Spaces:
Sleeping
Sleeping
File size: 9,728 Bytes
ce190ee c40855a ce190ee 7d48feb c40855a ae61fe0 c40855a bed8309 c40855a cd31093 c40855a ed4f2d1 c40855a ed4f2d1 c40855a ed4f2d1 c40855a ed4f2d1 ac56c6b ed4f2d1 c40855a ed4f2d1 c40855a ce190ee ae61fe0 ce190ee bed8309 c40855a ce190ee c40855a ce190ee c40855a ce190ee bed8309 ce190ee bed8309 ce190ee fe58151 e2d1ef3 fe58151 e2d1ef3 fe58151 bed8309 0776dea ae61fe0 51920a8 9da944e ae61fe0 18e383c ae61fe0 18e383c ae61fe0 18e383c ae61fe0 18e383c ae61fe0 9da944e ae61fe0 ce190ee 7d48feb ce190ee b75254c 95ba8da b75254c ae61fe0 ce190ee c40855a ac09955 c40855a ed4f2d1 c40855a 7e7950a c40855a ed4f2d1 c40855a ae61fe0 c40855a ae61fe0 c40855a ae61fe0 c40855a ae61fe0 c40855a ed4f2d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 |
# 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 (at least 640x640)
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)
|