# Copyright 2024 Anton Obukhov, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import functools
import os
import spaces
import gradio as gr
import numpy as np
import torch as torch
from PIL import Image
from gradio_imageslider import ImageSlider
from huggingface_hub import login
from extrude import extrude_depth_3d
from marigold_depth_estimation import MarigoldPipeline
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 process_3d(
input_image,
files,
size_longest_px,
size_longest_cm,
filter_size,
plane_near,
plane_far,
embossing,
frame_thickness,
frame_near,
frame_far,
):
if input_image is None or len(files) < 1:
raise gr.Error(
"Please upload an image (or use examples) and compute depth first"
)
if plane_near >= plane_far:
raise gr.Error("NEAR plane must have a value smaller than the FAR plane")
def _process_3d(
size_longest_px,
filter_size,
vertex_colors,
scene_lights,
output_model_scale=None,
prepare_for_3d_printing=False,
):
image_rgb = input_image
image_depth = files[0]
image_rgb_basename, image_rgb_ext = os.path.splitext(image_rgb)
image_depth_basename, image_depth_ext = os.path.splitext(image_depth)
image_rgb_content = Image.open(image_rgb)
image_rgb_w, image_rgb_h = image_rgb_content.width, image_rgb_content.height
image_rgb_d = max(image_rgb_w, image_rgb_h)
image_new_w = size_longest_px * image_rgb_w // image_rgb_d
image_new_h = size_longest_px * image_rgb_h // image_rgb_d
image_rgb_new = image_rgb_basename + f"_{size_longest_px}" + image_rgb_ext
image_depth_new = image_depth_basename + f"_{size_longest_px}" + image_depth_ext
image_rgb_content.resize((image_new_w, image_new_h), Image.LANCZOS).save(
image_rgb_new
)
Image.open(image_depth).resize((image_new_w, image_new_h), Image.BILINEAR).save(
image_depth_new
)
path_glb, path_stl = extrude_depth_3d(
image_rgb_new,
image_depth_new,
output_model_scale=(
size_longest_cm * 10
if output_model_scale is None
else output_model_scale
),
filter_size=filter_size,
coef_near=plane_near,
coef_far=plane_far,
emboss=embossing / 100,
f_thic=frame_thickness / 100,
f_near=frame_near / 100,
f_back=frame_far / 100,
vertex_colors=vertex_colors,
scene_lights=scene_lights,
prepare_for_3d_printing=prepare_for_3d_printing,
)
return path_glb, path_stl
path_viewer_glb, _ = _process_3d(
256, filter_size, vertex_colors=False, scene_lights=True, output_model_scale=1
)
path_files_glb, path_files_stl = _process_3d(
size_longest_px,
filter_size,
vertex_colors=True,
scene_lights=False,
prepare_for_3d_printing=True,
)
return path_viewer_glb, [path_files_glb, path_files_stl]
def run_demo_server(pipe):
process_pipe = spaces.GPU(functools.partial(process, pipe), duration=120)
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;
}
h1 {
text-align: center;
display: block;
}
h2 {
text-align: center;
display: block;
}
h3 {
text-align: center;
display: block;
}
""",
) as demo:
gr.Markdown(
"""
# Marigold Depth Estimation
Marigold is the state-of-the-art depth estimator for images in the wild.
Upload your image into the first pane, or click any of the examples below.
The result will be computed and appear in the second pane.
Scroll down to use the computed depth map for creating a 3D printable asset.
Check out Marigold-LCM — a FAST version of this 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=10,
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
],
[
"files/cat.jpg",
10, # ensemble_size
10, # denoise_steps
768, # processing_res
"files/cat_depth_16bit.png",
"files/cat_depth_fp32.npy",
"files/cat_depth_colored.png",
0.0, # plane_near
0.3, # plane_far
20, # embossing
3, # filter_size
0, # frame_near
],
[
"files/swings.jpg",
10, # ensemble_size
10, # denoise_steps
768, # processing_res
"files/swings_depth_16bit.png",
"files/swings_depth_fp32.npy",
"files/swings_depth_colored.png",
0.05, # plane_near
0.25, # plane_far
10, # embossing
1, # filter_size
0, # frame_near
],
[
"files/einstein.jpg",
10, # ensemble_size
10, # denoise_steps
768, # processing_res
"files/einstein_depth_16bit.png",
"files/einstein_depth_fp32.npy",
"files/einstein_depth_colored.png",
0.0, # plane_near
0.5, # plane_far
50, # embossing
3, # filter_size
-15, # 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,
],
)
def submit_3d_fn(*args):
out = list(process_3d(*args))
out = [gr.Button(interactive=False)] + out
return out
submit_3d.click(
fn=submit_3d_fn,
inputs=[
input_image,
files,
size_longest_px,
size_longest_cm,
filter_size,
plane_near,
plane_far,
embossing,
frame_thickness,
frame_near,
frame_far,
],
outputs=[submit_3d, viewer_3d, files_3d],
concurrency_limit=1,
)
def clear_3d_fn():
return [gr.Button(interactive=True), None, None]
clear_3d.click(
fn=clear_3d_fn,
inputs=[],
outputs=[submit_3d, viewer_3d, files_3d],
)
demo.queue(
api_open=False,
).launch(
server_name="0.0.0.0",
server_port=7860,
)
def main():
CHECKPOINT = "prs-eth/marigold-v1-0"
if "HF_TOKEN_LOGIN" in os.environ:
login(token=os.environ["HF_TOKEN_LOGIN"])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = MarigoldPipeline.from_pretrained(CHECKPOINT)
try:
import xformers
pipe.enable_xformers_memory_efficient_attention()
except:
pass # run without xformers
pipe = pipe.to(device)
run_demo_server(pipe)
if __name__ == "__main__":
main()