torchio.visualization.plot_volume() - Code Metrics - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

torchio.visualization.plot_volume() F
last analyzed 2022-07-04 22:05 UTC

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	78
Code Lines	69

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	69
dl	0
loc	78
rs	2.9998
c	0
b	0
f	0
cc	15
nop	12

How to fix Long Method Complexity Many Parameters

Long Method

Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.

For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.

Commonly applied refactorings include:

If many parameters/temporary variables are present:
- Replace temporary variables with Query
- Introduce parameter object; often combined with preserve whole object
- If the above two are insufficient: Replace method with method object
If you have long conditionals: Decompose Conditional
Otherwise: Extract method

Complexity

Complex classes like torchio.visualization.plot_volume() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.

Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.

Many Parameters

Methods with many parameters are not only hard to understand, but their parameters also often become inconsistent when you need more, or different data.

There are several approaches to avoid long parameter lists:

If you can get the data from another object that the method knows about already: Replace the parameter with method call
If several parameters are part of another object: Preserve Whole Object
If parameters are not yet connected: Introduce Parameter Object

import warnings

import torch
import numpy as np

from .typing import TypePath
from .data.subject import Subject
from .data.image import Image, LabelMap
from .transforms.preprocessing.spatial.to_canonical import ToCanonical
from .transforms.preprocessing.intensity.rescale import RescaleIntensity


def import_mpl_plt():
    try:
        import matplotlib as mpl
        import matplotlib.pyplot as plt
    except ImportError as e:
        raise ImportError('Install matplotlib for plotting support') from e
    return mpl, plt


def rotate(image, radiological=True, n=-1):
    # Rotate for visualization purposes
    image = np.rot90(image, n)
    if radiological:
        image = np.fliplr(image)
    return image


def plot_volume(
        image: Image,
        radiological=True,
        channel=-1,  # default to foreground for binary maps
        axes=None,
        cmap=None,
        output_path=None,
        show=True,
        xlabels=True,
        percentiles=(0.5, 99.5),
        figsize=None,
        reorient=True,
        indices=None,
        ):
    _, plt = import_mpl_plt()
    fig = None
    if axes is None:
        fig, axes = plt.subplots(1, 3, figsize=figsize)
    sag_axis, cor_axis, axi_axis = axes

    if reorient:
        image = ToCanonical()(image)
    data = image.data[channel]
    if indices is None:
        indices = np.array(data.shape) // 2
    i, j, k = indices
    slice_x = rotate(data[i, :, :], radiological=radiological)
    slice_y = rotate(data[:, j, :], radiological=radiological)
    slice_z = rotate(data[:, :, k], radiological=radiological)
    kwargs = {}
    is_label = isinstance(image, LabelMap)
    if isinstance(cmap, dict):
        slices = slice_x, slice_y, slice_z
        slice_x, slice_y, slice_z = color_labels(slices, cmap)
    else:
        if cmap is None:
            cmap = 'cubehelix' if is_label else 'gray'
        kwargs['cmap'] = cmap
    if is_label:
        kwargs['interpolation'] = 'none'

    sr, sa, ss = image.spacing
    kwargs['origin'] = 'lower'

    if percentiles is not None:
        p1, p2 = np.percentile(data, percentiles)
        kwargs['vmin'] = p1
        kwargs['vmax'] = p2

    sag_aspect = ss / sa
    sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs)
    if xlabels:
        sag_axis.set_xlabel('A')
    sag_axis.set_ylabel('S')
    sag_axis.invert_xaxis()
    sag_axis.set_title('Sagittal')

    cor_aspect = ss / sr
    cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs)
    if xlabels:
        cor_axis.set_xlabel('R')
    cor_axis.set_ylabel('S')
    cor_axis.invert_xaxis()
    cor_axis.set_title('Coronal')

    axi_aspect = sa / sr
    axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs)
    if xlabels:
        axi_axis.set_xlabel('R')
    axi_axis.set_ylabel('A')
    axi_axis.invert_xaxis()
    axi_axis.set_title('Axial')

    plt.tight_layout()
    if output_path is not None and fig is not None:
        fig.savefig(output_path)
    if show:
        plt.show()


def plot_subject(
        subject: Subject,
        cmap_dict=None,
        show=True,
        output_path=None,
        figsize=None,
        clear_axes=True,
        **kwargs,
        ):
    _, plt = import_mpl_plt()
    num_images = len(subject)
    many_images = num_images > 2
    subplots_kwargs = {'figsize': figsize}
    try:
        if clear_axes:
            subject.check_consistent_spatial_shape()
            subplots_kwargs['sharex'] = 'row' if many_images else 'col'
            subplots_kwargs['sharey'] = 'row' if many_images else 'col'
    except RuntimeError:  # different shapes in subject
        pass
    args = (3, num_images) if many_images else (num_images, 3)
    fig, axes = plt.subplots(*args, **subplots_kwargs)
    # The array of axes must be 2D so that it can be indexed correctly within
    # the plot_volume() function
    axes = axes.T if many_images else axes.reshape(-1, 3)
    iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
    axes_names = 'sagittal', 'coronal', 'axial'
    for image_index, (name, image) in iterable:
        image_axes = axes[image_index]
        cmap = None
        if cmap_dict is not None and name in cmap_dict:
            cmap = cmap_dict[name]
        last_row = image_index == len(axes) - 1
        plot_volume(
            image,
            axes=image_axes,
            show=False,
            cmap=cmap,
            xlabels=last_row,
            **kwargs,
        )
        for axis, axis_name in zip(image_axes, axes_names):
            axis.set_title(f'{name} ({axis_name})')
    plt.tight_layout()
    if output_path is not None:
        fig.savefig(output_path)
    if show:
        plt.show()
    plt.close(fig)


def color_labels(arrays, cmap_dict):
    results = []
    for array in arrays:
        si, sj = array.shape
        rgb = np.zeros((si, sj, 3), dtype=np.uint8)
        for label, color in cmap_dict.items():
            if isinstance(color, str):
                mpl, _ = import_mpl_plt()
                color = mpl.colors.to_rgb(color)
                color = [255 * n for n in color]
            rgb[array == label] = color
        results.append(rgb)
    return results


def make_gif(
        tensor: torch.Tensor,
        axis: int,
        duration: float,  # of full gif
        output_path: TypePath,
        loop: int = 0,
        optimize: bool = True,
        rescale: bool = True,
        reverse: bool = False,
        ) -> None:
    try:
        from PIL import Image as ImagePIL
    except ModuleNotFoundError as e:
        message = (
            'Please install Pillow to use Image.to_gif():'
            ' pip install Pillow'
        )
        raise RuntimeError(message) from e
    tensor = RescaleIntensity((0, 255))(tensor) if rescale else tensor
    single_channel = len(tensor) == 1

    # Move channels dimension to the end and bring selected axis to 0
    axes = np.roll(range(1, 4), -axis)
    tensor = tensor.permute(*axes, 0)

    if single_channel:
        mode = 'P'
        tensor = tensor[..., 0]
    else:
        mode = 'RGB'
    array = tensor.byte().numpy()
    n = 2 if axis == 1 else 1
    images = [ImagePIL.fromarray(rotate(i, n=n)).convert(mode) for i in array]
    num_images = len(images)
    images = list(reversed(images)) if reverse else images
    frame_duration_ms = duration / num_images * 1000
    if frame_duration_ms < 10:
        fps = round(1000 / frame_duration_ms)
        frame_duration_ms = 10
        new_duration = frame_duration_ms * num_images / 1000
        message = (
            'The computed frame rate from the given duration is too high'
            f' ({fps} fps). The highest possible frame rate in the GIF'
            ' file format specification is 100 fps. The duration has been set'
            f' to {new_duration:.1f} seconds, instead of {duration:.1f}'
        )
        warnings.warn(message)
    images[0].save(
        output_path,
        save_all=True,
        append_images=images[1:],
        optimize=optimize,
        duration=frame_duration_ms,
        loop=loop,
    )


1			import warnings
2
3			import torch
4			import numpy as np
5
6			from .typing import TypePath
7			from .data.subject import Subject
8			from .data.image import Image, LabelMap
9			from .transforms.preprocessing.spatial.to_canonical import ToCanonical
10			from .transforms.preprocessing.intensity.rescale import RescaleIntensity
11
12
13			def import_mpl_plt():
14			try:
15			import matplotlib as mpl
16			import matplotlib.pyplot as plt
17			except ImportError as e:
18			raise ImportError('Install matplotlib for plotting support') from e
19			return mpl, plt
20
21
22			def rotate(image, radiological=True, n=-1):
23			# Rotate for visualization purposes
24			image = np.rot90(image, n)
25			if radiological:
26			image = np.fliplr(image)
27			return image
28
29
30			def plot_volume(
31			image: Image,
32			radiological=True,
33			channel=-1, # default to foreground for binary maps
34			axes=None,
35			cmap=None,
36			output_path=None,
37			show=True,
38			xlabels=True,
39			percentiles=(0.5, 99.5),
40			figsize=None,
41			reorient=True,
42			indices=None,
43			):
44			_, plt = import_mpl_plt()
45			fig = None
46			if axes is None:
47			fig, axes = plt.subplots(1, 3, figsize=figsize)
48			sag_axis, cor_axis, axi_axis = axes
49
50			if reorient:
51			image = ToCanonical()(image)
52			data = image.data[channel]
53			if indices is None:
54			indices = np.array(data.shape) // 2
55			i, j, k = indices
56			slice_x = rotate(data[i, :, :], radiological=radiological)
57			slice_y = rotate(data[:, j, :], radiological=radiological)
58			slice_z = rotate(data[:, :, k], radiological=radiological)
59			kwargs = {}
60			is_label = isinstance(image, LabelMap)
61			if isinstance(cmap, dict):
62			slices = slice_x, slice_y, slice_z
63			slice_x, slice_y, slice_z = color_labels(slices, cmap)
64			else:
65			if cmap is None:
66			cmap = 'cubehelix' if is_label else 'gray'
67			kwargs['cmap'] = cmap
68			if is_label:
69			kwargs['interpolation'] = 'none'
70
71			sr, sa, ss = image.spacing
72			kwargs['origin'] = 'lower'
73
74			if percentiles is not None:
75			p1, p2 = np.percentile(data, percentiles)
76			kwargs['vmin'] = p1
77			kwargs['vmax'] = p2
78
79			sag_aspect = ss / sa
80			sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs)
81			if xlabels:
82			sag_axis.set_xlabel('A')
83			sag_axis.set_ylabel('S')
84			sag_axis.invert_xaxis()
85			sag_axis.set_title('Sagittal')
86
87			cor_aspect = ss / sr
88			cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs)
89			if xlabels:
90			cor_axis.set_xlabel('R')
91			cor_axis.set_ylabel('S')
92			cor_axis.invert_xaxis()
93			cor_axis.set_title('Coronal')
94
95			axi_aspect = sa / sr
96			axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs)
97			if xlabels:
98			axi_axis.set_xlabel('R')
99			axi_axis.set_ylabel('A')
100			axi_axis.invert_xaxis()
101			axi_axis.set_title('Axial')
102
103			plt.tight_layout()
104			if output_path is not None and fig is not None:
105			fig.savefig(output_path)
106			if show:
107			plt.show()
108
109
110			def plot_subject(
111			subject: Subject,
112			cmap_dict=None,
113			show=True,
114			output_path=None,
115			figsize=None,
116			clear_axes=True,
117			**kwargs,
118			):
119			_, plt = import_mpl_plt()
120			num_images = len(subject)
121			many_images = num_images > 2
122			subplots_kwargs = {'figsize': figsize}
123			try:
124			if clear_axes:
125			subject.check_consistent_spatial_shape()
126			subplots_kwargs['sharex'] = 'row' if many_images else 'col'
127			subplots_kwargs['sharey'] = 'row' if many_images else 'col'
128			except RuntimeError: # different shapes in subject
129			pass
130			args = (3, num_images) if many_images else (num_images, 3)
131			fig, axes = plt.subplots(args, *subplots_kwargs)
132			# The array of axes must be 2D so that it can be indexed correctly within
133			# the plot_volume() function
134			axes = axes.T if many_images else axes.reshape(-1, 3)
135			iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
136			axes_names = 'sagittal', 'coronal', 'axial'
137			for image_index, (name, image) in iterable:
138			image_axes = axes[image_index]
139			cmap = None
140			if cmap_dict is not None and name in cmap_dict:
141			cmap = cmap_dict[name]
142			last_row = image_index == len(axes) - 1
143			plot_volume(
144			image,
145			axes=image_axes,
146			show=False,
147			cmap=cmap,
148			xlabels=last_row,
149			**kwargs,
150			)
151			for axis, axis_name in zip(image_axes, axes_names):
152			axis.set_title(f'{name} ({axis_name})')
153			plt.tight_layout()
154			if output_path is not None:
155			fig.savefig(output_path)
156			if show:
157			plt.show()
158			plt.close(fig)
159
160
161			def color_labels(arrays, cmap_dict):
162			results = []
163			for array in arrays:
164			si, sj = array.shape
165			rgb = np.zeros((si, sj, 3), dtype=np.uint8)
166			for label, color in cmap_dict.items():
167			if isinstance(color, str):
168			mpl, _ = import_mpl_plt()
169			color = mpl.colors.to_rgb(color)
170			color = [255 * n for n in color]
171			rgb[array == label] = color
172			results.append(rgb)
173			return results
174
175
176			def make_gif(
177			tensor: torch.Tensor,
178			axis: int,
179			duration: float, # of full gif
180			output_path: TypePath,
181			loop: int = 0,
182			optimize: bool = True,
183			rescale: bool = True,
184			reverse: bool = False,
185			) -> None:
186			try:
187			from PIL import Image as ImagePIL
188			except ModuleNotFoundError as e:
189			message = (
190			'Please install Pillow to use Image.to_gif():'
191			' pip install Pillow'
192			)
193			raise RuntimeError(message) from e
194			tensor = RescaleIntensity((0, 255))(tensor) if rescale else tensor
195			single_channel = len(tensor) == 1
196
197			# Move channels dimension to the end and bring selected axis to 0
198			axes = np.roll(range(1, 4), -axis)
199			tensor = tensor.permute(*axes, 0)
200
201			if single_channel:
202			mode = 'P'
203			tensor = tensor[..., 0]
204			else:
205			mode = 'RGB'
206			array = tensor.byte().numpy()
207			n = 2 if axis == 1 else 1
208			images = [ImagePIL.fromarray(rotate(i, n=n)).convert(mode) for i in array]
209			num_images = len(images)
210			images = list(reversed(images)) if reverse else images
211			frame_duration_ms = duration / num_images * 1000
212			if frame_duration_ms < 10:
213			fps = round(1000 / frame_duration_ms)
214			frame_duration_ms = 10
215			new_duration = frame_duration_ms * num_images / 1000
216			message = (
217			'The computed frame rate from the given duration is too high'
218			f' ({fps} fps). The highest possible frame rate in the GIF'
219			' file format specification is 100 fps. The duration has been set'
220			f' to {new_duration:.1f} seconds, instead of {duration:.1f}'
221			)
222			warnings.warn(message)
223			images[0].save(
224			output_path,
225			save_all=True,
226			append_images=images[1:],
227			optimize=optimize,
228			duration=frame_duration_ms,
229			loop=loop,
230			)
231

fepegar / torchio

torchio.visualization.plot_volume() F last analyzed 2022-07-04 22:05 UTC

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How to fix Long Method Complexity Many Parameters

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torchio.visualization.plot_volume() F
last analyzed 2022-07-04 22:05 UTC