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# Halide tutorial lesson 2.
# This lesson demonstrates how to pass in input images.
# This lesson can be built by invoking the command:
# make test_tutorial_lesson_02_input_image
# in a shell with the current directory at python_bindings/
import halide as hl
import numpy as np
import halide.imageio
import os.path
def main():
# This program defines a single-stage imaging pipeline that
# brightens an image.
# First we'll load the input image we wish to brighten.
image_path = os.path.join(
os.path.dirname(__file__), "../../tutorial/images/rgb.png"
)
# We create a hl.Buffer object to wrap the numpy array
input = hl.Buffer(halide.imageio.imread(image_path))
assert input.type() == hl.UInt(8)
# Next we define our hl.Func object that represents our one pipeline
# stage.
brighter = hl.Func("brighter")
# Our hl.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.
x, y, c = hl.Var("x"), hl.Var("y"), hl.Var("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.
value = input[x, y, c]
assert type(value) is hl.Expr
# Cast it to a floating point value.
value = hl.cast(hl.Float(32), 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 * 1.5
# Clamp it to be less than 255, so we don't get overflow when we
# hl.cast it back to an 8-bit unsigned int.
value = hl.min(value, 255.0)
# Cast it back to an 8-bit unsigned integer.
value = hl.cast(hl.UInt(8), value)
# Define the function.
brighter[x, y, c] = value
# The equivalent one-liner to all of the above is:
#
# brighter(x, y, c) = hl.cast<uint8_t>(hl.min(input(x, y, c) * 1.5f, 255))
# brighter[x, y, c] = hl.cast(hl.UInt(8), hl.min(input[x, y, c] * 1.5, 255))
#
# In the shorter version:
# - I skipped the hl.cast to float, because multiplying by 1.5f does
# that automatically.
# - I also used integer constants in hl.clamp, because they get hl.cast
# to match the type of the first argument.
# - I left the h. off hl.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 hl.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.
output_image = brighter.realize([input.width(), input.height(), input.channels()])
assert output_image.type() == hl.UInt(8)
# Save the output for inspection. It should look like a bright parrot.
# python3-imageio versions <2.5 expect a numpy array
halide.imageio.imwrite("brighter.png", np.asanyarray(output_image))
print("Created brighter.png result file.")
print("Success!")
return 0
if __name__ == "__main__":
main()
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