app.py

from huggingface_hub import whoami   
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',
}

encoder = 'vits'
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)

# -------------------------------------
# OLD GRADIO COMPATIBILITY PATCH
# -------------------------------------
if not hasattr(gr.Blocks, "get_api_info"):
    gr.Blocks.get_api_info = lambda self: {}
# -------------------------------------

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()
        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]
    )

    if os.path.exists('assets/examples'):
        example_files = sorted(os.listdir('assets/examples'))
        example_files = [os.path.join('assets/examples', f) for f in example_files]
        gr.Examples(
            cache_examples=False,
            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)