GonzaloMG's picture
Update app.py
f5a0315 verified
raw
history blame
5.17 kB
###########################################################################################
# Code based on the Hugging Face Space of Depth Anything v2
# https://huggingface.co/spaces/depth-anything/Depth-Anything-V2/blob/main/app.py
###########################################################################################
import gradio as gr
import cv2
import matplotlib
import numpy as np
import os
from PIL import Image
import spaces
import torch
import tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download
from Marigold.marigold import MarigoldPipeline
from diffusers import AutoencoderKL, DDIMScheduler, UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
#download {
height: 62px;
}
"""
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
dtype = torch.float32
variant = None
checkpoint_path = "GonzaloMG/marigold-e2e-ft-depth"
unet = UNet2DConditionModel.from_pretrained(checkpoint_path, subfolder="unet")
vae = AutoencoderKL.from_pretrained(checkpoint_path, subfolder="vae")
text_encoder = CLIPTextModel.from_pretrained(checkpoint_path, subfolder="text_encoder")
tokenizer = CLIPTokenizer.from_pretrained(checkpoint_path, subfolder="tokenizer")
scheduler = DDIMScheduler.from_pretrained(checkpoint_path, timestep_spacing="trailing", subfolder="scheduler")
pipe = MarigoldPipeline.from_pretrained(pretrained_model_name_or_path = checkpoint_path,
unet=unet,
vae=vae,
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
variant=variant,
torch_dtype=dtype,
)
pipe = pipe.to(DEVICE)
pipe.unet.eval()
title = "# End-to-End Fine-Tuned Marigold for Depth Estimation"
description = """ Please refer to our [paper](https://arxiv.org/abs/2409.11355) and [GitHub](https://vision.rwth-aachen.de/diffusion-e2e-ft) for more details."""
@spaces.GPU
def predict_depth(image, processing_res_choice):
with torch.no_grad():
pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", normals=False, processing_res=processing_res_choice, match_input_res=True)
pred = pipe_out.depth_np
pred_colored = pipe_out.depth_colored
return pred, pred_colored
with gr.Blocks(css=css) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### Depth Prediction demo")
with gr.Row():
input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
with gr.Row():
submit = gr.Button(value="Compute Depth")
processing_res_choice = gr.Radio(
[
("Recommended (768)", 768),
("Native", 0),
],
label="Processing resolution",
value=768,
)
gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
raw_file = gr.File(label="Raw Depth Data (.npy)", elem_id="download")
cmap = matplotlib.colormaps.get_cmap('Spectral_r')
def on_submit(image, processing_res_choice):
if image is None:
print("No image uploaded.")
return None
pil_image = Image.fromarray(image.astype('uint8'))
depth_npy, depth_colored = predict_depth(pil_image, processing_res_choice)
# Save the npy data (raw depth map)
tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
np.save(tmp_npy_depth.name, depth_npy)
# Save the grayscale depth map
depth_gray = (depth_npy * 65535.0).astype(np.uint16)
tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
Image.fromarray(depth_gray).save(tmp_gray_depth.name, mode="I;16")
# Save the colored depth map
tmp_colored_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
depth_colored.save(tmp_colored_depth.name)
return [(image, depth_colored), tmp_gray_depth.name, tmp_npy_depth.name]
submit.click(on_submit, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider, gray_depth_file, raw_file])
example_files = os.listdir('assets/examples')
example_files.sort()
example_files = [os.path.join('assets/examples', filename) for filename in example_files]
example_files = [[image, 768] for image in example_files]
examples = gr.Examples(examples=example_files, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
if __name__ == '__main__':
demo.queue().launch(share=True)