torchio.visualization.make_gif() - Code Metrics - Inspection of "Add support to save GIFs (#680)" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 6deb01...ca4890 )

by Fernando

created 2021-10-03 13:24 UTC

torchio.visualization.make_gif() B

↳ Parent: torchio.visualization

Complexity

Conditions

Size

Total Lines	54
Code Lines	43

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	43
dl	0
loc	54
rs	7.448
c	0
b	0
f	0
cc	7
nop	8

How to fix Long Method Many Parameters

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,
        ):
    _, 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]
    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()


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

fepegar / torchio

Push — master ( 6deb01...ca4890 )

torchio.visualization.make_gif() B

Complexity

Size

Duplication

Importance

How to fix Long Method Many Parameters

Long Method

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