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+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+
+ +

All modules for which code is available

+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/crop_image/threshold_crop_background.html b/_modules/medipt/crop_image/threshold_crop_background.html new file mode 100644 index 0000000..15894f8 --- /dev/null +++ b/_modules/medipt/crop_image/threshold_crop_background.html @@ -0,0 +1,134 @@ + + + + + + medipt.crop_image.threshold_crop_background — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.crop_image.threshold_crop_background
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.crop_image.threshold_crop_background

+import SimpleITK as sitk
+from typing import Union
+
+
[docs]def threshold_crop_background(image: sitk.Image,
+                              inside_value: Union[int, float],
+                              outside_value: Union[int, float],
+                              *args, **kwargs) -> sitk.Image:
+
+
+    binary_image_filter = sitk.OtsuThresholdImageFilter()
+    binary_image_filter.SetInsideValue(inside_value)
+    binary_image_filter.SetOutsideValue(outside_value)
+
+    binary_image = binary_image_filter.Execute(image)
+
+    label_shape_filter = sitk.LabelShapeStatisticsImageFilter()
+    label_shape_filter.Execute(binary_image)
+    bounding_box = label_shape_filter.GetBoundingBox(outside_value)
+    # The bounding box's first "dim" entries are the starting index and last "dim" entries the size
+
+    cropped_image = sitk.RegionOfInterest(image, bounding_box[int(len(bounding_box)/2):], bounding_box[0:int(len(bounding_box)/2)])
+
+    return cropped_image

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/resample_image/resample_image.html b/_modules/medipt/resample_image/resample_image.html new file mode 100644 index 0000000..120af1f --- /dev/null +++ b/_modules/medipt/resample_image/resample_image.html @@ -0,0 +1,338 @@ + + + + + + medipt.resample_image.resample_image — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.resample_image.resample_image
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.resample_image.resample_image

+import numpy as np
+import SimpleITK as sitk
+from typing import Union, Tuple, List, Callable, Iterable
+
+
+
[docs]def get_sitk_interpolator(interpolator):
+    """
+    Return an sitk interpolator object for the given string.
+
+    :param interpolator: Interpolator type as string.
+                         'nearest': sitk.sitkNearestNeighbor
+                         'linear': sitk.sitkLinear
+                         'cubic': sitk.sitkBSpline
+                         'label_gaussian': sitk.sitkLabelGaussian
+                         'gaussian': sitk.sitkGaussian
+                         'lanczos': sitk.sitkLanczosWindowedSinc
+    :return: The sitk interpolator object.
+
+    """
+
+    if isinstance(interpolator, str):
+
+        if interpolator == 'nearest':
+            return sitk.sitkNearestNeighbor
+        elif interpolator == 'linear':
+            return sitk.sitkLinear
+        elif interpolator == 'cubic':
+            return sitk.sitkBSpline
+        elif interpolator == 'label_gaussian':
+            return sitk.sitkLabelGaussian
+        elif interpolator == 'gaussian':
+            return sitk.sitkGaussian
+        elif interpolator == 'lanczos':
+            return sitk.sitkLanczosWindowedSinc
+
+    elif isinstance(interpolator, int):
+        return interpolator
+
+    else:
+        raise Exception('invalid interpolator type')

+
+
+
+
+
[docs]def get_simpleitk_pix_type(pixel_type: Union[str, int]) -> int:
+    """
+    Returns the SimpleITK pixel type for the given pixel type string.
+
+    :param pixel_type: The pixel type as string.
+                       'float32': sitk.sitkFloat32
+                       'float64': sitk.sitkFloat64
+                       'int8': sitk.sitkInt8
+                       'int16': sitk.sitkInt16
+                       'int32': sitk.sitkInt32
+                       'int64': sitk.sitkInt64
+                       'uint8': sitk.sitkUInt8
+                       'uint16': sitk.sitkUInt16
+                       'uint32': sitk.sitkUInt32
+                       'uint64': sitk.sitkUInt64
+                       'complex64': sitk.sitkComplexFloat32
+                       'complex128': sitk.sitkComplexFloat64
+    :return: The SimpleITK pixel type.
+    """
+
+    if isinstance(pixel_type, str):
+
+        if pixel_type == 'float32':
+            return sitk.sitkFloat32
+        elif pixel_type == 'float64':
+            return sitk.sitkFloat64
+        elif pixel_type == 'int8':
+            return sitk.sitkInt8
+        elif pixel_type == 'int16':
+            return sitk.sitkInt16
+        elif pixel_type == 'int32':
+            return sitk.sitkInt32
+        elif pixel_type == 'int64':
+            return sitk.sitkInt64
+        elif pixel_type == 'uint8':
+            return sitk.sitkUInt8
+        elif pixel_type == 'uint16':
+            return sitk.sitkUInt16
+        elif pixel_type == 'uint32':
+            return sitk.sitkUInt32
+        elif pixel_type == 'uint64':
+            return sitk.sitkUInt64
+        elif pixel_type == 'complex64':
+            return sitk.sitkComplexFloat32
+        elif pixel_type == 'complex128':
+            return sitk.sitkComplexFloat64
+
+    elif isinstance(pixel_type, int):
+        return pixel_type
+
+    else:
+        raise Exception('invalid pixel type')

+
+
+
[docs]class ResampleImage:
+    def __init__(self,
+
+                 interpolator: Union[str, int] = 'linear',
+
+
+                 post_processing_sitk: Union[Callable, Union[List[Callable]], Tuple[Callable, ...]] = None,
+
+                 pixel_type: [str, int] = 'float32',
+                 default_pixel_value: Union[float, int, None] = None,
+
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+
+
+
+        self.used_dimensions = used_dimensions or [True] * dim
+        self.dim = dim
+
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+
+        self.interpolator = interpolator
+        self.post_processing_sitk = post_processing_sitk
+        self.pixel_type = pixel_type
+        self.default_pixel_value = default_pixel_value
+
+
+
[docs]    def resample_image(self,
+                       image: sitk.Image,
+                       input_output_space_dict: dict,
+                       reference_image: sitk.Image = None,
+                       transform: sitk.Transform = None,
+                       *args, **kwargs) -> sitk.Image:
+
+        # if image is None:
+        #     image = input_output_space_dict.get('input_image', None)
+        #     if image is None:
+        #         image = input_output_space_dict.get('image', None)
+        #         if image is None:
+        #             raise Exception('No image was provided.')
+
+        output_size = input_output_space_dict['output_size']
+        output_spacing = input_output_space_dict['output_spacing']
+        output_origin = input_output_space_dict['output_origin']
+        output_direction = input_output_space_dict['output_direction']
+
+        sitk_interpolator = get_sitk_interpolator(self.interpolator)
+
+        if self.default_pixel_value is None:
+            default_value_filter = sitk.MinimumMaximumImageFilter()
+            default_value_filter.Execute(image)
+            default_pixel_value = default_value_filter.GetMinimum()
+        else:
+            default_pixel_value = self.default_pixel_value
+
+        resample_filter = sitk.ResampleImageFilter()
+
+        if reference_image is not None:
+            resample_filter.SetReferenceImage(reference_image)
+
+        else:
+            resample_filter.SetOutputSpacing(output_spacing)
+            resample_filter.SetOutputOrigin(output_origin)
+            resample_filter.SetOutputDirection(output_direction)
+            resample_filter.SetSize(output_size)
+
+        resample_filter.SetDefaultPixelValue(default_pixel_value)
+
+        if transform is None:
+            resample_filter.SetTransform(sitk.AffineTransform(self.dim))
+
+        else:
+            resample_filter.SetTransform(transform)
+
+        resample_filter.SetInterpolator(sitk_interpolator)
+
+        if self.pixel_type is not None:
+            sitk_pix_type = get_simpleitk_pix_type(self.pixel_type)
+            resample_filter.SetOutputPixelType(sitk_pix_type)
+
+        resampled_image = resample_filter.Execute(image)
+
+        return resampled_image

+
+
+
+
[docs]    def get_images(self,
+                   images: Union[List[sitk.Image], Tuple[sitk.Image, ...], sitk.Image],
+                   input_output_space_dict: dict,
+
+                   reference_image: sitk.Image = None,
+                   transform: sitk.Transform = None,
+                   *args, **kwargs
+                   ):
+
+
+        if isinstance(images, (list, tuple)):
+            return [self.resample_image(image, input_output_space_dict, reference_image, transform, *args, **kwargs) for image in images]
+
+        else:
+            return self.resample_image(images, input_output_space_dict, reference_image, transform, *args, **kwargs)

+
+
[docs]    def get_resampled_images(self,
+                             image: Union[List[sitk.Image], Tuple[sitk.Image, ...], sitk.Image],
+                             input_output_space_dict: dict,
+                             reference_image: sitk.Image = None,
+                             transform: sitk.Transform = None,
+                             *args, **kwargs
+                             ) -> Union[List[sitk.Image], Tuple[sitk.Image, ...], sitk.Image]:
+
+        output_image_sitk = self.get_images(image, input_output_space_dict,
+                                            reference_image, transform,
+                                            *args, **kwargs)
+
+        if self.post_processing_sitk is not None:
+            if isinstance(self.post_processing_sitk, (list, tuple)):
+                for post_processing_sitk in self.post_processing_sitk:
+                    output_image_sitk = post_processing_sitk(output_image_sitk)
+
+            else:
+                output_image_sitk = self.post_processing_sitk(output_image_sitk)
+
+
+        return output_image_sitk

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/change_image_gamma.html b/_modules/medipt/transforms/intensity/change_image_gamma.html new file mode 100644 index 0000000..54167e8 --- /dev/null +++ b/_modules/medipt/transforms/intensity/change_image_gamma.html @@ -0,0 +1,144 @@ + + + + + + medipt.transforms.intensity.change_image_gamma — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.change_image_gamma
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.change_image_gamma

+import numpy as np
+from ...utils import random_uniform_float
+from typing import Union, Tuple, List
+
+
[docs]def change_gamma_unnormalized(img, l):
+    min_value = np.min(img)
+    max_value = np.max(img)
+    input_range = (min_value, max_value)
+    range_0_1 = (0, 1)
+    normalized = scale(img, input_range, range_0_1)
+    normalized = change_gamma(normalized, l)
+    return scale(normalized, range_0_1, input_range)

+
+
+
[docs]def change_gamma(img, l):
+    return np.power(img, l)

+
+
+
[docs]def scale(img, old_range, new_range):
+    shift = -old_range[0] + new_range[0] * (old_range[1] - old_range[0]) / (new_range[1] - new_range[0])
+    scale = (new_range[1] - new_range[0]) / (old_range[1] - old_range[0])
+    return (img + shift) * scale

+
+
+
+
[docs]def random_change_gamma(img, lambda_min=0.5, lambda_max=1.5,
+                        seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                        legacy_random_state: bool = True):
+
+    l = random_uniform_float(lambda_min, lambda_max,
+                             seed=seed,
+                             legacy_random_state=legacy_random_state)
+    return change_gamma_unnormalized(img, l)

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/clamp_intensity.html b/_modules/medipt/transforms/intensity/clamp_intensity.html new file mode 100644 index 0000000..e4edd82 --- /dev/null +++ b/_modules/medipt/transforms/intensity/clamp_intensity.html @@ -0,0 +1,149 @@ + + + + + + medipt.transforms.intensity.clamp_intensity — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.clamp_intensity
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.clamp_intensity

+from typing import Union, Tuple, List
+import numpy as np
+from .intensity_utils_np import clamp_intensity_np
+from .intensity_utils_sitk import clamp_intensity_sitk
+import SimpleITK as sitk
+
+
+
+
[docs]class ClampIntensity:
+    def __init__(self,
+                 window_min: Union[int, float] = None,
+                 window_max: Union[int, float] = None,
+                 *args, **kwargs):
+
+        self.window_min = window_min
+        self.window_max = window_max
+
+
+
+    def __call__(self, image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+
+        if isinstance(image, np.ndarray):
+
+            return clamp_intensity_np(image,
+                                      window_min=self.window_min,
+                                      window_max=self.window_max,
+                                      *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            return clamp_intensity_sitk(image,
+                                        window_min=self.window_min,
+                                        window_max=self.window_max,
+                                        *args, **kwargs)
+
+        else:
+            raise ImportError("image must be either a numpy array or a SimpleITK image.")

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/gaussian_blur.html b/_modules/medipt/transforms/intensity/gaussian_blur.html new file mode 100644 index 0000000..70ec39c --- /dev/null +++ b/_modules/medipt/transforms/intensity/gaussian_blur.html @@ -0,0 +1,179 @@ + + + + + + medipt.transforms.intensity.gaussian_blur — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.gaussian_blur
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.gaussian_blur

+from typing import Union, Tuple, List
+import numpy as np
+from ...utils import random_uniform_float
+from ...utils.random_float import initialize_rand_state
+from .intensity_utils_np import gaussian_blur_np
+from .intensity_utils_sitk import gaussian_blur_sitk
+import SimpleITK as sitk
+
+
+
[docs]class GaussianBlur:
+    def __init__(self,
+                 sigma: Union[int, float],
+                 *args, **kwargs):
+
+        self.sigma = sigma
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+        if isinstance(image, np.ndarray):
+            return gaussian_blur_np(image, self.sigma, *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            return gaussian_blur_sitk(image, self.sigma, *args, **kwargs)
+
+        else:
+            raise ImportError("Image must be either a numpy array or a SimpleITK image.")

+
+
[docs]class RandomGaussianBlur:
+
+    def __init__(self,
+                 min_sigma: Union[int, float],
+                 max_sigma: Union[int, float],
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, ** kwargs):
+
+        self.min_sigma = min_sigma
+        self.max_sigma = max_sigma
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        self.rand_init = initialize_rand_state(self.seed, self.legacy_random_state)
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+
+
+        sigma = random_uniform_float(low_value=self.min_sigma, high_value=self.max_sigma,
+                                     seed=self.seed,
+                                     legacy_random_state=self.legacy_random_state,
+                                     rand_init=self.rand_init)
+
+
+        if isinstance(image, np.ndarray):
+            return gaussian_blur_np(image, sigma, *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            return gaussian_blur_sitk(image, sigma, *args, **kwargs)
+
+        else:
+            raise ImportError("image must be either a numpy array or a SimpleITK image.")

+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/gaussian_noise.html b/_modules/medipt/transforms/intensity/gaussian_noise.html new file mode 100644 index 0000000..0602a1a --- /dev/null +++ b/_modules/medipt/transforms/intensity/gaussian_noise.html @@ -0,0 +1,234 @@ + + + + + + medipt.transforms.intensity.gaussian_noise — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.gaussian_noise
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.gaussian_noise

+from typing import Union, Tuple, List
+import SimpleITK as sitk
+import numpy as np
+from ...utils import random_uniform_float
+from ...utils.random_float import initialize_rand_state
+from .intensity_utils_np import gaussian_noise_np
+from .intensity_utils_sitk import gaussian_noise_sitk
+
+
+
[docs]class GaussianNoise:
+    def __init__(self,
+                 mean: Union[int, float],
+                 sigma: Union[int, float],
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        self.sigma = sigma
+        self.mean = mean
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+        if isinstance(image, np.ndarray):
+            return gaussian_noise_np(image, self.mean, self.sigma, *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            return gaussian_noise_sitk(image, self.mean, self.sigma, *args, **kwargs)
+
+        else:
+            raise ImportError("Image must be either a numpy array or a SimpleITK image.")

+
+
+
[docs]class RandomGaussianNoise:
+
+    def __init__(self,
+                 mean: Union[int, float] = None,
+                 min_mean: Union[int, float] = None,
+                 max_mean: Union[int, float] = None,
+
+                 sigma: Union[int, float] = None,
+                 min_sigma: Union[int, float] = None,
+                 max_sigma: Union[int, float] = None,
+
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+
+        self.sigma = sigma
+        self.min_sigma = min_sigma
+        self.max_sigma = max_sigma
+
+        self.mean = mean
+        self.min_mean = min_mean
+        self.max_mean = max_mean
+
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        self.rand_init = initialize_rand_state(seed=seed, legacy_random_state=legacy_random_state)
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+
+
+        if (self.min_sigma is None) and (self.max_sigma is None) and (self.min_mean is None) and (self.max_mean is None):
+            raise ValueError("If all min and max values are none, then use GaussianNoise instead")
+
+
+        if self.mean is None:
+            if (self.min_mean is None) and (self.max_mean is None):
+                raise ValueError("If mean is not provided, min_mean and max_mean must be specified")
+
+
+            mean = random_uniform_float(low_value=self.min_mean, high_value=self.max_mean,
+                                        seed=self.seed,
+                                        legacy_random_state=self.legacy_random_state,
+                                        rand_init=self.rand_init)
+
+        else:
+            mean = self.mean
+
+
+        if self.sigma is None:
+            if (self.min_sigma is None) and (self.max_sigma is None):
+                raise ValueError("If sigma is not provided, min_sigma and max_sigma must be specified")
+
+
+            sigma = random_uniform_float(low_value=self.min_sigma, high_value=self.max_sigma,
+                                         seed=self.seed,
+                                         legacy_random_state=self.legacy_random_state,
+                                         rand_init=self.rand_init)
+
+        else:
+            sigma = self.sigma
+
+
+        if isinstance(image, np.ndarray):
+            return gaussian_noise_np(image, mean, sigma, rand_init=self.rand_init, *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+
+            if self.seed is not None:
+                if self.legacy_random_state:
+                    seed = self.rand_init.get_state()[1][0]
+
+                else:
+                    seed = self.rand_init.__getstate__()['state']['state']
+            else:
+                seed = None
+
+            return gaussian_noise_sitk(image, mean, sigma, seed=seed, *args, **kwargs)
+
+        else:
+            raise ImportError("image must be either a numpy array or a SimpleITK image.")

+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/intensity_utils_np.html b/_modules/medipt/transforms/intensity/intensity_utils_np.html new file mode 100644 index 0000000..6405a9c --- /dev/null +++ b/_modules/medipt/transforms/intensity/intensity_utils_np.html @@ -0,0 +1,236 @@ + + + + + + medipt.transforms.intensity.intensity_utils_np — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.intensity_utils_np
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.intensity_utils_np

+import SimpleITK as sitk
+import numpy as np
+from scipy.ndimage import gaussian_filter
+from typing import Union, Tuple, List, Any, Callable, Optional
+from types import ModuleType
+
+
+
[docs]def clamp_intensity_np(image: np.ndarray,
+                       window_min: Union[int, float] = None,
+                       window_max: Union[int, float] = None,
+                       image_min: Union[int, float] = None,
+                       image_max: Union[int, float] = None,
+                       *args, **kwargs) -> np.ndarray:
+
+
+    if (window_min is None) and (window_max is None):
+        raise ValueError('window_min and window_max cannot both be None.')
+
+    if image_min is None or image_max is None:
+        if image_min is None:
+            image_min = image.min()
+        if image_max is None:
+            image_max = image.max()
+
+    if window_min is None:
+        window_min = image_min
+    if window_max is None:
+        window_max = image_max
+
+    clamped_image = np.clip(image, window_min, window_max)
+
+    return clamped_image

+
+
+
+
[docs]def shift_scale_intensity_np(image: np.ndarray,
+                             shift: Union[int, float] = None,
+                             scale: Union[int, float] = None,
+                             *args, **kwargs) -> np.ndarray:
+
+    shifted_image = image + shift
+    rescaled_image = shifted_image * scale
+
+    return rescaled_image

+
+
+
+
[docs]def rescale_intensity_np(image: np.ndarray,
+                         output_min: Union[int, float] = None,
+                         output_max: Union[int, float] = None,
+                         *args, **kwargs) -> np.ndarray:
+
+
+    if (output_min is None) or (output_max is None):
+        if output_min is None:
+            output_min = image.min()
+        if output_max is None:
+            output_max = image.min()
+    else:
+        raise ValueError('Need to provide output minimum and/or output maximum.')
+
+    input_min = image.min()
+    input_max = image.max()
+
+    rescaled_image = (image - input_min) * (output_max - output_min) / (input_max - input_min) + output_max
+
+    return rescaled_image

+
+
[docs]def rescale_intensity_window_np(image: np.ndarray,
+                                input_min: Union[int, float] = None,
+                                input_max: Union[int, float] = None,
+                                output_min: Union[int, float] = None,
+                                output_max: Union[int, float] = None,
+                                *args, **kwargs) -> np.ndarray:
+
+    if input_min is None:
+        input_min = image.min()
+
+    if input_max is None:
+        input_max = image.max()
+
+    if (output_min is None) and (output_max is None):
+        raise ValueError('Need to provide output minimum and/or output maximum.')
+    else:
+        if output_min is None:
+            output_min = input_min
+        if output_max is None:
+            output_max = input_max
+
+
+    rescaled_image = (image - input_min) * (output_max - output_min) / (input_max - input_min) + output_min
+
+    rescaled_image = np.clip(rescaled_image, a_min=output_min, a_max=output_max)
+
+    return rescaled_image

+
+
+
+
+
[docs]def gaussian_blur_np(image: np.ndarray,
+                     sigma: Union[int, float],
+                     *args, **kwargs) -> np.ndarray:
+    output_image = gaussian_filter(image, sigma=sigma)
+
+    return output_image

+
+
+
+
[docs]def gaussian_noise_np(image: np.ndarray,
+                      mean: Union[int, float],
+                      sigma: Union[int, float],
+                      rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+                      *args, **kwargs) -> np.ndarray:
+
+    gaussian = rand_init.normal(mean, sigma, image.shape)
+    output_image = image + gaussian
+    return output_image

+
+
+
[docs]def change_image_gamma_np(image: np.ndarray,
+                          gamma: Union[int, float],
+                          *args, **kwargs) -> np.ndarray:
+
+    output_image = np.power(image, gamma)
+    return output_image

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/intensity_utils_sitk.html b/_modules/medipt/transforms/intensity/intensity_utils_sitk.html new file mode 100644 index 0000000..444f80a --- /dev/null +++ b/_modules/medipt/transforms/intensity/intensity_utils_sitk.html @@ -0,0 +1,313 @@ + + + + + + medipt.transforms.intensity.intensity_utils_sitk — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.intensity_utils_sitk
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.intensity_utils_sitk

+import SimpleITK as sitk
+import numpy as np
+from typing import Union, Tuple, List
+
+
+
[docs]def min_max_intensity_sitk(image: sitk.Image,
+                           *args, **kwargs) -> Tuple[float, float]:
+
+    min_max_filter = sitk.MinimumMaximumImageFilter()
+    min_max_filter.Execute(image)
+
+    image_min = min_max_filter.GetMinimum()
+    image_max = min_max_filter.GetMaximum()
+
+    return image_min, image_max

+
+
+
+
[docs]def clamp_intensity_sitk(image: sitk.Image,
+                         window_min: Union[int, float] = None,
+                         window_max: Union[int, float] = None,
+                         image_min: Union[int, float] = None,
+                         image_max: Union[int, float] = None,
+                         *args, **kwargs) -> sitk.Image:
+
+
+    if (window_min is None) and (window_max is None):
+        raise ValueError('window_min and window_max cannot both be None.')
+
+    if image_min is None or image_max is None:
+        image_min_max = min_max_intensity_sitk(image)
+
+        if image_min is None:
+            image_min = image_min_max[0]
+        if image_max is None:
+            image_max = image_min_max[1]
+
+    if window_min is None:
+        window_min = image_min
+    if window_max is None:
+        window_max = image_max
+
+    clamp_filter = sitk.ClampImageFilter()
+
+    clamp_filter.SetLowerBound(window_min)
+    clamp_filter.SetUpperBound(window_max)
+
+    clamped_image = clamp_filter.Execute(image)
+
+    return clamped_image

+
+
+
+
[docs]def shift_scale_intensity_sitk(image: sitk.Image,
+                               shift: Union[int, float] = None,
+                               scale: Union[int, float] = None,
+                               *args, **kwargs) -> sitk.Image:
+
+    shift_scale_filter = sitk.ShiftScaleImageFilter()
+    shift_scale_filter.SetShift(shift)
+    shift_scale_filter.SetScale(scale)
+    shifted_scaled_image = shift_scale_filter.Execute(image)
+
+    return shifted_scaled_image

+
+
+
[docs]def rescale_intensity_sitk(image: sitk.Image,
+                           output_min: Union[int, float] = None,
+                           output_max: Union[int, float] = None,
+                           *args, **kwargs) -> sitk.Image:
+
+
+    if (output_min is None) and (output_max is None):
+        raise ValueError('Need to provide output minimum and/or output maximum.')
+    else:
+        image_min_max = min_max_intensity_sitk(image)
+        if output_min is None:
+            output_min = image_min_max[0]
+        if output_max is None:
+            output_max = image_min_max[1]
+
+
+    # if (output_min is None) or (output_max is None):
+    #     image_min_max = min_max_intensity_sitk(image)
+    #     if output_min is None:
+    #         output_min = image_min_max[0]
+    #     if output_max is None:
+    #         output_max = image_min_max[1]
+    # else:
+    #     raise ValueError('Need to provide output minimum and/or output maximum.')
+
+
+    rescale_filter = sitk.RescaleIntensityImageFilter()
+    rescale_filter.SetOutputMinimum(output_min)
+    rescale_filter.SetOutputMaximum(output_max)
+
+    rescaled_image = rescale_filter.Execute(image)
+
+    return rescaled_image

+
+
[docs]def rescale_intensity_window_sitk(image: sitk.Image,
+                           # output_min: Union[int, float] = None,
+                           # output_max: Union[int, float] = None,
+                           input_min: Union[int, float] = None,
+                           input_max: Union[int, float] = None,
+                           output_min: Union[int, float] = None,
+                           output_max: Union[int, float] = None,
+                           *args, **kwargs) -> sitk.Image:
+
+    image_min_max = min_max_intensity_sitk(image)
+
+    if input_min is None:
+        input_min = image_min_max[0]
+
+    if input_max is None:
+        input_max = image_min_max[1]
+
+
+    if (output_min is None) and (output_max is None):
+        raise ValueError('Need to provide output minimum and/or output maximum.')
+    else:
+        if output_min is None:
+            output_min = input_min
+        if output_max is None:
+            output_max = input_max
+
+
+
+
+    rescale_filter = sitk.IntensityWindowingImageFilter()
+
+    rescale_filter.SetOutputMaximum(output_max)
+    rescale_filter.SetOutputMinimum(output_min)
+
+    rescale_filter.SetWindowMaximum(input_max)
+    rescale_filter.SetWindowMinimum(input_min)
+
+
+    rescaled_image = rescale_filter.Execute(image)
+
+    return rescaled_image

+
+
+
+
+
[docs]def gaussian_blur_sitk(image: sitk.Image,
+                       sigma: Union[int, float],
+                       *args, **kwargs) -> sitk.Image:
+
+    image_default_pixel_type = image.GetPixelID()
+
+    gaussian_filter = sitk.SmoothingRecursiveGaussianImageFilter()
+    gaussian_filter.SetSigma(sigma)
+
+    rescast_filter = sitk.CastImageFilter()
+    rescast_filter.SetOutputPixelType(sitk.sitkFloat64)
+    image = rescast_filter.Execute(image)
+
+    output_image = gaussian_filter.Execute(image)
+
+    if image_default_pixel_type != sitk.sitkFloat64:
+        output_image = sitk.Cast(output_image, image_default_pixel_type)
+
+    return output_image

+
+
+
+
[docs]def gaussian_noise_sitk(image: sitk.Image,
+                        mean: Union[int, float],
+                        sigma: Union[int, float],
+                        seed: int = None,
+                        *args, **kwargs) -> sitk.Image:
+
+    image_default_pixel_type = image.GetPixelID()
+
+    gaussian_noise_filter = sitk.AdditiveGaussianNoiseImageFilter()
+    gaussian_noise_filter.SetMean(mean)
+    gaussian_noise_filter.SetStandardDeviation(sigma)
+
+    if seed is not None:
+        gaussian_noise_filter.SetSeed(seed)
+
+    rescast_filter = sitk.CastImageFilter()
+    rescast_filter.SetOutputPixelType(sitk.sitkFloat64)
+    image = rescast_filter.Execute(image)
+
+    output_image = gaussian_noise_filter.Execute(image)
+
+    if image_default_pixel_type != sitk.sitkFloat64:
+        output_image = sitk.Cast(output_image, image_default_pixel_type)
+
+    return output_image

+
+
+
[docs]def change_image_gamma_sitk(image: sitk.Image,
+                            gamma: Union[int, float],
+                            *args, **kwargs) -> sitk.Image:
+
+    exp_image_filter = sitk.PowImageFilter()
+    output_image = exp_image_filter.Execute(image, gamma)
+
+    return output_image

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/rescale_intensity.html b/_modules/medipt/transforms/intensity/rescale_intensity.html new file mode 100644 index 0000000..4df139b --- /dev/null +++ b/_modules/medipt/transforms/intensity/rescale_intensity.html @@ -0,0 +1,149 @@ + + + + + + medipt.transforms.intensity.rescale_intensity — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.rescale_intensity
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.rescale_intensity

+from typing import Union, Tuple, List
+import SimpleITK as sitk
+import numpy as np
+from .intensity_utils_np import rescale_intensity_np
+from .intensity_utils_sitk import rescale_intensity_sitk
+
+
+
+
[docs]class RescaleIntensity:
+    def __init__(self,
+                 new_min: Union[int, float] = None,
+                 new_max: Union[int, float] = None,
+                 *args, **kwargs):
+
+
+        self.new_min = new_min
+        self.new_max = new_max
+
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+
+        if isinstance(image, np.ndarray):
+            return rescale_intensity_np(image,
+                                        output_min=self.new_min,
+                                        output_max=self.new_max,
+                                        *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            return rescale_intensity_sitk(image,
+                                          output_min=self.new_min,
+                                          output_max=self.new_max,
+                                          *args, **kwargs)
+
+        else:
+            raise ImportError("image must be either a numpy array or a SimpleITK image.")

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/intensity/shift_scale_intensity.html b/_modules/medipt/transforms/intensity/shift_scale_intensity.html new file mode 100644 index 0000000..c54971b --- /dev/null +++ b/_modules/medipt/transforms/intensity/shift_scale_intensity.html @@ -0,0 +1,272 @@ + + + + + + medipt.transforms.intensity.shift_scale_intensity — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.intensity.shift_scale_intensity
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.intensity.shift_scale_intensity

+from typing import Union, Tuple, List
+import numpy as np
+from .intensity_utils_sitk import shift_scale_intensity_sitk
+from .intensity_utils_np import shift_scale_intensity_np
+from ...utils import random_uniform_float
+from ...utils.random_float import initialize_rand_state
+import SimpleITK as sitk
+import warnings
+
+
+# class ShiftScaleIntensity:
+#     def __init__(self,
+#                  shift: Union[int, float] = None,
+#                  scale: Union[int, float] = None,
+#                  *args, **kwargs):
+#
+#         self.shift = shift
+#         self.scale = scale
+#
+#
+#     def __call__(self,
+#                  image: Union[np.ndarray, sitk.Image],
+#                  *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+#
+#         if isinstance(image, np.ndarray):
+#             output_image = shift_scale_intensity_np(image,
+#                                                     shift=self.shift,
+#                                                     scale=self.scale,
+#                                                     *args, **kwargs)
+#         elif isinstance(image, sitk.Image):
+#             output_image = shift_scale_intensity_sitk(image,
+#                                                       shift=self.shift,
+#                                                       scale=self.scale,
+#                                                       *args, **kwargs)
+#
+#         else:
+#             raise ImportError("image must be either a numpy array or a SimpleITK image.")
+#
+#         return output_image
+
+
+
+
[docs]class ShiftScaleIntensity:
+
+    def __init__(self,
+                 shift: Union[int, float] = None,
+                 scale: Union[int, float] = None,
+                 input_min: Union[int, float] = None,
+                 input_max: Union[int, float] = None,
+                 output_min: Union[int, float] = None,
+                 output_max: Union[int, float] = None,
+                 random_shift: Union[int, float] = None,
+                 random_scale: Union[int, float] = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, ** kwargs):
+
+        self.shift = shift
+        self.scale = scale
+
+
+        self.input_min = input_min
+        self.input_max = input_max
+        self.output_min = output_min
+        self.output_max = output_max
+
+        self.random_shift = random_shift
+        self.random_scale = random_scale
+
+
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        self.rand_init = initialize_rand_state(self.seed, self.legacy_random_state)
+
+
+    def __call__(self,
+                 image: Union[np.ndarray, sitk.Image],
+                 *args, **kwargs) -> Union[np.ndarray, sitk.Image]:
+
+
+        if (self.shift is None) and (self.scale is None) and (self.input_min is None) and (self.input_max is None) and (self.output_min is None) and (self.output_max is None):
+            raise ValueError("At least one of shift, scale, input_min, input_max, output_min, output_max must be provided.")
+
+
+        if ((self.shift is None) and (self.scale is None)) and ((self.input_min is None) and (self.input_max is None) and (self.output_min is None) and (self.output_max is None)):
+            raise ValueError("Either shift and scale or input_min, input_max, output_min, output_max must be provided.")
+
+        if (self.shift is not None) and (self.scale is not None) and (self.input_min is not None) and (self.input_max is not None) and (self.output_min is not None) and (self.output_max is not None):
+            warnings.warn("Mixing both shift and scale with input_min, input_max, output_min, output_max. Input and output values will be set to 'None'.")
+            self.input_min = None
+            self.input_max = None
+            self.output_min = None
+            self.output_max = None
+
+
+        if (self.shift is None) and (self.scale is None) and (self.input_min is not None) and (self.input_max is not None) and (self.output_min is not None) and (self.output_max is not None):
+            self.shift = -self.input_min + self.output_min * (self.input_max - self.input_min) / (self.output_max - self.output_min)
+            self.scale = (self.output_max - self.output_min) / (self.input_max - self.input_min)
+
+
+
+
+        if isinstance(image, np.ndarray):
+            output_image = shift_scale_intensity_np(image,
+                                                    shift=self.shift,
+                                                    scale=self.scale,
+                                                    *args, **kwargs)
+
+        elif isinstance(image, sitk.Image):
+            output_image = shift_scale_intensity_sitk(image,
+                                                      shift=self.shift,
+                                                      scale=self.scale,
+                                                      *args, **kwargs)
+
+        else:
+            raise ImportError("image must be either a numpy array or a SimpleITK image.")
+
+
+        if (self.random_shift is not None) or (self.random_scale is not None):
+
+            if self.random_shift:
+                random_shift = random_uniform_float(low_value=-self.random_shift,
+                                                    high_value=self.random_shift,
+                                                    seed=self.seed,
+                                                    legacy_random_state=self.legacy_random_state,
+                                                    rand_init=self.rand_init)
+
+            else:
+                random_shift = 0
+
+            if self.random_scale:
+                random_scale = random_uniform_float(low_value=-self.random_scale,
+                                                    high_value=self.random_scale,
+                                                    seed=self.seed,
+                                                    legacy_random_state=self.legacy_random_state,
+                                                    rand_init=self.rand_init)
+
+            else:
+                random_scale = 1
+
+            random_scale = 1 + random_scale
+
+            if isinstance(image, np.ndarray):
+                output_image = shift_scale_intensity_np(output_image,
+                                                        shift=random_shift,
+                                                        scale=random_scale,
+                                                        *args, **kwargs)
+
+            elif isinstance(image, sitk.Image):
+                output_image = shift_scale_intensity_sitk(output_image,
+                                                          shift=random_shift,
+                                                          scale=random_scale,
+                                                          *args, **kwargs)
+
+            return output_image
+
+        else:
+
+            return output_image

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/composite_transform.html b/_modules/medipt/transforms/spatial/composite_transform.html new file mode 100644 index 0000000..5398696 --- /dev/null +++ b/_modules/medipt/transforms/spatial/composite_transform.html @@ -0,0 +1,200 @@ + + + + + + medipt.transforms.spatial.composite_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.composite_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.composite_transform

+import SimpleITK as sitk
+import numpy as np
+from typing import Union, Tuple, List
+from .spatial_transform import SpatialTransform
+from .elastic_deformation_transform import ElasticDeformation
+
+
[docs]def composite_transform(transforms: List[Union[sitk.AffineTransform, sitk.BSplineTransform, sitk.DisplacementFieldTransform]],
+                        dim: int = 3):
+
+    if sitk.Version_MajorVersion() == 1:
+        compos = sitk.Transform(dim, sitk.sitkIdentity)
+        for transformation in transforms:
+            compos.AddTransform(transformation)
+    else:
+        compos = sitk.CompositeTransform(transforms)
+
+
+    return compos

+
+
+
+
[docs]class CompositeTransform:
+    def __init__(self,
+                 *args, **kwargs):
+
+        self.transforms = []
+
+
+
[docs]    def add_transforms(self,
+                       transform: Union[List[Union[SpatialTransform, sitk.Transform]],
+                                        Tuple[Union[SpatialTransform, sitk.Transform], ...],
+                                        Union[SpatialTransform, sitk.Transform]]):
+
+        if isinstance(transform, (tuple, list)):
+            self.transforms.extend(transform)
+
+        else:
+            self.transforms.append(transform)

+
+
[docs]    def create_composite(self, dim: int = 3) -> sitk.Transform:
+        compos = sitk.CompositeTransform(dim)
+        for transform in self.transforms:
+            if isinstance(transform, SpatialTransform):
+                sitk_transform = transform.transform
+                compos.AddTransform(sitk_transform)
+            else:
+                compos.AddTransform(transform.transform)
+
+        return compos

+
+
[docs]    def create_inverse_composite(self, dim: int = 3,
+                                 use_displacement_field: bool = False) -> sitk.CompositeTransform:
+        compos = sitk.CompositeTransform(dim)
+
+        for transform in self.transforms[::-1]:
+            if isinstance(transform, SpatialTransform):
+
+                if isinstance(transform, ElasticDeformation):
+                    if use_displacement_field:
+                        transform.get_inverted_transform_from_displacement()
+                        sitk_inverse_transform = transform.inverted_transform_from_displacement
+
+                    else:
+                        transform.get_inverse_transform()
+                        sitk_inverse_transform = transform.inverse_transform
+
+                else:
+                    transform.get_inverse_transform()
+                    sitk_inverse_transform = transform.inverse_transform
+
+                # if use_displacement_field:
+                #     transform.get_inverted_transform_from_displacement()
+                #     sitk_inverse_transform = transform.inverted_transform_from_displacement
+                #
+                # else:
+                #     transform.get_inverse_transform()
+                #     sitk_inverse_transform = transform.inverse_transform
+
+
+                compos.AddTransform(sitk_inverse_transform)
+
+            else:
+                if use_displacement_field:
+                    raise Warning("The use_displacement_field option is not implemented for SimpleITK transforms.")
+                compos.AddTransform(transform.GetInverse())
+
+        return compos

+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/elastic_deformation_transform.html b/_modules/medipt/transforms/spatial/elastic_deformation_transform.html new file mode 100644 index 0000000..d7efc3a --- /dev/null +++ b/_modules/medipt/transforms/spatial/elastic_deformation_transform.html @@ -0,0 +1,476 @@ + + + + + + medipt.transforms.spatial.elastic_deformation_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.elastic_deformation_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.elastic_deformation_transform

+from typing import Union, Tuple, List
+from ...utils import random_uniform_float
+import SimpleITK as sitk
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+
+
+
[docs]class ElasticDeformation(SpatialTransform):
+    """
+    Rotation transformation base class.
+    """
+
+    def __init__(self,
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+        """
+        Initializer
+        :param dim: The dimension.
+        :param used_dimensions: Boolean list of which dimension indizes to use for the transformation.
+        :param args: Arguments passed to super init.
+        :param kwargs: Keyword arguments passed to super init.
+        """
+
+
+        super(ElasticDeformation, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+        self.spline_params = None
+        self.transform = None
+        self.inverse_transform = None
+        self.transform_created = False
+
+        self.displacement_field = None
+        self.inverted_displacement_field = None
+        self.inverted_transform_from_displacement = None
+
+        self.spline_order = None
+        self.num_grid_points = None
+
+        self.image_size = None
+        self.image_spacing = None
+        self.image_origin = None
+        self.image_direction = None
+
+
+
+    def _get_deform_transform(self,
+                              spline_params: List[float],
+                              *args, **kwargs) -> sitk.BSplineTransform:
+
+
+        # direction_reshape = np.reshape(self.image_direction, (self.dim, self.dim))
+        # physical_dimensions = np.matmul(direction_reshape * self.image_spacing, self.image_size)
+
+        physical_dimensions = np.array(self.image_spacing) * np.array(self.image_size)
+        physical_dimensions = physical_dimensions.astype('float')
+
+        mesh_size = [grid_node - self.spline_order for grid_node in self.num_grid_points]
+        t = sitk.BSplineTransform(self.dim, self.spline_order)
+        t.SetTransformDomainOrigin(self.image_origin)
+        t.SetTransformDomainMeshSize(mesh_size)
+        t.SetTransformDomainPhysicalDimensions(physical_dimensions)
+        t.SetTransformDomainDirection(self.image_direction)
+
+        t.SetParameters(spline_params)
+
+        return t
+
+
+
+    def _get_transform(
+            self,
+            spline_params: Union[List[float], np.ndarray],
+            image_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_direction: Union[List[Union[int, float]], Tuple[Union[int, float]]] = None,
+
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            # max_deformation_displacement: Union[List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray] = 25,
+            spline_order: int = 3,
+
+            *args, **kwargs):
+
+        self.spline_params = spline_params
+
+        self.spline_order = spline_order
+
+        if isinstance(num_grid_points, (int, float)):
+            num_grid_points = tuple([int(num_grid_points)] * self.dim)
+
+        else:
+            assert len(num_grid_points) == self.dim, 'Make sure dimensions align for num_grid_points'
+
+        self.num_grid_points = num_grid_points
+
+
+        image_params = self.get_image_params(image_size=image_size,
+                                             image_spacing=image_spacing,
+                                             image_origin=image_origin,
+                                             image_direction=image_direction,
+                                             *args, **kwargs)
+
+        self.image_size = image_params[0]
+        self.image_spacing = image_params[1]
+        self.image_origin = image_params[2]
+        self.image_direction = image_params[3]
+
+
+        if isinstance(spline_params, np.ndarray):
+            spline_params = spline_params.flatten().tolist()
+
+        self.transform = self._get_deform_transform(spline_params, *args, **kwargs)
+
+
+
[docs]    def get_transform(self,
+                      *args, **kwargs):
+
+        self._get_transform(*args, **kwargs)

+
+
+
[docs]    def get_inverse_transform(self, *args, **kwargs):
+        '''
+        This determines the inverse transform for the elastic deformation.
+
+        Note: this is not suitable for proper resampling the transformed image back to the original state, but is
+        intended to be used with coordinate transforms. For image resampling, use the inverted deformation field.
+        :param args:
+        :param kwargs:
+        :return:
+        '''
+
+        if self.transform is not None:
+            if isinstance(self.spline_params, np.ndarray):
+                spline_params_inv = -1 * self.spline_params
+            else:
+                spline_params_inv = [-1 * param for param in self.spline_params]
+
+            if isinstance(spline_params_inv, np.ndarray):
+                spline_params_inv = spline_params_inv.flatten().tolist()
+
+            self.inverse_transform = self._get_deform_transform(spline_params_inv, *args, **kwargs)
+
+            # self.t_inv = t_inv
+
+        else:
+            raise ValueError('No transform found. Call get_transform first.')

+
+
+
+
[docs]    def get_displacement_field(self, *args, **kwargs):
+
+        if self.transform is not None:
+
+            displacement_field = sitk.TransformToDisplacementField(self.transform,
+                                                                  size=self.image_size,
+                                                                  outputSpacing=self.image_spacing,
+                                                                  outputDirection=self.image_direction,
+                                                                  outputOrigin=self.image_origin)
+
+            self.displacement_field = displacement_field
+        else:
+            raise ValueError('No transform found. Call get_transform first.')

+
+
+
[docs]    def get_inverted_displacement_field(self, *args, **kwargs):
+
+        if self.displacement_field is None:
+            self.get_displacement_field()
+
+        self.inverted_displacement_field = sitk.InvertDisplacementField(self.displacement_field)

+
+
+
[docs]    def get_inverted_transform_from_displacement(self):
+
+        if self.inverted_displacement_field is None:
+            self.get_inverted_displacement_field()
+
+        self.inverted_transform_from_displacement = sitk.DisplacementFieldTransform(self.inverted_displacement_field)

+
+
+
+
[docs]class ElasticDeformationInputImage(ElasticDeformation):
+
+
[docs]    def get_transform_on_input(
+            self,
+            spline_params: Union[List[float], np.ndarray],
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            spline_order: int = 3,
+            *args, **kwargs):
+
+
+        self.get_image_params(*args, **kwargs)
+
+
+        self.get_transform(spline_params=spline_params,
+                           num_grid_points=num_grid_points,
+                           spline_order=spline_order,
+                           image_size=self.input_size,
+                           image_spacing=self.input_spacing,
+                           image_origin=self.input_origin,
+                           image_direction=self.input_direction,
+                           *args, **kwargs)

+
+        # return t
+
+
+
[docs]class ElasticDeformationOutputImage(ElasticDeformation):
+
+
[docs]    def get_transform_on_output(
+            self,
+            spline_params: Union[List[float], np.ndarray],
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            spline_order: int = 3,
+            *args, **kwargs):
+
+        self.get_image_params(*args, **kwargs)
+
+        self.get_transform(spline_params=spline_params,
+                           num_grid_points=num_grid_points,
+                           spline_order=spline_order,
+                           image_size=self.output_size,
+                           image_spacing=self.output_spacing,
+                           image_origin=self.output_size,
+                           image_direction=self.output_direction,
+                           *args, **kwargs)

+
+        # return t
+
+
+
[docs]class RandomElasticDeformation(ElasticDeformation):
+
+    def __init__(self,
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+
+
+        super(RandomElasticDeformation, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
+
+
[docs]    def get_random_spline_params(
+            self,
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray] = (6, 6, 6),
+            max_deformation_displacement: Union[List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray] = (25, 25, 25),
+            *args, **kwargs, ) -> np.ndarray:
+
+        output_size = *num_grid_points, self.dim
+
+        spline_params = random_uniform_float(-1, 1,
+                                             output_size=output_size,
+                                             seed=self.seed,
+                                             legacy_random_state=self.legacy_random_state,
+                                             rand_init=self.rand_init)
+
+        for dimension in range(self.dim):
+            spline_params[..., dimension] *= max_deformation_displacement[dimension]
+
+
+
+
+
+
+
+
+        # if isinstance(max_deformation_displacement, (list, tuple)):
+        #     spline_params = []
+        #     for v in max_deformation_displacement:
+        #         for i in range(int(np.prod(num_grid_points))):
+        #             spline_params.append(random_uniform_float(-v, v, seed=self.seed,
+        #                                                       legacy_random_state=self.legacy_random_state,
+        #                                                       rand_init=self.rand_init))
+
+
+
+        return spline_params

+
+
+
[docs]    def get_random_transform(
+            self,
+            image_size: Union[int, float, List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray],
+            image_spacing: Union[int, float, List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray],
+            image_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...], np.ndarray] = None,
+            image_direction: Union[List[Union[int, float]], Tuple[Union[int, float]], np.ndarray] = None,
+
+
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            max_deformation_displacement: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 25,
+
+
+            spline_order: int = 3,
+
+            *args, **kwargs):
+
+        if isinstance(num_grid_points, (int, float)):
+            num_grid_points = [num_grid_points] * self.dim
+
+        if isinstance(max_deformation_displacement, (int, float)):
+            max_deformation_displacement = [max_deformation_displacement] * self.dim
+
+        self.spline_params = self.get_random_spline_params(num_grid_points, max_deformation_displacement,
+                                                           *args, **kwargs)
+
+
+        self.get_transform(self.spline_params,
+                           image_size=image_size, image_spacing=image_spacing,
+                           image_origin=image_origin, image_direction=image_direction,
+                           num_grid_points=num_grid_points,
+                           max_deformation_displacement=max_deformation_displacement,
+                           spline_order=spline_order,
+                           *args, **kwargs)

+
+
+        # return self.t
+
+
+
+
[docs]class RandomElasticDeformationTransformInputImage(RandomElasticDeformation):
+
[docs]    def get_random_transform_on_input(
+            self,
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            max_deformation_displacement: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 25,
+            spline_order: int = 3,
+            *args, **kwargs):
+
+        self.get_image_params(*args, **kwargs)
+
+        self.get_random_transform(num_grid_points=num_grid_points,
+                                  max_deformation_displacement=max_deformation_displacement,
+                                  spline_order=spline_order,
+                                  image_size=self.input_size,
+                                  image_spacing=self.input_spacing,
+                                  image_origin=self.input_origin,
+                                  image_direction=self.input_direction,
+                                  *args, **kwargs)

+
+        # return t
+
+
+
[docs]class RandomElasticDeformationTransformOutputImage(RandomElasticDeformation):
+
[docs]    def get_random_transform_on_output(
+            self,
+            num_grid_points: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 6,
+            max_deformation_displacement: Union[List[Union[int, float]], Tuple[Union[int, float], ...], int, float] = 25,
+            spline_order: int = 3,
+            *args, **kwargs):
+
+        self.get_image_params(*args, **kwargs)
+
+        self.get_random_transform(num_grid_points=num_grid_points,
+                                  max_deformation_displacement=max_deformation_displacement,
+                                  spline_order=spline_order,
+                                  image_size=self.output_size,
+                                  image_spacing=self.output_spacing,
+                                  image_origin=self.output_size,
+                                  image_direction=self.output_direction,
+                                  *args, **kwargs)

+
+        # return t
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/flipping_transform.html b/_modules/medipt/transforms/spatial/flipping_transform.html new file mode 100644 index 0000000..b0adda7 --- /dev/null +++ b/_modules/medipt/transforms/spatial/flipping_transform.html @@ -0,0 +1,238 @@ + + + + + + medipt.transforms.spatial.flipping_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.flipping_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.flipping_transform

+from typing import Union, Tuple, List
+import SimpleITK as sitk
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+from ...utils import random_binomial
+
+
+
[docs]class FlippingTransform(SpatialTransform):
+
+    def _get_transform(self,
+                       flip_axes: Union[List[float], Tuple[float, ...], float],
+                       *args, **kwargs):
+
+
+
+        if isinstance(flip_axes, (tuple, list, np.ndarray)):
+            current_flip_axes = []
+            if len(flip_axes) > 1:
+                if len(flip_axes) != self.dim:
+                    raise ValueError(f'flip axes must be a tuple or list of length {self.dim}.')
+
+            for flip_axis in flip_axes:
+
+                current_flip_axes.append(bool(flip_axis))
+
+        elif isinstance(flip_axes, (int, float, np.integer, np.floating, np.bool_, bool)):
+            current_flip_axes = [bool(flip_axes)] * self.dim
+
+        else:
+            raise ValueError('flip axes must be tuples, lists, or numbers.')
+
+
+
+        self.transform = sitk.AffineTransform(self.dim)
+
+        scale_factors = [-1.0 if f else 1.0 for f in current_flip_axes]
+
+        self.transform.Scale(scale_factors)
+
+
[docs]    def get_transform(self,
+                      flip_axes: Union[List[float], Tuple[float, ...], float],
+                      *args, **kwargs
+                      ):
+
+        self._get_transform(flip_axes, *args, **kwargs)

+
+
+
+
[docs]class RandomFlipping(FlippingTransform):
+
+
[docs]    def get_random_transform(self,
+                             flip_axes: Union[
+                                 List[Union[int, float, bool]],
+                                 Tuple[Union[int, float, bool], ...],
+                                 Union[int, float, bool, np.integer, np.floating],
+                                 np.ndarray],
+                             *args, **kwargs,
+                             ):
+
+        if isinstance(flip_axes, (tuple, list, np.ndarray)):
+
+            if isinstance(flip_axes, np.ndarray):
+                probability = flip_axes * 0.5
+
+            elif isinstance(flip_axes, (list, tuple)):
+                probability = 0.5 * np.array(flip_axes)
+
+            else:
+                raise ValueError('flip axes must be tuples, lists, or numbers.')
+
+        elif isinstance(flip_axes, (int, float, np.integer, np.floating, np.bool_, bool)):
+
+            probability = 0.5 * flip_axes
+
+        else:
+            raise ValueError('flip axes must be tuples, lists, or numbers.')
+
+        current_flip_axis = random_binomial(n=1, p=probability,
+                                            seed=self.seed,
+                                            legacy_random_state=self.legacy_random_state,
+                                            rand_init=self.rand_init)
+
+        self._get_transform(current_flip_axis, *args, **kwargs)

+
+
+
+
+
+
+
+
+
+        #
+        # if isinstance(flip_axes, (tuple, list, np.ndarray)):
+        #     current_flip_axes = []
+        #     if len(flip_axes) > 1:
+        #         if len(flip_axes) != self.dim:
+        #             raise ValueError(f'flip axes must be a tuple or list of length {self.dim}.')
+        #
+        #     for flip_axis in flip_axes:
+        #
+        #         if flip_axis == 1:
+        #             current_flip_axis = random_binomial(n=1, p=0.5,
+        #                                                 seed=self.seed,
+        #                                                 legacy_random_state=self.legacy_random_state,
+        #                                                 rand_init=self.rand_init)
+        #         else:
+        #             current_flip_axis = 0
+        #
+        #         current_flip_axes.append(bool(current_flip_axis))
+        #
+        # elif isinstance(flip_axes, (int, float, np.integer, np.floating, np.bool_, bool)):
+        #     if flip_axes == 1:
+        #         current_flip_axis = random_binomial(n=1, p=0.5,
+        #                                             seed=self.seed,
+        #                                             legacy_random_state=self.legacy_random_state,
+        #                                             rand_init=self.rand_init)
+        #
+        #     else:
+        #         current_flip_axis = 0
+        #     current_flip_axes = [bool(current_flip_axis)] * self.dim
+        #
+        # else:
+        #     raise ValueError('flip axes must be tuples, lists, or numbers.')
+        #
+        # self._get_transform(current_flip_axes, *args, **kwargs)
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/random_affine_transform.html b/_modules/medipt/transforms/spatial/random_affine_transform.html new file mode 100644 index 0000000..d1eb65e --- /dev/null +++ b/_modules/medipt/transforms/spatial/random_affine_transform.html @@ -0,0 +1,198 @@ + + + + + + medipt.transforms.spatial.random_affine_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.random_affine_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.random_affine_transform

+from typing import Union, Tuple, List
+from types import ModuleType
+from ...utils import random_uniform_float
+
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+
+
+
+
[docs]class RandomAffineTransform(SpatialTransform):
+
+    def __init__(self,
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+                 *args, **kwargs
+                 ):
+
+
+        super(RandomAffineTransform, self).__init__(dim, used_dimensions, seed, legacy_random_state, rand_init, *args, **kwargs)
+
+
+        self.min_range = None
+        self.max_range = None
+
+
+
+    def _get_random_transform(self,
+                              min_range: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                              max_range: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                              value_offset: Union[int, float] = None,
+                              uniform: bool = False,
+                              transform_dict: dict = None,
+                              *args, **kwargs):
+
+        self.min_range = min_range
+        self.max_range = max_range
+
+
+        if isinstance(min_range, (tuple, list, np.ndarray)):
+            assert isinstance(self.max_range, (tuple, list, np.ndarray)), 'both min and max angles must be tuples, lists or np.ndarrays.'
+
+            if len(self.min_range) > 1 or len(self.max_range) > 1:
+                assert len(self.min_range) == self.dim, f'min angles must be a tuple or list of length {self.dim}.'
+                assert len(self.max_range) == self.dim, f'max angles must be a tuple or list of length {self.dim}.'
+
+                random_params = random_uniform_float(self.min_range, self.max_range,
+                                                     seed=self.seed, legacy_random_state=self.legacy_random_state,
+                                                     rand_init=self.rand_init)
+            else:
+                random_params = random_uniform_float(self.min_range[0], self.max_range[0], output_size=self.dim,
+                                                     seed=self.seed, legacy_random_state=self.legacy_random_state,
+                                                     rand_init=self.rand_init)
+
+
+        elif isinstance(self.min_range, (int, float, np.ndarray, np.floating, np.integer)):
+            assert isinstance(self.max_range, (int, float, np.ndarray, np.floating, np.integer)), 'both min and max rotations must be numbers.'
+
+
+            if uniform is False:
+
+                random_params = random_uniform_float(self.min_range, self.max_range, output_size=self.dim,
+                                                     seed=self.seed, legacy_random_state=self.legacy_random_state,
+                                                     rand_init=self.rand_init)
+            else:
+                random_params = random_uniform_float(self.min_range, self.max_range,
+                                                     seed=self.seed, legacy_random_state=self.legacy_random_state,
+                                                     rand_init=self.rand_init)
+
+                random_params = [random_params] * self.dim
+
+
+        else:
+            raise ValueError('min and max offsets must be tuples, lists, or numbers.')
+
+
+        if value_offset is not None:
+            if isinstance(random_params, (list, tuple)):
+               random_params = [x + value_offset for x in random_params]
+            elif isinstance(random_params, np.ndarray):
+                random_params += value_offset
+
+        self._get_transform(random_params, *args, **kwargs)

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/rotation_transform.html b/_modules/medipt/transforms/spatial/rotation_transform.html new file mode 100644 index 0000000..0218d59 --- /dev/null +++ b/_modules/medipt/transforms/spatial/rotation_transform.html @@ -0,0 +1,236 @@ + + + + + + medipt.transforms.spatial.rotation_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.rotation_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.rotation_transform

+from abc import ABC
+from typing import Union, Tuple, List
+from types import ModuleType
+import SimpleITK as sitk
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+from .random_affine_transform import RandomAffineTransform
+
+
+
[docs]class RotationTransform(SpatialTransform):
+    """
+    Rotation transformation base class.
+    """
+
+    # def __init__(self,
+    #              dim: int = 3,
+    #              used_dimensions: bool = None,
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #              rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+    #              *args, **kwargs):
+    #     """
+    #     Initializer
+    #     :param dim: The dimension.
+    #     :param used_dimensions: Boolean list of which dimension indizes to use for the transformation.
+    #     :param args: Arguments passed to super init.
+    #     :param kwargs: Keyword arguments passed to super init.
+    #     """
+    #
+    #
+    #     super(RotationTransform, self).__init__(dim, used_dimensions, seed, legacy_random_state, rand_init, *args, **kwargs)
+
+
+    def _get_transform(self,
+                       angles: Union[List[float], Tuple[float, ...], float],
+                       *args, **kwargs):
+
+        """
+        Returns the sitk transform based on the given parameters.
+        :param dim: The dimension.
+        :param angles: List of angles for each dimension (in radians).
+        :return: The sitk.AffineTransform().
+        """
+        if not isinstance(angles, (list, tuple)):
+            angles = [angles]
+        assert isinstance(angles, (list, tuple)), 'Angles parameter must be a list of floats, one for each dimension.'
+
+        assert len(angles) in [1, self.dim], 'Angles must be a list of length 1 for 2D, or 3 for 3D.'
+
+        self.transform = sitk.AffineTransform(self.dim)
+
+        if isinstance(angles, (tuple, list)):
+
+            if len(angles) == 1:
+                # 2D
+                self.transform.Rotate(0, 1, angle=angles[0])
+
+            elif len(angles) > 1:
+                assert len(angles) == self.dim, f'angles must be a list of length {self.dim}.'
+
+                # 3D
+                # rotate about x axis
+                self.transform.Rotate(1, 2, angle=angles[0])
+                # rotate about y axis
+                self.transform.Rotate(0, 2, angle=angles[1])
+                # rotate about z axis
+                self.transform.Rotate(0, 1, angle=angles[2])
+
+
+        elif isinstance(angles, (int, float, np.ndarray)):
+            self.transform.Rotate(0, 1, angle=angles)
+
+        # return self.t
+
+        a = 1
+
+
+
[docs]    def get_transform(self,
+                      angles: Union[List[float], Tuple[float, ...], float],
+                      *args, **kwargs):
+
+        self._get_transform(angles, *args, **kwargs)

+
+        # return t
+
+
[docs]class RandomRotation(RotationTransform, RandomAffineTransform):
+    """
+    A rotation transformation with random angles (in radian).
+    """
+    # def __init__(self,
+    #              dim: int,
+    #              used_dimensions: bool = None,
+    #              # min_angles: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray, None] = None,
+    #              # max_angles: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray, None] = None,
+    #
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #
+    #              *args, **kwargs):
+    #     """
+    #     Initializer.
+    #     :param dim: The dimension.
+    #     :param random_angles: List of random angles per dimension. Random angle is calculated uniformly within [-random_angles[i], random_angles[i]]
+    #     :param args: Arguments passed to super init.
+    #     :param kwargs: Keyword arguments passed to super init.
+    #     """
+    #
+    #
+    #     super(RandomRotation, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
+
+
+
[docs]    def get_random_transform(self,
+                             min_angles: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray, None] = None,
+                             max_angles: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray, None] = None,
+                             transformation_dict: dict = None,
+                             *args, **kwargs):
+
+
+        self._get_random_transform(min_angles, max_angles, transformation_dict=transformation_dict, *args, **kwargs)

+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/scaling_transform.html b/_modules/medipt/transforms/spatial/scaling_transform.html new file mode 100644 index 0000000..c3d8ad8 --- /dev/null +++ b/_modules/medipt/transforms/spatial/scaling_transform.html @@ -0,0 +1,256 @@ + + + + + + medipt.transforms.spatial.scaling_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.scaling_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.scaling_transform

+from typing import Union, Tuple, List
+from types import ModuleType
+import SimpleITK as sitk
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+from .random_affine_transform import RandomAffineTransform
+
+
+
[docs]class ScalingTransform(SpatialTransform):
+    """
+    Scale transformation base class.
+    """
+
+    # def __init__(self,
+    #              dim: int = 3,
+    #              used_dimensions: bool = None,
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #              rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+    #              *args, **kwargs):
+    #     """
+    #     Initializer
+    #     :param dim: The dimension.
+    #     :param used_dimensions: Boolean list of which dimension indizes to use for the transformation.
+    #     :param args: Arguments passed to super init.
+    #     :param kwargs: Keyword arguments passed to super init.
+    #     """
+    #
+    #     super(ScalingTransform, self).__init__(dim, used_dimensions, seed, legacy_random_state, rand_init, *args, **kwargs)
+
+    def _get_transform(self,
+                      scale: Union[List[float], Tuple[float, ...], float, int],
+                      *args, **kwargs):
+
+
+        if isinstance(scale, (list, tuple)):
+            assert len(scale) == self.dim, 'Length of scale must be equal to dim.'
+
+        elif isinstance(scale, (float, int)):
+            scale = [scale] * self.dim
+
+        else:
+            raise ValueError('Scale must be a list, tuple, float or int.')
+
+        t = sitk.AffineTransform(self.dim)
+        t.Scale(scale)
+
+        self.transform = t
+
+        # return t
+
+
[docs]    def get_transform(self,
+                      scale: Union[List[float], Tuple[float, ...], float, int],
+                      *args, **kwargs):
+
+        self._get_transform(scale, *args, **kwargs)

+
+
[docs]    def get_scale_transform(self, dim, scale, direction) -> sitk.AffineTransform:
+        """
+        Returns the sitk transform based on the given parameters.
+        :param dim: The dimension.
+        :param scale: List of scale factors for each dimension.
+        :return: The sitk.AffineTransform().
+        """
+        if isinstance(scale, list) or isinstance(scale, tuple):
+            assert len(scale) == dim, 'Length of scale must be equal to dim.'
+
+        s = sitk.AffineTransform(dim)
+        s.Scale(scale)
+
+        scale_matrix = np.array(s.GetMatrix()).reshape([self.dim, self.dim])
+
+        aff_matrix = np.matmul(np.array(direction).reshape([self.dim, self.dim]), scale_matrix)
+        s.SetMatrix(aff_matrix.flatten())
+        return s

+
+
+
+
+
[docs]class RandomScaling(ScalingTransform, RandomAffineTransform):
+
+    # def __init__(self,
+    #              dim: int,
+    #              used_dimensions: bool = None,
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #
+    #              *args, **kwargs):
+    #
+    #     super(RandomScaling, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
+
+
[docs]    def get_random_transform(self,
+                 min_scaling: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                 max_scaling: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                 transformation_dict: dict = None,
+                 *args, **kwargs):
+
+
+        self._get_random_transform(min_scaling, max_scaling,
+                                   transform_dict=transformation_dict,
+                                   value_offset=1.0,
+                                   *args, **kwargs)

+
+        # return self.t
+
+
+
+
+
[docs]class RandomUniformScaling(ScalingTransform, RandomAffineTransform):
+
+    # def __init__(self,
+    #              dim: int,
+    #              used_dimensions: bool = None,
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #
+    #              *args, **kwargs):
+    #
+    #     super(RandomUniformScaling, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
+
+
[docs]    def get_random_transform(
+            self,
+            min_scaling: Union[int, float, np.integer, np.floating, np.ndarray] = None,
+            max_scaling: Union[int, float, np.integer, np.floating, np.ndarray] = None,
+            transformation_dict: dict = None,
+            *args, **kwargs):
+
+
+        self._get_random_transform(min_scaling, max_scaling,
+                                   transform_dict=transformation_dict,
+                                   value_offset=1.0,
+                                   uniform=True,
+                                   *args, **kwargs)

+
+        # return self.t
+
+
+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/spatial_transform.html b/_modules/medipt/transforms/spatial/spatial_transform.html new file mode 100644 index 0000000..2a4f66c --- /dev/null +++ b/_modules/medipt/transforms/spatial/spatial_transform.html @@ -0,0 +1,379 @@ + + + + + + medipt.transforms.spatial.spatial_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.spatial_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.spatial_transform

+from abc import ABC, abstractmethod
+import numpy as np
+import SimpleITK as sitk
+from typing import Union, Tuple, List
+from types import ModuleType
+from ...utils import image_index_to_phys
+from ...utils.random_float import initialize_rand_state
+from ...utils.image_input_output_space import resolve_input_output_space
+
+
[docs]class SpatialTransform(ABC):
+    """
+    A generic spatial transform that can be applied to 2D and 3D images.
+    """
+    def __init__(self,
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+                 *args, **kwargs):
+        """
+        Initializer.
+        :param dim: The dimension of the transformation.
+        :param args: Arguments passed to super init.
+        :param kwargs: Keyword arguments passed to super init.
+        """
+
+
+        self.used_dimensions = used_dimensions or [True] * dim
+        self.dim = dim
+
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        self.transform = None
+
+        self.inverse_transform = None
+
+        self.displacement_field = None
+        self.inverted_displacement_field = None
+        self.inverted_transform_from_displacement = None
+
+        if rand_init is None:
+            self.rand_init = initialize_rand_state(self.seed, self.legacy_random_state)
+        else:
+            self.rand_init = rand_init
+
+        assert len(self.used_dimensions) == dim, 'Length of used_dimensions must be equal to dim.'
+
+
+        self.input_size = None
+        self.input_space = None
+        self.input_origin = None
+        self.input_direction = None
+
+        self.image_params_gathered = False
+
+
+        self.output_size = None
+        self.output_spacing = None
+        self.output_origin = None
+        self.output_direction = None
+
+
+    @abstractmethod
+    def _get_transform(self,
+                      transform_params: Union[List[float], Tuple[float, ...], float],
+                      *args, **kwargs):
+
+        raise NotImplementedError('This method must be implemented in a child class.')
+
+
+
+
[docs]    def get_inverse_transform(self, *args, **kwargs):
+
+
+        if self.transform is not None:
+            self.inverse_transform = self.transform.GetInverse()
+
+        else:
+            raise ValueError('No transform found. Call get_transform first.')

+
+        # return self.inverse_transform
+
+
[docs]    def get_displacement_field(self, *args, **kwargs):
+        raise NotImplementedError('This method must be implemented in a child class.')

+
+
[docs]    def get_inverted_displacement_field(self, *args, **kwargs):
+        raise NotImplementedError('This method must be implemented in a child class.')

+
+
[docs]    def get_inverted_transform_from_displacement(self, *args, **kwargs):
+        self.get_inverse_transform(*args, **kwargs)

+
+
[docs]    def get_image_params(
+            self,
+            image: sitk.Image = None,
+            image_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+            image_direction: Union[List[Union[int, float]], Tuple[Union[int, float]]] = None,
+            *args, **kwargs) -> Tuple[
+        Union[List[Union[int, float]], Tuple[Union[int, float]], np.ndarray],
+        Union[List[Union[int, float]], Tuple[Union[int, float]], np.ndarray],
+        Union[List[Union[int, float]], Tuple[Union[int, float]], np.ndarray],
+        Union[List[Union[int, float]], Tuple[Union[int, float]], np.ndarray]]:
+
+
+        if image is not None:
+            image_size = image.GetSize()
+            image_spacing = image.GetSpacing()
+            image_origin = image.GetOrigin()
+            image_direction = image.GetDirection()
+
+        else:
+            image_size = kwargs.get('image_size', image_size)
+            image_spacing = kwargs.get('image_spacing', image_spacing)
+            image_origin = kwargs.get('image_origin', image_origin)
+            image_direction = kwargs.get('image_direction', image_direction)
+
+
+
+        if image_spacing is None:
+            image_spacing = [1.0] * self.dim
+
+        if image_origin is None:
+            image_origin = np.zeros(self.dim)
+
+        if image_direction is None:
+            image_direction = np.eye(self.dim).flatten(order='F').tolist()
+
+
+        return image_size, image_spacing, image_origin, image_direction

+
+
[docs]    def get_input_output_space(self,
+                               # input_image: sitk.Image = None,
+                               # input_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # output_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # input_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               #
+                               #
+                               # output_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # input_direction: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # output_direction: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # input_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               # output_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                               dict: dict = None,
+                               *args, **kwargs):
+
+        if dict is None:
+            dict = resolve_input_output_space(*args, **kwargs)
+
+        elif len(dict) == 0:
+            dict = resolve_input_output_space(*args, **kwargs)
+
+
+        input_size = dict['input_size']
+        input_spacing = dict['input_spacing']
+        input_origin = dict['input_origin']
+        input_direction = dict['input_direction']
+
+        output_size = dict['output_size']
+        output_spacing = dict['output_spacing']
+        output_origin = dict['output_origin']
+        output_direction = dict['output_direction']
+
+
+        self.input_size = input_size
+        self.input_spacing = input_spacing
+        self.input_origin = input_origin
+        self.input_direction = input_direction
+
+        self.output_size = output_size
+        self.output_spacing = output_spacing
+        self.output_origin = output_origin
+        self.output_direction = output_direction
+
+        self.image_params_gathered = True

+
+
+
[docs]    def get_input_origin(self,
+                         *args, **kwargs) -> List[float]:
+
+        if self.image_params_gathered is False:
+            self.get_input_output_space(*args, **kwargs)
+
+        input_origin_index = (0.0,) * self.dim
+
+
+        input_origin_phys = image_index_to_phys(dim=self.dim,
+                                                index_coords=input_origin_index,
+                                                spacing=self.input_spacing,
+                                                direction=self.input_direction,
+                                                origin=self.input_origin)
+
+        return input_origin_phys

+
+
[docs]    def get_output_origin(self, *args, **kwargs) -> List[float]:
+
+        if self.image_params_gathered is False:
+            self.get_input_output_space(*args, **kwargs)
+
+        output_origin_index = (0.0,) * self.dim
+
+        output_origin_phys = image_index_to_phys(dim=self.dim,
+                                                 index_coords=output_origin_index,
+                                                 spacing=self.output_spacing,
+                                                 direction=self.output_direction,
+                                                 origin=self.output_origin)
+
+        return output_origin_phys

+
+
[docs]    def get_input_center(self, *args, **kwargs) -> List[float]:
+        if self.image_params_gathered is False:
+            self.get_input_output_space(*args, **kwargs)
+
+
+        input_center_index = tuple([(i - 1) / 2 for i in self.input_size])
+
+        input_center_phys = image_index_to_phys(dim=self.dim,
+                                                index_coords=input_center_index,
+                                                spacing=self.input_spacing,
+                                                direction=self.input_direction,
+                                                origin=self.input_origin)
+
+        return input_center_phys

+
+
+
[docs]    def get_random_point(self,
+                         *args, **kwargs) -> List[float]:
+
+        if self.image_params_gathered is False:
+            self.get_input_output_space(*args, **kwargs)
+
+        input_center_index = tuple([(i - 1) / 2 for i in self.input_size])
+
+        input_center_index_min = np.array(input_center_index) - (np.array(input_center_index) // 4)
+        input_center_index_max = np.array(input_center_index) + (np.array(input_center_index) // 4)
+
+        input_index = self.rand_init.randint(low=input_center_index_min, high=input_center_index_max)
+
+        input_index = (int(input_index[0]), int(input_index[1]), int(input_index[2]))
+
+        input_center_phys = image_index_to_phys(dim=self.dim,
+                                                index_coords=input_index,
+                                                spacing=self.input_spacing,
+                                                direction=self.input_direction,
+                                                origin=self.input_origin)
+
+        return input_center_phys

+
+
+
+
[docs]    def get_output_center(self, *args, **kwargs) -> List[float]:
+        if self.image_params_gathered is False:
+            self.get_input_output_space(*args, **kwargs)
+
+        output_center_index = tuple([(i - 1) / 2 for i in self.output_size])
+
+        output_center_phys = image_index_to_phys(dim=self.dim,
+                                                 index_coords=output_center_index,
+                                                 spacing=self.output_spacing,
+                                                 direction=self.output_direction,
+                                                 origin=self.output_origin)
+
+        return output_center_phys

+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/spatial_transform_init.html b/_modules/medipt/transforms/spatial/spatial_transform_init.html new file mode 100644 index 0000000..b5e6ec9 --- /dev/null +++ b/_modules/medipt/transforms/spatial/spatial_transform_init.html @@ -0,0 +1,429 @@ + + + + + + medipt.transforms.spatial.spatial_transform_init — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.spatial_transform_init
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.spatial_transform_init

+from abc import ABC, abstractmethod
+import numpy as np
+import SimpleITK as sitk
+from typing import Union, Tuple, List
+from types import ModuleType
+from ...utils.random_float import initialize_rand_state
+
+from .composite_transform import CompositeTransform
+from .rotation_transform import RotationTransform, RandomRotation
+from .scaling_transform import ScalingTransform, RandomScaling, RandomUniformScaling
+from .flipping_transform import FlippingTransform, RandomFlipping
+
+from .elastic_deformation_transform import (
+    ElasticDeformation,
+    RandomElasticDeformation,
+    ElasticDeformationInputImage,
+    RandomElasticDeformationTransformInputImage,
+    RandomElasticDeformationTransformOutputImage)
+
+from .translation_transform import (
+    TranslationTransform,
+    RandomTranslation,
+    RandomCoordTranslation,
+    RandomBBoxTranslation,
+    TranslateInputCenterToOutputCenter,
+    TranslateInputOriginToOutputCenter,
+    TranslateInputCenterToInputOrigin,
+    TranslateRandomInputCenterToInputOrigin)
+
+    # TranslateInputOriginToInputCenter, TranslateInputOriginToOutputOrigin, \
+    # TranslateInputCenterToOutputOrigin, \
+
+
+
+
+
[docs]class SpatialTransformInit:
+    def __init__(self,
+                 dim: int = 3,
+                 used_dimensions: bool = None,
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None,
+                 *args, **kwargs
+                 ):
+
+        self.used_dimensions = used_dimensions or [True] * dim
+        self.dim = dim
+        self.seed = seed
+        self.legacy_random_state = legacy_random_state
+
+        if rand_init is None:
+            self.rand_init = initialize_rand_state(self.seed, self.legacy_random_state)
+        else:
+            self.rand_init = rand_init
+
+    # Rotation Transforms
+
+
[docs]    def rotation_transform(self,
+                           *args, **kwargs):
+        return RotationTransform(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_rotation_transform(self,
+                                  *args, **kwargs):
+
+        return RandomRotation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+    # Scaling Transforms
+
+
[docs]    def scaling_transform(self,
+                            *args, **kwargs):
+        return ScalingTransform(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_scaling_transform(self,
+                                 *args, **kwargs):
+        return RandomScaling(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_uniform_scaling_transform(self,
+                                         *args, **kwargs):
+        return RandomUniformScaling(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+    # Flipping Transforms
+
+
[docs]    def flipping_transform(self,
+                           *args, **kwargs):
+        return FlippingTransform(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_flipping_transform(self,
+                                  *args, **kwargs):
+        return RandomFlipping(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
+
+
+    # Translation Transforms
+
+
[docs]    def translation_transform(self,
+                              *args, **kwargs):
+        return TranslationTransform(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_translation_transform(self,
+                                     *args, **kwargs):
+        return RandomTranslation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
[docs]    def random_coord_translation(self,
+                                 *args, **kwargs):
+        return RandomCoordTranslation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
[docs]    def random_bbox_translation(self,
+                                *args, **kwargs):
+        return RandomBBoxTranslation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
[docs]    def translate_input_center_to_input_origin(self,
+                                               *args, **kwargs):
+        return TranslateInputCenterToInputOrigin(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
[docs]    def translate_random_input_center_to_input_origin(self,
+                                               *args, **kwargs):
+        return TranslateRandomInputCenterToInputOrigin(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
[docs]    def translate_input_origin_to_output_center(self,
+                                                *args, **kwargs):
+        return TranslateInputOriginToOutputCenter(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+
+
[docs]    def translate_input_center_to_output_center(self,
+                                                *args, **kwargs):
+        return TranslateInputCenterToOutputCenter(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
+    # def translate_input_origin_to_input_center(self,
+    #                                            *args, **kwargs):
+    #     return TranslateInputOriginToInputCenter(
+    #         dim=self.dim,
+    #         used_dimensions=self.used_dimensions,
+    #         seed=self.seed,
+    #         legacy_random_state=self.legacy_random_state,
+    #         *args, **kwargs)
+    #
+    # def translate_input_origin_to_output_origin(self,
+    #                                             *args, **kwargs):
+    #     return TranslateInputOriginToOutputOrigin(
+    #         dim=self.dim,
+    #         used_dimensions=self.used_dimensions,
+    #         seed=self.seed,
+    #         legacy_random_state=self.legacy_random_state,
+    #         *args, **kwargs)
+    #
+    # def translate_input_center_to_output_origin(self,
+    #                                             *args, **kwargs):
+    #     return TranslateInputCenterToOutputOrigin(
+    #         dim=self.dim,
+    #         used_dimensions=self.used_dimensions,
+    #         seed=self.seed,
+    #         legacy_random_state=self.legacy_random_state,
+    #         *args, **kwargs)
+    #
+    #
+    # def transate_input_origin_to_output_center(self,
+    #                                            *args, **kwargs):
+    #     return TranslateInputOriginToOutputCenter(
+    #         dim=self.dim,
+    #         used_dimensions=self.used_dimensions,
+    #         seed=self.seed,
+    #         legacy_random_state=self.legacy_random_state,
+    #         *args, **kwargs)
+
+
+
+    # Elastic Deformation Transforms
+
+
[docs]    def elastic_deformation_transform(self,
+                                      *args, **kwargs):
+        return ElasticDeformation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_elastic_deformation_transform(self,
+                                             *args, **kwargs):
+        return RandomElasticDeformation(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def elastic_deformation_input_image(self,
+                                        *args, **kwargs):
+        return ElasticDeformationInputImage(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_elastic_deformation_input_image(self,
+                                               *args, **kwargs):
+        return RandomElasticDeformationTransformInputImage(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+
[docs]    def random_elastic_deformation_output_image(self,
+                                                *args, **kwargs):
+        return RandomElasticDeformationTransformOutputImage(
+            dim=self.dim,
+            used_dimensions=self.used_dimensions,
+            seed=self.seed,
+            legacy_random_state=self.legacy_random_state,
+            rand_init=self.rand_init,
+            *args, **kwargs)

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/spatial/translation_transform.html b/_modules/medipt/transforms/spatial/translation_transform.html new file mode 100644 index 0000000..0445ea1 --- /dev/null +++ b/_modules/medipt/transforms/spatial/translation_transform.html @@ -0,0 +1,615 @@ + + + + + + medipt.transforms.spatial.translation_transform — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.spatial.translation_transform
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.spatial.translation_transform

+from typing import Union, Tuple, List
+from ...utils import random_uniform_float
+from ...utils import physical_image_size_no_origin, physical_image_size
+import SimpleITK as sitk
+import numpy as np
+
+from .spatial_transform import SpatialTransform
+from .random_affine_transform import RandomAffineTransform
+
+
+
+
+
[docs]class TranslationTransform(SpatialTransform):
+    """
+    Translation transformation base class.
+    """
+    # def __init__(self,
+    #              dim: int = 3,
+    #              used_dimensions: bool = None,
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #              *args, **kwargs):
+    #     """
+    #     Initializer
+    #     :param dim: The dimension.
+    #     :param used_dimensions: Boolean list of which dimension indizes to use for the transformation.
+    #     :param args: Arguments passed to super init.
+    #     :param kwargs: Keyword arguments passed to super init.
+    #     """
+    #
+    #     super(TranslationTransform, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+    #
+    #     assert len(self.used_dimensions) == dim, 'Length of used_dimensions must be equal to dim.'
+
+    def _get_transform(self,
+                      translation: Union[List[float], Tuple[float, ...], float],
+                      *args, **kwargs):
+        """
+        Returns the sitk transform based on the given parameters.
+        :param dim: The dimension.
+        :param offset: List of offsets for each dimension.
+        :return: The sitk.AffineTransform().
+        """
+
+        assert len(translation) == self.dim, 'Length of offset must be equal to dim.'
+
+        t = sitk.AffineTransform(self.dim)
+        t.Translate(translation)
+
+        self.transform = t
+        # return t
+
+
[docs]    def get_transform(self,
+                      translation: Union[List[float], Tuple[float, ...], float],
+                      *args, **kwargs):
+
+        self._get_transform(translation, *args, **kwargs)

+
+
+
[docs]class RandomTranslation(RandomAffineTransform, TranslationTransform):
+    """
+    A translation transformation with a random offset.
+    """
+    # def __init__(self,
+    #              # min_trans: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+    #              # max_trans: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+    #              dim: Union[int, None] = 3,
+    #              used_dimensions: bool = None,
+    #
+    #              seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+    #              legacy_random_state: bool = True,
+    #              *args, **kwargs):
+    #     """
+    #     Initializer.
+    #     :param dim: The dimension.
+    #     :param random_offset: List of random offsets per dimension. Random offset is calculated uniformly within [-random_offset[i], random_offset[i]]
+    #     :param args: Arguments passed to super init.
+    #     :param kwargs: Keyword arguments passed to super init.
+    #     """
+    #
+    #     super(RandomTranslation, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
[docs]    def get_random_transform(self,
+                             min_trans: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                             max_trans: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                             transformation_dict=None,
+                             *args, **kwargs):
+
+
+        self._get_random_transform(min_trans, max_trans, *args, **kwargs)

+        # return self.transform
+
+
+
[docs]class RandomCoordTranslation(RandomAffineTransform, TranslationTransform):
+    def __init__(self,
+                 dim: Union[int, None] = 3,
+                 used_dimensions: bool = None,
+
+                 seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+                 legacy_random_state: bool = True,
+                 *args, **kwargs):
+        """
+        Initializer.
+        :param dim: The dimension.
+        :param random_offset: List of random offsets per dimension. Random offset is calculated uniformly within [-random_offset[i], random_offset[i]]
+        :param args: Arguments passed to super init.
+        :param kwargs: Keyword arguments passed to super init.
+        """
+
+        super(RandomCoordTranslation, self).__init__(dim, used_dimensions, seed, legacy_random_state, *args, **kwargs)
+
+
+
[docs]    def get_random_transform(self,
+                             coord: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                             translation_extent: Union[Union[List[Union[int, float]], Tuple[Union[int, float], ...]], int, float, np.integer, np.floating, np.ndarray],
+                             transformation_dict: dict = None,
+                             *args, **kwargs):
+
+
+
+        if isinstance(translation_extent, (int, float, np.floating, np.integer)):
+            translation_extent = np.ndarray([translation_extent] * self.dim)
+        elif isinstance(translation_extent, (tuple, list)):
+            translation_extent = np.array(translation_extent)
+        elif isinstance(translation_extent, np.ndarray):
+            if len(translation_extent) < self.dim:
+                translation_extent = np.array([translation_extent] * self.dim)
+
+
+        if isinstance(coord, (int, float, np.floating, np.integer)):
+            coord = np.ndarray([coord] * self.dim)
+        elif isinstance(coord, (tuple, list)):
+            coord = np.array(coord)
+
+        assert len(coord) == len(translation_extent), "coord and translation_extent must have the same length."
+
+
+        coord_min = coord - translation_extent
+        coord_max = coord + translation_extent
+
+        self._get_random_transform(min_range=coord_min, max_range=coord_max, transformation_dict=transformation_dict,
+                                   offset=coord, *args, **kwargs)

+
+
+
[docs]class TranslateInputCenterToInputOrigin(TranslationTransform):
+
[docs]    def get_transform(self,
+                      *args, **kwargs):
+
+        input_center_phys = self.get_input_center(*args, **kwargs)
+        input_origin_phys = self.get_input_origin(*args, **kwargs)
+
+        # translation = tuple([input_center_phys[i] - input_origin_phys[i] for i in range(self.dim)])
+        translation = tuple([input_center_phys[i] for i in range(self.dim)])
+
+        self._get_transform(translation)

+
+        # return self.t
+
+
+
+
[docs]class TranslateRandomInputCenterToInputOrigin(TranslationTransform):
+
[docs]    def get_transform(self,
+                      *args, **kwargs):
+
+        input_center_phys = self.get_random_point(*args, **kwargs)
+        input_origin_phys = self.get_input_origin(*args, **kwargs)
+
+        # translation = tuple([input_center_phys[i] - input_origin_phys[i] for i in range(self.dim)])
+        translation = tuple([input_center_phys[i] for i in range(self.dim)])
+
+        self._get_transform(translation)

+
+        # return self.t
+
+
+
+
[docs]class TranslateInputOriginToOutputCenter(TranslationTransform):
+
[docs]    def get_transform(self,
+                      *args, **kwargs):
+
+        input_origin_phys = self.get_input_origin(*args, **kwargs)
+        output_center_phys = self.get_output_center(*args, **kwargs)
+
+        # translation = tuple([input_origin_phys[i] - output_center_phys[i] for i in range(self.dim)])
+        translation = tuple([-output_center_phys[i] for i in range(self.dim)])
+
+        self._get_transform(translation)

+
+
+class TranslateInputCenterToOutputCenter(TranslationTransform):
+    def get_transform(self,
+                      *args, **kwargs):
+
+        input_center_phys = self.get_input_center(*args, **kwargs)
+        output_center_phys = self.get_output_center(*args, **kwargs)
+
+
+        translation = tuple([input_center_phys[i] - output_center_phys[i] for i in range(self.dim)])
+
+        self._get_transform(translation)
+
+        # return self.t
+
+
+
[docs]class TranslateInputCenterToOutputCenter(TranslationTransform):
+
[docs]    def get_transform(self,
+                      *args, **kwargs):
+
+        input_center_phys = self.get_input_center(*args, **kwargs)
+        output_center_phys = self.get_output_center(*args, **kwargs)
+
+
+        translation = tuple([input_center_phys[i] - output_center_phys[i] for i in range(self.dim)])
+
+        self._get_transform(translation)

+
+        # return self.t
+
+
+
+#
+# class TranslateInputOriginToInputCenter(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_origin_phys = self.get_input_origin(*args, **kwargs)
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#
+#         translation = tuple([input_origin_phys[i] - input_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+#
+#
+# class TranslateInputOriginToOutputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_origin_phys = self.get_input_origin(*args, **kwargs)
+#         output_origin_phys = self.get_output_origin(*args, **kwargs)
+#
+#
+#         translation = tuple([input_origin_phys[i] - output_origin_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+#
+#
+#
+#
+#
+#
+# class TranslateInputCenterToOutputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#         output_origin_phys = self.get_input_origin(*args, **kwargs)
+#
+#
+#         translation = tuple([input_center_phys[i] - output_origin_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# class TranslateInputCenterToInputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_origin_phys = self.get_input_origin(*args, **kwargs)
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#
+#
+#         translation = tuple([input_center_phys[i] - input_origin_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+#
+#
+# class TranslateInputOriginToOutputCenter(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#         input_origin_phys = self.get_input_origin(*args, **kwargs)
+#
+#
+#         translation = tuple([input_origin_phys[i] - output_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+# class TranslateOutputOriginToOutputCenter(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#         output_origin_phys = self.get_output_origin(*args, **kwargs)
+#
+#         translation = tuple([output_origin_phys[i] - output_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+# class TranslateInputCenterToOutputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#         # output_origin_phys = self.get_output_origin(*args, **kwargs)
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#
+#         translation = tuple([-input_center_phys[i] + output_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+# class TranslateInputOriginToOutputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_origin_phys = self.get_input_origin(*args, **kwargs)
+#         # output_origin_phys = self.get_output_origin(*args, **kwargs)
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#
+#         translation = tuple([input_origin_phys[i] - output_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+#
+#
+#
+# class TranslateOutputCenterToOutputOrigin(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#         output_origin_phys = self.get_output_origin(*args, **kwargs)
+#
+#         translation = tuple([output_center_phys[i] - output_origin_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+# class TranslateInputCenterToOutputCenter(TranslationTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#
+#         translation = tuple([input_center_phys[i] - output_center_phys[i] for i in range(self.dim)])
+#
+#         self._get_transform(translation)
+#
+#         # return self.t
+
+
+
+
+# class TranslateInputCenterToOutputCenter(TranslateTransform):
+#     def get_transform(self,
+#                       *args, **kwargs):
+#
+#         input_center_phys = self.get_input_center(*args, **kwargs)
+#         output_center_phys = self.get_output_center(*args, **kwargs)
+#
+#         # additional = [70, 70, 0]
+#         additional = [0, 0, 0]
+#         translation = tuple([input_center_phys[i] - output_center_phys[i] + additional[i] for i in range(self.dim)])
+#
+#
+#
+#
+#         # translation = self.get_output_center(*args, **kwargs) self.get_input_origin(*args, **kwargs)
+#         #
+#         # translation = [-1 * t for t in translation]
+#
+#         self._get_transform(translation)
+
+
+
[docs]class RandomBBoxTranslation(RandomAffineTransform, TranslationTransform):
+
[docs]    def get_random_transform(self,
+                             transformation_dict: dict = None,
+                             *args, **kwargs):
+
+        bbox_center_start = kwargs.get('centroids_bb_start', None)
+        bbox_center_end = kwargs.get('centroids_bb_end', None)
+
+
+
+        if (bbox_center_start is None) and (bbox_center_end is None):
+            raise ValueError('Centroid bounding box start and end must be provided when using '
+                             '"RandomBBoxTranslation".')
+
+        bbox_extent = bbox_center_end - bbox_center_start
+
+        output_size = kwargs.get('output_size', None)
+        output_spacing = kwargs.get('output_spacing', None)
+        output_direction = kwargs.get('output_direction', None)
+        dim = kwargs.get('dim', None)
+
+        output_image_size_phys = physical_image_size_no_origin(dim=dim,
+                                                               size=output_size,
+                                                               spacing=output_spacing,
+                                                               direction=output_direction)
+
+        try:
+            extra_space = bbox_extent - np.array(output_image_size_phys)
+        except:
+            a = 1
+
+        # Only translate if the bbox is larger than the output image size
+        extra_space[extra_space < 0] = 0
+
+        max_trans = extra_space / 2
+        min_trans = extra_space / 2 * -1
+
+
+        self._get_random_transform(min_trans, max_trans, transformation_dict=transformation_dict,
+                                   *args, **kwargs)

+
+
+
+
+
+
+
+
+
+
+
+
[docs]class RandomFactorInput(TranslationTransform):
+    """
+    A translation transform that translates the input image by a random factor, such that it will be cropped.
+    The input center should usually be at the origin before this transformation.
+    The actual translation value per dimension will be calculated as follows:
+    (input_size[i] * input_spacing[i] - self.remove_border[i]) * float_uniform(-self.random_factor[i], self.random_factor[i]) for each dimension.
+    """
+
[docs]    def get_transform(self,
+                      random_factor,
+                      remove_border=None,
+                      *args, **kwargs):
+        """
+        Returns the sitk transform based on the given parameters.
+        :param kwargs: Must contain either 'image', or 'input_size' and 'input_spacing', which define the input image physical space.
+        :return: The sitk.AffineTransform().
+        """
+
+        # TODO check to see if this works for arbitrary input directions and origins
+
+
+        remove_border = remove_border or [0.0] * self.dim
+
+        self.get_input_output_space(*args, **kwargs)
+
+        # assert np.allclose(self.input_direction, np.eye(self.dim).flatten()), 'this transformation only works for eye direction, is: ' + self.input_direction
+        # assert np.allclose(self.input_origin, np.zeros(self.dim)), 'this transformation only works for zeros origin, is: ' + self.input_origin
+
+
+        phys_input_size = physical_image_size_no_origin(dim=self.dim,
+                                                        size=self.input_size,
+                                                        spacing=self.input_spacing,
+                                                        direction=self.input_direction)
+
+        phys_input_size = np.array(phys_input_size) - np.array(remove_border)
+
+        random_factor = np.array(random_factor)
+
+        random_scaling = np.array(random_uniform_float(low_value=-random_factor, high_value=random_factor,
+                                                       output_size=self.dim,
+                                                       seed=self.seed, legacy_random_state=self.legacy_random_state))
+
+        translation = list(phys_input_size * random_scaling)
+
+        self.transform = self._get_transform(translation)

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/transforms/transform_coordinate.html b/_modules/medipt/transforms/transform_coordinate.html new file mode 100644 index 0000000..1aebe63 --- /dev/null +++ b/_modules/medipt/transforms/transform_coordinate.html @@ -0,0 +1,131 @@ + + + + + + medipt.transforms.transform_coordinate — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • medipt.transforms.transform_coordinate
    • +
  • +
    • +
+
+
+
+
+ +

Source code for medipt.transforms.transform_coordinate

+from typing import Union, Tuple, List
+import SimpleITK as sitk
+import numpy as np
+
+
[docs]class TransformCoordinate:
+
+    def __init__(self,
+                 *args, **kwargs):
+
+        pass
+
+
+
[docs]    def transform_coordinate(self,
+                             phys_coordinate: Union[List[Union[float, int]], Tuple[Union[float, int], ...], float],
+                             transform: sitk.Transform,
+                             *args, **kwargs) -> Union[List[Union[float, int]], Tuple[Union[float, int], ...], float]:
+
+        transform_coord = transform.TransformPoint(phys_coordinate)
+
+        return transform_coord

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/utils.html b/_modules/medipt/utils.html new file mode 100644 index 0000000..78dfbf4 --- /dev/null +++ b/_modules/medipt/utils.html @@ -0,0 +1,118 @@ + + + + + + medipt.utils — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for medipt.utils

+from .random_float import random_uniform_float, random_binomial
+from .spatial_utils import *
+import SimpleITK as sitk
+
+
+
[docs]def set_num_workers(n_threads):
+    sitk.ProcessObject.SetGlobalDefaultNumberOfThreads(n_threads)

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/utils/image_input_output_space.html b/_modules/medipt/utils/image_input_output_space.html new file mode 100644 index 0000000..1cee151 --- /dev/null +++ b/_modules/medipt/utils/image_input_output_space.html @@ -0,0 +1,236 @@ + + + + + + medipt.utils.image_input_output_space — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for medipt.utils.image_input_output_space

+import SimpleITK as sitk
+import numpy as np
+from typing import Union, Tuple, List
+
+from .spatial_utils import get_output_size_from_spacing, get_output_spacing_from_size
+
+
+
+
[docs]def resolve_input_output_space(
+        dim: int = 3,
+        input_image: sitk.Image = None,
+        reference_image: sitk.Image = None,
+        input_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        output_size: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        input_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+
+        output_spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        input_direction: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        output_direction: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        input_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        output_origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+        *args, **kwargs):
+
+
+    if input_image is not None:
+        input_size = input_image.GetSize()
+        input_spacing = input_image.GetSpacing()
+        input_origin = input_image.GetOrigin()
+        input_direction = input_image.GetDirection()
+
+    else:
+        input_size = kwargs.get('input_size', input_size)
+        input_spacing = kwargs.get('input_spacing', input_spacing)
+        input_origin = kwargs.get('input_origin', input_origin)
+        input_direction = kwargs.get('input_direction', input_direction)
+
+        if input_spacing is None:
+            input_spacing = [1.0] * dim
+
+        if input_origin is None:
+            input_origin = [0] * dim
+
+        if input_direction is None:
+            input_direction = np.eye(dim).flatten().tolist()
+
+    if reference_image is not None:
+        output_size = reference_image.GetSize()
+        output_direction = reference_image.GetDirection()
+        output_origin = reference_image.GetOrigin()
+        output_spacing = reference_image.GetSpacing()
+    else:
+        output_size = kwargs.get('output_size', output_size)
+        output_spacing = kwargs.get('output_spacing', output_spacing)
+        output_origin = kwargs.get('output_origin', output_origin)
+        output_direction = kwargs.get('output_direction', output_direction)
+
+
+    if (output_size is None) and (output_spacing is None) and (input_image is None):
+        raise ValueError('One of output_size, output_spacing or input_image must be provided.')
+
+    if output_direction is None:
+        output_direction = input_direction
+
+    if output_origin is None:
+        output_origin = input_origin
+
+    if (output_size is None) and (output_spacing is not None):
+        output_size = get_output_size_from_spacing(dim=dim,
+                                                   input_size=input_size,
+                                                   input_spacing=input_spacing,
+                                                   input_direction=input_direction,
+                                                   output_spacing=output_spacing,
+                                                   output_direction=output_direction,
+                                                   *args, **kwargs)
+
+        output_size = [int(np.round(x)) for x in output_size]
+
+    elif (output_size is not None) and (output_spacing is None):
+        output_spacing = get_output_spacing_from_size(dim=dim,
+                                                      input_size=input_size,
+                                                      input_spacing=input_spacing,
+                                                      input_direction=input_direction,
+                                                      output_size=output_size,
+                                                      output_direction=output_direction,
+                                                      *args, **kwargs)
+
+
+
+    elif (output_spacing is None) and (output_size is None):
+        output_spacing = input_spacing
+        output_size = input_size
+
+    kwargs['input_image'] = input_image
+    kwargs['input_size'] = input_size
+    kwargs['input_spacing'] = input_spacing
+    kwargs['input_origin'] = input_origin
+    kwargs['input_direction'] = input_direction
+
+    kwargs['output_size'] = output_size
+    kwargs['output_spacing'] = output_spacing
+    kwargs['output_origin'] = output_origin
+    kwargs['output_direction'] = output_direction
+
+    return kwargs

+
+
+
+if __name__ == '__main__':
+
+
+    dim = 3
+
+    input_image = sitk.ReadImage('../../testing_files/sub-verse260.nii.gz')
+
+    output_size = [256, 256, 256]
+    output_spacing = [3.0, 5.0, 2.0]
+
+    hold = resolve_input_output_space(dim=dim,
+                                      input_image=input_image,
+                                      output_size=output_size,
+                                      # output_spacing=output_spacing,                               )
+                                      )
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/utils/random_float.html b/_modules/medipt/utils/random_float.html new file mode 100644 index 0000000..e6ae7ae --- /dev/null +++ b/_modules/medipt/utils/random_float.html @@ -0,0 +1,406 @@ + + + + + + medipt.utils.random_float — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for medipt.utils.random_float

+import numpy as np
+import random
+from typing import Union, Tuple, List, Any, Callable, Optional
+from types import ModuleType
+
+
+
[docs]def initialize_rand_state(
+        seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+        legacy_random_state: bool = True, ) -> Union[ModuleType, np.random.Generator, np.random.BitGenerator]:
+    if seed is not None:
+        if isinstance(seed, int):
+            if legacy_random_state:
+                np.random.seed(seed)
+                random.seed(seed)
+
+            else:
+                ran_gen = np.random.default_rng(seed)
+                random.seed(seed)
+
+        elif isinstance(seed, np.random.RandomState):
+            np.random.set_state(np.random.get_state())
+            random.setstate(random.getstate())
+
+        elif isinstance(seed, (np.random.Generator, np.random.BitGenerator)):
+            ran_gen = np.random.default_rng(seed)
+
+    else:
+        if legacy_random_state:
+            np.random.set_state(np.random.get_state())
+            random.setstate(random.getstate())
+
+        else:
+            ran_gen = np.random.default_rng()
+
+    if legacy_random_state:
+        rand_init = np.random
+
+    else:
+        rand_init = ran_gen
+
+    return rand_init

+
+
+
[docs]def random_binomial(
+        n: Union[int, float, np.integer, np.floating],
+        p: Union[int, float, np.integer, np.floating],
+        output_size: Union[List[Union[int, float]], Tuple[Union[int, float]], int, float, np.integer, np.floating] = None,
+
+        ignore_axis: Union[None, List[int], Tuple[int], int] = None,
+
+        seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+
+        legacy_random_state: bool = True,
+
+        rand_init:  Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None) -> Union[np.ndarray, int]:
+    '''
+
+    :param n:
+    :param p:
+    :param output_size:
+    :param ignore_axis: Ignores the axis specified. If None, then no axis is ignored. Works only for 1D right now.
+    :param seed:
+    :param legacy_random_state:
+    :return:
+    '''
+
+    if rand_init is None:
+        rand_init = initialize_rand_state(seed=seed, legacy_random_state=legacy_random_state)
+
+    binomial_out = rand_init.binomial(n=n, p=p, size=output_size)
+
+    if (output_size is not None) and (ignore_axis is not None):
+        if isinstance(ignore_axis, int):
+            binomial_out[ignore_axis] = 1
+
+        elif isinstance(ignore_axis, (list, tuple)):
+            for idx in ignore_axis:
+                binomial_out[idx] = 1
+
+        else:
+            raise ValueError('ignore_axis must be an int, list, or tuple.')
+
+    return binomial_out

+
+
+
[docs]def random_uniform_float(
+        low_value: Union[float, int, List[Union[int, float]], Tuple[Union[int, float], ...], np.integer, np.floating, np.ndarray],
+        high_value: Union[float, int, List[Union[int, float]], Tuple[Union[int, float], ...], np.integer, np.floating, np.ndarray],
+        output_size: Union[float, int, List[Union[int, float]], Tuple[Union[int, float], ...], np.integer, np.floating, np.ndarray, None] = None,
+
+        dim: Union[int, None] = None,
+
+        seed: Union[np.random.RandomState, np.random.Generator, np.random.BitGenerator, int, None] = None,
+
+        legacy_random_state: bool = True,
+
+        rand_init: Union[ModuleType, np.random.Generator, np.random.BitGenerator] = None
+
+
+) -> Union[float, List[float], np.ndarray]:
+
+    if rand_init is None:
+        rand_init = initialize_rand_state(seed=seed, legacy_random_state=legacy_random_state)
+
+    # if seed is not None:
+    #     if isinstance(seed, int):
+    #         if legacy_random_state:
+    #             np.random.seed(seed)
+    #             random.seed(seed)
+    #
+    #         else:
+    #             ran_gen = np.random.default_rng(seed)
+    #             random.seed(seed)
+    #
+    #     elif isinstance(seed, np.random.RandomState):
+    #         np.random.set_state(np.random.get_state())
+    #         random.setstate(random.getstate())
+    #
+    #     elif isinstance(seed, (np.random.Generator, np.random.BitGenerator)):
+    #         ran_gen = np.random.default_rng(seed)
+    #
+    # else:
+    #     if legacy_random_state:
+    #         np.random.set_state(np.random.get_state())
+    #         random.setstate(random.getstate())
+    #
+    #     else:
+    #         ran_gen = np.random.default_rng()
+    #
+    #
+    # if legacy_random_state:
+    #     rand_init = np.random
+    #
+    # else:
+    #     rand_init = ran_gen
+
+    if (output_size is not None) and (dim is not None):
+        if isinstance(output_size, (int, float, np.integer, np.floating)):
+
+            output_size = (output_size, dim)
+
+        elif isinstance(output_size, (list, tuple, np.ndarray)):
+            output_size = *output_size, dim
+
+        else:
+            raise ValueError('output_size must be a number, list, tuple, or numpy array.')
+
+    if isinstance(low_value, np.ndarray) or (isinstance(high_value, np.ndarray)):
+        if isinstance(low_value, np.ndarray) and (isinstance(high_value, np.ndarray)):
+            if low_value.shape != high_value.shape:
+                raise ValueError('shape of low_value must be equal to shape of high_value.')
+
+            else:
+                if output_size is None:
+                    random_uniform = [float(rand_init.uniform(low=l, high=h)) for l, h in zip(low_value, high_value)]
+                    return random_uniform
+
+                else:
+                    random_uniform = rand_init.uniform(low=low_value, high=high_value, size=output_size)
+                    return random_uniform
+
+
+        elif isinstance(low_value, np.ndarray) and (isinstance(high_value, (int, float, np.integer, np.floating))):
+            if len(low_value) > 1:
+                raise ValueError('size of low_value must be equal to size of high_value.')
+
+            else:
+                low_value = low_value[0]
+                random_uniform = float(rand_init.uniform(low=low_value, high=high_value))
+                return random_uniform
+
+        elif isinstance(low_value, (int, float, np.integer, np.floating)) and (isinstance(high_value, np.ndarray)):
+            if len(high_value) > 1:
+                raise ValueError('size of low_value must be equal to size of high_value.')
+            else:
+                high_value = high_value[0]
+                random_uniform = float(rand_init.uniform(low=low_value, high=high_value))
+                return random_uniform
+
+            # if output_size is None:
+            #     random_uniform = np.random.uniform(low=low_value, high=high_value)
+            #     return random_uniform
+            #
+            # else:
+            #     random_uniform = np.random.uniform(low=low_value, hig
+
+    elif (isinstance(low_value, (int, float, np.integer, np.floating)) is False) and (
+            isinstance(high_value, (int, float, np.integer, np.floating)) is False):
+        if len(low_value) != len(high_value):
+            raise ValueError('size of low_value must be equal to size of high_value.')
+
+    elif (isinstance(low_value, (int, float, np.integer, np.floating)) is False) and (
+    isinstance(high_value, (int, float, np.integer, np.floating))):
+        high_value = [high_value] * len(low_value)
+
+    elif (isinstance(low_value, (int, float, np.integer, np.floating))) and (
+            isinstance(high_value, (int, float, np.integer, np.floating)) is False):
+        low_value = [low_value] * len(high_value)
+
+
+
+
+
+    else:
+        random_uniform = rand_init.uniform(low=low_value, high=high_value, size=output_size)
+        return random_uniform
+
+    if output_size is None:
+        random_uniform = [float(rand_init.uniform(low=l, high=h)) for l, h in zip(low_value, high_value)]
+        return random_uniform
+
+    else:
+        random_uniform = np.stack(
+            [rand_init.uniform(low=l, high=h, size=output_size) for l, h in zip(low_value, high_value)])
+
+        return random_uniform

+
+    # random_val_list = None
+    # output_size_no_channel = None
+    # if output_size:
+    #     if len(output_size) > dim:
+    #         output_size_no_channel = output_size[:dim]
+    #
+    # if isinstance(value, (int, float)):
+    #     if output_size is not None:
+    #         random_val_list = np.random.uniform(low=-value, high=value, size=output_size)
+    #     else:
+    #         random_val_list = list(np.random.uniform(low=-value, high=value, size=dim))
+    #
+    # elif isinstance(value, (tuple, list)):
+    #     if isinstance(value[0], (float, int)):
+    #         if len(value) == 1:
+    #             if output_size is None:
+    #                 random_val_list = list(np.random.uniform(low=-value[0], high=value[0], size=dim))
+    #
+    #             else:
+    #                 random_val_list = np.zeros(tuple(output_size))
+    #                 random_val_list[..., 0] = np.random.uniform(low=-value[0], high=value[0],
+    #                                                             size=output_size_no_channel)
+    #         elif len(value) == dim:
+    #             if output_size is None:
+    #                 random_val_list = [np.random.uniform(low=-val, high=val) for val in value]
+    #
+    #             else:
+    #                 random_val_list = np.zeros(tuple(output_size))
+    #                 for val_idx, val in enumerate(value):
+    #                     random_val_list[..., val_idx] = np.random.uniform(low=-val, high=val,
+    #                                                                       size=output_size_no_channel)
+    #
+    #
+    #         elif len(value) == 2:
+    #             if output_size is not None:
+    #                 random_val_list = np.random.uniform(low=value[0], high=value[1], size=output_size)
+    #             else:
+    #                 random_val_list = list(np.random.uniform(low=value[0], high=value[1], size=dim))
+    #
+    #         elif len(value) == dim * 2:
+    #             val_reshape = []
+    #             counter = 0
+    #             for v in value:
+    #                 if counter == 0:
+    #                     v_hold = []
+    #                     v_hold.append(v)
+    #                     counter = 1
+    #                 elif counter == 1:
+    #                     v_hold.append(v)
+    #                     val_reshape.append(v_hold)
+    #                     counter = 0
+    #
+    #             if output_size is None:
+    #                 random_val_list = [np.random.uniform(low=val[0], high=val[1]) for val in val_reshape]
+    #
+    #             else:
+    #                 random_val_list = np.zeros(tuple(output_size))
+    #                 for val_idx, value in enumerate(value):
+    #                     random_val_list[..., val_idx] = np.random.uniform(low=value[val_idx][0], high=value[val_idx][1],
+    #                                                                       size=output_size_no_channel)
+    #
+    #     elif isinstance(value[0], (tuple, list)):
+    #         random_val_list = [np.random.uniform(low=val[0], high=val[1]) for val in value]
+    #
+    #     elif isinstance(value[0], np.ndarray):
+    #         raise ValueError('Numpy arrays are not currently supported. Please use list or tuples.')
+    #     else:
+    #         raise ValueError('value was found to have an incorrect shape')
+    #
+    #
+    # elif isinstance(value, np.ndarray):
+    #     raise ValueError('Numpy arrays are not currently supported. Please use list, tuples, floats or ints')
+    # else:
+    #     raise ValueError('value was found to have an incorrect shape')
+    #
+    #
+    # return random_val_list
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/medipt/utils/spatial_utils.html b/_modules/medipt/utils/spatial_utils.html new file mode 100644 index 0000000..82f50d2 --- /dev/null +++ b/_modules/medipt/utils/spatial_utils.html @@ -0,0 +1,229 @@ + + + + + + medipt.utils.spatial_utils — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for medipt.utils.spatial_utils

+import SimpleITK as sitk
+import numpy as np
+from typing import Union, Tuple, List
+
+
+
[docs]def image_center(input_size: Tuple[int, ...],
+                 *args, **kwargs):
+
+    # -1 is important, as it is always the center pixel.
+    input_size_half = [(size - 1) * 0.5 for size in input_size]
+    return input_size_half

+
+
+
[docs]def physical_image_size(dim: int,
+                        size: Tuple[int, ...],
+                        spacing: Tuple[float, ...],
+                        direction: Tuple[float, ...],
+                        origin: Tuple[float, ...],
+                        *args, **kwargs) -> List[float]:
+
+    phys_size = np.array(origin) + np.matmul(size, np.matmul(np.array(direction).reshape([dim, dim]), np.diag(spacing)))
+
+    return list(phys_size)

+
+
+
[docs]def physical_image_size_no_origin(dim: int,
+                                  size: Tuple[int, ...],
+                                  spacing: Tuple[float, ...],
+                                  direction: Tuple[float, ...],
+                                  *args, **kwargs) -> List[float]:
+
+    phys_size = np.matmul(size, np.matmul(np.array(direction).reshape([dim, dim]), np.diag(spacing)))
+
+    return list(phys_size)

+
+
+
+
[docs]def get_output_size_from_spacing(dim: int,
+                                 input_size: Union[List[int], Tuple[int, ...]],
+                                 input_spacing: Union[List[int], Tuple[int, ...]],
+                                 input_direction: Union[List[int], Tuple[int, ...]],
+                                 output_spacing: Union[List[int], Tuple[int, ...]],
+                                 output_direction: Union[List[int], Tuple[int, ...]] = None,
+                                 *args, **kwargs):
+
+    input_total_space = physical_image_size_no_origin(dim=dim,
+                                                      size=input_size,
+                                                      spacing=input_spacing,
+                                                      direction=input_direction)
+
+    output_direction_reshape = np.array(output_direction).reshape([dim, dim])
+
+
+    out_spacing_direction = np.matmul(output_direction_reshape, np.diag(output_spacing))
+
+    output_size = np.matmul(input_total_space, np.linalg.inv(out_spacing_direction))
+
+    return output_size

+
+
+
[docs]def get_output_spacing_from_size(dim: int,
+                                 input_size: Union[List[int], Tuple[int, ...]],
+                                 input_spacing: Union[List[int], Tuple[int, ...]],
+                                 input_direction: Union[List[int], Tuple[int, ...]],
+                                 output_size: Union[List[int], Tuple[int, ...]],
+                                 output_direction: Union[List[int], Tuple[int, ...]] = None,
+                                 *args, **kwargs):
+
+    input_total_space = physical_image_size_no_origin(dim=dim,
+                                                      size=input_size,
+                                                      spacing=input_spacing,
+                                                      direction=input_direction)
+
+    output_direction_reshape = np.array(output_direction).reshape([dim, dim])
+
+
+    out_size_direction = np.matmul(np.diag(output_size), output_direction_reshape)
+
+    output_spacing = np.matmul(np.linalg.inv(out_size_direction), input_total_space)
+
+    return output_spacing

+
+
+
[docs]def image_index_to_phys(dim: int,
+                        index_coords: Union[List[Union[int, float]], Tuple[Union[int, float], ...]],
+                        image: sitk.Image = None,
+                        size: Tuple[int, ...] = None,
+                        origin: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                        spacing: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                        direction: Union[List[Union[int, float]], Tuple[Union[int, float], ...]] = None,
+                        *args, **kwargs) -> List[float]:
+
+
+    if image is not None:
+        phys_coord = image.TransformContinuousIndexToPhysicalPoint(index_coords)
+        return phys_coord
+
+
+    if spacing is None:
+        spacing = [1.0] * dim
+
+    if origin is None:
+        origin = [0] * dim
+
+    if direction is None:
+        direction = np.eye(dim).flatten().tolist()
+
+    phys_coord = np.array(origin) + np.matmul(
+        np.matmul(np.array(direction).reshape([dim, dim]), np.diag(spacing)),
+        np.array(index_coords)
+    )
+
+
+    return list(phys_coord)

+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_sources/documentation/medipt.crop_image.rst.txt b/_sources/documentation/medipt.crop_image.rst.txt new file mode 100644 index 0000000..ffedaa8 --- /dev/null +++ b/_sources/documentation/medipt.crop_image.rst.txt @@ -0,0 +1,21 @@ +medipt.crop\_image package +========================== + +Submodules +---------- + +medipt.crop\_image.threshold\_crop\_background module +----------------------------------------------------- + +.. automodule:: medipt.crop_image.threshold_crop_background + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.crop_image + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.resample_image.rst.txt b/_sources/documentation/medipt.resample_image.rst.txt new file mode 100644 index 0000000..5e8c625 --- /dev/null +++ b/_sources/documentation/medipt.resample_image.rst.txt @@ -0,0 +1,21 @@ +medipt.resample\_image package +============================== + +Submodules +---------- + +medipt.resample\_image.resample\_image module +--------------------------------------------- + +.. automodule:: medipt.resample_image.resample_image + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.resample_image + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.rst.txt b/_sources/documentation/medipt.rst.txt new file mode 100644 index 0000000..71e8d47 --- /dev/null +++ b/_sources/documentation/medipt.rst.txt @@ -0,0 +1,20 @@ +medipt package +============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + medipt.resample_image + medipt.transforms + medipt.utils + +Module contents +--------------- + +.. automodule:: medipt + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.transforms.intensity.rst.txt b/_sources/documentation/medipt.transforms.intensity.rst.txt new file mode 100644 index 0000000..6b04228 --- /dev/null +++ b/_sources/documentation/medipt.transforms.intensity.rst.txt @@ -0,0 +1,77 @@ +medipt.transforms.intensity package +=================================== + +Submodules +---------- + +medipt.transforms.intensity.change\_image\_gamma module +------------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.change_image_gamma + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.clamp\_intensity module +--------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.clamp_intensity + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.gaussian\_blur module +------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.gaussian_blur + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.gaussian\_noise module +-------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.gaussian_noise + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.intensity\_utils\_np module +------------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.intensity_utils_np + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.intensity\_utils\_sitk module +--------------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.intensity_utils_sitk + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.rescale\_intensity module +----------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.rescale_intensity + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.intensity.shift\_scale\_intensity module +---------------------------------------------------------- + +.. automodule:: medipt.transforms.intensity.shift_scale_intensity + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.transforms.intensity + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.transforms.rst.txt b/_sources/documentation/medipt.transforms.rst.txt new file mode 100644 index 0000000..b6553f4 --- /dev/null +++ b/_sources/documentation/medipt.transforms.rst.txt @@ -0,0 +1,30 @@ +medipt.transforms package +========================= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + medipt.transforms.intensity + medipt.transforms.spatial + +Submodules +---------- + +medipt.transforms.transform\_coordinate module +---------------------------------------------- + +.. automodule:: medipt.transforms.transform_coordinate + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.transforms + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.transforms.spatial.functional_legacy.rst.txt b/_sources/documentation/medipt.transforms.spatial.functional_legacy.rst.txt new file mode 100644 index 0000000..143154f --- /dev/null +++ b/_sources/documentation/medipt.transforms.spatial.functional_legacy.rst.txt @@ -0,0 +1,45 @@ +medipt.transforms.spatial.functional\_legacy package +==================================================== + +Submodules +---------- + +medipt.transforms.spatial.functional\_legacy.flipping module +------------------------------------------------------------ + +.. automodule:: medipt.transforms.spatial.functional_legacy.flipping + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.functional\_legacy.rotation module +------------------------------------------------------------ + +.. automodule:: medipt.transforms.spatial.functional_legacy.rotation + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.functional\_legacy.scaling module +----------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.functional_legacy.scaling + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.functional\_legacy.translation module +--------------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.functional_legacy.translation + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.transforms.spatial.functional_legacy + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.transforms.spatial.rst.txt b/_sources/documentation/medipt.transforms.spatial.rst.txt new file mode 100644 index 0000000..b35f366 --- /dev/null +++ b/_sources/documentation/medipt.transforms.spatial.rst.txt @@ -0,0 +1,93 @@ +medipt.transforms.spatial package +================================= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + medipt.transforms.spatial.functional_legacy + +Submodules +---------- + +medipt.transforms.spatial.composite\_transform module +----------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.composite_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.elastic\_deformation\_transform module +---------------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.elastic_deformation_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.flipping\_transform module +---------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.flipping_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.random\_affine\_transform module +---------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.random_affine_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.rotation\_transform module +---------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.rotation_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.scaling\_transform module +--------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.scaling_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.spatial\_transform module +--------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.spatial_transform + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.spatial\_transform\_init module +--------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.spatial_transform_init + :members: + :undoc-members: + :show-inheritance: + +medipt.transforms.spatial.translation\_transform module +------------------------------------------------------- + +.. automodule:: medipt.transforms.spatial.translation_transform + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.transforms.spatial + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/medipt.utils.rst.txt b/_sources/documentation/medipt.utils.rst.txt new file mode 100644 index 0000000..97aadf6 --- /dev/null +++ b/_sources/documentation/medipt.utils.rst.txt @@ -0,0 +1,37 @@ +medipt.utils package +==================== + +Submodules +---------- + +medipt.utils.image\_input\_output\_space module +----------------------------------------------- + +.. automodule:: medipt.utils.image_input_output_space + :members: + :undoc-members: + :show-inheritance: + +medipt.utils.random\_float module +--------------------------------- + +.. automodule:: medipt.utils.random_float + :members: + :undoc-members: + :show-inheritance: + +medipt.utils.spatial\_utils module +---------------------------------- + +.. automodule:: medipt.utils.spatial_utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: medipt.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/_sources/documentation/modules.rst.txt b/_sources/documentation/modules.rst.txt new file mode 100644 index 0000000..5cefcba --- /dev/null +++ b/_sources/documentation/modules.rst.txt @@ -0,0 +1,7 @@ +medipt +====== + +.. toctree:: + :maxdepth: 4 + + medipt diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..f833da3 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,40 @@ +.. ImageProcessingTools documentation master file, created by + sphinx-quickstart on Thu Jul 20 11:57:56 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to ImageProcessingTools's documentation! +================================================ + +Documentation for the ImageProcessingTools package. + + +.. toctree:: + :maxdepth: 2 + :caption: Install Instructions: + + install/installation + +.. toctree:: + :maxdepth: 2 + :caption: Package References: + + documentation/medipt + + +.. toctree:: + :maxdepth: 2 + :caption: Examples: + + examples/example1 + examples/example2 + + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_sources/install/installation.rst.txt b/_sources/install/installation.rst.txt new file mode 100644 index 0000000..ea1f9c5 --- /dev/null +++ b/_sources/install/installation.rst.txt @@ -0,0 +1,36 @@ +Installation +================== + +MedIPT is available for Python 3.7 to 3.11, and can be installed via pip or conda. + +Installation via Anaconda +------------------------- + +The easiest way to install **MedIPT** is with `Anaconda `_ and is available for all major OS platforms. + +If **SimpleITK** is already installed, **MedIPT** can be installed with the following command: +.. code-block:: + + conda install -c medipt medipt + +Otherwise, `MedIPT` can be installed with the following command: +.. code-block:: + + conda install -c medipt -c simpleitk -c medipt + + +On **Windows**, if **SimpleITK** is not installed, ensure **conda-forge** is being used to install. +.. code-block:: + + conda install -c medipt -c simpleitk -c conda-forge medipt + + + +Installation via pip +-------------------- + +Pip installation can be done by: +.. code-block:: + + pip install -i https://pypi.anaconda.org/medipt/simple medipt + diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..7577acb --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,903 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + 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+ stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 0000000..d96755f Binary files /dev/null and b/_static/minus.png differ diff --git a/_static/plus.png b/_static/plus.png new file mode 100644 index 0000000..7107cec Binary files /dev/null and b/_static/plus.png differ diff --git a/_static/pygments.css b/_static/pygments.css new file mode 100644 index 0000000..84ab303 --- /dev/null +++ b/_static/pygments.css @@ -0,0 +1,75 @@ +pre { line-height: 125%; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/documentation/medipt.crop_image.html b/documentation/medipt.crop_image.html new file mode 100644 index 0000000..2c070ae --- /dev/null +++ b/documentation/medipt.crop_image.html @@ -0,0 +1,140 @@ + + + + + + + medipt.crop_image package — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.crop_image package

+
+

Submodules

+
+
+

medipt.crop_image.threshold_crop_background module

+
+
+medipt.crop_image.threshold_crop_background.threshold_crop_background(image, inside_value, outside_value, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
    +

  • image (Image) –

    • +

  • inside_value (int | float) –

    • +

  • outside_value (int | float) –

    • +
+
+
+
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.html b/documentation/medipt.html new file mode 100644 index 0000000..3b373d3 --- /dev/null +++ b/documentation/medipt.html @@ -0,0 +1,224 @@ + + + + + + + medipt package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt package

+
+

Subpackages

+
+ +
+
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.resample_image.html b/documentation/medipt.resample_image.html new file mode 100644 index 0000000..ce1e898 --- /dev/null +++ b/documentation/medipt.resample_image.html @@ -0,0 +1,263 @@ + + + + + + + medipt.resample_image package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.resample_image package

+
+

Submodules

+
+
+

medipt.resample_image.resample_image module

+
+
+class medipt.resample_image.resample_image.ResampleImage(interpolator='linear', post_processing_sitk=None, pixel_type='float32', default_pixel_value=None, dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+
+get_images(images, input_output_space_dict, reference_image=None, transform=None, *args, **kwargs)[source]
+
+
Parameters:
+
    +

  • images (List[Image] | Tuple[Image, ...] | Image) –

    • +

  • input_output_space_dict (dict) –

    • +

  • reference_image (Image) –

    • +

  • transform (Transform) –

    • +
+
+
+
+ +
+
+get_resampled_images(image, input_output_space_dict, reference_image=None, transform=None, *args, **kwargs)[source]
+
+
Return type:
+

Union[List[Image], Tuple[Image, ...], Image]

+
+
Parameters:
+
    +

  • image (List[Image] | Tuple[Image, ...] | Image) –

    • +

  • input_output_space_dict (dict) –

    • +

  • reference_image (Image) –

    • +

  • transform (Transform) –

    • +
+
+
+
+ +
+
+resample_image(image, input_output_space_dict, reference_image=None, transform=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
    +

  • image (Image) –

    • +

  • input_output_space_dict (dict) –

    • +

  • reference_image (Image) –

    • +

  • transform (Transform) –

    • +
+
+
+
+ +
+ +
+
+medipt.resample_image.resample_image.get_simpleitk_pix_type(pixel_type)[source]
+

Returns the SimpleITK pixel type for the given pixel type string.

+
+
Parameters:
+

pixel_type (Union[str, int]) – The pixel type as string. +‘float32’: sitk.sitkFloat32 +‘float64’: sitk.sitkFloat64 +‘int8’: sitk.sitkInt8 +‘int16’: sitk.sitkInt16 +‘int32’: sitk.sitkInt32 +‘int64’: sitk.sitkInt64 +‘uint8’: sitk.sitkUInt8 +‘uint16’: sitk.sitkUInt16 +‘uint32’: sitk.sitkUInt32 +‘uint64’: sitk.sitkUInt64 +‘complex64’: sitk.sitkComplexFloat32 +‘complex128’: sitk.sitkComplexFloat64

+
+
Return type:
+

int

+
+
Returns:
+

The SimpleITK pixel type.

+
+
+
+ +
+
+medipt.resample_image.resample_image.get_sitk_interpolator(interpolator)[source]
+

Return an sitk interpolator object for the given string.

+
+
Parameters:
+

interpolator – Interpolator type as string. +‘nearest’: sitk.sitkNearestNeighbor +‘linear’: sitk.sitkLinear +‘cubic’: sitk.sitkBSpline +‘label_gaussian’: sitk.sitkLabelGaussian +‘gaussian’: sitk.sitkGaussian +‘lanczos’: sitk.sitkLanczosWindowedSinc

+
+
Returns:
+

The sitk interpolator object.

+
+
+
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.transforms.html b/documentation/medipt.transforms.html new file mode 100644 index 0000000..365a7c4 --- /dev/null +++ b/documentation/medipt.transforms.html @@ -0,0 +1,411 @@ + + + + + + + medipt.transforms package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.transforms package

+
+

Subpackages

+
+ +
+
+
+

Submodules

+
+
+

medipt.transforms.transform_coordinate module

+
+
+class medipt.transforms.transform_coordinate.TransformCoordinate(*args, **kwargs)[source]
+

Bases: object

+
+
+transform_coordinate(phys_coordinate, transform, *args, **kwargs)[source]
+
+
Return type:
+

Union[List[Union[int, float]], Tuple[Union[int, float], ...], float]

+
+
Parameters:
+
+
+
+
+ +
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.transforms.intensity.html b/documentation/medipt.transforms.intensity.html new file mode 100644 index 0000000..00160cc --- /dev/null +++ b/documentation/medipt.transforms.intensity.html @@ -0,0 +1,564 @@ + + + + + + + medipt.transforms.intensity package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.transforms.intensity package

+
+

Submodules

+
+
+

medipt.transforms.intensity.change_image_gamma module

+
+
+medipt.transforms.intensity.change_image_gamma.change_gamma(img, l)[source]
+
+ +
+
+medipt.transforms.intensity.change_image_gamma.change_gamma_unnormalized(img, l)[source]
+
+ +
+
+medipt.transforms.intensity.change_image_gamma.random_change_gamma(img, lambda_min=0.5, lambda_max=1.5, seed=None, legacy_random_state=True)[source]
+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.change_image_gamma.scale(img, old_range, new_range)[source]
+
+ +
+
+

medipt.transforms.intensity.clamp_intensity module

+
+
+class medipt.transforms.intensity.clamp_intensity.ClampIntensity(window_min=None, window_max=None, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.gaussian_blur module

+
+
+class medipt.transforms.intensity.gaussian_blur.GaussianBlur(sigma, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+

sigma (int | float) –

+
+
+
+ +
+
+class medipt.transforms.intensity.gaussian_blur.RandomGaussianBlur(min_sigma, max_sigma, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.gaussian_noise module

+
+
+class medipt.transforms.intensity.gaussian_noise.GaussianNoise(mean, sigma, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+class medipt.transforms.intensity.gaussian_noise.RandomGaussianNoise(mean=None, min_mean=None, max_mean=None, sigma=None, min_sigma=None, max_sigma=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.intensity_utils_np module

+
+
+medipt.transforms.intensity.intensity_utils_np.change_image_gamma_np(image, gamma, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.clamp_intensity_np(image, window_min=None, window_max=None, image_min=None, image_max=None, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.gaussian_blur_np(image, sigma, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.gaussian_noise_np(image, mean, sigma, rand_init=None, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.rescale_intensity_np(image, output_min=None, output_max=None, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.rescale_intensity_window_np(image, input_min=None, input_max=None, output_min=None, output_max=None, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_np.shift_scale_intensity_np(image, shift=None, scale=None, *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.intensity_utils_sitk module

+
+
+medipt.transforms.intensity.intensity_utils_sitk.change_image_gamma_sitk(image, gamma, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
    +

  • image (Image) –

    • +

  • gamma (int | float) –

    • +
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.clamp_intensity_sitk(image, window_min=None, window_max=None, image_min=None, image_max=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.gaussian_blur_sitk(image, sigma, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
    +

  • image (Image) –

    • +

  • sigma (int | float) –

    • +
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.gaussian_noise_sitk(image, mean, sigma, seed=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.min_max_intensity_sitk(image, *args, **kwargs)[source]
+
+
Return type:
+

Tuple[float, float]

+
+
Parameters:
+

image (Image) –

+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.rescale_intensity_sitk(image, output_min=None, output_max=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
    +

  • image (Image) –

    • +

  • output_min (int | float) –

    • +

  • output_max (int | float) –

    • +
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.rescale_intensity_window_sitk(image, input_min=None, input_max=None, output_min=None, output_max=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.transforms.intensity.intensity_utils_sitk.shift_scale_intensity_sitk(image, shift=None, scale=None, *args, **kwargs)[source]
+
+
Return type:
+

Image

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.rescale_intensity module

+
+
+class medipt.transforms.intensity.rescale_intensity.RescaleIntensity(new_min=None, new_max=None, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.intensity.shift_scale_intensity module

+
+
+class medipt.transforms.intensity.shift_scale_intensity.ShiftScaleIntensity(shift=None, scale=None, input_min=None, input_max=None, output_min=None, output_max=None, random_shift=None, random_scale=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.transforms.spatial.functional_legacy.html b/documentation/medipt.transforms.spatial.functional_legacy.html new file mode 100644 index 0000000..473b69c --- /dev/null +++ b/documentation/medipt.transforms.spatial.functional_legacy.html @@ -0,0 +1,155 @@ + + + + + + + medipt.transforms.spatial.functional_legacy package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.transforms.spatial.functional_legacy package

+
+

Submodules

+
+
+

medipt.transforms.spatial.functional_legacy.flipping module

+
+
+

medipt.transforms.spatial.functional_legacy.rotation module

+
+
+

medipt.transforms.spatial.functional_legacy.scaling module

+
+
+

medipt.transforms.spatial.functional_legacy.translation module

+
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.transforms.spatial.html b/documentation/medipt.transforms.spatial.html new file mode 100644 index 0000000..ada7788 --- /dev/null +++ b/documentation/medipt.transforms.spatial.html @@ -0,0 +1,1169 @@ + + + + + + + medipt.transforms.spatial package — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.transforms.spatial package

+
+

Subpackages

+
+ +
+
+
+

Submodules

+
+
+

medipt.transforms.spatial.composite_transform module

+
+
+class medipt.transforms.spatial.composite_transform.CompositeTransform(*args, **kwargs)[source]
+

Bases: object

+
+
+add_transforms(transform)[source]
+
+
Parameters:
+

transform (List[SpatialTransform | Transform] | Tuple[SpatialTransform | Transform, ...] | SpatialTransform | Transform) –

+
+
+
+ +
+
+create_composite(dim=3)[source]
+
+
Return type:
+

Transform

+
+
Parameters:
+

dim (int) –

+
+
+
+ +
+
+create_inverse_composite(dim=3, use_displacement_field=False)[source]
+
+
Return type:
+

CompositeTransform

+
+
Parameters:
+
    +

  • dim (int) –

    • +

  • use_displacement_field (bool) –

    • +
+
+
+
+ +
+ +
+
+medipt.transforms.spatial.composite_transform.composite_transform(transforms, dim=3)[source]
+
+
Parameters:
+
    +

  • transforms (List[AffineTransform | BSplineTransform | DisplacementFieldTransform]) –

    • +

  • dim (int) –

    • +
+
+
+
+ +
+
+

medipt.transforms.spatial.elastic_deformation_transform module

+
+
+class medipt.transforms.spatial.elastic_deformation_transform.ElasticDeformation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: SpatialTransform

+

Rotation transformation base class.

+
+
Parameters:
+
+
+
+
+
+get_displacement_field(*args, **kwargs)[source]
+
+ +
+
+get_inverse_transform(*args, **kwargs)[source]
+

This determines the inverse transform for the elastic deformation.

+

Note: this is not suitable for proper resampling the transformed image back to the original state, but is +intended to be used with coordinate transforms. For image resampling, use the inverted deformation field. +:type args: +:param args: +:type kwargs: +:param kwargs: +:return:

+
+ +
+
+get_inverted_displacement_field(*args, **kwargs)[source]
+
+ +
+
+get_inverted_transform_from_displacement()[source]
+
+ +
+
+get_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+class medipt.transforms.spatial.elastic_deformation_transform.ElasticDeformationInputImage(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: ElasticDeformation

+
+
Parameters:
+
+
+
+
+
+get_transform_on_input(spline_params, num_grid_points=6, spline_order=3, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.elastic_deformation_transform.ElasticDeformationOutputImage(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: ElasticDeformation

+
+
Parameters:
+
+
+
+
+
+get_transform_on_output(spline_params, num_grid_points=6, spline_order=3, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.elastic_deformation_transform.RandomElasticDeformation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: ElasticDeformation

+
+
Parameters:
+
+
+
+
+
+get_random_spline_params(num_grid_points=(6, 6, 6), max_deformation_displacement=(25, 25, 25), *args, **kwargs)[source]
+
+
Return type:
+

ndarray

+
+
Parameters:
+
+
+
+
+ +
+
+get_random_transform(image_size, image_spacing, image_origin=None, image_direction=None, num_grid_points=6, max_deformation_displacement=25, spline_order=3, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.elastic_deformation_transform.RandomElasticDeformationTransformInputImage(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: RandomElasticDeformation

+
+
Parameters:
+
+
+
+
+
+get_random_transform_on_input(num_grid_points=6, max_deformation_displacement=25, spline_order=3, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.elastic_deformation_transform.RandomElasticDeformationTransformOutputImage(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: RandomElasticDeformation

+
+
Parameters:
+
+
+
+
+
+get_random_transform_on_output(num_grid_points=6, max_deformation_displacement=25, spline_order=3, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+

medipt.transforms.spatial.flipping_transform module

+
+
+class medipt.transforms.spatial.flipping_transform.FlippingTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: SpatialTransform

+
+
Parameters:
+
+
+
+
+
+get_transform(flip_axes, *args, **kwargs)[source]
+
+
Parameters:
+

flip_axes (List[float] | Tuple[float, ...] | float) –

+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.flipping_transform.RandomFlipping(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: FlippingTransform

+
+
Parameters:
+
+
+
+
+
+get_random_transform(flip_axes, *args, **kwargs)[source]
+
+
Parameters:
+

flip_axes (List[int | float | bool] | Tuple[int | float | bool, ...] | int | float | bool | integer | floating | ndarray) –

+
+
+
+ +
+ +
+
+

medipt.transforms.spatial.random_affine_transform module

+
+
+class medipt.transforms.spatial.random_affine_transform.RandomAffineTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: SpatialTransform

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.transforms.spatial.rotation_transform module

+
+
+class medipt.transforms.spatial.rotation_transform.RandomRotation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: RotationTransform, RandomAffineTransform

+

A rotation transformation with random angles (in radian).

+
+
Parameters:
+
+
+
+
+
+get_random_transform(min_angles=None, max_angles=None, transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.rotation_transform.RotationTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: SpatialTransform

+

Rotation transformation base class.

+
+
Parameters:
+
+
+
+
+
+get_transform(angles, *args, **kwargs)[source]
+
+
Parameters:
+

angles (List[float] | Tuple[float, ...] | float) –

+
+
+
+ +
+ +
+
+

medipt.transforms.spatial.scaling_transform module

+
+
+class medipt.transforms.spatial.scaling_transform.RandomScaling(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: ScalingTransform, RandomAffineTransform

+
+
Parameters:
+
+
+
+
+
+get_random_transform(min_scaling, max_scaling, transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.scaling_transform.RandomUniformScaling(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: ScalingTransform, RandomAffineTransform

+
+
Parameters:
+
+
+
+
+
+get_random_transform(min_scaling=None, max_scaling=None, transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.scaling_transform.ScalingTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: SpatialTransform

+

Scale transformation base class.

+
+
Parameters:
+
+
+
+
+
+get_scale_transform(dim, scale, direction)[source]
+

Returns the sitk transform based on the given parameters. +:type dim: +:param dim: The dimension. +:type scale: +:param scale: List of scale factors for each dimension. +:rtype: AffineTransform +:return: The sitk.AffineTransform().

+
+
Return type:
+

AffineTransform

+
+
+
+ +
+
+get_transform(scale, *args, **kwargs)[source]
+
+
Parameters:
+

scale (List[float] | Tuple[float, ...] | float | int) –

+
+
+
+ +
+ +
+
+

medipt.transforms.spatial.spatial_transform module

+
+
+class medipt.transforms.spatial.spatial_transform.SpatialTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: ABC

+

A generic spatial transform that can be applied to 2D and 3D images.

+
+
Parameters:
+
+
+
+
+
+get_displacement_field(*args, **kwargs)[source]
+
+ +
+
+get_image_params(image=None, image_size=None, image_spacing=None, image_origin=None, image_direction=None, *args, **kwargs)[source]
+
+
Return type:
+

Tuple[Union[List[Union[int, float]], Tuple[Union[int, float]], ndarray], Union[List[Union[int, float]], Tuple[Union[int, float]], ndarray], Union[List[Union[int, float]], Tuple[Union[int, float]], ndarray], Union[List[Union[int, float]], Tuple[Union[int, float]], ndarray]]

+
+
Parameters:
+
+
+
+
+ +
+
+get_input_center(*args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
+
+ +
+
+get_input_origin(*args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
+
+ +
+
+get_input_output_space(dict=None, *args, **kwargs)[source]
+
+
Parameters:
+

dict (dict) –

+
+
+
+ +
+
+get_inverse_transform(*args, **kwargs)[source]
+
+ +
+
+get_inverted_displacement_field(*args, **kwargs)[source]
+
+ +
+
+get_inverted_transform_from_displacement(*args, **kwargs)[source]
+
+ +
+
+get_output_center(*args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
+
+ +
+
+get_output_origin(*args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
+
+ +
+
+get_random_point(*args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
+
+ +
+ +
+
+

medipt.transforms.spatial.spatial_transform_init module

+
+
+class medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: object

+
+
Parameters:
+
+
+
+
+
+elastic_deformation_input_image(*args, **kwargs)[source]
+
+ +
+
+elastic_deformation_transform(*args, **kwargs)[source]
+
+ +
+
+flipping_transform(*args, **kwargs)[source]
+
+ +
+
+random_bbox_translation(*args, **kwargs)[source]
+
+ +
+
+random_coord_translation(*args, **kwargs)[source]
+
+ +
+
+random_elastic_deformation_input_image(*args, **kwargs)[source]
+
+ +
+
+random_elastic_deformation_output_image(*args, **kwargs)[source]
+
+ +
+
+random_elastic_deformation_transform(*args, **kwargs)[source]
+
+ +
+
+random_flipping_transform(*args, **kwargs)[source]
+
+ +
+
+random_rotation_transform(*args, **kwargs)[source]
+
+ +
+
+random_scaling_transform(*args, **kwargs)[source]
+
+ +
+
+random_translation_transform(*args, **kwargs)[source]
+
+ +
+
+random_uniform_scaling_transform(*args, **kwargs)[source]
+
+ +
+
+rotation_transform(*args, **kwargs)[source]
+
+ +
+
+scaling_transform(*args, **kwargs)[source]
+
+ +
+
+translate_input_center_to_input_origin(*args, **kwargs)[source]
+
+ +
+
+translate_input_center_to_output_center(*args, **kwargs)[source]
+
+ +
+
+translate_input_origin_to_output_center(*args, **kwargs)[source]
+
+ +
+
+translate_random_input_center_to_input_origin(*args, **kwargs)[source]
+
+ +
+
+translation_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+

medipt.transforms.spatial.translation_transform module

+
+
+class medipt.transforms.spatial.translation_transform.RandomBBoxTranslation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: RandomAffineTransform, TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_random_transform(transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+

transformation_dict (dict) –

+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.RandomCoordTranslation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, *args, **kwargs)[source]
+

Bases: RandomAffineTransform, TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_random_transform(coord, translation_extent, transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.RandomFactorInput(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: TranslationTransform

+

A translation transform that translates the input image by a random factor, such that it will be cropped. +The input center should usually be at the origin before this transformation. +The actual translation value per dimension will be calculated as follows: +(input_size[i] * input_spacing[i] - self.remove_border[i]) * float_uniform(-self.random_factor[i], self.random_factor[i]) for each dimension.

+
+
Parameters:
+
+
+
+
+
+get_transform(random_factor, remove_border=None, *args, **kwargs)[source]
+

Returns the sitk transform based on the given parameters. +:type kwargs: +:param kwargs: Must contain either ‘image’, or ‘input_size’ and ‘input_spacing’, which define the input image physical space. +:return: The sitk.AffineTransform().

+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.RandomTranslation(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: RandomAffineTransform, TranslationTransform

+

A translation transformation with a random offset.

+
+
Parameters:
+
+
+
+
+
+get_random_transform(min_trans, max_trans, transformation_dict=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.TranslateInputCenterToInputOrigin(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.TranslateInputCenterToOutputCenter(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.TranslateInputOriginToOutputCenter(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.TranslateRandomInputCenterToInputOrigin(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: TranslationTransform

+
+
Parameters:
+
+
+
+
+
+get_transform(*args, **kwargs)[source]
+
+ +
+ +
+
+class medipt.transforms.spatial.translation_transform.TranslationTransform(dim=3, used_dimensions=None, seed=None, legacy_random_state=True, rand_init=None, *args, **kwargs)[source]
+

Bases: SpatialTransform

+

Translation transformation base class.

+
+
Parameters:
+
+
+
+
+
+get_transform(translation, *args, **kwargs)[source]
+
+
Parameters:
+

translation (List[float] | Tuple[float, ...] | float) –

+
+
+
+ +
+ +
+
+

Module contents

+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/medipt.utils.html b/documentation/medipt.utils.html new file mode 100644 index 0000000..2920926 --- /dev/null +++ b/documentation/medipt.utils.html @@ -0,0 +1,339 @@ + + + + + + + medipt.utils package — MedIPT + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

medipt.utils package

+
+

Submodules

+
+
+

medipt.utils.image_input_output_space module

+
+
+medipt.utils.image_input_output_space.resolve_input_output_space(dim=3, input_image=None, reference_image=None, input_size=None, output_size=None, input_spacing=None, output_spacing=None, input_direction=None, output_direction=None, input_origin=None, output_origin=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.utils.random_float module

+
+
+medipt.utils.random_float.initialize_rand_state(seed=None, legacy_random_state=True)[source]
+
+
Return type:
+

Union[ModuleType, Generator, BitGenerator]

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.utils.random_float.random_binomial(n, p, output_size=None, ignore_axis=None, seed=None, legacy_random_state=True, rand_init=None)[source]
+
+
Parameters:
+
+
+
Return type:
+

Union[ndarray, int]

+
+
Returns:
+

+
+
+
+ +
+
+medipt.utils.random_float.random_uniform_float(low_value, high_value, output_size=None, dim=None, seed=None, legacy_random_state=True, rand_init=None)[source]
+
+
Return type:
+

Union[float, List[float], ndarray]

+
+
Parameters:
+
+
+
+
+ +
+
+

medipt.utils.spatial_utils module

+
+
+medipt.utils.spatial_utils.get_output_size_from_spacing(dim, input_size, input_spacing, input_direction, output_spacing, output_direction=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+
+medipt.utils.spatial_utils.get_output_spacing_from_size(dim, input_size, input_spacing, input_direction, output_size, output_direction=None, *args, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+
+medipt.utils.spatial_utils.image_center(input_size, *args, **kwargs)[source]
+
+
Parameters:
+

input_size (Tuple[int, ...]) –

+
+
+
+ +
+
+medipt.utils.spatial_utils.image_index_to_phys(dim, index_coords, image=None, size=None, origin=None, spacing=None, direction=None, *args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.utils.spatial_utils.physical_image_size(dim, size, spacing, direction, origin, *args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
Parameters:
+
+
+
+
+ +
+
+medipt.utils.spatial_utils.physical_image_size_no_origin(dim, size, spacing, direction, *args, **kwargs)[source]
+
+
Return type:
+

List[float]

+
+
Parameters:
+
+
+
+
+ +
+
+

Module contents

+
+
+medipt.utils.set_num_workers(n_threads)[source]
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation/modules.html b/documentation/modules.html new file mode 100644 index 0000000..52c7e36 --- /dev/null +++ b/documentation/modules.html @@ -0,0 +1,146 @@ + + + + + + + medipt — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..887cc12 --- /dev/null +++ b/genindex.html @@ -0,0 +1,769 @@ + + + + + + Index — MedIPT + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ A + | C + | E + | F + | G + | I + | M + | P + | R + | S + | T + +
+

A

+ + +
+ +

C

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

I

+ + + +
+ +

M

+ + + +
    +
  • + medipt + +
    • +
  • + medipt.crop_image + +
    • +
  • + medipt.crop_image.threshold_crop_background + +
    • +
  • + medipt.resample_image + +
    • +
  • + medipt.resample_image.resample_image + +
    • +
  • + medipt.transforms + +
    • +
  • + medipt.transforms.intensity + +
    • +
  • + medipt.transforms.intensity.change_image_gamma + +
    • +
  • + medipt.transforms.intensity.clamp_intensity + +
    • +
  • + medipt.transforms.intensity.gaussian_blur + +
    • +
  • + medipt.transforms.intensity.gaussian_noise + +
    • +
  • + medipt.transforms.intensity.intensity_utils_np + +
    • +
  • + medipt.transforms.intensity.intensity_utils_sitk + +
    • +
  • + medipt.transforms.intensity.rescale_intensity + +
    • +
  • + medipt.transforms.intensity.shift_scale_intensity + +
    • +
  • + medipt.transforms.spatial + +
    • +
  • + medipt.transforms.spatial.composite_transform + +
    • +
  • + medipt.transforms.spatial.elastic_deformation_transform + +
    • +
  • + medipt.transforms.spatial.flipping_transform + +
    • +
  • + medipt.transforms.spatial.random_affine_transform + +
    • +
  • + medipt.transforms.spatial.rotation_transform + +
    • +
  • + medipt.transforms.spatial.scaling_transform + +
    • +
  • + medipt.transforms.spatial.spatial_transform + +
    • +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..63b1643 --- /dev/null +++ b/index.html @@ -0,0 +1,148 @@ + + + + + + + Welcome to ImageProcessingTools’s documentation! — MedIPT + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Welcome to ImageProcessingTools’s documentation!

+

Documentation for the ImageProcessingTools package.

+
+

Install Instructions:

+ +
+
+

Package References:

+ +
+
+
+
+
+

Indices and tables

+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/install/installation.html b/install/installation.html new file mode 100644 index 0000000..feb817b --- /dev/null +++ b/install/installation.html @@ -0,0 +1,151 @@ + + + + + + + Installation — MedIPT + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Installation

+

MedIPT is available for Python 3.7 to 3.11, and can be installed via pip or conda.

+
+

Installation via Anaconda

+

The easiest way to install MedIPT is with Anaconda and is available for all major OS platforms.

+

If SimpleITK is already installed, MedIPT can be installed with the following command: +.. code-block:

+
conda install -c medipt medipt
+
+
+

Otherwise, MedIPT can be installed with the following command: +.. code-block:

+
conda install -c medipt -c simpleitk -c medipt
+
+
+

On Windows, if SimpleITK is not installed, ensure conda-forge is being used to install. +.. code-block:

+
conda install -c medipt -c simpleitk -c conda-forge medipt
+
+
+
+
+

Installation via pip

+

Pip installation can be done by: +.. code-block:

+
pip install -i https://pypi.anaconda.org/medipt/simple medipt
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..3045f55 Binary files /dev/null and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 0000000..cf5fed8 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,275 @@ + + + + + + Python Module Index — MedIPT + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
  • +
    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ m +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ m
+ medipt +
    + medipt.crop_image +
    + medipt.crop_image.threshold_crop_background +
    + medipt.resample_image +
    + medipt.resample_image.resample_image +
    + medipt.transforms +
    + medipt.transforms.intensity +
    + medipt.transforms.intensity.change_image_gamma +
    + medipt.transforms.intensity.clamp_intensity +
    + medipt.transforms.intensity.gaussian_blur +
    + medipt.transforms.intensity.gaussian_noise +
    + medipt.transforms.intensity.intensity_utils_np +
    + medipt.transforms.intensity.intensity_utils_sitk +
    + medipt.transforms.intensity.rescale_intensity +
    + medipt.transforms.intensity.shift_scale_intensity +
    + medipt.transforms.spatial +
    + medipt.transforms.spatial.composite_transform +
    + medipt.transforms.spatial.elastic_deformation_transform +
    + medipt.transforms.spatial.flipping_transform +
    + medipt.transforms.spatial.random_affine_transform +
    + medipt.transforms.spatial.rotation_transform +
    + medipt.transforms.spatial.scaling_transform +
    + medipt.transforms.spatial.spatial_transform +
    + medipt.transforms.spatial.spatial_transform_init +
    + medipt.transforms.spatial.translation_transform +
    + medipt.transforms.transform_coordinate +
    + medipt.utils +
    + medipt.utils.image_input_output_space +
    + medipt.utils.random_float +
    + medipt.utils.spatial_utils +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 0000000..a02689f --- /dev/null +++ b/search.html @@ -0,0 +1,130 @@ + + + + + + Search — MedIPT + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
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  • Search
    • +
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+
+
+
+
+ + + + +
+ +
+ +
+
+ +
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+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..58201fa --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["documentation/medipt", "documentation/medipt.crop_image", "documentation/medipt.resample_image", "documentation/medipt.transforms", "documentation/medipt.transforms.intensity", "documentation/medipt.transforms.spatial", "documentation/medipt.transforms.spatial.functional_legacy", "documentation/medipt.utils", "documentation/modules", "index", "install/installation"], "filenames": ["documentation/medipt.rst", "documentation/medipt.crop_image.rst", "documentation/medipt.resample_image.rst", "documentation/medipt.transforms.rst", "documentation/medipt.transforms.intensity.rst", "documentation/medipt.transforms.spatial.rst", "documentation/medipt.transforms.spatial.functional_legacy.rst", "documentation/medipt.utils.rst", "documentation/modules.rst", "index.rst", "install/installation.rst"], "titles": ["medipt package", "medipt.crop_image package", "medipt.resample_image package", "medipt.transforms package", "medipt.transforms.intensity package", "medipt.transforms.spatial package", "medipt.transforms.spatial.functional_legacy package", "medipt.utils package", "medipt", "Welcome to ImageProcessingTools\u2019s documentation!", "Installation"], "terms": {"resample_imag": [0, 8], "submodul": [0, 8], "resampleimag": [0, 2], "get_imag": [0, 2], "get_resampled_imag": [0, 2], "get_simpleitk_pix_typ": [0, 2], "get_sitk_interpol": [0, 2], "transform": [0, 2, 8], "intens": [0, 3], "change_image_gamma": [0, 3], "clamp_intens": [0, 3], "gaussian_blur": [0, 3], "gaussian_nois": [0, 3], "intensity_utils_np": [0, 3], "intensity_utils_sitk": [0, 3], "rescale_intens": [0, 3], "shift_scale_intens": [0, 3], "spatial": [0, 3], "composite_transform": [0, 3], "elastic_deformation_transform": [0, 3], "flipping_transform": [0, 3], "random_affine_transform": [0, 3], "rotation_transform": [0, 3], "scaling_transform": [0, 3], "spatial_transform": [0, 3], "spatial_transform_init": [0, 3], "translation_transform": [0, 3], "transform_coordin": [0, 8], "transformcoordin": [0, 3], "util": [0, 8], "image_input_output_spac": [0, 8], "resolve_input_output_spac": [0, 7], "random_float": [0, 8], "initialize_rand_st": [0, 7], "random_binomi": [0, 7], "random_uniform_float": [0, 7], "spatial_util": [0, 8], "get_output_size_from_spac": [0, 7], "get_output_spacing_from_s": [0, 7], "image_cent": [0, 7], "image_index_to_phi": [0, 7], "physical_image_s": [0, 7], "physical_image_size_no_origin": [0, 7], "set_num_work": [0, 7], "imag": [1, 2, 4, 5, 7], "inside_valu": 1, "outside_valu": 1, "arg": [1, 2, 3, 4, 5, 7], "kwarg": [1, 2, 3, 4, 5, 7], "sourc": [1, 2, 3, 4, 5, 7], "return": [1, 2, 3, 4, 5, 7], "type": [1, 2, 3, 4, 5, 7], "paramet": [1, 2, 3, 4, 5, 7], "int": [1, 2, 3, 4, 5, 7], "float": [1, 2, 3, 4, 5, 7], "class": [2, 3, 4, 5], "interpol": 2, "linear": 2, "post_processing_sitk": 2, "none": [2, 4, 5, 7], "pixel_typ": 2, "float32": 2, "default_pixel_valu": 2, "dim": [2, 5, 7], "3": [2, 5, 7, 10], "used_dimens": [2, 5], "seed": [2, 4, 5, 7], "legacy_random_st": [2, 4, 5, 7], "true": [2, 4, 5, 7], "base": [2, 3, 4, 5], "object": [2, 3, 4, 5], "str": 2, "callabl": 2, "list": [2, 3, 5, 7], "tupl": [2, 3, 4, 5, 7], "bool": [2, 4, 5, 7], "randomst": [2, 4, 5, 7], "gener": [2, 4, 5, 7], "bitgener": [2, 4, 5, 7], "input_output_space_dict": 2, "reference_imag": [2, 7], "dict": [2, 5], "union": [2, 3, 5, 7], "simpleitk": [2, 10], "pixel": 2, "given": [2, 5], "string": 2, "The": [2, 5, 10], "sitk": [2, 5], "sitkfloat32": 2, "float64": 2, "sitkfloat64": 2, "int8": 2, "sitkint8": 2, "int16": 2, "sitkint16": 2, "int32": 2, "sitkint32": 2, "int64": 2, "sitkint64": 2, "uint8": 2, "sitkuint8": 2, "uint16": 2, "sitkuint16": 2, "uint32": 2, "sitkuint32": 2, "uint64": 2, "sitkuint64": 2, "complex64": 2, "sitkcomplexfloat32": 2, "complex128": 2, "sitkcomplexfloat64": 2, "an": 2, "nearest": 2, "sitknearestneighbor": 2, "sitklinear": 2, "cubic": 2, "sitkbsplin": 2, "label_gaussian": 2, "sitklabelgaussian": 2, "gaussian": 2, "sitkgaussian": 2, "lanczo": 2, "sitklanczoswindowedsinc": 2, "change_gamma": [3, 4], "change_gamma_unnorm": [3, 4], "random_change_gamma": [3, 4], "scale": [3, 4, 5], "clampintens": [3, 4], "gaussianblur": [3, 4], "randomgaussianblur": [3, 4], "gaussiannois": [3, 4], "randomgaussiannois": [3, 4], "change_image_gamma_np": [3, 4], "clamp_intensity_np": [3, 4], "gaussian_blur_np": [3, 4], "gaussian_noise_np": [3, 4], "rescale_intensity_np": [3, 4], "rescale_intensity_window_np": [3, 4], "shift_scale_intensity_np": [3, 4], "change_image_gamma_sitk": [3, 4], "clamp_intensity_sitk": [3, 4], "gaussian_blur_sitk": [3, 4], "gaussian_noise_sitk": [3, 4], "min_max_intensity_sitk": [3, 4], "rescale_intensity_sitk": [3, 4], "rescale_intensity_window_sitk": [3, 4], "shift_scale_intensity_sitk": [3, 4], "rescaleintens": [3, 4], "shiftscaleintens": [3, 4], "functional_legaci": [3, 5], "flip": [3, 5], "rotat": [3, 5], "translat": [3, 5], "compositetransform": [3, 5], "add_transform": [3, 5], "create_composit": [3, 5], "create_inverse_composit": [3, 5], "elasticdeform": [3, 5], "get_displacement_field": [3, 5], "get_inverse_transform": [3, 5], "get_inverted_displacement_field": [3, 5], "get_inverted_transform_from_displac": [3, 5], "get_transform": [3, 5], "elasticdeformationinputimag": [3, 5], "get_transform_on_input": [3, 5], "elasticdeformationoutputimag": [3, 5], "get_transform_on_output": [3, 5], "randomelasticdeform": [3, 5], "get_random_spline_param": [3, 5], "get_random_transform": [3, 5], "randomelasticdeformationtransforminputimag": [3, 5], "get_random_transform_on_input": [3, 5], "randomelasticdeformationtransformoutputimag": [3, 5], "get_random_transform_on_output": [3, 5], "flippingtransform": [3, 5], "randomflip": [3, 5], "randomaffinetransform": [3, 5], "randomrot": [3, 5], "rotationtransform": [3, 5], "randomsc": [3, 5], "randomuniformsc": [3, 5], "scalingtransform": [3, 5], "get_scale_transform": [3, 5], "spatialtransform": [3, 5], "get_image_param": [3, 5], "get_input_cent": [3, 5], "get_input_origin": [3, 5], "get_input_output_spac": [3, 5], "get_output_cent": [3, 5], "get_output_origin": [3, 5], "get_random_point": [3, 5], "spatialtransforminit": [3, 5], "elastic_deformation_input_imag": [3, 5], "random_bbox_transl": [3, 5], "random_coord_transl": [3, 5], "random_elastic_deformation_input_imag": [3, 5], "random_elastic_deformation_output_imag": [3, 5], "random_elastic_deformation_transform": [3, 5], "random_flipping_transform": [3, 5], "random_rotation_transform": [3, 5], "random_scaling_transform": [3, 5], "random_translation_transform": [3, 5], "random_uniform_scaling_transform": [3, 5], "translate_input_center_to_input_origin": [3, 5], "translate_input_center_to_output_cent": [3, 5], "translate_input_origin_to_output_cent": [3, 5], 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"medipt.transforms": [[3, "module-medipt.transforms"]], "medipt.transforms.transform_coordinate": [[3, "module-medipt.transforms.transform_coordinate"]], "transform_coordinate() (medipt.transforms.transform_coordinate.transformcoordinate method)": [[3, "medipt.transforms.transform_coordinate.TransformCoordinate.transform_coordinate"]], "clampintensity (class in medipt.transforms.intensity.clamp_intensity)": [[4, "medipt.transforms.intensity.clamp_intensity.ClampIntensity"]], "gaussianblur (class in medipt.transforms.intensity.gaussian_blur)": [[4, "medipt.transforms.intensity.gaussian_blur.GaussianBlur"]], "gaussiannoise (class in medipt.transforms.intensity.gaussian_noise)": [[4, "medipt.transforms.intensity.gaussian_noise.GaussianNoise"]], "randomgaussianblur (class in medipt.transforms.intensity.gaussian_blur)": [[4, "medipt.transforms.intensity.gaussian_blur.RandomGaussianBlur"]], "randomgaussiannoise (class in medipt.transforms.intensity.gaussian_noise)": [[4, "medipt.transforms.intensity.gaussian_noise.RandomGaussianNoise"]], "rescaleintensity (class in medipt.transforms.intensity.rescale_intensity)": [[4, "medipt.transforms.intensity.rescale_intensity.RescaleIntensity"]], "shiftscaleintensity (class in medipt.transforms.intensity.shift_scale_intensity)": [[4, "medipt.transforms.intensity.shift_scale_intensity.ShiftScaleIntensity"]], "change_gamma() (in module medipt.transforms.intensity.change_image_gamma)": [[4, "medipt.transforms.intensity.change_image_gamma.change_gamma"]], "change_gamma_unnormalized() (in module medipt.transforms.intensity.change_image_gamma)": [[4, "medipt.transforms.intensity.change_image_gamma.change_gamma_unnormalized"]], "change_image_gamma_np() (in module medipt.transforms.intensity.intensity_utils_np)": [[4, "medipt.transforms.intensity.intensity_utils_np.change_image_gamma_np"]], "change_image_gamma_sitk() (in module medipt.transforms.intensity.intensity_utils_sitk)": [[4, 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[[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.random_translation_transform"]], "random_uniform_scaling_transform() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.random_uniform_scaling_transform"]], "rotation_transform() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.rotation_transform"]], "scaling_transform() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.scaling_transform"]], "translate_input_center_to_input_origin() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.translate_input_center_to_input_origin"]], "translate_input_center_to_output_center() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.translate_input_center_to_output_center"]], "translate_input_origin_to_output_center() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.translate_input_origin_to_output_center"]], "translate_random_input_center_to_input_origin() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.translate_random_input_center_to_input_origin"]], "translation_transform() (medipt.transforms.spatial.spatial_transform_init.spatialtransforminit method)": [[5, "medipt.transforms.spatial.spatial_transform_init.SpatialTransformInit.translation_transform"]], "get_output_size_from_spacing() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.get_output_size_from_spacing"]], "get_output_spacing_from_size() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.get_output_spacing_from_size"]], "image_center() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.image_center"]], "image_index_to_phys() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.image_index_to_phys"]], "initialize_rand_state() (in module medipt.utils.random_float)": [[7, "medipt.utils.random_float.initialize_rand_state"]], "medipt.utils": [[7, "module-medipt.utils"]], "medipt.utils.image_input_output_space": [[7, "module-medipt.utils.image_input_output_space"]], "medipt.utils.random_float": [[7, "module-medipt.utils.random_float"]], "medipt.utils.spatial_utils": [[7, "module-medipt.utils.spatial_utils"]], "physical_image_size() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.physical_image_size"]], "physical_image_size_no_origin() (in module medipt.utils.spatial_utils)": [[7, "medipt.utils.spatial_utils.physical_image_size_no_origin"]], "random_binomial() (in module medipt.utils.random_float)": [[7, "medipt.utils.random_float.random_binomial"]], "random_uniform_float() (in module medipt.utils.random_float)": [[7, "medipt.utils.random_float.random_uniform_float"]], "resolve_input_output_space() (in module medipt.utils.image_input_output_space)": [[7, "medipt.utils.image_input_output_space.resolve_input_output_space"]], "set_num_workers() (in module medipt.utils)": [[7, "medipt.utils.set_num_workers"]]}}) \ No newline at end of file