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Added tests for integration with nanoarrow
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wiredfool
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281
Tests/test_nanoarrow.py
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281
Tests/test_nanoarrow.py
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from __future__ import annotations
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import json
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from typing import Any, NamedTuple
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import pytest
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from PIL import Image
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from .helper import (
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assert_deep_equal,
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assert_image_equal,
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hopper,
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is_big_endian,
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)
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TYPE_CHECKING = False
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if TYPE_CHECKING:
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import nanoarrow
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else:
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nanoarrow = pytest.importorskip("nanoarrow", reason="Nanoarrow not installed")
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TEST_IMAGE_SIZE = (10, 10)
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def _test_img_equals_pyarray(
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img: Image.Image, arr: Any, mask: list[int] | None, elts_per_pixel: int = 1
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) -> None:
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assert img.height * img.width * elts_per_pixel == len(arr)
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px = img.load()
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assert px is not None
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if elts_per_pixel > 1 and mask is None:
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# have to do element-wise comparison when we're comparing
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# flattened r,g,b,a to a pixel.
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mask = list(range(elts_per_pixel))
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for x in range(0, img.size[0], int(img.size[0] / 10)):
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for y in range(0, img.size[1], int(img.size[1] / 10)):
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if mask:
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pixel = px[x, y]
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assert isinstance(pixel, tuple)
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for ix, elt in enumerate(mask):
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if elts_per_pixel == 1:
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assert pixel[ix] == arr[y * img.width + x].as_py()[elt]
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else:
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assert (
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pixel[ix]
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== arr[(y * img.width + x) * elts_per_pixel + elt].as_py()
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)
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else:
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assert_deep_equal(px[x, y], arr[y * img.width + x].as_py())
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def _test_img_equals_int32_pyarray(
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img: Image.Image, arr: Any, mask: list[int] | None, elts_per_pixel: int = 1
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) -> None:
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assert img.height * img.width * elts_per_pixel == len(arr)
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px = img.load()
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assert px is not None
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if mask is None:
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# have to do element-wise comparison when we're comparing
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# flattened rgba in an uint32 to a pixel.
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mask = list(range(elts_per_pixel))
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for x in range(0, img.size[0], int(img.size[0] / 10)):
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for y in range(0, img.size[1], int(img.size[1] / 10)):
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pixel = px[x, y]
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assert isinstance(pixel, tuple)
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arr_pixel_int = arr[y * img.width + x].as_py()
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arr_pixel_tuple = (
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arr_pixel_int % 256,
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(arr_pixel_int // 256) % 256,
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(arr_pixel_int // 256**2) % 256,
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(arr_pixel_int // 256**3),
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)
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if is_big_endian():
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arr_pixel_tuple = arr_pixel_tuple[::-1]
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for ix, elt in enumerate(mask):
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assert pixel[ix] == arr_pixel_tuple[elt]
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fl_uint8_4_type = nanoarrow.fixed_size_list(value_type=nanoarrow.uint8(nullable=False),
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list_size=4,
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nullable=False)
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@pytest.mark.parametrize(
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"mode, dtype, mask",
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(
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("L", nanoarrow.uint8(nullable=False), None),
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("I", nanoarrow.int32(nullable=False), None),
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("F", nanoarrow.float32(nullable=False), None),
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("LA", fl_uint8_4_type, [0, 3]),
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("RGB", fl_uint8_4_type, [0, 1, 2]),
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("RGBA", fl_uint8_4_type, None),
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("RGBX", fl_uint8_4_type, None),
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("CMYK", fl_uint8_4_type, None),
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("YCbCr", fl_uint8_4_type, [0, 1, 2]),
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("HSV", fl_uint8_4_type, [0, 1, 2]),
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),
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)
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def test_to_array(mode: str, dtype: nanoarrow, mask: list[int] | None) -> None:
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img = hopper(mode)
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# Resize to non-square
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img = img.crop((3, 0, 124, 127))
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assert img.size == (121, 127)
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arr = nanoarrow.Array(img) # type: ignore[call-overload]
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_test_img_equals_pyarray(img, arr, mask)
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assert arr.schema.type == dtype.type
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assert arr.schema.nullable == dtype.nullable
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reloaded = Image.fromarrow(arr, mode, img.size)
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assert reloaded
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assert_image_equal(img, reloaded)
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def test_lifetime() -> None:
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# valgrind shouldn't error out here.
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# arrays should be accessible after the image is deleted.
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img = hopper("L")
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arr_1 = nanoarrow.Array(img) # type: ignore[call-overload]
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arr_2 = nanoarrow.Array(img) # type: ignore[call-overload]
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del img
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assert sum(arr_1.iter_py()) > 0
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del arr_1
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assert sum(arr_2.iter_py()) > 0
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del arr_2
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def test_lifetime2() -> None:
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# valgrind shouldn't error out here.
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# img should remain after the arrays are collected.
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img = hopper("L")
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arr_1 = nanoarrow.Array(img) # type: ignore[call-overload]
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arr_2 = nanoarrow.Array(img) # type: ignore[call-overload]
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assert sum(arr_1.iter_py()) > 0
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del arr_1
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assert sum(arr_2.iter_py()) > 0
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del arr_2
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img2 = img.copy()
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px = img2.load()
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assert px # make mypy happy
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assert isinstance(px[0, 0], int)
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class DataShape(NamedTuple):
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dtype: nanoarrow
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# Strictly speaking, elt should be a pixel or pixel component, so
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# list[uint8][4], float, int, uint32, uint8, etc. But more
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# correctly, it should be exactly the dtype from the line above.
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elt: Any
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elts_per_pixel: int
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UINT_ARR = DataShape(
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dtype=fl_uint8_4_type,
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elt=[1, 2, 3, 4], # array of 4 uint8 per pixel
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elts_per_pixel=1, # only one array per pixel
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)
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UINT = DataShape(
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dtype=nanoarrow.uint8(),
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elt=3, # one uint8,
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elts_per_pixel=4, # but repeated 4x per pixel
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)
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UINT32 = DataShape(
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dtype=nanoarrow.uint32(),
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elt=0xABCDEF45, # one packed int, doesn't fit in a int32 > 0x80000000
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elts_per_pixel=1, # one per pixel
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)
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INT32 = DataShape(
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dtype=nanoarrow.uint32(),
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elt=0x12CDEF45, # one packed int
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elts_per_pixel=1, # one per pixel
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)
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@pytest.mark.xfail(reason="Support for nested array creation is not available in nanoarrow/python")
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@pytest.mark.parametrize(
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"mode, data_tp, mask",
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(
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("L", DataShape(nanoarrow.uint8(), 3, 1), None),
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("I", DataShape(nanoarrow.int32(), 1 << 24, 1), None),
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("F", DataShape(nanoarrow.float32(), 3.14159, 1), None),
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("LA", UINT_ARR, [0, 3]),
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("LA", UINT, [0, 3]),
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("RGB", UINT_ARR, [0, 1, 2]),
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("RGBA", UINT_ARR, None),
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("CMYK", UINT_ARR, None),
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("YCbCr", UINT_ARR, [0, 1, 2]),
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("HSV", UINT_ARR, [0, 1, 2]),
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("RGB", UINT, [0, 1, 2]),
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("RGBA", UINT, None),
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("CMYK", UINT, None),
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("YCbCr", UINT, [0, 1, 2]),
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("HSV", UINT, [0, 1, 2]),
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),
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)
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def test_fromarray(mode: str, data_tp: DataShape, mask: list[int] | None) -> None:
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(dtype, elt, elts_per_pixel) = data_tp
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ct_pixels = TEST_IMAGE_SIZE[0] * TEST_IMAGE_SIZE[1]
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if dtype == fl_uint8_4_type:
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# Apparently there's no good way to create this array from python using nanoarrow
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# https://github.com/apache/arrow-nanoarrow/issues/620
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# the following lines will fail.
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tmp_arr = nanoarrow.c_array(elt * (ct_pixels * elts_per_pixel), schema=nanoarrow.uint8())
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arr = nanoarrow.Array(tmp_arr, schema=dtype)
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else:
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arr = nanoarrow.Array(nanoarrow.c_array([elt] * (ct_pixels * elts_per_pixel), schema=dtype))
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img = Image.fromarrow(arr, mode, TEST_IMAGE_SIZE)
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_test_img_equals_pyarray(img, arr, mask, elts_per_pixel)
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@pytest.mark.parametrize(
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"mode, data_tp, mask",
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(
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("LA", UINT32, [0, 3]),
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("RGB", UINT32, [0, 1, 2]),
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("RGBA", UINT32, None),
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("CMYK", UINT32, None),
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("YCbCr", UINT32, [0, 1, 2]),
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("HSV", UINT32, [0, 1, 2]),
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("LA", INT32, [0, 3]),
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("RGB", INT32, [0, 1, 2]),
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("RGBA", INT32, None),
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("CMYK", INT32, None),
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("YCbCr", INT32, [0, 1, 2]),
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("HSV", INT32, [0, 1, 2]),
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),
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)
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def test_from_int32array(mode: str, data_tp: DataShape, mask: list[int] | None) -> None:
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(dtype, elt, elts_per_pixel) = data_tp
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ct_pixels = TEST_IMAGE_SIZE[0] * TEST_IMAGE_SIZE[1]
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arr = nanoarrow.Array(nanoarrow.c_array([elt] * (ct_pixels * elts_per_pixel), schema=dtype))
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img = Image.fromarrow(arr, mode, TEST_IMAGE_SIZE)
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_test_img_equals_int32_pyarray(img, arr, mask, elts_per_pixel)
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@pytest.mark.parametrize(
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"mode, metadata",
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(
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("LA", ["L", "X", "X", "A"]),
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("RGB", ["R", "G", "B", "X"]),
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("RGBX", ["R", "G", "B", "X"]),
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("RGBA", ["R", "G", "B", "A"]),
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("CMYK", ["C", "M", "Y", "K"]),
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("YCbCr", ["Y", "Cb", "Cr", "X"]),
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("HSV", ["H", "S", "V", "X"]),
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),
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)
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def test_image_nested_metadata(mode: str, metadata: list[str]) -> None:
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img = hopper(mode)
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arr = nanoarrow.Array(img) # type: ignore[call-overload]
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assert arr.schema.value_type.metadata
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assert arr.schema.value_type.metadata[b"image"]
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parsed_metadata = json.loads(arr.schema.value_type.metadata[b"image"].decode("utf8"))
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assert "bands" in parsed_metadata
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assert parsed_metadata["bands"] == metadata
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@ -60,6 +60,7 @@ optional-dependencies.test-arrow = [
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"arro3-compute",
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"arro3-core",
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"pyarrow",
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"nanoarrow",
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]
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optional-dependencies.tests = [
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