sum += hist.data[i];
this._hist[i] = (int)((sum - min) * (hist_size - 1) / ((hist_total - min)));
}
//変換
this._tone_table.doFilter(this._hist, i_output);
return;
}
public void createHistogram(int i_l, int i_t, int i_w, int i_h, int i_skip, NyARHistogram o_histogram)
{
o_histogram.reset();
int[] input = (int[])this._gsr.getBuffer();
NyARIntSize s = this._gsr.getSize();
int skip = (i_skip * s.w - i_w);
int pix_count = i_w;
int pix_mod_part = pix_count - (pix_count % 8);
//左上から1行づつ走査していく
int pt = (i_t * s.w + i_l);
int[] data = o_histogram.data;
for (int y = i_h - 1; y >= 0; y -= i_skip)
{
int x;
for (x = pix_count - 1; x >= pix_mod_part; x--)
{
data[input[pt++]]++;
}
for (; x >= 0; x -= 8)
{
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
}
//スキップ
pt += skip;
}
o_histogram.total_of_data = i_w * i_h / i_skip;
return;
}
public void createHistogram(int i_skip, NyARHistogram o_histogram)
{
NyARIntSize s = this._gsr.getSize();
this.createHistogram(0, 0, s.w, s.h, i_skip, o_histogram);
}
public void createHistogram(int i_skip, NyARHistogram o_histogram)
{
NyARIntSize s = this._gsr.getSize();
this.createHistogram(0, 0, s.w, s.h, i_skip, o_histogram);
}
public void createHistogram(int i_l, int i_t, int i_w, int i_h, int i_skip, NyARHistogram o_histogram)
{
o_histogram.reset();
int[] input = (int[])this._gsr.getBuffer();
NyARIntSize s = this._gsr.getSize();
int skip = (i_skip * s.w - i_w);
int pix_count = i_w;
int pix_mod_part = pix_count - (pix_count % 8);
//左上から1行づつ走査していく
int pt = (i_t * s.w + i_l);
int[] data = o_histogram.data;
for (int y = i_h - 1; y >= 0; y -= i_skip)
{
int x;
for (x = pix_count - 1; x >= pix_mod_part; x--)
{
data[input[pt++]]++;
}
for (; x >= 0; x -= 8)
{
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
data[input[pt++]]++;
}
//スキップ
pt += skip;
}
o_histogram.total_of_data = i_w * i_h / i_skip;
return;
}
