import spaces
import functools
import os
import shutil
import sys
import git
import gradio as gr
import numpy as np
import torch as torch
from PIL import Image
from gradio_imageslider import ImageSlider
def process(
pipe,
path_input,
ensemble_size,
denoise_steps,
processing_res,
path_out_16bit=None,
path_out_fp32=None,
path_out_vis=None,
_input_3d_plane_near=None,
_input_3d_plane_far=None,
_input_3d_embossing=None,
_input_3d_filter_size=None,
_input_3d_frame_near=None,
):
if path_out_vis is not None:
return (
[path_out_16bit, path_out_vis],
[path_out_16bit, path_out_fp32, path_out_vis],
)
input_image = Image.open(path_input)
pipe_out = pipe(
input_image,
ensemble_size=ensemble_size,
denoising_steps=denoise_steps,
processing_res=processing_res,
batch_size=1 if processing_res == 0 else 0,
show_progress_bar=True,
)
depth_pred = pipe_out.depth_np
depth_colored = pipe_out.depth_colored
depth_16bit = (depth_pred * 65535.0).astype(np.uint16)
path_output_dir = os.path.splitext(path_input)[0] + "_output"
os.makedirs(path_output_dir, exist_ok=True)
name_base = os.path.splitext(os.path.basename(path_input))[0]
path_out_fp32 = os.path.join(path_output_dir, f"{name_base}_depth_fp32.npy")
path_out_16bit = os.path.join(path_output_dir, f"{name_base}_depth_16bit.png")
path_out_vis = os.path.join(path_output_dir, f"{name_base}_depth_colored.png")
np.save(path_out_fp32, depth_pred)
Image.fromarray(depth_16bit).save(path_out_16bit, mode="I;16")
depth_colored.save(path_out_vis)
return (
[path_out_16bit, path_out_vis],
[path_out_16bit, path_out_fp32, path_out_vis],
)
def run_demo_server(pipe):
process_pipe = functools.partial(process, pipe)
os.environ["GRADIO_ALLOW_FLAGGING"] = "never"
with gr.Blocks(
analytics_enabled=False,
title="Marigold Depth Estimation",
css="""
#download {
height: 118px;
}
.slider .inner {
width: 5px;
background: #FFF;
}
.viewport {
aspect-ratio: 4/3;
}
""",
) as demo:
gr.Markdown(
"""
Marigold Depth Estimation
Marigold is the new state-of-the-art depth estimator for images in the wild.
Upload your image into the left side, or click any of the examples below.
The result will be computed and appear on the right in the output comparison window.
NEW: Scroll down to the new 3D printing part of the demo!
"""
)
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Input Image",
type="filepath",
)
with gr.Accordion("Advanced options", open=False):
ensemble_size = gr.Slider(
label="Ensemble size",
minimum=1,
maximum=20,
step=1,
value=10,
)
denoise_steps = gr.Slider(
label="Number of denoising steps",
minimum=1,
maximum=20,
step=1,
value=10,
)
processing_res = gr.Radio(
[
("Native", 0),
("Recommended", 768),
],
label="Processing resolution",
value=768,
)
input_output_16bit = gr.File(
label="Predicted depth (16-bit)",
visible=False,
)
input_output_fp32 = gr.File(
label="Predicted depth (32-bit)",
visible=False,
)
input_output_vis = gr.File(
label="Predicted depth (red-near, blue-far)",
visible=False,
)
with gr.Row():
submit_btn = gr.Button(value="Compute Depth", variant="primary")
clear_btn = gr.Button(value="Clear")
with gr.Column():
output_slider = ImageSlider(
label="Predicted depth (red-near, blue-far)",
type="filepath",
show_download_button=True,
show_share_button=True,
interactive=False,
elem_classes="slider",
position=0.25,
)
files = gr.Files(
label="Depth outputs",
elem_id="download",
interactive=False,
)
demo_3d_header = gr.Markdown(
"""
3D Printing Depth Maps
This part of the demo uses Marigold depth maps estimated in the previous step to create a
3D-printable model. The models are watertight, with correct normals, and exported in the STL format.
We recommended creating the first model with the default parameters and iterating on it until the best
result (see Pro Tips below).
""",
render=False,
)
demo_3d = gr.Row(render=False)
with demo_3d:
with gr.Column():
with gr.Accordion("3D printing demo: Main options", open=True):
plane_near = gr.Slider(
label="Relative position of the near plane (between 0 and 1)",
minimum=0.0,
maximum=1.0,
step=0.001,
value=0.0,
)
plane_far = gr.Slider(
label="Relative position of the far plane (between near and 1)",
minimum=0.0,
maximum=1.0,
step=0.001,
value=1.0,
)
embossing = gr.Slider(
label="Embossing level",
minimum=0,
maximum=100,
step=1,
value=20,
)
with gr.Accordion("3D printing demo: Advanced options", open=False):
size_longest_px = gr.Slider(
label="Size (px) of the longest side",
minimum=256,
maximum=1024,
step=256,
value=512,
)
size_longest_cm = gr.Slider(
label="Size (cm) of the longest side",
minimum=1,
maximum=100,
step=1,
value=10,
)
filter_size = gr.Slider(
label="Size (px) of the smoothing filter",
minimum=1,
maximum=5,
step=2,
value=3,
)
frame_thickness = gr.Slider(
label="Frame thickness",
minimum=0,
maximum=100,
step=1,
value=5,
)
frame_near = gr.Slider(
label="Frame's near plane offset",
minimum=-100,
maximum=100,
step=1,
value=1,
)
frame_far = gr.Slider(
label="Frame's far plane offset",
minimum=1,
maximum=10,
step=1,
value=1,
)
with gr.Row():
submit_3d = gr.Button(value="Create 3D", variant="primary")
clear_3d = gr.Button(value="Clear 3D")
gr.Markdown(
"""
Pro Tips
- Re-render with new parameters: Click "Clear 3D" and then "Create 3D".
- Adjust 3D scale and cut-off focus: Set the frame's near plane offset to the
minimum and use 3D preview to evaluate depth scaling. Repeat until the scale is correct and
everything important is in the focus. Set the optimal value for frame's near
plane offset as a last step.
- Increase details: Decrease size of the smoothing filter (also increases noise).
"""
)
with gr.Column():
viewer_3d = gr.Model3D(
camera_position=(75.0, 90.0, 1.25),
elem_classes="viewport",
label="3D preview (low-res, relief highlight)",
interactive=False,
)
files_3d = gr.Files(
label="3D model outputs (high-res)",
elem_id="download",
interactive=False,
)
blocks_settings_depth = [ensemble_size, denoise_steps, processing_res]
blocks_settings_3d = [plane_near, plane_far, embossing, size_longest_px, size_longest_cm, filter_size,
frame_thickness, frame_near, frame_far]
blocks_settings = blocks_settings_depth + blocks_settings_3d
map_id_to_default = {b._id: b.value for b in blocks_settings}
inputs = [
input_image,
ensemble_size,
denoise_steps,
processing_res,
input_output_16bit,
input_output_fp32,
input_output_vis,
plane_near,
plane_far,
embossing,
filter_size,
frame_near,
]
outputs = [
submit_btn,
input_image,
output_slider,
files,
]
def submit_depth_fn(*args):
out = list(process_pipe(*args))
out = [gr.Button(interactive=False), gr.Image(interactive=False)] + out
return out
submit_btn.click(
fn=submit_depth_fn,
inputs=inputs,
outputs=outputs,
concurrency_limit=1,
)
gr.Examples(
fn=submit_depth_fn,
examples=[
[
"files/bee.jpg",
10, # ensemble_size
10, # denoise_steps
768, # processing_res
"files/bee_depth_16bit.png",
"files/bee_depth_fp32.npy",
"files/bee_depth_colored.png",
0.0, # plane_near
0.5, # plane_far
20, # embossing
3, # filter_size
0, # frame_near
],
],
inputs=inputs,
outputs=outputs,
cache_examples=True,
)
demo_3d_header.render()
demo_3d.render()
def clear_fn():
out = []
for b in blocks_settings:
out.append(map_id_to_default[b._id])
out += [
gr.Button(interactive=True),
gr.Button(interactive=True),
gr.Image(value=None, interactive=True),
None, None, None, None, None, None, None,
]
return out
clear_btn.click(
fn=clear_fn,
inputs=[],
outputs=blocks_settings + [
submit_btn,
submit_3d,
input_image,
input_output_16bit,
input_output_fp32,
input_output_vis,
output_slider,
files,
viewer_3d,
files_3d,
],
)
demo.queue(
api_open=False,
).launch(
server_name="0.0.0.0",
server_port=7860,
)
def prefetch_hf_cache(pipe):
process(pipe, "files/bee.jpg", 1, 1, 64)
shutil.rmtree("files/bee_output")
def main():
REPO_URL = "https://github.com/lemonaddie/geowizard.git"
CHECKPOINT = "lemonaddie/Geowizard"
REPO_DIR = "geowizard"
if os.path.isdir(REPO_DIR):
shutil.rmtree(REPO_DIR)
repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
sys.path.append(os.path.join(os.getcwd(), REPO_DIR))
from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
try:
import xformers
pipe.enable_xformers_memory_efficient_attention()
except:
pass # run without xformers
pipe = pipe.to(device)
try:
import xformers
pipe.enable_xformers_memory_efficient_attention()
except:
pass # run without xformers
pipe = pipe.to(device)
prefetch_hf_cache(pipe)
run_demo_server(pipe)
if __name__ == "__main__":
main()