Spaces:
Running
on
Zero
Running
on
Zero
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 depth_anything_v2.dpt import DepthAnythingV2 | |
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' | |
model_configs = { | |
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, | |
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, | |
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, | |
'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} | |
} | |
encoder2name = { | |
'vits': 'Small', | |
'vitb': 'Base', | |
'vitl': 'Large', | |
'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint | |
} | |
encoder = 'vitl' | |
model_name = encoder2name[encoder] | |
model = DepthAnythingV2(**model_configs[encoder]) | |
filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model") | |
state_dict = torch.load(filepath, map_location="cpu") | |
model.load_state_dict(state_dict) | |
model = model.to(DEVICE).eval() | |
title = "# Depth Anything V2" | |
description1 = """Official demo for **Depth Anything V2**. | |
Please refer to our [paper](https://arxiv.org/abs/2406.09414) for more details.""" | |
description2 = """**Due to the issue with our V2 Github repositories, we temporarily upload the content to [Huggingface space](https://huggingface.co/spaces/depth-anything/Depth-Anything-V2/blob/main/README_Github.md)**""" | |
def predict_depth(image): | |
return model.infer_image(image).cpu() | |
with gr.Blocks(css=css) as demo: | |
gr.Markdown(title) | |
gr.Markdown(description1) | |
gr.Markdown(description2) | |
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) | |
submit = gr.Button(value="Compute Depth") | |
gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",) | |
raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",) | |
cmap = matplotlib.colormaps.get_cmap('Spectral_r') | |
def on_submit(image): | |
original_image = image.copy() | |
h, w = image.shape[:2] | |
depth = predict_depth(image[:, :, ::-1]) | |
raw_depth = Image.fromarray(depth.numpy().astype('uint16')) | |
tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
raw_depth.save(tmp_raw_depth.name) | |
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 | |
depth = depth.numpy().astype(np.uint8) | |
colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8) | |
gray_depth = Image.fromarray(depth) | |
tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
gray_depth.save(tmp_gray_depth.name) | |
return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name] | |
submit.click(on_submit, inputs=[input_image], 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] | |
examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit) | |
if __name__ == '__main__': | |
demo.queue().launch(share=True) | |