def test_perf_to_rgb_2d():
hsl = np.random.rand(19200, 3) * 100
import nphusl
nphusl.enable_standard_fns()
cs = None
t1 = timeit.timeit("nphusl.to_rgb(hsl, chunksize=cs)", number=1, globals=locals())
nphusl.enable_numexpr_fns()
t2 = timeit.timeit("nphusl.to_rgb(hsl, chunksize=cs)", number=1, globals=locals())
nphusl.enable_cython_fns()
t3 = timeit.timeit("nphusl.to_rgb(hsl, chunksize=cs)", number=1, globals=locals())
print("to_rgb()\nCython: {}\nNumExpr: {}\nNumpy: {}".format(
t3, t2, t1))
assert t3 < t2 < t1
def test_perf_rgb_to_husl_2d():
rgb = np.random.rand(19200, 3)
nphusl.enable_cython_fns()
go_cy = cy.rgb_to_husl
t_cy = timeit.timeit("go_cy(rgb)", number=1, globals=locals())
nphusl.enable_standard_fns()
nphusl.enable_numexpr_fns()
go_ex = nphusl.rgb_to_husl
t_ex = timeit.timeit("go_ex(rgb)", number=1, globals=locals())
nphusl.enable_standard_fns()
go_np = nphusl.rgb_to_husl
t_np = timeit.timeit("go_np(rgb)", number=1, globals=locals())
print("\n2D RGB->HUSL: "
"\nCython: {}s\nNumExpr: {}s\nNumpy: {}s".format(
t_cy, t_ex, t_np))
assert t_cy < t_ex and t_cy < t_np
def test_perf_cython_husl_to_rgb():
hsl = np.random.rand(1920, 1080, 3) * 100
nphusl.enable_cython_fns()
go_cy = cy.husl_to_rgb
t_cy = timeit.timeit("go_cy(hsl)", number=1, globals=locals())
nphusl.enable_standard_fns()
nphusl.enable_numexpr_fns()
go_ex = nphusl.husl_to_rgb
t_ex = timeit.timeit("go_ex(hsl)", number=1, globals=locals())
nphusl.enable_standard_fns()
go_np = nphusl.husl_to_rgb
t_np = timeit.timeit("go_np(hsl)", number=1, globals=locals())
print("\n1080p image HUSL->RGB: "
"\nCython: {}s\nNumExpr: {}s\nNumpy: {}s".format(
t_cy, t_ex, t_np))
assert t_cy < t_ex < t_np
assert t_np / t_cy > 2
def with_expr(*args, **kwargs):
assert hasattr(nphusl, "_nphusl_expr")
nphusl.enable_numexpr_fns()
fn(*args, **kwargs)
nphusl.enable_standard_fns()
i[1] = 1.0 # ensure we get all white
IMG_CACHED[0] = np.ascontiguousarray(i)
return IMG_CACHED[0]
def main(img_int, chunksize):
img_float = img_int / 255.0
out = np.zeros(img_float.shape, dtype=np.float)
chunks = nphusl.chunk_img(img_float, chunksize=chunksize)
nphusl.chunk_transform(nphusl.rgb_to_husl, chunks, out)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("image", type=str)
parser.add_argument("--optimizations", default="cython",
choices=("standard", "cython", "numexpr"))
parser.add_argument("--image-size", type=int, default=2000)
parser.add_argument("--chunk-size", type=int, default=1000)
args = parser.parse_args()
if args.optimizations == "standard":
nphusl.enable_standard_fns()
elif args.optimizations == "numexpr":
nphusl.enable_numexpr_fns()
else:
nphusl.enable_cython_fns()
n = args.image_size
img_int = imread.imread(args.image)[:n, :n]
main(img_int, args.chunk_size)
