refactor code to replace np.clip
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@ -998,6 +998,27 @@ def solveBilinearTransform(points1, points2):
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return matrix
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def clip_array(arr, vmin, vmax, out=None):
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# replacement for np.clip due to regression in
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# performance since numpy 1.17
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# https://github.com/numpy/numpy/issues/14281
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if out is None:
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out = np.empty_like(arr)
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if vmin is not None:
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arr = np.core.umath.maximum(arr, vmin, out=out)
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if vmax is not None:
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arr = np.core.umath.minimum(arr, vmax, out=out)
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# np.core.umath.clip performs slightly better than
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# the above on platforms compiled with GCC (e.g. Linux),
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# but worse for CLANG (e.g. macOS) and MSVC (Windows)
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return out
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def rescaleData(data, scale, offset, dtype=None, clip=None):
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"""Return data rescaled and optionally cast to a new dtype.
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@ -1010,16 +1031,12 @@ def rescaleData(data, scale, offset, dtype=None, clip=None):
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else:
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dtype = np.dtype(dtype)
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vmin, vmax = None, None
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if dtype.kind in 'ui':
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lim = np.iinfo(dtype)
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if clip is None:
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# don't let rescale cause integer overflow
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vmin, vmax = lim.min, lim.max
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else:
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vmin, vmax = max(clip[0], lim.min), min(clip[1], lim.max)
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elif clip is not None:
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vmin, vmax = clip
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clip = lim.min, lim.max
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clip = max(clip[0], lim.min), min(clip[1], lim.max)
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if np.can_cast(data, np.float32):
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work_dtype = np.float32
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@ -1030,11 +1047,8 @@ def rescaleData(data, scale, offset, dtype=None, clip=None):
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d2 *= scale
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# Clip before converting dtype to avoid overflow
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# regression in np.clip performance since numpy 1.17
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if vmin is not None:
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np.core.umath.maximum(d2, vmin, out=d2)
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if vmax is not None:
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np.core.umath.minimum(d2, vmax, out=d2)
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if clip is not None:
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clip_array(d2, clip[0], clip[1], out=d2)
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# don't copy if no change in dtype
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data = d2.astype(dtype, copy=False)
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