// Compute the Otsu threshold(s) for the given image rectangle, making one
// for each channel. Each channel is always one byte per pixel.
// Returns an array of threshold values and an array of hi_values, such
// that a pixel value >threshold[channel] is considered foreground if
// hi_values[channel] is 0 or background if 1. A hi_value of -1 indicates
// that there is no apparent foreground. At least one hi_value will not be -1.
// thresholds and hi_values are assumed to be of bytes_per_pixel size.
void TessBaseAPI::OtsuThreshold(const unsigned char* imagedata,
int bytes_per_pixel,
int bytes_per_line,
int left, int top, int right, int bottom,
int* thresholds,
int* hi_values) {
// Of all channels with no good hi_value, keep the best so we can always
// produce at least one answer.
int best_hi_value = 0;
int best_hi_index = 0;
bool any_good_hivalue = false;
double best_hi_dist = 0.0;
for (int ch = 0; ch < bytes_per_pixel; ++ch) {
thresholds[ch] = 0;
hi_values[ch] = -1;
// Compute the histogram of the image rectangle.
int histogram[256];
HistogramRect(imagedata + ch, bytes_per_pixel, bytes_per_line,
left, top, right, bottom, histogram);
int H;
int best_omega_0;
int best_t = OtsuStats(histogram, &H, &best_omega_0);
if (best_omega_0 == 0 || best_omega_0 == H) {
// This channel is empty.
continue;
}
// To be a convincing foreground we must have a small fraction of H
// or to be a convincing background we must have a large fraction of H.
// In between we assume this channel contains no thresholding information.
int hi_value = best_omega_0 < H * 0.5;
thresholds[ch] = best_t;
if (best_omega_0 > H * 0.75) {
any_good_hivalue = true;
hi_values[ch] = 0;
}
else if (best_omega_0 < H * 0.25) {
any_good_hivalue = true;
hi_values[ch] = 1;
}
else {
// In case all channels are like this, keep the best of the bad lot.
double hi_dist = hi_value ? (H - best_omega_0) : best_omega_0;
if (hi_dist > best_hi_dist) {
best_hi_dist = hi_dist;
best_hi_value = hi_value;
best_hi_index = ch;
}
}
}
if (!any_good_hivalue) {
// Use the best of the ones that were not good enough.
hi_values[best_hi_index] = best_hi_value;
}
}
// Compute the Otsu threshold(s) for the given image rectangle, making one
// for each channel. Each channel is always one byte per pixel.
// Returns an array of threshold values and an array of hi_values, such
// that a pixel value >threshold[channel] is considered foreground if
// hi_values[channel] is 0 or background if 1. A hi_value of -1 indicates
// that there is no apparent foreground. At least one hi_value will not be -1.
// Delete thresholds and hi_values with delete [] after use.
void OtsuThreshold(const unsigned char* imagedata,
int bytes_per_pixel, int bytes_per_line,
int left, int top, int width, int height,
int** thresholds, int** hi_values) {
// Of all channels with no good hi_value, keep the best so we can always
// produce at least one answer.
int best_hi_value = 1;
int best_hi_index = 0;
bool any_good_hivalue = false;
double best_hi_dist = 0.0;
*thresholds = new int[bytes_per_pixel];
*hi_values = new int[bytes_per_pixel];
int *histogramAllChannels = new int[kHistogramSize*bytes_per_pixel];// all of channel 0 then all of channel 1...
#ifdef USE_OPENCL
// Calculate Histogram on GPU
OpenclDevice od;
//printf("Histograming Rect: height=%i, width=%i, channels=%i\n", height, width, bytes_per_pixel);
od.HistogramRectOCL(
imagedata,
bytes_per_pixel,
bytes_per_line,
left,
top,
width,
height,
kHistogramSize,
histogramAllChannels);
// Calculate Threshold from Histogram on cpu
for (int ch = 0; ch < bytes_per_pixel; ++ch) {
(*thresholds)[ch] = -1;
(*hi_values)[ch] = -1;
int *histogram = &histogramAllChannels[kHistogramSize*ch];
int H;
int best_omega_0;
int best_t = OtsuStats(histogram, &H, &best_omega_0);
if (best_omega_0 == 0 || best_omega_0 == H) {
// This channel is empty.
continue;
}
// To be a convincing foreground we must have a small fraction of H
// or to be a convincing background we must have a large fraction of H.
// In between we assume this channel contains no thresholding information.
int hi_value = best_omega_0 < H * 0.5;
(*thresholds)[ch] = best_t;
if (best_omega_0 > H * 0.75) {
any_good_hivalue = true;
(*hi_values)[ch] = 0;
} else if (best_omega_0 < H * 0.25) {
any_good_hivalue = true;
(*hi_values)[ch] = 1;
} else {
// In case all channels are like this, keep the best of the bad lot.
double hi_dist = hi_value ? (H - best_omega_0) : best_omega_0;
if (hi_dist > best_hi_dist) {
best_hi_dist = hi_dist;
best_hi_value = hi_value;
best_hi_index = ch;
}
}
}
delete[] histogramAllChannels;
#else
for (int ch = 0; ch < bytes_per_pixel; ++ch) {
(*thresholds)[ch] = -1;
(*hi_values)[ch] = -1;
// Compute the histogram of the image rectangle.
int histogram[kHistogramSize];
HistogramRect(imagedata + ch, bytes_per_pixel, bytes_per_line,
left, top, width, height, histogram);
int H;
int best_omega_0;
int best_t = OtsuStats(histogram, &H, &best_omega_0);
if (best_omega_0 == 0 || best_omega_0 == H) {
// This channel is empty.
continue;
}
// To be a convincing foreground we must have a small fraction of H
// or to be a convincing background we must have a large fraction of H.
// In between we assume this channel contains no thresholding information.
int hi_value = best_omega_0 < H * 0.5;
(*thresholds)[ch] = best_t;
if (best_omega_0 > H * 0.75) {
any_good_hivalue = true;
(*hi_values)[ch] = 0;
} else if (best_omega_0 < H * 0.25) {
any_good_hivalue = true;
(*hi_values)[ch] = 1;
} else {
// In case all channels are like this, keep the best of the bad lot.
double hi_dist = hi_value ? (H - best_omega_0) : best_omega_0;
if (hi_dist > best_hi_dist) {
best_hi_dist = hi_dist;
best_hi_value = hi_value;
best_hi_index = ch;
}
}
}
#endif // USE_OPENCL
if (!any_good_hivalue) {
// Use the best of the ones that were not good enough.
(*hi_values)[best_hi_index] = best_hi_value;
}
}
