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Running
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Zero
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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"
description = """Official demo for **Depth Anything V2**.
Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), and [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
@spaces.GPU
def predict_depth(image):
return model.infer_image(image)
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)
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.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.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)
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