app.py

import gradio as gr
import cv2
import matplotlib
import numpy as np
import os
from PIL import Image
import mmap
import json
import spaces
import torch
import tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download
import safetensors

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="depth-anything/Depth-Anything-V2-Metric-Indoor-Large-hf", filename="model.safetensors", repo_type="model")

def create_tensor(storage, info, offset):
    DTYPES = {"F32": torch.float32}
    dtype = DTYPES[info["dtype"]]
    shape = info["shape"]
    start, stop = info["data_offsets"]
    return torch.asarray(storage[start + offset : stop + offset], dtype=torch.uint8).view(dtype=dtype).reshape(shape)

def load_file(filename):
    with open(filename, mode="r", encoding="utf8") as file_obj:
        with mmap.mmap(file_obj.fileno(), length=0, access=mmap.ACCESS_READ) as m:
            header = m.read(8)
            n = int.from_bytes(header, "little")
            metadata_bytes = m.read(n)
            metadata = json.loads(metadata_bytes)

    size = os.stat(filename).st_size
    storage = torch.ByteStorage.from_file(filename, shared=False, size=size).untyped()
    offset = n + 8
    return {name: create_tensor(storage, info, offset) for name, info in metadata.items() if name != "__metadata__"}

tensor_data = safetensors.load(filepath)

# Convert to PyTorch tensor
if isinstance(tensor_data, np.ndarray):
    pytorch_tensor = torch.tensor(tensor_data)
elif isinstance(tensor_data, safetensors.Tensor):
    pytorch_tensor = torch.tensor(tensor_data.numpy())  # Assuming safetensors Tensor has a .numpy() method
else:
    raise TypeError("Unsupported data type from safetensors")

#state_dict = torch.load(filepath, map_location="cpu", weights_only=True)
#state_dict = load_file(filepath)
state_dict = pytorch_tensor

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)