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# Before reading this file, see lesson_10_aot_compilation_generate.py
# This is the code that actually uses the Halide pipeline we've
# compiled. It does not depend on libHalide, so we won't do
# "import halide".
#
# Instead, it depends on the header file that lesson_10_generate
# produced when we ran it:
import lesson_10_halide
import numpy as np
def main():
# Have a look at the generated files above (they won't exist until you've run
# lesson_10_generate): lesson_10_halide.py.cpp, lesson_10_halide.h
#
# In the header file, the generated function is represented like this:
# int lesson_10_halide(halide_buffer_t*, uint8_t, halide_buffer_t*);
#
# lesson_10_halide.py.cpp creates a Python wrapper around this function.
# Buffers are converted using the Python buffer API:
#
# https://docs.python.org/2/c-api/buffer.html
# https://docs.python.org/3/c-api/buffer.html
#
# In other words, you can pass numpy arrays directly to the generated
# code.
# Let's make some input data to test with:
input = np.empty((640, 480), dtype=np.uint8, order="F")
for y in range(480):
for x in range(640):
input[x, y] = (x ^ (y + 1)) & 0xFF
# And the memory where we want to write our output:
output = np.empty((640, 480), dtype=np.uint8, order="F")
offset_value = 5
lesson_10_halide.lesson_10_halide(input, offset_value, output)
# Now let's check the filter performed as advertised. It was
# supposed to add the offset to every input pixel.
correct_val = np.empty((1), dtype=np.uint8)
for y in range(480):
for x in range(640):
input_val = input[x, y]
output_val = output[x, y]
correct_val[0] = input_val
# we add over a uint8 value (will properly model overflow)
correct_val[0] += offset_value
assert output_val == correct_val[0], (
f"output({x}, {y}) was {output_val} instead of {correct_val}"
)
# Everything worked!
print("Success!")
return 0
if __name__ == "__main__":
main()
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