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import numpy as np
from skimage.transform import SimilarityTransform, warp, resize_local_mean
import warnings
import functools
import inspect
try:
from skimage.util.dtype import _convert as convert
except ImportError:
from skimage.util.dtype import convert
class WarpSuite:
params = (
[np.uint8, np.uint16, np.float32, np.float64],
[128, 1024, 4096],
[0, 1, 3],
# [np.float32, np.float64]
)
# param_names = ['dtype_in', 'N', 'order', 'dtype_tform']
param_names = ['dtype_in', 'N', 'order']
# def setup(self, dtype_in, N, order, dtype_tform):
def setup(self, dtype_in, N, order):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Possible precision loss")
self.image = convert(np.random.random((N, N)), dtype=dtype_in)
self.tform = SimilarityTransform(
scale=1, rotation=np.pi / 10, translation=(0, 4)
)
self.tform.params = self.tform.params.astype('float32')
self.order = order
if 'dtype' in inspect.signature(warp).parameters:
self.warp = functools.partial(warp, dtype=self.image.dtype)
else:
# Keep a call to functools to have the same number of python
# function calls
self.warp = functools.partial(warp)
# def time_same_type(self, dtype_in, N, order, dtype_tform):
def time_same_type(self, dtype_in, N, order):
"""Test the case where the users wants to preserve their same low
precision data type."""
result = self.warp(
self.image, self.tform, order=self.order, preserve_range=True
)
# convert back to input type, no-op if same type
result = result.astype(dtype_in, copy=False)
# def time_to_float64(self, dtype_in, N, order, dtype_form):
def time_to_float64(self, dtype_in, N, order):
"""Test the case where want to upvert to float64 for continued
transformations."""
warp(self.image, self.tform, order=self.order, preserve_range=True)
class ResizeLocalMeanSuite:
params = (
[np.float32, np.float64],
[(512, 512), (2048, 2048), (48, 48, 48), (192, 192, 192)],
[(512, 512), (2048, 2048), (48, 48, 48), (192, 192, 192)],
)
param_names = ['dtype', 'shape_in', 'shape_out']
timeout = 180
def setup(self, dtype, shape_in, shape_out):
if len(shape_in) != len(shape_out):
raise NotImplementedError("shape_in, shape_out must have same dimension")
self.image = np.zeros(shape_in, dtype=dtype)
def time_resize_local_mean(self, dtype, shape_in, shape_out):
resize_local_mean(self.image, shape_out)
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