FilesExpand file tree

 master

/

utils.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

138 lines (115 loc) · 4.98 KB

 master

/

utils.py

Copy path

Top

File metadata and controls

• Code
• Blame

138 lines (115 loc) · 4.98 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

""" This file contains different utility functions that are not connected

in anyway to the networks presented in the tutorials, but rather help in

processing the outputs into a more understandable way.

For example ``tile_raster_images`` helps in generating a easy to grasp

image from a set of samples or weights.

"""

import numpy

def scale_to_unit_interval(ndar, eps=1e-8):

""" Scales all values in the ndarray ndar to be between 0 and 1 """

ndar = ndar.copy()

ndar -= ndar.min()

ndar *= 1.0 / (ndar.max() + eps)

return ndar

def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),

scale_rows_to_unit_interval=True,

output_pixel_vals=True):

"""

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).

:type X: a 2-D ndarray or a tuple of 4 channels, elements of which can

be 2-D ndarrays or None;

:param X: a 2-D array in which every row is a flattened image.

:type img_shape: tuple; (height, width)

:param img_shape: the original shape of each image

:type tile_shape: tuple; (rows, cols)

:param tile_shape: the number of images to tile (rows, cols)

:param output_pixel_vals: if output should be pixel values (i.e. int8

values) or floats

:param scale_rows_to_unit_interval: if the values need to be scaled before

being plotted to [0,1] or not

:returns: array suitable for viewing as an image.

(See:`Image.fromarray`.)

:rtype: a 2-d array with same dtype as X.

"""

assert len(img_shape) == 2

assert len(tile_shape) == 2

assert len(tile_spacing) == 2

# The expression below can be re-written in a more C style as

# follows :

#

# out_shape = [0,0]

# 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

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:

# if we should scale values to be between 0 and 1

# do this by calling the `scale_to_unit_interval`

# function

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