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277 changes: 277 additions & 0 deletions Tests/test_arro3.py
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from __future__ import annotations

import json
from typing import Any, NamedTuple

import pytest

from PIL import Image

from .helper import (
assert_deep_equal,
assert_image_equal,
hopper,
is_big_endian,
)

TYPE_CHECKING = False
if TYPE_CHECKING:
from arro3 import compute
from arro3.core import Array, DataType, Field, fixed_size_list_array
else:
arro3 = pytest.importorskip("arro3", reason="Arro3 not installed")
from arro3 import compute
from arro3.core import Array, DataType, Field, fixed_size_list_array

TEST_IMAGE_SIZE = (10, 10)


def _test_img_equals_pyarray(
img: Image.Image, arr: Any, mask: list[int] | None, elts_per_pixel: int = 1
) -> None:
assert img.height * img.width * elts_per_pixel == len(arr)
px = img.load()
assert px is not None
if elts_per_pixel > 1 and mask is None:
# have to do element-wise comparison when we're comparing
# flattened r,g,b,a to a pixel.
mask = list(range(elts_per_pixel))
for x in range(0, img.size[0], int(img.size[0] / 10)):
for y in range(0, img.size[1], int(img.size[1] / 10)):
if mask:
pixel = px[x, y]
assert isinstance(pixel, tuple)
for ix, elt in enumerate(mask):
if elts_per_pixel == 1:
assert pixel[ix] == arr[y * img.width + x].as_py()[elt]
else:
assert (
pixel[ix]
== arr[(y * img.width + x) * elts_per_pixel + elt].as_py()
)
else:
assert_deep_equal(px[x, y], arr[y * img.width + x].as_py())


def _test_img_equals_int32_pyarray(
img: Image.Image, arr: Any, mask: list[int] | None, elts_per_pixel: int = 1
) -> None:
assert img.height * img.width * elts_per_pixel == len(arr)
px = img.load()
assert px is not None
if mask is None:
# have to do element-wise comparison when we're comparing
# flattened rgba in an uint32 to a pixel.
mask = list(range(elts_per_pixel))
for x in range(0, img.size[0], int(img.size[0] / 10)):
for y in range(0, img.size[1], int(img.size[1] / 10)):
pixel = px[x, y]
assert isinstance(pixel, tuple)
arr_pixel_int = arr[y * img.width + x].as_py()
arr_pixel_tuple = (
arr_pixel_int % 256,
(arr_pixel_int // 256) % 256,
(arr_pixel_int // 256**2) % 256,
(arr_pixel_int // 256**3),
)
if is_big_endian():
arr_pixel_tuple = arr_pixel_tuple[::-1]

for ix, elt in enumerate(mask):
assert pixel[ix] == arr_pixel_tuple[elt]


fl_uint8_4_type = DataType.list(Field("_", DataType.uint8()).with_nullable(False), 4)


@pytest.mark.parametrize(
"mode, dtype, mask",
(
("L", DataType.uint8(), None),
("I", DataType.int32(), None),
("F", DataType.float32(), None),
("LA", fl_uint8_4_type, [0, 3]),
("RGB", fl_uint8_4_type, [0, 1, 2]),
("RGBA", fl_uint8_4_type, None),
("RGBX", fl_uint8_4_type, None),
("CMYK", fl_uint8_4_type, None),
("YCbCr", fl_uint8_4_type, [0, 1, 2]),
("HSV", fl_uint8_4_type, [0, 1, 2]),
),
)
def test_to_array(mode: str, dtype: DataType, mask: list[int] | None) -> None:
img = hopper(mode)

# Resize to non-square
img = img.crop((3, 0, 124, 127))
assert img.size == (121, 127)

arr = Array(img) # type: ignore[call-overload]
_test_img_equals_pyarray(img, arr, mask)
assert arr.type == dtype

reloaded = Image.fromarrow(arr, mode, img.size)

assert reloaded

assert_image_equal(img, reloaded)


def test_lifetime() -> None:
# valgrind shouldn't error out here.
# arrays should be accessible after the image is deleted.

img = hopper("L")

arr_1 = Array(img) # type: ignore[call-overload]
arr_2 = Array(img) # type: ignore[call-overload]

del img

assert compute.sum(arr_1).as_py() > 0
del arr_1

assert compute.sum(arr_2).as_py() > 0
del arr_2


def test_lifetime2() -> None:
# valgrind shouldn't error out here.
# img should remain after the arrays are collected.

img = hopper("L")

arr_1 = Array(img) # type: ignore[call-overload]
arr_2 = Array(img) # type: ignore[call-overload]

assert compute.sum(arr_1).as_py() > 0
del arr_1

assert compute.sum(arr_2).as_py() > 0
del arr_2

img2 = img.copy()
px = img2.load()
assert px # make mypy happy
assert isinstance(px[0, 0], int)


class DataShape(NamedTuple):
dtype: DataType
# Strictly speaking, elt should be a pixel or pixel component, so
# list[uint8][4], float, int, uint32, uint8, etc. But more
# correctly, it should be exactly the dtype from the line above.
elt: Any
elts_per_pixel: int


UINT_ARR = DataShape(
dtype=fl_uint8_4_type,
elt=[1, 2, 3, 4], # array of 4 uint8 per pixel
elts_per_pixel=1, # only one array per pixel
)

UINT = DataShape(
dtype=DataType.uint8(),
elt=3, # one uint8,
elts_per_pixel=4, # but repeated 4x per pixel
)

UINT32 = DataShape(
dtype=DataType.uint32(),
elt=0xABCDEF45, # one packed int, doesn't fit in a int32 > 0x80000000
elts_per_pixel=1, # one per pixel
)

INT32 = DataShape(
dtype=DataType.uint32(),
elt=0x12CDEF45, # one packed int
elts_per_pixel=1, # one per pixel
)


@pytest.mark.parametrize(
"mode, data_tp, mask",
(
("L", DataShape(DataType.uint8(), 3, 1), None),
("I", DataShape(DataType.int32(), 1 << 24, 1), None),
("F", DataShape(DataType.float32(), 3.14159, 1), None),
("LA", UINT_ARR, [0, 3]),
("LA", UINT, [0, 3]),
("RGB", UINT_ARR, [0, 1, 2]),
("RGBA", UINT_ARR, None),
("CMYK", UINT_ARR, None),
("YCbCr", UINT_ARR, [0, 1, 2]),
("HSV", UINT_ARR, [0, 1, 2]),
("RGB", UINT, [0, 1, 2]),
("RGBA", UINT, None),
("CMYK", UINT, None),
("YCbCr", UINT, [0, 1, 2]),
("HSV", UINT, [0, 1, 2]),
),
)
def test_fromarray(mode: str, data_tp: DataShape, mask: list[int] | None) -> None:
(dtype, elt, elts_per_pixel) = data_tp

ct_pixels = TEST_IMAGE_SIZE[0] * TEST_IMAGE_SIZE[1]
if dtype == fl_uint8_4_type:
tmp_arr = Array(elt * (ct_pixels * elts_per_pixel), type=DataType.uint8())
arr = fixed_size_list_array(tmp_arr, 4)
else:
arr = Array([elt] * (ct_pixels * elts_per_pixel), type=dtype)
img = Image.fromarrow(arr, mode, TEST_IMAGE_SIZE)

_test_img_equals_pyarray(img, arr, mask, elts_per_pixel)


@pytest.mark.parametrize(
"mode, data_tp, mask",
(
("LA", UINT32, [0, 3]),
("RGB", UINT32, [0, 1, 2]),
("RGBA", UINT32, None),
("CMYK", UINT32, None),
("YCbCr", UINT32, [0, 1, 2]),
("HSV", UINT32, [0, 1, 2]),
("LA", INT32, [0, 3]),
("RGB", INT32, [0, 1, 2]),
("RGBA", INT32, None),
("CMYK", INT32, None),
("YCbCr", INT32, [0, 1, 2]),
("HSV", INT32, [0, 1, 2]),
),
)
def test_from_int32array(mode: str, data_tp: DataShape, mask: list[int] | None) -> None:
(dtype, elt, elts_per_pixel) = data_tp

ct_pixels = TEST_IMAGE_SIZE[0] * TEST_IMAGE_SIZE[1]
arr = Array([elt] * (ct_pixels * elts_per_pixel), type=dtype)
img = Image.fromarrow(arr, mode, TEST_IMAGE_SIZE)

_test_img_equals_int32_pyarray(img, arr, mask, elts_per_pixel)


@pytest.mark.parametrize(
"mode, metadata",
(
("LA", ["L", "X", "X", "A"]),
("RGB", ["R", "G", "B", "X"]),
("RGBX", ["R", "G", "B", "X"]),
("RGBA", ["R", "G", "B", "A"]),
("CMYK", ["C", "M", "Y", "K"]),
("YCbCr", ["Y", "Cb", "Cr", "X"]),
("HSV", ["H", "S", "V", "X"]),
),
)
def test_image_metadata(mode: str, metadata: list[str]) -> None:
img = hopper(mode)

arr = Array(img) # type: ignore[call-overload]

assert arr.type.value_field.metadata
assert arr.type.value_field.metadata[b"image"]

parsed_metadata = json.loads(arr.type.value_field.metadata[b"image"].decode("utf8"))

assert "bands" in parsed_metadata
assert parsed_metadata["bands"] == metadata
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