void NamedWindow::showImage2D(Halide::Image<uint8_t> im)
{
static Halide::Func convert("convertToMat2D");
static Halide::ImageParam ip(Halide::UInt(8), 2);
static Halide::Var x, y;
if (!convert.defined())
{
convert(x, y) = ip(x, y);
convert.vectorize(x, 4).parallel(y, 4);
}
ip.set(im);
cv::Mat mat(im.height(), im.width(), CV_8UC1, cv::Scalar(0));
convert.realize(Halide::Buffer(Halide::UInt(8), im.width(), im.height(), 0, 0, mat.data));
cv::imshow(name, mat);
}
void NamedWindow::showImage3D(Halide::Image<float> im)
{
static Halide::Func convert("convertToMat3D");
static Halide::ImageParam ip(Halide::Float(32), 3);
static Halide::Var x, y, c;
if (!convert.defined())
{
convert(c, x, y) = Halide::cast<uint8_t>(ip(x, y, 2 - c) * 255);
convert.vectorize(x, 4).parallel(y, 4);
}
ip.set(im);
cv::Mat mat(im.height(), im.width(), CV_8UC3, cv::Scalar(0));
convert.realize(Halide::Buffer(Halide::UInt(8), im.channels(), im.width(), im.height(), 0, mat.data));
cv::imshow(name, mat);
}
int sobel_example(int argc, const char **argv) {
Halide::Image<uint8_t> input = load<uint8_t>(argv[0]);
Halide::Var x,y,c;
Halide::Func padded;
padded(x,y) = input(clamp(x, 0, input.width()-1), clamp(y, 0, input.height()-1));
Halide::Image<uint8_t> sobel_output(input.width(), input.height());
// smooth the image
// Halide::Func gaussian = gaussian_3x3(padded, true);
// calculate the horizontal and vertical gradients (GX, Gy)
std::pair<Halide::Func, Halide::Func> sobel = sobel_3x3(padded, true);
Halide::Func Gx, Gy;
Gx(x,y) = AS_UINT8(sobel.first(x,y));
Gx.realize(sobel_output);
save(sobel_output, "output/sobel_gx.png");
Gy(x,y) = AS_UINT8(sobel.second(x,y));
Gy.realize(sobel_output);
save(sobel_output, "output/sobel_gy.png");
// Calculate gradient magnitudes and scale them to image intensity
// Described in http://patrick-fuller.com/gradients-image-processing-for-scientists-and-engineers-part-3/
Halide::RDom r(input);
Halide::Func maxpix, minpix, luminosity, mag, mag_uint8;
// calculate the {min,max} luminosity values of the original grayscale image
Halide::Image<int32_t> max_out, min_out;
maxpix(x) = 0; minpix(x) = 0;
maxpix(0) = max(input(r.x, r.y), maxpix(0));
minpix(0) = min(input(r.x, r.y), minpix(0));
max_out = maxpix.realize(1);
min_out = minpix.realize(1);
// calculate the magnitude of Gx, Gy
mag = grad_magnitude(sobel.first, sobel.second);
// scale the magnitude by the luminosity range
luminosity(x,y) = 255.0f * ((mag(x,y)-min_out(0)) / (max_out(0)-min_out(0)));
TO_2D_UINT8_LAMBDA(luminosity).realize(sobel_output);
save(sobel_output, "output/sobel_mag.png");
printf("%s DONE\n", __func__);
return EXIT_SUCCESS;
}
int main(int argc, char **argv) {
Halide::Func cell("cell");
Halide::Var x, y;
cell(x, y) = 0;
cell(2, 1) = 1;
cell(3, 2) = 1;
cell(1, 3) = 1;
cell(2, 3) = 1;
cell(3, 3) = 1;
Halide::Image<int32_t> output;
output = cell.realize(NX,NY);
Halide::ImageParam input(Halide::Int(32), 2); // int32_t 2D
Halide::Func cell2;
cell2(x,y)=input(x,y)+1;
Halide::Func nbd("nbd");
Halide::Func bc("bc");
for (int t=0; t<10; ++t) {
Halide::Image<int32_t> output = cell.realize(NX,NY);
for (int j = 0; j < output.height(); j++) {
for (int i = 0; i < output.width(); i++) {
printf("%1d", output(i, j));
}
printf("\n");
}
printf("\n");
bc(x,y)=select((x>=0)&&(x<NX)&&(y>=0)&&(y<NY)
,output(min(NX-1,max(x,0)),min(NY-1,max(y,0))),0);
nbd(x,y)=-bc(x,y);
// staged programming!
for(int dy=-1; dy<=1; ++dy) {
for(int dx=-1; dx<=1; ++dx) {
nbd(x,y)+=bc(x+dx,y+dy);
}
}
cell(x,y)=select((bc(x,y)==1&&nbd(x,y)==3)||(bc(x,y)==1&&nbd(x,y)==2)
|| (bc(x,y)==0&&nbd(x,y)==3)
,1,0);
}
return 0;
}
int main(int argc, char **argv) {
// This program defines a single-stage imaging pipeline that
// brightens an image.
// First we'll load the input image we wish to brighten.
Halide::Image<uint8_t> input = load<uint8_t>("../apps/images/rgb.png");
// Next we define our Func object that represents our one pipeline
// stage.
Halide::Func brighter;
// Our Func will have three arguments, representing the position
// in the image and the color channel. Halide treats color
// channels as an extra dimension of the image.
Halide::Var x, y, c;
// Normally we'd probably write the whole function definition on
// one line. Here we'll break it apart so we can explain what
// we're doing at every step.
// For each pixel of the input image.
Halide::Expr value = input(x, y, c);
// Cast it to a floating point value.
value = Halide::cast<float>(value);
// Multiply it by 1.5 to brighten it. Halide represents real
// numbers as floats, not doubles, so we stick an 'f' on the end
// of our constant.
value = value * 1.5f;
// Clamp it to be less than 255, so we don't get overflow when we
// cast it back to an 8-bit unsigned int.
value = Halide::min(value, 255.0f);
// Cast it back to an 8-bit unsigned integer.
value = Halide::cast<uint8_t>(value);
// Define the function.
brighter(x, y, c) = value;
// The equivalent one-liner to all of the above is:
//
// brighter(x, y, c) = Halide::cast<uint8_t>(min(input(x, y, c) * 1.5f, 255));
//
// In the shorter version:
// - I skipped the cast to float, because multiplying by 1.5f does
// that automatically.
// - I also used integer constants in clamp, because they get cast
// to match the type of the first argument.
// - I left the Halide:: off clamp. It's unnecessary due to Koenig
// lookup.
// Remember. All we've done so far is build a representation of a
// Halide program in memory. We haven't actually processed any
// pixels yet. We haven't even compiled that Halide program yet.
// So now we'll realize the Func. The size of the output image
// should match the size of the input image. If we just wanted to
// brighten a portion of the input image we could request a
// smaller size. If we request a larger size Halide will throw an
// error at runtime telling us we're trying to read out of bounds
// on the input image.
Halide::Image<uint8_t> output = brighter.realize(input.width(), input.height(), input.channels());
// Save the output for inspection. It should look like a bright parrot.
save(output, "brighter.png");
printf("Success!\n");
return 0;
}
int main(int argc, char **argv) {
// This program defines a single-stage imaging pipeline that
// outputs a grayscale diagonal gradient.
// A 'Func' object represents a pipeline stage. It's a pure
// function that defines what value each pixel should have. You
// can think of it as a computed image.
Halide::Func gradient;
// Var objects are names to use as variables in the definition of
// a Func. They have no meaning by themselves.
Halide::Var x, y;
// We typically use Vars named 'x' and 'y' to correspond to the x
// and y axes of an image, and we write them in that order. If
// you're used to thinking of images as having rows and columns,
// then x is the column index, and y is the row index.
// Funcs are defined at any integer coordinate of its variables as
// an Expr in terms of those variables and other functions.
// Here, we'll define an Expr which has the value x + y. Vars have
// appropriate operator overloading so that expressions like
// 'x + y' become 'Expr' objects.
Halide::Expr e = x + y;
// Now we'll add a definition for the Func object. At pixel x, y,
// the image will have the value of the Expr e. On the left hand
// side we have the Func we're defining and some Vars. On the right
// hand side we have some Expr object that uses those same Vars.
gradient(x, y) = e;
// This is the same as writing:
//
// gradient(x, y) = x + y;
//
// which is the more common form, but we are showing the
// intermediate Expr here for completeness.
// That line of code defined the Func, but it didn't actually
// compute the output image yet. At this stage it's just Funcs,
// Exprs, and Vars in memory, representing the structure of our
// imaging pipeline. We're meta-programming. This C++ program is
// constructing a Halide program in memory. Actually computing
// pixel data comes next.
// Now we 'realize' the Func, which JIT compiles some code that
// implements the pipeline we've defined, and then runs it. We
// also need to tell Halide the domain over which to evaluate the
// Func, which determines the range of x and y above, and the
// resolution of the output image. Halide.h also provides a basic
// templatized Image type we can use. We'll make an 800 x 600
// image.
Halide::Image<int32_t> output = gradient.realize(800, 600);
// Halide does type inference for you. Var objects represent
// 32-bit integers, so the Expr object 'x + y' also represents a
// 32-bit integer, and so 'gradient' defines a 32-bit image, and
// so we got a 32-bit signed integer image out when we call
// 'realize'. Halide types and type-casting rules are equivalent
// to C.
// Let's check everything worked, and we got the output we were
// expecting:
for (int j = 0; j < output.height(); j++) {
for (int i = 0; i < output.width(); i++) {
// We can access a pixel of an Image object using similar
// syntax to defining and using functions.
if (output(i, j) != i + j) {
printf("Something went wrong!\n"
"Pixel %d, %d was supposed to be %d, but instead it's %d\n",
i, j, i+j, output(i, j));
return -1;
}
}
}
// Everything worked! We defined a Func, then called 'realize' on
// it to generate and run machine code that produced an Image.
printf("Success!\n");
return 0;
}