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 12:59 UTC

torchio.visualization.plot_volume() F

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	83
Code Lines	73

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	73
dl	0
loc	83
rs	2.9836
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')
    
    axes.axis('off')
    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			axes.axis('off')
104			sag_axis.axis('off')
105			cor_axis.axis('off')
106			axi_axis.axis('off')
107
108			plt.tight_layout()
109			if output_path is not None and fig is not None:
110			fig.savefig(output_path, transparent=True)
111			if show:
112			plt.show()
113
114
115			def plot_subject(
116			subject: Subject,
117			cmap_dict=None,
118			show=True,
119			output_path=None,
120			figsize=None,
121			clear_axes=True,
122			**kwargs,
123			):
124			_, plt = import_mpl_plt()
125			num_images = len(subject)
126			many_images = num_images > 2
127			subplots_kwargs = {'figsize': figsize}
128			try:
129			if clear_axes:
130			subject.check_consistent_spatial_shape()
131			subplots_kwargs['sharex'] = 'row' if many_images else 'col'
132			subplots_kwargs['sharey'] = 'row' if many_images else 'col'
133			except RuntimeError: # different shapes in subject
134			pass
135			args = (3, num_images) if many_images else (num_images, 3)
136			fig, axes = plt.subplots(args, *subplots_kwargs)
137			# The array of axes must be 2D so that it can be indexed correctly within
138			# the plot_volume() function
139			axes = axes.T if many_images else axes.reshape(-1, 3)
140			iterable = enumerate(subject.get_images_dict(intensity_only=False).items())
141			axes_names = 'sagittal', 'coronal', 'axial'
142			for image_index, (name, image) in iterable:
143			image_axes = axes[image_index]
144			cmap = None
145			if cmap_dict is not None and name in cmap_dict:
146			cmap = cmap_dict[name]
147			last_row = image_index == len(axes) - 1
148			plot_volume(
149			image,
150			axes=image_axes,
151			show=False,
152			cmap=cmap,
153			xlabels=last_row,
154			**kwargs,
155			)
156			for axis, axis_name in zip(image_axes, axes_names):
157			axis.set_title(f'{name} ({axis_name})')
158			plt.tight_layout()
159			if output_path is not None:
160			fig.savefig(output_path)
161			if show:
162			plt.show()
163			plt.close(fig)
164
165
166			def color_labels(arrays, cmap_dict):
167			results = []
168			for array in arrays:
169			si, sj = array.shape
170			rgb = np.zeros((si, sj, 3), dtype=np.uint8)
171			for label, color in cmap_dict.items():
172			if isinstance(color, str):
173			mpl, _ = import_mpl_plt()
174			color = mpl.colors.to_rgb(color)
175			color = [255 * n for n in color]
176			rgb[array == label] = color
177			results.append(rgb)
178			return results
179
180
181			def make_gif(
182			tensor: torch.Tensor,
183			axis: int,
184			duration: float, # of full gif
185			output_path: TypePath,
186			loop: int = 0,
187			optimize: bool = True,
188			rescale: bool = True,
189			reverse: bool = False,
190			) -> None:
191			try:
192			from PIL import Image as ImagePIL
193			except ModuleNotFoundError as e:
194			message = (
195			'Please install Pillow to use Image.to_gif():'
196			' pip install Pillow'
197			)
198			raise RuntimeError(message) from e
199			tensor = RescaleIntensity((0, 255))(tensor) if rescale else tensor
200			single_channel = len(tensor) == 1
201
202			# Move channels dimension to the end and bring selected axis to 0
203			axes = np.roll(range(1, 4), -axis)
204			tensor = tensor.permute(*axes, 0)
205
206			if single_channel:
207			mode = 'P'
208			tensor = tensor[..., 0]
209			else:
210			mode = 'RGB'
211			array = tensor.byte().numpy()
212			n = 2 if axis == 1 else 1
213			images = [ImagePIL.fromarray(rotate(i, n=n)).convert(mode) for i in array]
214			num_images = len(images)
215			images = list(reversed(images)) if reverse else images
216			frame_duration_ms = duration / num_images * 1000
217			if frame_duration_ms < 10:
218			fps = round(1000 / frame_duration_ms)
219			frame_duration_ms = 10
220			new_duration = frame_duration_ms * num_images / 1000
221			message = (
222			'The computed frame rate from the given duration is too high'
223			f' ({fps} fps). The highest possible frame rate in the GIF'
224			' file format specification is 100 fps. The duration has been set'
225			f' to {new_duration:.1f} seconds, instead of {duration:.1f}'
226			)
227			warnings.warn(message)
228			images[0].save(
229			output_path,
230			save_all=True,
231			append_images=images[1:],
232			optimize=optimize,
233			duration=frame_duration_ms,
234			loop=loop,
235			)
236

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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