# based on https://huggingface.co/spaces/NimaBoscarino/climategan/blob/main/app.py # noqa: E501
# thank you @NimaBoscarino
import os
from textwrap import dedent
from urllib import parse
from requests import get
import googlemaps
import gradio as gr
import numpy as np
from gradio.components import (
HTML,
Button,
Column,
Dropdown,
Image,
Markdown,
Radio,
Row,
Textbox,
)
from skimage import io
from datetime import datetime
from climategan_wrapper import ClimateGAN
TEXTS = [
dedent(
"""
<p>
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>
</p>
<br>
<p>
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.
</p>
<br>
<p>
This is <strong>NOT</strong> an exercise in climate prediction,
rather an exercise of empathy, to put yourself in others' shoes,
as if Climate Change came crushing on your doorstep.
</p>
<br>
<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>
<br>
<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>
<br>
<p style='text-align: center'>
Visit
<a href='https://thisclimatedoesnotexist.com/'>
ThisClimateDoesNotExist.com</a>
for more information
|
Original
<a href='https://github.com/cc-ai/climategan'>
ClimateGAN GitHub Repo
</a>
|
Read the original
<a
href='https://openreview.net/forum?id=EZNOb_uNpJk'
target='_blank'>
ICLR 2021 ClimateGAN paper
</a>
</p>
"""
),
dedent(
"""
## How to use this Space
1. Enter an address or upload a Street View image
2. Select the type of Painter you'd like to use for the flood renderings
3. Click on the "See for yourself!" button
4. Wait for the inference to complete, typically around 30 seconds
(plus queue time)
5. Enjoy the results!
1. The prompt for Stable Diffusion is `An HD picture of a street with
dirty water after a heavy flood`
2. Pay attention to potential "inventions" by Stable Diffusion's in-painting
3. The "restricted to masked area" SD output is the result of:
`y = mask * flooded + (1-mask) * input`
"""
),
]
CSS = dedent(
"""
a {
color: #0088ff;
text-decoration: underline;
}
strong {
color: #c34318;
font-weight: bolder;
}
#how-to-use-md li {
margin: 0.1em;
}
#how-to-use-md li p {
margin: 0.1em;
}
"""
)
def toggle(radio):
if "address" in radio.lower():
return [
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=True),
]
else:
return [
gr.update(visible=False),
gr.update(visible=True),
gr.update(visible=True),
]
def predict(cg: ClimateGAN, api_key):
def _predict(*args):
print(f"Starting inference ({str(datetime.now())})")
image = place = painter = radio = None
if api_key:
radio, image, place, painter = args
else:
image, painter = args
if api_key and place and "address" in radio.lower():
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)
print("Using GSV image")
else:
print("Using user image")
img_np = image
painters = {
"ClimateGAN Painter": "climategan",
"Stable Diffusion Painter": "stable_diffusion",
"Both": "both",
}
print("Using painter", painters[painter])
output_dict = cg.infer_single(
img_np,
painters[painter],
concats=[
"input",
"masked_input",
"climategan_flood",
"stable_copy_flood",
],
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"], 3, axis=-1)
segmentation = output_dict["segmentation"]
climategan_flood = output_dict.get(
"climategan_flood",
np.ones(input_image.shape, dtype=np.uint8) * 255,
)
stable_flood = output_dict.get(
"stable_flood",
np.ones(input_image.shape, dtype=np.uint8) * 255,
)
stable_copy_flood = output_dict.get(
"stable_copy_flood",
np.ones(input_image.shape, dtype=np.uint8) * 255,
)
concat = output_dict.get(
"concat",
np.ones(input_image.shape, dtype=np.uint8) * 255,
)
return (
input_image,
masked_input,
segmentation,
depth,
climategan_flood,
stable_flood,
stable_copy_flood,
concat,
wildfire,
smog,
)
return _predict
if __name__ == "__main__":
ip = get("https://api.ipify.org").content.decode("utf8")
print("My public IP address is: {}".format(ip))
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()
radio = address = None
pred_ins = []
pred_outs = []
with gr.Blocks(css=CSS) as app:
with Row():
with Column():
Markdown("# ClimateGAN: Visualize Climate Change")
HTML(TEXTS[0])
with Column():
Markdown(TEXTS[1], elem_id="how-to-use-md")
with Row():
HTML("<hr><br><h2 style='font-size: 1.5rem;'>Choose Inputs</h2>")
with Row():
with Column():
if api_key:
radio = Radio(["From Address", "From Image"], label="Input Type")
pred_ins += [radio]
im_inp = Image(label="Input Image", visible=not api_key)
pred_ins += [im_inp]
if api_key:
address = Textbox(label="Address or place name", visible=False)
pred_ins += [address]
with Column():
pred_ins += [
Dropdown(
choices=[
"ClimateGAN Painter",
"Stable Diffusion Painter",
"Both",
],
label="Choose Flood Painter",
value="Both",
)
]
btn = Button(
"See for yourself!",
label="Run",
variant="primary",
visible=not api_key,
)
with Row():
Markdown("## Outputs")
with Row():
pred_outs += [Image(type="numpy", label="Original image")]
pred_outs += [Image(type="numpy", label="Masked input image")]
pred_outs += [Image(type="numpy", label="Segmentation map")]
pred_outs += [Image(type="numpy", label="Depth map")]
with Row():
pred_outs += [Image(type="numpy", label="ClimateGAN-Flooded image")]
pred_outs += [Image(type="numpy", label="Stable Diffusion-Flooded image")]
pred_outs += [
Image(
type="numpy",
label="Stable Diffusion-Flooded image (restricted to masked area)",
)
]
with Row():
pred_outs += [Image(type="numpy", label="Comparison of flood images")]
with Row():
pred_outs += [Image(type="numpy", label="Wildfire")]
pred_outs += [Image(type="numpy", label="Smog")]
Image(type="numpy", label="Empty on purpose", interactive=False)
btn.click(predict(cg, api_key), inputs=pred_ins, outputs=pred_outs)
if api_key:
radio.change(toggle, inputs=[radio], outputs=[address, im_inp, btn])
app.launch(show_api=False)