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| # based on https://huggingface.co/spaces/NimaBoscarino/climategan/blob/main/app.py # noqa: E501 | |
| # thank you @NimaBoscarino | |
| import os | |
| import gradio as gr | |
| import googlemaps | |
| from skimage import io | |
| from urllib import parse | |
| import numpy as np | |
| from climategan_wrapper import ClimateGAN | |
| def predict(cg: ClimateGAN, api_key): | |
| def _predict(*args): | |
| image = place = painter = None | |
| if len(args) == 2: | |
| image = args[0] | |
| painter = args[1] | |
| else: | |
| assert len(args) == 3, "Unknown number of inputs {}".format(len(args)) | |
| image, place, painter = args | |
| if api_key and place: | |
| geocode_result = gmaps.geocode(place) | |
| address = geocode_result[0]["formatted_address"] | |
| static_map_url = f"https://maps.googleapis.com/maps/api/streetview?size=640x640&location={parse.quote(address)}&source=outdoor&key={api_key}" | |
| img_np = io.imread(static_map_url) | |
| else: | |
| img_np = image | |
| painters = { | |
| "ClimateGAN Painter": "climategan", | |
| "Stable Diffusion Painter": "stable_diffusion", | |
| "Both": "both", | |
| } | |
| output_dict = cg.infer_single(img_np, painters[painter], as_pil_image=True) | |
| input_image = output_dict["input"] | |
| masked_input = output_dict["masked_input"] | |
| wildfire = output_dict["wildfire"] | |
| smog = output_dict["smog"] | |
| depth = np.repeat(output_dict["depth"][..., None], 3, axis=-1) | |
| segmentation = output_dict["segmentation"] | |
| climategan_flood = output_dict.get( | |
| "climategan_flood", | |
| np.ones(input_image.shape) * 255, | |
| ) | |
| stable_flood = output_dict.get( | |
| "stable_flood", | |
| np.ones(input_image.shape) * 255, | |
| ) | |
| stable_copy_flood = output_dict.get( | |
| "stable_copy_flood", | |
| np.ones(input_image.shape) * 255, | |
| ) | |
| concat = output_dict.get( | |
| "concat", | |
| np.ones(input_image.shape) * 255, | |
| ) | |
| return ( | |
| input_image, | |
| masked_input, | |
| segmentation, | |
| depth, | |
| climategan_flood, | |
| stable_flood, | |
| stable_copy_flood, | |
| concat, | |
| wildfire, | |
| smog, | |
| ) | |
| return _predict | |
| if __name__ == "__main__": | |
| api_key = os.environ.get("GMAPS_API_KEY") | |
| gmaps = None | |
| if api_key is not None: | |
| gmaps = googlemaps.Client(key=api_key) | |
| cg = ClimateGAN( | |
| model_path="config/model/masker", | |
| dev_mode=os.environ.get("CG_DEV_MODE", "").lower() == "true", | |
| ) | |
| cg._setup_stable_diffusion() | |
| with gr.Blocks() as blocks: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("# ClimateGAN: Visualize Climate Change") | |
| gr.HTML( | |
| 'Climate change does not impact everyone equally. This Space shows the effects of the climate emergency, "one address at a time". Visit the original experience at <a href="https://thisclimatedoesnotexist.com/">ThisClimateDoesNotExist.com</a>.<br>Enter an address or upload a Street View image, and ClimateGAN will generate images showing how the location could be impacted by flooding, wildfires, or smog if it happened there.' # noqa: E501 | |
| + "<br><br>This is <strong>not</strong> an exercise in climate prediction, rather an exercise of empathy, to put yourself in other's shoes, as if Climate Change came crushing on your doorstep." # noqa: E501 | |
| ) | |
| with gr.Column(): | |
| gr.HTML( | |
| "<p style='text-align: center'>Visit <a href='https://thisclimatedoesnotexist.com/'>ThisClimateDoesNotExist</a> for more information. | Original <a href='https://github.com/cc-ai/climategan'>ClimateGAN GitHub Repo</a></p>" # noqa: E501 | |
| + "<p>After you have selected an image and started the inference you will see all the outputs of ClimateGAN, including intermediate outputs such as the flood mask, the segmentation map and the depth maps used to produce the 3 events</p>" | |
| + "<p>This Space makes use of recent Stable Diffusion in-painting pipelines to replace ClimateGAN's original Painter. If you select 'Both' painters, you will see a comparison</p>" | |
| + "<p>Read the original <a href='https://openreview.net/forum?id=EZNOb_uNpJk', target='_blank'>ICLR 2021 ClimateGAN paper</a></p>" | |
| ) | |
| with gr.Row(): | |
| gr.Markdown("## Inputs") | |
| with gr.Row(): | |
| with gr.Column(): | |
| inputs = [gr.inputs.Image(label="Input Image")] | |
| with gr.Column(): | |
| if api_key: | |
| inputs += [gr.inputs.Textbox(label="Address or place name")] | |
| inputs += [ | |
| gr.inputs.Dropdown( | |
| choices=[ | |
| "ClimateGAN Painter", | |
| "Stable Diffusion Painter", | |
| "Both", | |
| ], | |
| label="Choose Flood Painter", | |
| default="Both", | |
| ) | |
| ] | |
| btn = gr.Button("See for yourself!", label="Run") | |
| with gr.Row(): | |
| gr.Markdown("## Outputs") | |
| with gr.Row(): | |
| outputs = [] | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Original image"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Masked input image"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Segmentation map"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Depth map"), | |
| ) | |
| with gr.Row(): | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="ClimateGAN-Flooded image"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Stable Diffusion-Flooded image"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image( | |
| type="numpy", | |
| label="Stable Diffusion-Flooded image (restricted to masked area)", | |
| ) | |
| ), | |
| with gr.Row(): | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Comparison of previous images"), | |
| ) | |
| with gr.Row(): | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Wildfire"), | |
| ) | |
| outputs.append( | |
| gr.outputs.Image(type="numpy", label="Smog"), | |
| ) | |
| btn.click(predict(cg, api_key), inputs=inputs, outputs=outputs) | |
| blocks.launch() | |