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import numpy as np |
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from .data.image import Image, LabelMap |
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from .data.subject import Subject |
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from .transforms.preprocessing.spatial.to_canonical import ToCanonical |
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8
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def import_pyplot(): |
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try: |
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import matplotlib.pyplot as plt |
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except ImportError as e: |
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raise ImportError('Install matplotlib for plotting support') from e |
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return plt |
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def rotate(image): |
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return np.rot90(image) |
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def plot_image( |
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image: Image, |
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channel=0, |
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axes=None, |
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show=True, |
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cmap=None, |
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): |
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plt = import_pyplot() |
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if axes is None: |
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_, axes = plt.subplots(1, 3) |
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image = ToCanonical()(image) |
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data = image.data[channel] |
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indices = np.array(data.shape) // 2 |
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i, j, k = indices |
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slice_x = rotate(data[i, :, :]) |
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slice_y = rotate(data[:, j, :]) |
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slice_z = rotate(data[:, :, k]) |
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kwargs = {} |
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is_label = isinstance(image, LabelMap) |
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if isinstance(cmap, dict): |
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slices = slice_x, slice_y, slice_z |
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slice_x, slice_y, slice_z = color_labels(slices, cmap) |
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else: |
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if cmap is None: |
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cmap = 'inferno' if is_label else 'gray' |
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kwargs['cmap'] = cmap |
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if is_label: |
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kwargs['interpolation'] = 'none' |
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x_extent, y_extent, z_extent = [tuple(b) for b in image.bounds.T] |
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axes[0].imshow(slice_x, extent=y_extent + z_extent, **kwargs) |
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axes[1].imshow(slice_y, extent=x_extent + z_extent, **kwargs) |
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axes[2].imshow(slice_z, extent=x_extent + y_extent, **kwargs) |
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plt.tight_layout() |
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if show: |
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plt.show() |
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def plot_subject( |
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subject: Subject, |
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cmap_dict=None, |
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): |
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plt = import_pyplot() |
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_, axes = plt.subplots(len(subject), 3) |
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iterable = enumerate(subject.get_images_dict(intensity_only=False).items()) |
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axes_names = 'sagittal', 'coronal', 'axial' |
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for row, (name, image) in iterable: |
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row_axes = axes[row] |
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cmap = None |
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if cmap_dict is not None and name in cmap_dict: |
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cmap = cmap_dict[name] |
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plot_image(image, axes=row_axes, show=False, cmap=cmap) |
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for axis, axis_name in zip(row_axes, axes_names): |
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axis.set_title(f'{name} ({axis_name})') |
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plt.tight_layout() |
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plt.show() |
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def color_labels(arrays, cmap_dict): |
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results = [] |
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for array in arrays: |
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si, sj = array.shape |
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rgb = np.zeros((si, sj, 3), dtype=np.uint8) |
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for label, value in cmap_dict.items(): |
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rgb[array == label] = value |
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results.append(rgb) |
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return results |
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fepegar /
torchio
