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| import gradio as gr | |
| import numpy as np | |
| import nrrd | |
| import matplotlib.pyplot as plt | |
| import io | |
| from PIL import Image | |
| # Set Matplotlib to use the 'Agg' backend | |
| plt.switch_backend('Agg') | |
| def load_nrrd(file_obj): | |
| data, _ = nrrd.read(file_obj) | |
| return data | |
| def get_num_slices(data, view): | |
| if view == 'Axial': | |
| return data.shape[2] | |
| elif view == 'Coronal': | |
| return data.shape[1] | |
| elif view == 'Sagittal': | |
| return data.shape[0] | |
| def extract_slice(data, view, slice_index): | |
| if view == 'Axial': | |
| return data[:, :, slice_index] | |
| elif view == 'Coronal': | |
| return data[:, slice_index, :] | |
| elif view == 'Sagittal': | |
| return data[slice_index, :, :] | |
| def resize_slice(slice_image, view, data_shape): | |
| if view == 'Axial': | |
| return slice_image # No resizing needed for Axial view | |
| else: | |
| # For Coronal and Sagittal views, find the two largest dimensions | |
| if view == 'Coronal': | |
| resize_dims = (data_shape[0], data_shape[2]) # (x, z) | |
| elif view == 'Sagittal': | |
| resize_dims = (data_shape[1], data_shape[2]) # (y, z) | |
| # Resize the slice image while maintaining the aspect ratio | |
| resized_image = Image.fromarray(slice_image).resize(resize_dims, Image.ANTIALIAS) | |
| return np.array(resized_image) | |
| def visualize_slice(file_obj, view, slice_index): | |
| data = load_nrrd(file_obj) | |
| data_shape = data.shape # Get the original x, y, z dimensions | |
| slice_image = extract_slice(data, view, slice_index) | |
| # Resize the slice image if necessary | |
| slice_image = resize_slice(slice_image, view, data_shape) | |
| # Plot the slice | |
| fig, ax = plt.subplots() | |
| ax.imshow(slice_image, cmap='gray') | |
| plt.axis('off') | |
| # Convert matplotlib figure to PIL Image | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format='png') | |
| plt.close(fig) | |
| buf.seek(0) | |
| pil_img = Image.open(buf) | |
| return pil_img | |
| def update_slider(file_obj, view): | |
| data = load_nrrd(file_obj) | |
| num_slices = get_num_slices(data, view) | |
| return gr.update(maximum=num_slices-1, value=0) | |
| with gr.Blocks() as app: | |
| gr.Markdown("## NRRD Slice Visualizer") | |
| gr.Markdown("Upload an NRRD file, select a view, and use the slider to select and visualize slices.") | |
| file_input = gr.File(label="Upload NRRD File") | |
| view_selector = gr.Radio(choices=["Axial", "Coronal", "Sagittal"], label="View Selector", value="Axial") | |
| slider = gr.Slider(minimum=0, maximum=1, step=1, value=0, label="Slice Selector") | |
| image_output = gr.Image(type="pil", label="Selected Slice") | |
| file_input.change(fn=update_slider, inputs=[file_input, view_selector], outputs=slider) | |
| view_selector.change(fn=update_slider, inputs=[file_input, view_selector], outputs=slider) | |
| file_input.change(fn=visualize_slice, inputs=[file_input, view_selector, slider], outputs=image_output) | |
| slider.change(fn=visualize_slice, inputs=[file_input, view_selector, slider], outputs=image_output) | |
| view_selector.change(fn=visualize_slice, inputs=[file_input, view_selector, slider], outputs=image_output) | |
| app.launch() |