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jnistefanv
jni
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Use fused types in denoise, warp (#3486)
* MNT: Add a fused numeric type to make fused_types more constent. * MNT: make the interpolation use fused types for any->any type interpolation of images. * MNT: Move even more numpy function calls from Cython to Python. * MNT: More explicit type specifying * BUG: Use fused floats in denoise Pull all denoise array allocation out into python Function used in python should be def not cdef Have the correct number of arguments * MNT: Add additional type check for denoise, more docs * PEP8 fixes * Remove redundant array u and view cu * Mutate output array in Cython and trim in Python * Replace height and width with existing rows and cols * Change iteration order to match array order * Move range_lut and color_lut properly from Cy to Py * Ravel range LUT which is expected to be 1D * Update comment in denoise bilateral tests * Fix relative import in interpolation.pyx * BENCH: Benchmark for warping with many types * Rename warp benchmark file * Update pointer syntax Co-Authored-By: jni <juan.nunez-iglesias@monash.edu>
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‎benchmarks/benchmark_transform_warp.py‎

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

‎skimage/_shared/fused_numerics.pxd‎

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cimport numpy as cnp
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import numpy as np
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ctypedef fused np_ints:
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cnp.int8_t
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cnp.int16_t
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cnp.int32_t
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cnp.int64_t
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ctypedef fused np_uints:
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cnp.uint8_t
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cnp.uint16_t
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cnp.uint32_t
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cnp.uint64_t
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ctypedef fused np_anyint:
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np_uints
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np_ints
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ctypedef fused np_floats:
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cnp.float32_t
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cnp.float64_t
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ctypedef fused np_complexes:
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cnp.complex64_t
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cnp.complex128_t
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ctypedef fused np_real_numeric:
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np_anyint
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np_floats
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ctypedef fused np_numeric:
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np_real_numeric
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np_complexes

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