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