Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import sys | |
| import csv | |
| import numpy as np | |
| import gradio as gr | |
| import nibabel as nib | |
| import matplotlib.pyplot as plt | |
| from scipy import ndimage | |
| from huggingface_hub import from_pretrained_keras | |
| csv.field_size_limit(sys.maxsize) | |
| def read_nifti_file(filepath): | |
| """Read and load volume""" | |
| # Read file | |
| scan = nib.load(filepath) | |
| # Get raw data | |
| scan = scan.get_fdata() | |
| return scan | |
| def normalize(volume): | |
| """Normalize the volume""" | |
| min = -1000 | |
| max = 400 | |
| volume[volume < min] = min | |
| volume[volume > max] = max | |
| volume = (volume - min) / (max - min) | |
| volume = volume.astype("float32") | |
| return volume | |
| def resize_volume(img): | |
| """Resize across z-axis""" | |
| # Set the desired depth | |
| desired_depth = 64 | |
| desired_width = 128 | |
| desired_height = 128 | |
| # Get current depth | |
| current_depth = img.shape[-1] | |
| current_width = img.shape[0] | |
| current_height = img.shape[1] | |
| # Compute depth factor | |
| depth = current_depth / desired_depth | |
| width = current_width / desired_width | |
| height = current_height / desired_height | |
| depth_factor = 1 / depth | |
| width_factor = 1 / width | |
| height_factor = 1 / height | |
| # Rotate | |
| img = ndimage.rotate(img, 90, reshape=False) | |
| # Resize across z-axis | |
| img = ndimage.zoom(img, (width_factor, height_factor, depth_factor), order=1) | |
| return img | |
| def process_scan(path): | |
| """Read and resize volume""" | |
| # Read scan | |
| volume = read_nifti_file(path) | |
| # Normalize | |
| volume = normalize(volume) | |
| # Resize width, height and depth | |
| volume = resize_volume(volume) | |
| return volume | |
| def plot_slices(num_rows, num_columns, width, height, data): | |
| """Plot a montage of 20 CT slices""" | |
| data = np.rot90(np.array(data)) | |
| data = np.transpose(data) | |
| data = np.reshape(data, (num_rows, num_columns, width, height)) | |
| rows_data, columns_data = data.shape[0], data.shape[1] | |
| heights = [slc[0].shape[0] for slc in data] | |
| widths = [slc.shape[1] for slc in data[0]] | |
| fig_width = 12.0 | |
| fig_height = fig_width * sum(heights) / sum(widths) | |
| f, axarr = plt.subplots( | |
| rows_data, | |
| columns_data, | |
| figsize=(fig_width, fig_height), | |
| gridspec_kw={"height_ratios": heights}, | |
| ) | |
| for i in range(rows_data): | |
| for j in range(columns_data): | |
| axarr[i, j].imshow(data[i][j], cmap="gray") | |
| axarr[i, j].axis("off") | |
| return f | |
| def infer(filename): | |
| vol = process_scan(filename.name) | |
| vol = np.expand_dims(vol, axis=0) | |
| prediction = model.predict(vol)[0] | |
| scores = [1 - prediction[0], prediction[0]] | |
| class_names = ["normal", "abnormal"] | |
| result = [] | |
| for score, name in zip(scores, class_names): | |
| result = result + [f"This model is {(100 * score):.2f} percent confident that CT scan is {name}"] | |
| return result, plot_slices(2, 10, 128, 128, vol[0, :, :, :20]) | |
| if __name__ == "__main__": | |
| model = from_pretrained_keras('keras-io/3D_CNN_Pneumonia') | |
| inputs = gr.inputs.File() | |
| outputs = [gr.outputs.Textbox(), 'plot'] | |
| title = "Predicting Viral Pneumonia in CT scans using 3D CNN" | |
| description = "This space implements 3D convolutional neural network to predict the presence of viral pneumonia in computer tomography scans." | |
| article = """<p style='text-align: center'> | |
| <a href='https://keras.io/examples/vision/3D_image_classification/' target='_blank'>Keras Example given by Hasib Zunair</a> | |
| <br> | |
| Space by Faizan Shaikh | |
| </p> | |
| """ | |
| iface = gr.Interface( | |
| infer, | |
| inputs, | |
| outputs, | |
| title=title, | |
| article=article, | |
| description=description, | |
| examples=['example_1_normal.nii.gz'] | |
| ) | |
| iface.launch(enable_queue=True) |