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annotate pylearn/io/image_tiling.py @ 1473:91a475ca9b6d

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image_tiling with better scaling for integer inputs
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 18 May 2011 10:52:22 -0400
parents 9d941cd77479
children
rev   line source
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1 """
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2 Illustrate filters (or data) in a grid of small image-shaped tiles.
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3 """
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4
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5 import numpy
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6 from PIL import Image
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7
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8 def scale_to_unit_interval(ndar,eps=1e-8):
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9 ndar = ndar.copy()
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10 ndar -= ndar.min()
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11 ndar *= 1.0 / max(ndar.max(),eps)
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12 return ndar
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13
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14 def tile_raster_images(X, img_shape,
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15 tile_shape=None, tile_spacing=(1,1),
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16 scale_rows_to_unit_interval=True,
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17 output_pixel_vals=True,
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18 min_dynamic_range=1e-4,
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19 ):
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20 """
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21 Transform an array with one flattened image per row, into an array in which images are
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22 reshaped and layed out like tiles on a floor.
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23
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24 This function is useful for visualizing datasets whose rows are images, and also columns of
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25 matrices for transforming those rows (such as the first layer of a neural net).
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26
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27 :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can be 2-D ndarrays or None
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28 :param X: a 2-D array in which every row is a flattened image.
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29 :type img_shape: tuple; (height, width)
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30 :param img_shape: the original shape of each image
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31 :type tile_shape: tuple; (rows, cols)
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32 :param tile_shape: the number of images to tile (rows, cols) (Defaults to a square-ish
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33 shape with the right area for the number of images)
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34 :type min_dynamic_range: positive float
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35 :param min_dynamic_range: the dynamic range of each image is used in scaling to the unit
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36 interval, but images with less dynamic range than this will be scaled as if this were
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37 the dynamic range.
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38
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39 :returns: array suitable for viewing as an image. (See:`PIL.Image.fromarray`.)
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40 :rtype: a 2-d array with same dtype as X.
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41
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42 """
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43 # This is premature when tile_slices_to_image is not documented at all yet,
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44 # but ultimately true:
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45 #print >> sys.stderr, "WARN: tile_raster_images sucks, use tile_slices_to_image"
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46 if len(img_shape)==3 and img_shape[2]==3:
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47 # make this save an rgb image
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48 if scale_rows_to_unit_interval:
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49 print >> sys.stderr, "WARN: tile_raster_images' scaling routine messes up colour - try tile_slices_to_image"
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50 return tile_raster_images(
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51 (X[:,0::3], X[:,1::3], X[:,2::3], None),
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52 img_shape=img_shape[:2],
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53 tile_shape=tile_shape,
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54 tile_spacing=tile_spacing,
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55 scale_rows_to_unit_interval=scale_rows_to_unit_interval,
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56 output_pixel_vals=output_pixel_vals,
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57 min_dynamic_range=min_dynamic_range)
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58
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59 if isinstance(X, tuple):
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60 n_images_in_x = X[0].shape[0]
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61 else:
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62 n_images_in_x = X.shape[0]
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63
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64 if tile_shape is None:
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65 tile_shape = most_square_shape(n_images_in_x)
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66
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67 assert len(img_shape) == 2
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68 assert len(tile_shape) == 2
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69 assert len(tile_spacing) == 2
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70
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71 #out_shape is the shape in pixels of the returned image array
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72 out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp
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73 in zip(img_shape, tile_shape, tile_spacing)]
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74
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75 if isinstance(X, tuple):
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76 if scale_rows_to_unit_interval:
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77 raise NotImplementedError()
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78 assert len(X) == 4
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79 if output_pixel_vals:
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80 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')
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81 else:
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82 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)
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83
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84 #colors default to 0, alpha defaults to 1 (opaque)
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85 if output_pixel_vals:
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86 channel_defaults = [0,0,0,255]
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87 else:
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88 channel_defaults = [0.,0.,0.,1.]
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89
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90 for i in xrange(4):
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91 if X[i] is None:
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92 out_array[:,:,i] = numpy.zeros(out_shape,
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93 dtype='uint8' if output_pixel_vals else out_array.dtype
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94 )+channel_defaults[i]
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95 else:
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96 out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)
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97 return out_array
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98
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99 else:
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100 H, W = img_shape
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101 Hs, Ws = tile_spacing
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102
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103 out_scaling = 1
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104 if output_pixel_vals and str(X.dtype).startswith('float'):
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105 out_scaling = 255
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106
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107 out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)
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108 for tile_row in xrange(tile_shape[0]):
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109 for tile_col in xrange(tile_shape[1]):
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110 if tile_row * tile_shape[1] + tile_col < X.shape[0]:
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111 if scale_rows_to_unit_interval:
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112 try:
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113 this_img = scale_to_unit_interval(
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114 X[tile_row * tile_shape[1] + tile_col].reshape(img_shape),
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115 eps=min_dynamic_range)
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116 except ValueError:
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117 raise ValueError('Failed to reshape array of shape %s to shape %s'
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118 % (
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119 X[tile_row*tile_shape[1] + tile_col].shape
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120 , img_shape
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121 ))
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122 else:
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123 this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)
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124 out_array[
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125 tile_row * (H+Hs):tile_row*(H+Hs)+H,
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126 tile_col * (W+Ws):tile_col*(W+Ws)+W
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127 ] \
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128 = this_img * out_scaling
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129 return out_array
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130
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131
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132 def most_square_shape(N):
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133 """rectangle (height, width) with area N that is closest to sqaure
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134 """
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135 for i in xrange(int(numpy.sqrt(N)),0, -1):
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136 if 0 == N % i:
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137 return (i, N/i)
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138
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139 def save_tiled_raster_images(tiled_img, filename):
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140 """Save a a return value from `tile_raster_images` to `filename`.
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141
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142 Returns the PIL image that was saved
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143 """
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144 if tiled_img.ndim==2:
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145 img = Image.fromarray( tiled_img, 'L')
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146 elif tiled_img.ndim==3:
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147 img = Image.fromarray(tiled_img, 'RGBA')
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148 else:
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149 raise TypeError('bad ndim', tiled_img)
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150
950
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151 img.save(filename)
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152 return img
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153
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154 def tile_slices_to_image_uint8(X, tile_shape=None):
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155 if str(X.dtype) != 'uint8':
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156 raise TypeError(X)
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157 if tile_shape is None:
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158 #how many tile rows and cols
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159 (TR, TC) = most_square_shape(X.shape[0])
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160 H, W = X.shape[1], X.shape[2]
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161
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162 Hs = H+1 #spacing between tiles
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163 Ws = W+1 #spacing between tiles
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164
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165 trows, tcols= most_square_shape(X.shape[0])
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166 outrows = trows * Hs - 1
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167 outcols = tcols * Ws - 1
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168 out = numpy.zeros((outrows, outcols,3), dtype='uint8')
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169 tr_stride= 1+X.shape[1]
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170 for tr in range(trows):
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171 for tc in range(tcols):
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172 Xrc = X[tr*tcols+tc]
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173 if Xrc.ndim==2: # if no color channel make it broadcast
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174 Xrc=Xrc[:,:,None]
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175 #print Xrc.shape
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176 #print out[tr*Hs:tr*Hs+H,tc*Ws:tc*Ws+W].shape
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177 out[tr*Hs:tr*Hs+H,tc*Ws:tc*Ws+W] = Xrc
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178 img = Image.fromarray(out, 'RGB')
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179 return img
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180
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181 def tile_slices_to_image(X,
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182 tile_shape=None,
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183 scale_each=True,
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184 min_dynamic_range=1e-4):
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185 #always returns an RGB image
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186 def scale_0_255(x):
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187 xmin = x.min()
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188 xmax = x.max()
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189 return numpy.asarray(
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190 255 * (x - xmin) / max(xmax - xmin, min_dynamic_range),
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191 dtype='uint8')
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192
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193 if scale_each:
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194 uintX = numpy.empty(X.shape, dtype='uint8')
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195 for i, Xi in enumerate(X):
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196 uintX[i] = scale_0_255(Xi)
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197 X = uintX
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198 else:
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199 X = scale_0_255(X)
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200 return tile_slices_to_image_uint8(X, tile_shape=tile_shape)