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| a | b/ants/contrib/sampling/affine2d.py | ||
|---|---|---|---|
| 1 | """ |
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| 2 | Affine transforms |
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| 3 | |||
| 4 | See http://www.cs.cornell.edu/courses/cs4620/2010fa/lectures/03transforms3D.pdf |
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| 5 | """ |
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| 6 | |||
| 7 | __all__ = [ |
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| 8 | "Zoom2D", |
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| 9 | "RandomZoom2D", |
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| 10 | "Rotate2D", |
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| 11 | "RandomRotate2D", |
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| 12 | "Shear2D", |
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| 13 | "RandomShear2D", |
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| 14 | "Translate2D", |
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| 15 | "RandomTranslate2D", |
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| 16 | ] |
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| 17 | |||
| 18 | import random |
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| 19 | import math |
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| 20 | import numpy as np |
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| 21 | |||
| 22 | from ...core import ants_transform as tio |
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| 23 | |||
| 24 | |||
| 25 | class Translate2D(object): |
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| 26 | """ |
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| 27 | Create an ANTs Affine Transform with a specified translation. |
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| 28 | """ |
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| 29 | |||
| 30 | def __init__(self, translation, reference=None, lazy=False): |
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| 31 | """ |
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| 32 | Initialize a Translate2D object |
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| 33 | |||
| 34 | Arguments |
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| 35 | --------- |
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| 36 | translation : list or tuple |
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| 37 | translation values for each axis, in degrees. |
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| 38 | Negative values can be used for translation in the |
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| 39 | other direction |
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| 40 | |||
| 41 | reference : ANTsImage (optional but recommended) |
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| 42 | image providing the reference space for the transform. |
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| 43 | this will also set the transform fixed parameters. |
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| 44 | |||
| 45 | lazy : boolean (default = False) |
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| 46 | if True, calling the `transform` method only returns |
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| 47 | the randomly generated transform and does not actually |
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| 48 | transform the image |
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| 49 | """ |
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| 50 | if (not isinstance(translation, (list, tuple))) or (len(translation) != 2): |
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| 51 | raise ValueError("translation argument must be list/tuple with two values!") |
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| 52 | |||
| 53 | self.translation = translation |
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| 54 | self.lazy = lazy |
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| 55 | self.reference = reference |
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| 56 | |||
| 57 | self.tx = tio.ANTsTransform( |
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| 58 | precision="float", dimension=2, transform_type="AffineTransform" |
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| 59 | ) |
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| 60 | if self.reference is not None: |
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| 61 | self.tx.set_fixed_parameters(self.reference.get_center_of_mass()) |
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| 62 | |||
| 63 | def transform(self, X=None, y=None): |
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| 64 | """ |
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| 65 | Transform an image using an Affine transform with the given |
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| 66 | translation parameters. Return the transform if X=None. |
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| 67 | |||
| 68 | Arguments |
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| 69 | --------- |
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| 70 | X : ANTsImage |
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| 71 | Image to transform |
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| 72 | |||
| 73 | y : ANTsImage (optional) |
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| 74 | Another image to transform |
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| 75 | |||
| 76 | Returns |
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| 77 | ------- |
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| 78 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 79 | |||
| 80 | Examples |
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| 81 | -------- |
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| 82 | >>> import ants |
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| 83 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 84 | >>> tx = ants.contrib.Translate2D(translation=(10,0)) |
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| 85 | >>> img2_x = tx.transform(img) |
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| 86 | >>> tx = ants.contrib.Translate2D(translation=(-10,0)) # other direction |
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| 87 | >>> img2_x = tx.transform(img) |
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| 88 | >>> tx = ants.contrib.Translate2D(translation=(0,10)) |
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| 89 | >>> img2_z = tx.transform(img) |
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| 90 | >>> tx = ants.contrib.Translate2D(translation=(10,10)) |
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| 91 | >>> img2 = tx.transform(img) |
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| 92 | """ |
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| 93 | # convert to radians and unpack |
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| 94 | translation_x, translation_y = self.translation |
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| 95 | |||
| 96 | translation_matrix = np.array([[1, 0, translation_x], [0, 1, translation_y]]) |
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| 97 | self.tx.set_parameters(translation_matrix) |
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| 98 | if self.lazy or X is None: |
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| 99 | return self.tx |
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| 100 | else: |
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| 101 | if y is None: |
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| 102 | return self.tx.apply_to_image(X, reference=self.reference) |
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| 103 | else: |
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| 104 | return ( |
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| 105 | self.tx.apply_to_image(X, reference=self.reference), |
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| 106 | self.tx.apply_to_image(y, reference=self.reference), |
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| 107 | ) |
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| 108 | |||
| 109 | |||
| 110 | class RandomTranslate2D(object): |
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| 111 | """ |
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| 112 | Apply a Translate2D transform to an image, but with the |
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| 113 | parameters randomly generated from a user-specified range. |
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| 114 | The range is determined by a mean (first parameter) and standard deviation |
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| 115 | (second parameter) via calls to random.gauss. |
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| 116 | """ |
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| 117 | |||
| 118 | def __init__(self, translation_range, reference=None, lazy=False): |
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| 119 | """ |
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| 120 | Initialize a RandomTranslate2D object |
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| 121 | |||
| 122 | Arguments |
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| 123 | --------- |
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| 124 | translation_range : list or tuple |
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| 125 | Lower and Upper bounds on rotation parameter, in degrees. |
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| 126 | e.g. translation_range = (-10,10) will result in a random |
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| 127 | draw of the rotation parameters between -10 and 10 degrees |
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| 128 | |||
| 129 | reference : ANTsImage (optional but recommended) |
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| 130 | image providing the reference space for the transform. |
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| 131 | this will also set the transform fixed parameters. |
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| 132 | |||
| 133 | lazy : boolean (default = False) |
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| 134 | if True, calling the `transform` method only returns |
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| 135 | the randomly generated transform and does not actually |
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| 136 | transform the image |
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| 137 | """ |
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| 138 | if (not isinstance(translation_range, (list, tuple))) or ( |
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| 139 | len(translation_range) != 2 |
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| 140 | ): |
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| 141 | raise ValueError("shear_range argument must be list/tuple with two values!") |
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| 142 | |||
| 143 | self.translation_range = translation_range |
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| 144 | self.reference = reference |
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| 145 | self.lazy = lazy |
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| 146 | |||
| 147 | def transform(self, X=None, y=None): |
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| 148 | """ |
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| 149 | Transform an image using an Affine transform with |
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| 150 | translation parameters randomly generated from the user-specified |
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| 151 | range. Return the transform if X=None. |
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| 152 | |||
| 153 | Arguments |
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| 154 | --------- |
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| 155 | X : ANTsImage |
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| 156 | Image to transform |
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| 157 | |||
| 158 | y : ANTsImage (optional) |
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| 159 | Another image to transform |
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| 160 | |||
| 161 | Returns |
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| 162 | ------- |
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| 163 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 164 | |||
| 165 | Examples |
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| 166 | -------- |
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| 167 | >>> import ants |
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| 168 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 169 | >>> tx = ants.contrib.RandomShear2D(translation_range=(-10,10)) |
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| 170 | >>> img2 = tx.transform(img) |
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| 171 | """ |
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| 172 | # random draw in translation range |
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| 173 | translation_x = random.gauss( |
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| 174 | self.translation_range[0], self.translation_range[1] |
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| 175 | ) |
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| 176 | translation_y = random.gauss( |
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| 177 | self.translation_range[0], self.translation_range[1] |
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| 178 | ) |
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| 179 | self.params = (translation_x, translation_y) |
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| 180 | |||
| 181 | tx = Translate2D( |
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| 182 | (translation_x, translation_y), reference=self.reference, lazy=self.lazy |
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| 183 | ) |
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| 184 | |||
| 185 | return tx.transform(X, y) |
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| 186 | |||
| 187 | |||
| 188 | class Shear2D(object): |
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| 189 | """ |
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| 190 | Create an ANTs Affine Transform with a specified shear. |
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| 191 | """ |
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| 192 | |||
| 193 | def __init__(self, shear, reference=None, lazy=False): |
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| 194 | """ |
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| 195 | Initialize a Shear2D object |
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| 196 | |||
| 197 | Arguments |
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| 198 | --------- |
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| 199 | shear : list or tuple |
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| 200 | shear values for each axis, in degrees. |
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| 201 | Negative values can be used for shear in the |
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| 202 | other direction |
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| 203 | |||
| 204 | reference : ANTsImage (optional but recommended) |
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| 205 | image providing the reference space for the transform. |
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| 206 | this will also set the transform fixed parameters. |
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| 207 | |||
| 208 | lazy : boolean (default = False) |
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| 209 | if True, calling the `transform` method only returns |
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| 210 | the randomly generated transform and does not actually |
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| 211 | transform the image |
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| 212 | """ |
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| 213 | if (not isinstance(shear, (list, tuple))) or (len(shear) != 2): |
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| 214 | raise ValueError("shear argument must be list/tuple with two values!") |
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| 215 | |||
| 216 | self.shear = shear |
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| 217 | self.lazy = lazy |
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| 218 | self.reference = reference |
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| 219 | |||
| 220 | self.tx = tio.ANTsTransform( |
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| 221 | precision="float", dimension=2, transform_type="AffineTransform" |
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| 222 | ) |
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| 223 | if self.reference is not None: |
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| 224 | self.tx.set_fixed_parameters(self.reference.get_center_of_mass()) |
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| 225 | |||
| 226 | def transform(self, X=None, y=None): |
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| 227 | """ |
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| 228 | Transform an image using an Affine transform with the given |
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| 229 | shear parameters. Return the transform if X=None. |
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| 230 | |||
| 231 | Arguments |
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| 232 | --------- |
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| 233 | X : ANTsImage |
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| 234 | Image to transform |
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| 235 | |||
| 236 | y : ANTsImage (optional) |
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| 237 | Another image to transform |
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| 238 | |||
| 239 | Returns |
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| 240 | ------- |
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| 241 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 242 | |||
| 243 | Examples |
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| 244 | -------- |
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| 245 | >>> import ants |
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| 246 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 247 | >>> tx = ants.contrib.Shear2D(shear=(10,0,0)) |
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| 248 | >>> img2_x = tx.transform(img)# x axis stays same |
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| 249 | >>> tx = ants.contrib.Shear2D(shear=(-10,0,0)) # other direction |
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| 250 | >>> img2_x = tx.transform(img)# x axis stays same |
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| 251 | >>> tx = ants.contrib.Shear2D(shear=(0,10,0)) |
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| 252 | >>> img2_y = tx.transform(img) # y axis stays same |
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| 253 | >>> tx = ants.contrib.Shear2D(shear=(0,0,10)) |
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| 254 | >>> img2_z = tx.transform(img) # z axis stays same |
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| 255 | >>> tx = ants.contrib.Shear2D(shear=(10,10,10)) |
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| 256 | >>> img2 = tx.transform(img) |
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| 257 | """ |
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| 258 | # convert to radians and unpack |
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| 259 | shear = [math.pi / 180 * s for s in self.shear] |
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| 260 | shear_x, shear_y = shear |
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| 261 | |||
| 262 | shear_matrix = np.array([[1, shear_x, 0], [shear_y, 1, 0]]) |
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| 263 | self.tx.set_parameters(shear_matrix) |
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| 264 | if self.lazy or X is None: |
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| 265 | return self.tx |
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| 266 | else: |
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| 267 | if y is None: |
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| 268 | return self.tx.apply_to_image(X, reference=self.reference) |
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| 269 | else: |
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| 270 | return ( |
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| 271 | self.tx.apply_to_image(X, reference=self.reference), |
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| 272 | self.tx.apply_to_image(y, reference=self.reference), |
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| 273 | ) |
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| 274 | |||
| 275 | |||
| 276 | class RandomShear2D(object): |
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| 277 | """ |
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| 278 | Apply a Shear2D transform to an image, but with the shear |
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| 279 | parameters randomly generated from a user-specified range. |
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| 280 | The range is determined by a mean (first parameter) and standard deviation |
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| 281 | (second parameter) via calls to random.gauss. |
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| 282 | """ |
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| 283 | |||
| 284 | def __init__(self, shear_range, reference=None, lazy=False): |
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| 285 | """ |
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| 286 | Initialize a RandomShear2D object |
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| 287 | |||
| 288 | Arguments |
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| 289 | --------- |
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| 290 | shear_range : list or tuple |
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| 291 | Lower and Upper bounds on rotation parameter, in degrees. |
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| 292 | e.g. shear_range = (-10,10) will result in a random |
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| 293 | draw of the rotation parameters between -10 and 10 degrees |
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| 294 | |||
| 295 | reference : ANTsImage (optional but recommended) |
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| 296 | image providing the reference space for the transform. |
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| 297 | this will also set the transform fixed parameters. |
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| 298 | |||
| 299 | lazy : boolean (default = False) |
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| 300 | if True, calling the `transform` method only returns |
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| 301 | the randomly generated transform and does not actually |
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| 302 | transform the image |
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| 303 | """ |
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| 304 | if (not isinstance(shear_range, (list, tuple))) or (len(shear_range) != 2): |
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| 305 | raise ValueError("shear_range argument must be list/tuple with two values!") |
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| 306 | |||
| 307 | self.shear_range = shear_range |
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| 308 | self.reference = reference |
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| 309 | self.lazy = lazy |
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| 310 | |||
| 311 | def transform(self, X=None, y=None): |
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| 312 | """ |
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| 313 | Transform an image using an Affine transform with |
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| 314 | shear parameters randomly generated from the user-specified |
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| 315 | range. Return the transform if X=None. |
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| 316 | |||
| 317 | Arguments |
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| 318 | --------- |
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| 319 | X : ANTsImage |
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| 320 | Image to transform |
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| 321 | |||
| 322 | y : ANTsImage (optional) |
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| 323 | Another image to transform |
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| 324 | |||
| 325 | Returns |
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| 326 | ------- |
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| 327 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 328 | |||
| 329 | Examples |
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| 330 | -------- |
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| 331 | >>> import ants |
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| 332 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 333 | >>> tx = ants.contrib.RandomShear2D(shear_range=(-10,10)) |
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| 334 | >>> img2 = tx.transform(img) |
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| 335 | """ |
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| 336 | # random draw in shear range |
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| 337 | shear_x = random.gauss(self.shear_range[0], self.shear_range[1]) |
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| 338 | shear_y = random.gauss(self.shear_range[0], self.shear_range[1]) |
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| 339 | self.params = (shear_x, shear_y) |
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| 340 | |||
| 341 | tx = Shear2D((shear_x, shear_y), reference=self.reference, lazy=self.lazy) |
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| 342 | |||
| 343 | return tx.transform(X, y) |
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| 344 | |||
| 345 | |||
| 346 | class Rotate2D(object): |
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| 347 | """ |
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| 348 | Create an ANTs Affine Transform with a specified level |
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| 349 | of rotation. |
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| 350 | """ |
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| 351 | |||
| 352 | def __init__(self, rotation, reference=None, lazy=False): |
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| 353 | """ |
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| 354 | Initialize a Rotate2D object |
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| 355 | |||
| 356 | Arguments |
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| 357 | --------- |
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| 358 | rotation : scalar |
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| 359 | rotation value in degrees. |
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| 360 | Negative values can be used for rotation in the |
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| 361 | other direction |
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| 362 | |||
| 363 | reference : ANTsImage (optional but recommended) |
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| 364 | image providing the reference space for the transform. |
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| 365 | this will also set the transform fixed parameters. |
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| 366 | |||
| 367 | lazy : boolean (default = False) |
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| 368 | if True, calling the `transform` method only returns |
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| 369 | the randomly generated transform and does not actually |
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| 370 | transform the image |
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| 371 | """ |
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| 372 | self.rotation = rotation |
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| 373 | self.lazy = lazy |
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| 374 | self.reference = reference |
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| 375 | |||
| 376 | self.tx = tio.ANTsTransform( |
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| 377 | precision="float", dimension=2, transform_type="AffineTransform" |
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| 378 | ) |
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| 379 | if self.reference is not None: |
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| 380 | self.tx.set_fixed_parameters(self.reference.get_center_of_mass()) |
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| 381 | |||
| 382 | def transform(self, X=None, y=None): |
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| 383 | """ |
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| 384 | Transform an image using an Affine transform with the given |
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| 385 | rotation parameters. Return the transform if X=None. |
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| 386 | |||
| 387 | Arguments |
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| 388 | --------- |
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| 389 | X : ANTsImage |
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| 390 | Image to transform |
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| 391 | |||
| 392 | y : ANTsImage (optional) |
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| 393 | Another image to transform |
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| 394 | |||
| 395 | Returns |
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| 396 | ------- |
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| 397 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 398 | |||
| 399 | Examples |
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| 400 | -------- |
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| 401 | >>> import ants |
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| 402 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 403 | >>> tx = ants.contrib.Rotate2D(rotation=(10,-5,12)) |
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| 404 | >>> img2 = tx.transform(img) |
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| 405 | """ |
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| 406 | # unpack zoom range |
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| 407 | rotation = self.rotation |
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| 408 | |||
| 409 | # Rotation about X axis |
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| 410 | theta = math.pi / 180 * rotation |
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| 411 | rotation_matrix = np.array( |
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| 412 | [[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0]] |
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| 413 | ) |
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| 414 | |||
| 415 | self.tx.set_parameters(rotation_matrix) |
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| 416 | if self.lazy or X is None: |
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| 417 | return self.tx |
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| 418 | else: |
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| 419 | if y is None: |
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| 420 | return self.tx.apply_to_image(X, reference=self.reference) |
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| 421 | else: |
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| 422 | return ( |
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| 423 | self.tx.apply_to_image(X, reference=self.reference), |
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| 424 | self.tx.apply_to_image(y, reference=self.reference), |
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| 425 | ) |
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| 426 | |||
| 427 | |||
| 428 | class RandomRotate2D(object): |
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| 429 | """ |
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| 430 | Apply a Rotated2D transform to an image, but with the zoom |
||
| 431 | parameters randomly generated from a user-specified range. |
||
| 432 | The range is determined by a mean (first parameter) and standard deviation |
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| 433 | (second parameter) via calls to random.gauss. |
||
| 434 | """ |
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| 435 | |||
| 436 | def __init__(self, rotation_range, reference=None, lazy=False): |
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| 437 | """ |
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| 438 | Initialize a RandomRotate2D object |
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| 439 | |||
| 440 | Arguments |
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| 441 | --------- |
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| 442 | rotation_range : list or tuple |
||
| 443 | Lower and Upper bounds on rotation parameter, in degrees. |
||
| 444 | e.g. rotation_range = (-10,10) will result in a random |
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| 445 | draw of the rotation parameters between -10 and 10 degrees |
||
| 446 | |||
| 447 | reference : ANTsImage (optional but recommended) |
||
| 448 | image providing the reference space for the transform. |
||
| 449 | this will also set the transform fixed parameters. |
||
| 450 | |||
| 451 | lazy : boolean (default = False) |
||
| 452 | if True, calling the `transform` method only returns |
||
| 453 | the randomly generated transform and does not actually |
||
| 454 | transform the image |
||
| 455 | """ |
||
| 456 | if (not isinstance(rotation_range, (list, tuple))) or ( |
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| 457 | len(rotation_range) != 2 |
||
| 458 | ): |
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| 459 | raise ValueError( |
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| 460 | "rotation_range argument must be list/tuple with two values!" |
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| 461 | ) |
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| 462 | |||
| 463 | self.rotation_range = rotation_range |
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| 464 | self.reference = reference |
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| 465 | self.lazy = lazy |
||
| 466 | |||
| 467 | def transform(self, X=None, y=None): |
||
| 468 | """ |
||
| 469 | Transform an image using an Affine transform with |
||
| 470 | rotation parameters randomly generated from the user-specified |
||
| 471 | range. Return the transform if X=None. |
||
| 472 | |||
| 473 | Arguments |
||
| 474 | --------- |
||
| 475 | X : ANTsImage |
||
| 476 | Image to transform |
||
| 477 | |||
| 478 | y : ANTsImage (optional) |
||
| 479 | Another image to transform |
||
| 480 | |||
| 481 | Returns |
||
| 482 | ------- |
||
| 483 | ANTsImage if y is None, else a tuple of ANTsImage types |
||
| 484 | |||
| 485 | Examples |
||
| 486 | -------- |
||
| 487 | >>> import ants |
||
| 488 | >>> img = ants.image_read(ants.get_data('r16')) |
||
| 489 | >>> tx = ants.contrib.RandomRotate2D(rotation_range=(-10,10)) |
||
| 490 | >>> img2 = tx.transform(img) |
||
| 491 | """ |
||
| 492 | # random draw in rotation range |
||
| 493 | rotation = random.gauss(self.rotation_range[0], self.rotation_range[1]) |
||
| 494 | self.params = rotation |
||
| 495 | |||
| 496 | tx = Rotate2D(rotation, reference=self.reference, lazy=self.lazy) |
||
| 497 | |||
| 498 | return tx.transform(X, y) |
||
| 499 | |||
| 500 | |||
| 501 | class Zoom2D(object): |
||
| 502 | """ |
||
| 503 | Create an ANTs Affine Transform with a specified level |
||
| 504 | of zoom. Any value greater than 1 implies a "zoom-out" and anything |
||
| 505 | less than 1 implies a "zoom-in". |
||
| 506 | """ |
||
| 507 | |||
| 508 | def __init__(self, zoom, reference=None, lazy=False): |
||
| 509 | """ |
||
| 510 | Initialize a Zoom2D object |
||
| 511 | |||
| 512 | Arguments |
||
| 513 | --------- |
||
| 514 | zoom_range : list or tuple |
||
| 515 | Lower and Upper bounds on zoom parameter. |
||
| 516 | e.g. zoom_range = (0.7,0.9) will result in a random |
||
| 517 | draw of the zoom parameters between 0.7 and 0.9 |
||
| 518 | |||
| 519 | reference : ANTsImage (optional but recommended) |
||
| 520 | image providing the reference space for the transform. |
||
| 521 | this will also set the transform fixed parameters. |
||
| 522 | |||
| 523 | lazy : boolean (default = False) |
||
| 524 | if True, calling the `transform` method only returns |
||
| 525 | the randomly generated transform and does not actually |
||
| 526 | transform the image |
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| 527 | """ |
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| 528 | if (not isinstance(zoom, (list, tuple))) or (len(zoom) != 2): |
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| 529 | raise ValueError("zoom_range argument must be list/tuple with two values!") |
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| 530 | |||
| 531 | self.zoom = zoom |
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| 532 | self.lazy = lazy |
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| 533 | self.reference = reference |
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| 534 | |||
| 535 | self.tx = tio.ANTsTransform( |
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| 536 | precision="float", dimension=2, transform_type="AffineTransform" |
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| 537 | ) |
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| 538 | if self.reference is not None: |
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| 539 | self.tx.set_fixed_parameters(self.reference.get_center_of_mass()) |
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| 540 | |||
| 541 | def transform(self, X=None, y=None): |
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| 542 | """ |
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| 543 | Transform an image using an Affine transform with the given |
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| 544 | zoom parameters. Return the transform if X=None. |
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| 545 | |||
| 546 | Arguments |
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| 547 | --------- |
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| 548 | X : ANTsImage |
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| 549 | Image to transform |
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| 550 | |||
| 551 | y : ANTsImage (optional) |
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| 552 | Another image to transform |
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| 553 | |||
| 554 | Returns |
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| 555 | ------- |
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| 556 | ANTsImage if y is None, else a tuple of ANTsImage types |
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| 557 | |||
| 558 | Examples |
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| 559 | -------- |
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| 560 | >>> import ants |
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| 561 | >>> img = ants.image_read(ants.get_data('r16')) |
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| 562 | >>> tx = ants.contrib.Zoom2D(zoom=(0.8,0.8,0.8)) |
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| 563 | >>> img2 = tx.transform(img) |
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| 564 | """ |
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| 565 | # unpack zoom range |
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| 566 | zoom_x, zoom_y = self.zoom |
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| 567 | |||
| 568 | self.params = (zoom_x, zoom_y) |
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| 569 | zoom_matrix = np.array([[zoom_x, 0, 0], [0, zoom_y, 0]]) |
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| 570 | self.tx.set_parameters(zoom_matrix) |
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| 571 | if self.lazy or X is None: |
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| 572 | return self.tx |
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| 573 | else: |
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| 574 | if y is None: |
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| 575 | return self.tx.apply_to_image(X, reference=self.reference) |
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| 576 | else: |
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| 577 | return ( |
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| 578 | self.tx.apply_to_image(X, reference=self.reference), |
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| 579 | self.tx.apply_to_image(y, reference=self.reference), |
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| 580 | ) |
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| 581 | |||
| 582 | |||
| 583 | class RandomZoom2D(object): |
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| 584 | """ |
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| 585 | Apply a Zoom2D transform to an image, but with the zoom |
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| 586 | parameters randomly generated from a user-specified range. |
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| 587 | The range is determined by a mean (first parameter) and standard deviation |
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| 588 | (second parameter) via calls to random.gauss. |
||
| 589 | """ |
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| 590 | |||
| 591 | def __init__(self, zoom_range, reference=None, lazy=False): |
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| 592 | """ |
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| 593 | Initialize a RandomZoom2D object |
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| 594 | |||
| 595 | Arguments |
||
| 596 | --------- |
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| 597 | zoom_range : list or tuple |
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| 598 | Lower and Upper bounds on zoom parameter. |
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| 599 | e.g. zoom_range = (0.7,0.9) will result in a random |
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| 600 | draw of the zoom parameters between 0.7 and 0.9 |
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| 601 | |||
| 602 | reference : ANTsImage (optional but recommended) |
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| 603 | image providing the reference space for the transform. |
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| 604 | this will also set the transform fixed parameters. |
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| 605 | |||
| 606 | lazy : boolean (default = False) |
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| 607 | if True, calling the `transform` method only returns |
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| 608 | the randomly generated transform and does not actually |
||
| 609 | transform the image |
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| 610 | """ |
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| 611 | if (not isinstance(zoom_range, (list, tuple))) or (len(zoom_range) != 2): |
||
| 612 | raise ValueError("zoom_range argument must be list/tuple with two values!") |
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| 613 | |||
| 614 | self.zoom_range = zoom_range |
||
| 615 | self.reference = reference |
||
| 616 | self.lazy = lazy |
||
| 617 | |||
| 618 | def transform(self, X=None, y=None): |
||
| 619 | """ |
||
| 620 | Transform an image using an Affine transform with |
||
| 621 | zoom parameters randomly generated from the user-specified |
||
| 622 | range. Return the transform if X=None. |
||
| 623 | |||
| 624 | Arguments |
||
| 625 | --------- |
||
| 626 | X : ANTsImage |
||
| 627 | Image to transform |
||
| 628 | |||
| 629 | y : ANTsImage (optional) |
||
| 630 | Another image to transform |
||
| 631 | |||
| 632 | Returns |
||
| 633 | ------- |
||
| 634 | ANTsImage if y is None, else a tuple of ANTsImage types |
||
| 635 | |||
| 636 | Examples |
||
| 637 | -------- |
||
| 638 | >>> import ants |
||
| 639 | >>> img = ants.image_read(ants.get_data('r16')) |
||
| 640 | >>> tx = ants.contrib.RandomZoom2D(zoom_range=(0.8,0.9)) |
||
| 641 | >>> img2 = tx.transform(img) |
||
| 642 | """ |
||
| 643 | # random draw in zoom range |
||
| 644 | zoom_x = np.exp( |
||
| 645 | random.gauss(np.log(self.zoom_range[0]), np.log(self.zoom_range[1])) |
||
| 646 | ) |
||
| 647 | zoom_y = np.exp( |
||
| 648 | random.gauss(np.log(self.zoom_range[0]), np.log(self.zoom_range[1])) |
||
| 649 | ) |
||
| 650 | self.params = (zoom_x, zoom_y) |
||
| 651 | |||
| 652 | tx = Zoom2D((zoom_x, zoom_y), reference=self.reference, lazy=self.lazy) |
||
| 653 | |||
| 654 | return tx.transform(X, y) |