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| import gradio as gr | |
| import earthview as ev | |
| import utils | |
| import random | |
| import pandas as pd | |
| import os | |
| # Load the Satellogic dataset | |
| dataset = ev.load_dataset("satellogic", streaming=True).shuffle(seed=42) | |
| data_iter = iter(dataset) | |
| # File to store labels (will create if it doesn't exist) | |
| label_file = "labels.csv" | |
| # Initialize a DataFrame to hold labels (or load existing) | |
| if os.path.exists(label_file): | |
| labels_df = pd.read_csv(label_file) | |
| else: | |
| labels_df = pd.DataFrame(columns=["image_id", "bounds", "rating", "google_maps_link"]) | |
| def get_next_image(): | |
| global data_iter, labels_df | |
| while True: # Keep iterating until we find an unlabeled image | |
| try: | |
| sample = next(data_iter) | |
| except StopIteration: | |
| #refresh the dataset if we reach the end | |
| dataset = ev.load_dataset("satellogic", streaming=True).shuffle(seed=random.randint(0, 1000000)) | |
| data_iter = iter(dataset) | |
| continue | |
| sample = ev.item_to_images("satellogic", sample) | |
| image = sample["rgb"][0] # Get the first RGB image | |
| metadata = sample["metadata"] | |
| bounds = metadata["bounds"] | |
| google_maps_link = utils.get_google_map_link(sample, "satellogic") | |
| #generate a unique image ID: | |
| image_id = (str(bounds)) | |
| # Check if image is already labeled | |
| if image_id not in labels_df["image_id"].values: | |
| return image, image_id, bounds, google_maps_link | |
| def rate_image(image_id, bounds, rating, google_maps_link): | |
| global labels_df | |
| # Add the rating to the DataFrame | |
| new_row = pd.DataFrame({"image_id": [image_id], "bounds": [bounds], "rating": [rating], "google_maps_link": [google_maps_link]}) | |
| labels_df = pd.concat([labels_df, new_row], ignore_index=True) | |
| # Save the DataFrame to CSV | |
| labels_df.to_csv(label_file, index=False) | |
| # Get the next image and its details | |
| next_image, next_image_id, next_bounds, next_google_maps_link = get_next_image() | |
| return next_image, next_image_id, next_bounds, next_google_maps_link | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=rate_image, | |
| inputs=[ | |
| gr.Textbox(label="Image ID", visible=False), | |
| gr.Textbox(label="Bounds", visible=False), | |
| gr.Radio(["Cool", "Not Cool"], label="Rating"), | |
| gr.Textbox(label="Google Maps Link"), | |
| ], | |
| outputs=[ | |
| gr.Image(label="Satellite Image"), | |
| gr.Textbox(label="Image ID", visible=False), | |
| gr.Textbox(label="Bounds", visible=False), | |
| gr.Textbox(label="Google Maps Link"), | |
| ], | |
| title="TerraNomaly - Satellite Image Labeling", | |
| description="Rate satellite images as 'Cool' or 'Not Cool'.", | |
| live=False, | |
| ) | |
| # Get the first image and its details | |
| initial_image, initial_image_id, initial_bounds, initial_google_maps_link = get_next_image() | |
| # Set the initial values for the output components | |
| iface.launch( | |
| share=True, | |
| initial_outputs=[ | |
| initial_image, | |
| initial_image_id, | |
| initial_bounds, | |
| initial_google_maps_link, | |
| ], | |
| ) |