TheanoLearning/utils.py at master · awp4211/TheanoLearning · GitHub

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# -*- coding: utf-8 -*-
"""
工具类
"""
import numpy

""" Scales all values in the ndarray ndar to be between 0 and 1 """
def scale_to_unit_interval(ndar, eps=1e-8):
  ndar = ndar.copy()
  ndar -= ndar.min()
  ndar *= 1.0 / (ndar.max() + eps)
  return ndar

"""
Transform an array with one flattened image per row, into an array in
which images are reshaped and layed out like tiles on a floor.

This function is useful for visualizing datasets whose rows are images,
and also columns of matrices for transforming those rows
(such as the first layer of a neural net).
用于可视化每行（列）是一个图像的数据集
X:2维数组，每行是一个flatten的图像
img_shape:原始图像每张图的大小(height,width)
tile_shape:图像个数(rows,cols)
scale_rows_to_unit_interval:是否要将图像数据缩放到[0,1]
output_pixel_vals:是否输出像素风格的图像 True表示int8，否则为原数据的类型
returns: array suitable for viewing as an image.(See:`Image.fromarray`.)
"""
def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0,0),
                       scale_rows_to_unit_interval=True,
                       output_pixel_vals=True):
    assert len(img_shape)==2
    assert len(tile_shape)==2
    assert len(tile_spacing)==2

    # out_shape    = [0,0]输出图像的大小
    # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
    #                tile_spacing[0]
    # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
    #                tile_spacing[1]
    out_shape = [
        (ishp + tsp) * tshp - tsp
        for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)
    ]
    if isinstance(X, tuple):
        assert len(X) == 4
        # Create an output numpy ndarray to store the image
        if output_pixel_vals:
            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
                                    dtype='uint8')
        else:
            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
                                    dtype=X.dtype)
        #colors default to 0, alpha defaults to 1 (opaque)
        if output_pixel_vals:
            channel_defaults = [0, 0, 0, 255]
        else:
            channel_defaults = [0., 0., 0., 1.]

        for i in range(4):
            if X[i] is None:
                # if channel is None, fill it with zeros of the correct
                # dtype
                dt = out_array.dtype
                if output_pixel_vals:
                    dt = 'uint8'
                out_array[:, :, i] = numpy.zeros(
                    out_shape,
                    dtype=dt
                ) + channel_defaults[i]
            else:
                # use a recurrent call to compute the channel and store it
                # in the output
                out_array[:, :, i] = tile_raster_images(
                    X[i], img_shape, tile_shape, tile_spacing,
                    scale_rows_to_unit_interval, output_pixel_vals)
        return out_array

    else:
        # if we are dealing with only one channel(单通道图像)
        H, W = img_shape
        Hs, Ws = tile_spacing

        # generate a matrix to store the output
        dt = X.dtype
        if output_pixel_vals:
            dt = 'uint8'
        out_array = numpy.zeros(out_shape, dtype=dt)

        for tile_row in range(tile_shape[0]):
            for tile_col in range(tile_shape[1]):
                if tile_row * tile_shape[1] + tile_col < X.shape[0]:
                    this_x = X[tile_row * tile_shape[1] + tile_col]
                    if scale_rows_to_unit_interval:
                        this_img = scale_to_unit_interval(
                            this_x.reshape(img_shape))
                    else:
                        this_img = this_x.reshape(img_shape)
                    # add the slice to the corresponding position in the
                    # output array
                    c = 1
                    if output_pixel_vals:
                        c = 255
                    out_array[
                        tile_row * (H + Hs): tile_row * (H + Hs) + H,
                        tile_col * (W + Ws): tile_col * (W + Ws) + W
                    ] = this_img * c
        return out_array