from enum import Enum
from typing import Dict
import numpy as np
from PIL import Image
[docs]
class ResampleMethod(Enum):
PIL_NEAREST = Image.Resampling.NEAREST # 0
PIL_LANCZOS = Image.Resampling.LANCZOS # 1
PIL_BILINEAR = Image.Resampling.BILINEAR # 2
PIL_BICUBIC = Image.Resampling.BICUBIC # 3
PIL_BOX = Image.Resampling.BOX # 4
PIL_HAMMING = Image.Resampling.HAMMING # 5
PIL_MAX_ENUM = 5
NP_MEAN = 6
NP_MEDIAN = 7
NP_MODE = 8
NP_MAX = 9
NP_MIN = 10
NP_NEAREST = 11
NP_MAX_COLOR = 12
NP_MIN_COLOR = 13
[docs]
def pilResize(
tile: np.ndarray,
new_shape: Dict,
resample_method: ResampleMethod,
) -> np.ndarray:
# Only NEAREST works for 16 bit images
img = Image.fromarray(tile)
resized_img = img.resize(
(new_shape['width'], new_shape['height']),
resample=resample_method.value,
)
result = np.array(resized_img).astype(tile.dtype)
return result
[docs]
def numpyResize(
tile: np.ndarray,
new_shape: Dict,
resample_method: ResampleMethod,
) -> np.ndarray:
if resample_method == ResampleMethod.NP_NEAREST:
return tile[::2, ::2]
else:
if tile.shape[0] % 2 != 0:
tile = np.append(tile, np.expand_dims(tile[-1], axis=0), axis=0)
if tile.shape[1] % 2 != 0:
tile = np.append(tile, np.expand_dims(tile[:, -1], axis=1), axis=1)
pixel_selection = None
subarrays = np.asarray(
[
tile[0::2, 0::2],
tile[1::2, 0::2],
tile[0::2, 1::2],
tile[1::2, 1::2],
],
)
if resample_method == ResampleMethod.NP_MEAN:
return np.mean(subarrays, axis=0).astype(tile.dtype)
elif resample_method == ResampleMethod.NP_MEDIAN:
return np.median(subarrays, axis=0).astype(tile.dtype)
elif resample_method == ResampleMethod.NP_MAX:
return np.max(subarrays, axis=0).astype(tile.dtype)
elif resample_method == ResampleMethod.NP_MIN:
return np.min(subarrays, axis=0).astype(tile.dtype)
elif resample_method == ResampleMethod.NP_MAX_COLOR:
summed = np.sum(subarrays, axis=3)
pixel_selection = np.argmax(summed, axis=0)
elif resample_method == ResampleMethod.NP_MIN_COLOR:
summed = np.sum(subarrays, axis=3)
pixel_selection = np.argmin(summed, axis=0)
elif resample_method == ResampleMethod.NP_MODE:
# if a pixel occurs twice in a set of four, it is a mode
# if no mode, default to pixel 0. check for minimal matches 1=2, 1=3, 2=3
pixel_selection = np.where(
(
(subarrays[1] == subarrays[2]).all(axis=2) |
(subarrays[1] == subarrays[3]).all(axis=2)
),
1, np.where(
(subarrays[2] == subarrays[3]).all(axis=2),
2, 0,
),
)
if pixel_selection is not None:
if len(tile.shape) > 2:
pixel_selection = np.expand_dims(pixel_selection, axis=2)
pixel_selection = np.repeat(pixel_selection, tile.shape[2], axis=2)
return np.choose(pixel_selection, subarrays).astype(tile.dtype)
else:
msg = f'Unknown resample method {resample_method}.'
raise ValueError(msg)
[docs]
def downsampleTileHalfRes(
tile: np.ndarray,
resample_method: ResampleMethod,
) -> np.ndarray:
new_shape = {
'height': (tile.shape[0] + 1) // 2,
'width': (tile.shape[1] + 1) // 2,
'bands': 1,
}
if len(tile.shape) > 2:
new_shape['bands'] = tile.shape[-1]
if resample_method.value <= ResampleMethod.PIL_MAX_ENUM.value:
if new_shape['bands'] > 4:
result = np.empty(
(new_shape['height'], new_shape['width'], new_shape['bands']),
dtype=tile.dtype,
)
for band_index in range(new_shape['bands']):
result[(..., band_index)] = pilResize(
tile[(..., band_index)],
new_shape,
resample_method,
)
return result
else:
return pilResize(tile, new_shape, resample_method)
else:
return numpyResize(tile, new_shape, resample_method)