void RGB24Buffer::drawIsolines(
double zx, double zy,
double zh, double zw,
double steps,
FunctionArgs &f)
{
int i,j;
if (f.inputs < 2)
return;
AbstractBuffer<double> *values = new AbstractBuffer<double>(this->getSize());
double *in = new double[f.inputs ];
double *out = new double[f.outputs];
for (int i = 0; i < f.inputs; i++) {
in[i] = 0.0;
}
for (i = 0; i < h; i++)
{
for (j = 0; j < w; j++)
{
in[0] = (i * zh / h) + zy;
in[1] = (j * zw / w) + zx;
f(in, out);
values->element(i,j) = out[0];
/* if (j == w / 2) {
printf("out: [%d %d] %lf\n", i, j, out[0]);
}*/
}
}
for (i = 1; i < h - 1; i++)
{
for (j = 1; j < w - 1; j++)
{
double c = values->element(i,j);
double r = values->element(i,j + 1);
double l = values->element(i + 1,j);
double ls = ((int) (c * steps)) / steps;
double hs = ((int) (c * steps) + 1) / steps;
if (r < ls || r > hs ||
l < ls || l > hs )
element(i,j) = RGBColor::gray(128);
}
}
deletearr_safe(in);
deletearr_safe(out);
delete_safe(values);
}
void ADCensus::disparityMapFromRGB(QUrl leftImageUrl, QUrl rightImageUrl) {
std::string left = leftImageUrl.toLocalFile().toStdString();
std::string right = rightImageUrl.toLocalFile().toStdString();
cout << "Opening [" << left << " " << right << "]" << endl;
RGB24Buffer *leftImage = BMPLoader().loadRGB(left);
RGB24Buffer *rightImage = BMPLoader().loadRGB(right);
G8Buffer *leftGray = leftImage->getChannel(ImageChannel::GRAY);
G8Buffer *rightGray = rightImage->getChannel(ImageChannel::GRAY);
AbstractBuffer<int32_t> disparities = constructDisparityMap(leftImage, rightImage, leftGray, rightGray);
G8Buffer *result = new G8Buffer(disparities.getSize());
for (int x = 0; x < result->w; ++x) {
for (int y = 0; y < result->h; ++y) {
result->element(y,x) = (disparities.element(y, x) / (double)result->w * 255 * 3);
}
}
BMPLoader().save("result.bmp", result);
cout << "Resulting disparity map saved to result.bmp\n";
delete leftImage;
delete rightImage;
delete leftGray;
delete rightGray;
delete result;
}
AbstractBuffer<int32_t> ADCensus::constructDisparityMap(const AbstractBuffer<pixel> *leftImage, const AbstractBuffer<pixel> *rightImage,
const AbstractBuffer<grayPixel> *leftGrayImage, const AbstractBuffer<grayPixel> *rightGrayImage) {
// Initialization
int width = leftImage->w;
int height = leftImage->h;
BaseTimeStatisticsCollector collector;
Statistics outerStats;
outerStats.setValue("H", height);
outerStats.setValue("W", width);
AbstractBuffer<int32_t> bestDisparities = AbstractBuffer<int32_t>(height, width);
AbstractBuffer<COST_TYPE> minCosts = AbstractBuffer<COST_TYPE>(height, width);
minCosts.fillWith(-1);
// Disparity computation
outerStats.startInterval();
AbstractBuffer<int64_t> leftCensus = AbstractBuffer<int64_t>(height, width);
AbstractBuffer<int64_t> rightCensus = AbstractBuffer<int64_t>(height, width);
makeCensus(leftGrayImage, leftCensus);
makeCensus(rightGrayImage, rightCensus);
outerStats.resetInterval("Making census");
makeAggregationCrosses(leftImage);
outerStats.resetInterval("Making aggregation crosses");
for (uint i = 0; i < CORE_COUNT_OF(table1); i++)
{
table1[i] = robust(i, lambdaCT);
table2[i] = robust(i, lambdaAD);
}
bool parallelDisp = true;
parallelable_for(0, width / 3,
[this, &minCosts, &bestDisparities, &leftImage, &rightImage,
&leftCensus, &rightCensus, &collector, height, width, parallelDisp](const BlockedRange<int> &r)
{
for (int d = r.begin(); d != r.end(); ++d) {
Statistics stats;
stats.startInterval();
AbstractBuffer<COST_TYPE> costs = AbstractBuffer<COST_TYPE>(height, width);
stats.resetInterval("Matrix construction");
parallelable_for(windowHh, height - windowHh,
[this, &costs, &leftImage, &rightImage, &leftCensus, &rightCensus, d, width](const BlockedRange<int> &r)
{
for (int y = r.begin(); y != r.end(); ++y) {
auto *im1 = &leftImage->element(y, windowWh + d);
auto *im2 = &rightImage->element(y, windowWh);
int64_t *cen1 = &leftCensus.element(y, windowWh + d);
int64_t *cen2 = &rightCensus.element(y, windowWh);
int x = windowWh + d;
#ifdef WITH_SSE
for (; x < width - windowWh; x += 8) {
FixedVector<Int16x8, 4> c1 = SSEReader8BBBB_DDDD::read((uint32_t *)im1);
FixedVector<Int16x8, 4> c2 = SSEReader8BBBB_DDDD::read((uint32_t *)im2);
UInt16x8 dr = SSEMath::difference(UInt16x8(c1[RGBColor::FIELD_R]), UInt16x8(c2[RGBColor::FIELD_R]));
UInt16x8 dg = SSEMath::difference(UInt16x8(c1[RGBColor::FIELD_G]), UInt16x8(c2[RGBColor::FIELD_G]));
UInt16x8 db = SSEMath::difference(UInt16x8(c1[RGBColor::FIELD_B]), UInt16x8(c2[RGBColor::FIELD_B]));
UInt16x8 ad = (dr + dg + db) >> 2;
Int16x8 cost_ad = Int16x8(robustLUTAD(ad[0]),
robustLUTAD(ad[1]),
robustLUTAD(ad[2]),
robustLUTAD(ad[3]),
robustLUTAD(ad[4]),
robustLUTAD(ad[5]),
robustLUTAD(ad[6]),
robustLUTAD(ad[7]));
Int64x2 cen10(&cen1[0]);
Int64x2 cen12(&cen1[2]);
Int64x2 cen14(&cen1[4]);
Int64x2 cen16(&cen1[6]);
Int64x2 cen20(&cen2[0]);
Int64x2 cen22(&cen2[2]);
Int64x2 cen24(&cen2[4]);
Int64x2 cen26(&cen2[6]);
Int64x2 diff0 = cen10 ^ cen20;
Int64x2 diff2 = cen12 ^ cen22;
Int64x2 diff4 = cen14 ^ cen24;
Int64x2 diff6 = cen16 ^ cen26;
Int16x8 cost_ct(robustLUTCen(_mm_popcnt_u64(diff0.getInt(0))), robustLUTCen(_mm_popcnt_u64(diff0.getInt(1))),
robustLUTCen(_mm_popcnt_u64(diff2.getInt(0))), robustLUTCen(_mm_popcnt_u64(diff2.getInt(1))),
robustLUTCen(_mm_popcnt_u64(diff4.getInt(0))), robustLUTCen(_mm_popcnt_u64(diff4.getInt(1))),
robustLUTCen(_mm_popcnt_u64(diff6.getInt(0))), robustLUTCen(_mm_popcnt_u64(diff6.getInt(1))));
Int16x8 cost_total = cost_ad + cost_ct;
for (int i = 0; i < 8; ++i) {
costs.element(y, x + i) = cost_total[i];
}
im1 += 8;
im2 += 8;
cen1+= 8;
cen2+= 8;
}
#else
for (; x < width - windowWh; ++x) {
uint8_t c_ad = costAD(*im1, *im2);
uint8_t c_census = hammingDist(*cen1, *cen2);
costs.element(y, x) = robustLUTCen(c_census) + robustLUTAD(c_ad);
im1 ++;
im2 ++;
cen1++;
cen2++;
}
#endif
}
}, !parallelDisp
);
stats.resetInterval("Cost computation");
aggregateCosts(&costs, windowWh + d, windowHh, width - windowWh, height - windowHh);
stats.resetInterval("Cost aggregation");
for (int x = windowWh + d; x < width - windowWh; ++x) {
for (int y = windowHh; y < height - windowHh; ++y) {
tbb::mutex::scoped_lock(bestDisparitiesMutex);
if(costs.element(y, x) < minCosts.element(y, x)) {
minCosts.element(y, x) = costs.element(y, x);
bestDisparities.element(y, x) = d;
//result.element(y,x) = (bestDisparities.element(y, x) / (double)width * 255 * 3);
}
}
}
//BMPLoader().save("../../result.bmp", result);
stats.endInterval("Comparing with previous minimum");
collector.addStatistics(stats);
}
}, parallelDisp);