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#include "Halide.h"
namespace {
using namespace Halide::ConciseCasts;
class Hist : public Halide::Generator<Hist> {
public:
Input<Buffer<uint8_t, 3>> input{"input"};
Output<Buffer<uint8_t, 3>> output{"output"};
void generate() {
Var x("x"), y("y"), c("c");
// Algorithm
Func Y("Y");
Y(x, y) = (0.299f * input(x, y, 0) +
0.587f * input(x, y, 1) +
0.114f * input(x, y, 2));
Func Cr("Cr");
Expr R = input(x, y, 0);
Cr(x, y) = (R - Y(x, y)) * 0.713f + 128;
Func Cb("Cb");
Expr B = input(x, y, 2);
Cb(x, y) = (B - Y(x, y)) * 0.564f + 128;
Func hist_rows("hist_rows");
hist_rows(x, y) = 0;
RDom rx(0, input.width());
Expr bin = cast<int>(clamp(Y(rx, y), 0, 255));
hist_rows(bin, y) += 1;
Func hist("hist");
hist(x) = 0;
RDom ry(0, input.height());
hist(x) += hist_rows(x, ry);
Func cdf("cdf");
cdf(x) = hist(0);
RDom b(1, 255);
cdf(b.x) = cdf(b.x - 1) + hist(b.x);
Func cdf_bin("cdf_bin");
cdf_bin(x, y) = u8(clamp(Y(x, y), 0, 255));
Func eq("equalize");
eq(x, y) = clamp(cdf(cdf_bin(x, y)) * (255.0f / (input.height() * input.width())), 0, 255);
Expr red = u8(clamp(eq(x, y) + (Cr(x, y) - 128) * 1.4f, 0, 255));
Expr green = u8(clamp(eq(x, y) - 0.343f * (Cb(x, y) - 128) - 0.711f * (Cr(x, y) - 128), 0, 255));
Expr blue = u8(clamp(eq(x, y) + 1.765f * (Cb(x, y) - 128), 0, 255));
output(x, y, c) = mux(c, {red, green, blue});
// Estimates (for autoscheduler; ignored otherwise)
{
input.dim(0).set_estimate(0, 1536);
input.dim(1).set_estimate(0, 2560);
input.dim(2).set_estimate(0, 3);
output.dim(0).set_estimate(0, 1536);
output.dim(1).set_estimate(0, 2560);
output.dim(2).set_estimate(0, 3);
}
// Schedule
if (!using_autoscheduler()) {
cdf.bound(x, 0, 256);
Var xi("xi"), yi("yi");
if (get_target().has_gpu_feature()) {
// 0.197ms on a 2060 RTX
Var yii;
RVar rxo, rxi;
if (get_target().has_feature(Target::CUDA)) {
// Each thread below will use atomic integer adds
// to shared to compute the histogram of a single
// row.
hist_rows
.in()
.compute_root()
.split(x, x, xi, 64)
.vectorize(xi, 2)
.unroll(x)
.gpu_lanes(xi)
.gpu_blocks(y);
hist_rows
.store_in(MemoryType::GPUShared)
.compute_at(hist_rows.in(), y)
.split(x, x, xi, 64)
.vectorize(xi, 2)
.unroll(x)
.gpu_lanes(xi)
.update()
.split(rx, rxo, rxi, 32)
.reorder(rxi, rxo, y)
.atomic()
.gpu_lanes(rxi);
Y.clone_in(hist_rows)
.compute_at(hist_rows, rxo)
.store_in(MemoryType::Register)
.gpu_lanes(x);
} else {
hist_rows.compute_root()
.gpu_tile(x, y, xi, yi, 32, 8);
const int slice_width = 256;
// Get more parallelism by not just taking
// histograms of rows, but histograms of small
// pieces of each row.
hist_rows.update()
.split(rx, rxo, rxi, slice_width);
Var z, zi;
Func intm = hist_rows.update().rfactor(rxo, z);
intm.in()
.compute_root()
.gpu_tile(y, z, yi, zi, 16, 1);
intm.compute_at(intm.in(), y)
.split(x, x, xi, 16)
.gpu_threads(xi)
.update()
.gpu_threads(y);
// hist_rows now just sums up the mini-histograms
// along the z dimension.
hist_rows.update().gpu_tile(x, y, xi, yi, 32, 8);
if (!get_target().has_feature(Target::Metal) &&
!get_target().has_feature(Target::D3D12Compute)) {
// bound_extent doesn't currently work inside
// metal & d3d12compute kernels because we can't compile the
// assertion. For metal & d3d12compute we just inline the
// luma computation.
Y.clone_in(intm)
.compute_at(intm.in(), y)
.split(x, x, xi, 16)
.bound_extent(x, 16)
.gpu_threads(xi);
}
}
hist.compute_root()
.gpu_tile(x, xi, 16)
.update()
.gpu_tile(x, xi, 16);
cdf.compute_root()
.gpu_tile(x, xi, 16)
.update()
.gpu_single_thread();
output.compute_root()
.reorder(c, x, y)
.bound(c, 0, 3)
.unroll(c)
.gpu_tile(x, y, xi, yi, 128, 4)
.vectorize(xi, 4);
Cb.compute_at(output, xi).vectorize(x);
Cr.compute_at(output, xi).vectorize(x);
eq.compute_at(output, xi).vectorize(x);
// Stage the LUT into shared memory
cdf.in()
.compute_at(output, x)
.split(x, x, xi, 64)
.vectorize(xi, 2)
.gpu_threads(xi, x);
} else {
// Runtime is noisy. 0.8ms - 1.1ms on an Intel
// i9-9960X using 16 threads
const int vec = natural_vector_size<float>();
// Make separate copies of Y to use while
// histogramming and while computing the output. It's
// better to redundantly luminance than reload it, but
// you don't want to inline it into the histogram
// computation because then it doesn't vectorize.
RVar rxo, rxi;
Y.clone_in(hist_rows)
.compute_at(hist_rows.in(), y)
.vectorize(x, vec);
hist_rows.update(0).unscheduled();
hist_rows.in()
.compute_root()
.vectorize(x, vec)
.parallel(y, 4);
hist_rows.compute_at(hist_rows.in(), y)
.vectorize(x, vec)
.update()
.reorder(y, rx)
.unroll(y);
hist.compute_root()
.vectorize(x, vec)
.update()
.reorder(x, ry)
.vectorize(x, vec)
.unroll(x, 4)
.parallel(x)
.reorder(ry, x);
cdf.compute_root().update().unscheduled();
output.reorder(c, x, y)
.bound(c, 0, 3)
.unroll(c)
.parallel(y, 8)
.vectorize(x, vec * 2);
}
}
}
};
} // namespace
HALIDE_REGISTER_GENERATOR(Hist, hist)
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