torchio.visualization.plot_volume() - Code Metrics - Inspection of "Add plot_volume indices parameter" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#682)

unknown

created 2021-10-06 13:02 UTC

torchio.visualization.plot_volume() F

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	82
Code Lines	72

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	72
dl	0
loc	82
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')
    
    sag_axis.axis('off')
    cor_axis.axis('off')
    axi_axis.axis('off')

    plt.tight_layout()
    if output_path is not None and fig is not None:
        fig.savefig(output_path, transparent=True)
    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			sag_axis.axis('off')
104			cor_axis.axis('off')
105			axi_axis.axis('off')
106
107			plt.tight_layout()
108			if output_path is not None and fig is not None:
109			fig.savefig(output_path, transparent=True)
110			if show:
111			plt.show()
112
113
114			def plot_subject(
115			subject: Subject,
116			cmap_dict=None,
117			show=True,
118			output_path=None,
119			figsize=None,
120			clear_axes=True,
121			**kwargs,
122			):
123			_, plt = import_mpl_plt()
124			num_images = len(subject)
125			many_images = num_images > 2
126			subplots_kwargs = {'figsize': figsize}
127			try:
128			if clear_axes:
129			subject.check_consistent_spatial_shape()
130			subplots_kwargs['sharex'] = 'row' if many_images else 'col'
131			subplots_kwargs['sharey'] = 'row' if many_images else 'col'
132			except RuntimeError: # different shapes in subject
133			pass
134			args = (3, num_images) if many_images else (num_images, 3)
135			fig, axes = plt.subplots(args, *subplots_kwargs)
136			# The array of axes must be 2D so that it can be indexed correctly within
137			# the plot_volume() function
138			axes = axes.T if many_images else axes.reshape(-1, 3)
139			iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
140			axes_names = 'sagittal', 'coronal', 'axial'
141			for image_index, (name, image) in iterable:
142			image_axes = axes[image_index]
143			cmap = None
144			if cmap_dict is not None and name in cmap_dict:
145			cmap = cmap_dict[name]
146			last_row = image_index == len(axes) - 1
147			plot_volume(
148			image,
149			axes=image_axes,
150			show=False,
151			cmap=cmap,
152			xlabels=last_row,
153			**kwargs,
154			)
155			for axis, axis_name in zip(image_axes, axes_names):
156			axis.set_title(f'{name} ({axis_name})')
157			plt.tight_layout()
158			if output_path is not None:
159			fig.savefig(output_path)
160			if show:
161			plt.show()
162			plt.close(fig)
163
164
165			def color_labels(arrays, cmap_dict):
166			results = []
167			for array in arrays:
168			si, sj = array.shape
169			rgb = np.zeros((si, sj, 3), dtype=np.uint8)
170			for label, color in cmap_dict.items():
171			if isinstance(color, str):
172			mpl, _ = import_mpl_plt()
173			color = mpl.colors.to_rgb(color)
174			color = [255 * n for n in color]
175			rgb[array == label] = color
176			results.append(rgb)
177			return results
178
179
180			def make_gif(
181			tensor: torch.Tensor,
182			axis: int,
183			duration: float, # of full gif
184			output_path: TypePath,
185			loop: int = 0,
186			optimize: bool = True,
187			rescale: bool = True,
188			reverse: bool = False,
189			) -> None:
190			try:
191			from PIL import Image as ImagePIL
192			except ModuleNotFoundError as e:
193			message = (
194			'Please install Pillow to use Image.to_gif():'
195			' pip install Pillow'
196			)
197			raise RuntimeError(message) from e
198			tensor = RescaleIntensity((0, 255))(tensor) if rescale else tensor
199			single_channel = len(tensor) == 1
200
201			# Move channels dimension to the end and bring selected axis to 0
202			axes = np.roll(range(1, 4), -axis)
203			tensor = tensor.permute(*axes, 0)
204
205			if single_channel:
206			mode = 'P'
207			tensor = tensor[..., 0]
208			else:
209			mode = 'RGB'
210			array = tensor.byte().numpy()
211			n = 2 if axis == 1 else 1
212			images = [ImagePIL.fromarray(rotate(i, n=n)).convert(mode) for i in array]
213			num_images = len(images)
214			images = list(reversed(images)) if reverse else images
215			frame_duration_ms = duration / num_images * 1000
216			if frame_duration_ms < 10:
217			fps = round(1000 / frame_duration_ms)
218			frame_duration_ms = 10
219			new_duration = frame_duration_ms * num_images / 1000
220			message = (
221			'The computed frame rate from the given duration is too high'
222			f' ({fps} fps). The highest possible frame rate in the GIF'
223			' file format specification is 100 fps. The duration has been set'
224			f' to {new_duration:.1f} seconds, instead of {duration:.1f}'
225			)
226			warnings.warn(message)
227			images[0].save(
228			output_path,
229			save_all=True,
230			append_images=images[1:],
231			optimize=optimize,
232			duration=frame_duration_ms,
233			loop=loop,
234			)
235

fepegar / torchio

Pull Request — master (#682)

torchio.visualization.plot_volume() F

Complexity

Size

Duplication

Importance

How to fix Long Method Complexity Many Parameters

Long Method

Complexity

Many Parameters

Duplication Side-by-Side

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