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import numpy as np |
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from PIL import Image |
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_errstr = "Mode is unknown or incompatible with input array shape." |
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def bytescale(data, cmin=None, cmax=None, high=255, low=0): |
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""" |
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Byte scales an array (image). |
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Byte scaling means converting the input image to uint8 dtype and scaling |
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the range to ``(low, high)`` (default 0-255). |
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If the input image already has dtype uint8, no scaling is done. |
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This function is only available if Python Imaging Library (PIL) is installed. |
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Parameters |
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---------- |
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data : ndarray |
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PIL image data array. |
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cmin : scalar, optional |
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Bias scaling of small values. Default is ``data.min()``. |
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cmax : scalar, optional |
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Bias scaling of large values. Default is ``data.max()``. |
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high : scalar, optional |
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Scale max value to `high`. Default is 255. |
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low : scalar, optional |
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Scale min value to `low`. Default is 0. |
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Returns |
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------- |
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img_array : uint8 ndarray |
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The byte-scaled array. |
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Examples |
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-------- |
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>>> from scipy.misc import bytescale |
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>>> img = np.array([[ 91.06794177, 3.39058326, 84.4221549 ], |
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... [ 73.88003259, 80.91433048, 4.88878881], |
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... [ 51.53875334, 34.45808177, 27.5873488 ]]) |
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>>> bytescale(img) |
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array([[255, 0, 236], |
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[205, 225, 4], |
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[140, 90, 70]], dtype=uint8) |
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>>> bytescale(img, high=200, low=100) |
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array([[200, 100, 192], |
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[180, 188, 102], |
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[155, 135, 128]], dtype=uint8) |
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>>> bytescale(img, cmin=0, cmax=255) |
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array([[91, 3, 84], |
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[74, 81, 5], |
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[52, 34, 28]], dtype=uint8) |
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""" |
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if data.dtype == np.uint8: |
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return data |
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if high > 255: |
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raise ValueError("`high` should be less than or equal to 255.") |
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if low < 0: |
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raise ValueError("`low` should be greater than or equal to 0.") |
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if high < low: |
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raise ValueError("`high` should be greater than or equal to `low`.") |
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if cmin is None: |
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cmin = data.min() |
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if cmax is None: |
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cmax = data.max() |
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cscale = cmax - cmin |
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if cscale < 0: |
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raise ValueError("`cmax` should be larger than `cmin`.") |
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elif cscale == 0: |
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cscale = 1 |
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scale = float(high - low) / cscale |
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bytedata = (data - cmin) * scale + low |
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return (bytedata.clip(low, high) + 0.5).astype(np.uint8) |
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def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None, |
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mode=None, channel_axis=None): |
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"""Takes a numpy array and returns a PIL image. |
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This function is only available if Python Imaging Library (PIL) is installed. |
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The mode of the PIL image depends on the array shape and the `pal` and |
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`mode` keywords. |
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For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values |
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(from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode |
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is given as 'F' or 'I' in which case a float and/or integer array is made. |
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.. warning:: |
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This function uses `bytescale` under the hood to rescale images to use |
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the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``. |
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It will also cast data for 2-D images to ``uint32`` for ``mode=None`` |
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(which is the default). |
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Notes |
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----- |
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For 3-D arrays, the `channel_axis` argument tells which dimension of the |
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array holds the channel data. |
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For 3-D arrays if one of the dimensions is 3, the mode is 'RGB' |
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by default or 'YCbCr' if selected. |
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The numpy array must be either 2 dimensional or 3 dimensional. |
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""" |
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data = np.asarray(arr) |
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if np.iscomplexobj(data): |
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raise ValueError("Cannot convert a complex-valued array.") |
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shape = list(data.shape) |
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valid = len(shape) == 2 or ((len(shape) == 3) and |
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((3 in shape) or (4 in shape))) |
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if not valid: |
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raise ValueError("'arr' does not have a suitable array shape for " |
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"any mode.") |
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if len(shape) == 2: |
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shape = (shape[1], shape[0]) # columns show up first |
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if mode == 'F': |
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data32 = data.astype(np.float32) |
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image = Image.frombytes(mode, shape, data32.tostring()) |
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return image |
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if mode in [None, 'L', 'P']: |
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bytedata = bytescale(data, high=high, low=low, |
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cmin=cmin, cmax=cmax) |
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image = Image.frombytes('L', shape, bytedata.tostring()) |
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if pal is not None: |
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image.putpalette(np.asarray(pal, dtype=np.uint8).tostring()) |
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# Becomes a mode='P' automagically. |
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elif mode == 'P': # default gray-scale |
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pal = (np.arange(0, 256, 1, dtype=np.uint8)[:, np.newaxis] * |
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np.ones((3,), dtype=np.uint8)[np.newaxis, :]) |
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image.putpalette(np.asarray(pal, dtype=np.uint8).tostring()) |
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return image |
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if mode == '1': # high input gives threshold for 1 |
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bytedata = (data > high) |
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image = Image.frombytes('1', shape, bytedata.tostring()) |
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return image |
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if cmin is None: |
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cmin = np.amin(np.ravel(data)) |
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if cmax is None: |
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cmax = np.amax(np.ravel(data)) |
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data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low |
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if mode == 'I': |
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data32 = data.astype(np.uint32) |
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image = Image.frombytes(mode, shape, data32.tostring()) |
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else: |
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raise ValueError(_errstr) |
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return image |
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# if here then 3-d array with a 3 or a 4 in the shape length. |
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# Check for 3 in datacube shape --- 'RGB' or 'YCbCr' |
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if channel_axis is None: |
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if (3 in shape): |
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ca = np.flatnonzero(np.asarray(shape) == 3)[0] |
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else: |
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ca = np.flatnonzero(np.asarray(shape) == 4) |
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if len(ca): |
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ca = ca[0] |
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else: |
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raise ValueError("Could not find channel dimension.") |
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else: |
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ca = channel_axis |
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numch = shape[ca] |
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if numch not in [3, 4]: |
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raise ValueError("Channel axis dimension is not valid.") |
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bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax) |
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if ca == 2: |
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strdata = bytedata.tostring() |
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shape = (shape[1], shape[0]) |
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elif ca == 1: |
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strdata = np.transpose(bytedata, (0, 2, 1)).tostring() |
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shape = (shape[2], shape[0]) |
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elif ca == 0: |
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strdata = np.transpose(bytedata, (1, 2, 0)).tostring() |
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shape = (shape[2], shape[1]) |
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if mode is None: |
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if numch == 3: |
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mode = 'RGB' |
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else: |
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mode = 'RGBA' |
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if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']: |
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raise ValueError(_errstr) |
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if mode in ['RGB', 'YCbCr']: |
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if numch != 3: |
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raise ValueError("Invalid array shape for mode.") |
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if mode in ['RGBA', 'CMYK']: |
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if numch != 4: |
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raise ValueError("Invalid array shape for mode.") |
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# Here we know data and mode is correct |
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image = Image.frombytes(mode, shape, strdata) |
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return image |
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