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| import gradio as gr | |
| import rasterio | |
| import matplotlib.pyplot as plt | |
| def visualize_image(file_path): | |
| # Read the 10-band image using rasterio | |
| dataset = rasterio.open(file_path) | |
| # Get the number of bands in the image | |
| num_bands = dataset.count | |
| # Calculate the number of rows required for subplots | |
| num_rows = (num_bands + 1) // 2 | |
| # Create subplots to visualize each band | |
| fig, axes = plt.subplots(num_rows, 2, figsize=(10, 6 * num_rows)) | |
| # Loop through each band and plot it | |
| for band_index in range(1, num_bands + 1): | |
| # Read the band data | |
| band = dataset.read(band_index) | |
| # Determine the subplot position | |
| row_index = (band_index - 1) // 2 | |
| col_index = (band_index - 1) % 2 | |
| # Plot the band on the corresponding subplot | |
| axes[row_index, col_index].imshow(band, cmap='terrain') | |
| axes[row_index, col_index].set_title(f"Band {band_index}") | |
| # Remove any empty subplots if there are odd number of bands | |
| if num_bands % 2 != 0: | |
| fig.delaxes(axes[num_rows - 1, 1]) | |
| # Adjust the layout and display the visualization | |
| plt.tight_layout() | |
| # Save the figure to a temporary file and close it to release resources | |
| temp_file = "/tmp/visualization.png" | |
| plt.savefig(temp_file) | |
| plt.close() | |
| # Return the temporary file path, which will be used by Gradio to display the visualization | |
| return temp_file | |
| # Interface for the Gradio app | |
| iface = gr.Interface( | |
| fn=visualize_image, | |
| inputs=gr.inputs.File(), | |
| outputs="image", | |
| live=True, | |
| capture_session=True, | |
| title="Earth Engine Image Segmentation", | |
| description="Upload a TIFF image and segmentation all its bands.", | |
| ) | |
| # Launch the Gradio app | |
| iface.launch() |