88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
import glob
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import gradio as gr
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import matplotlib
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import numpy as np
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from PIL import Image
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import torch
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import tempfile
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from gradio_imageslider import ImageSlider
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from depth_anything_v2.dpt import DepthAnythingV2
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css = """
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#img-display-container {
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max-height: 100vh;
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}
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#img-display-input {
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max-height: 80vh;
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}
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#img-display-output {
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max-height: 80vh;
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}
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#download {
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height: 62px;
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}
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"""
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DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
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model_configs = {
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'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
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'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
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'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
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'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
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}
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encoder = 'vitl'
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model = DepthAnythingV2(**model_configs[encoder])
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state_dict = torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location="cpu")
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model.load_state_dict(state_dict)
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model = model.to(DEVICE).eval()
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title = "# Depth Anything V2"
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description = """Official demo for **Depth Anything V2**.
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Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
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def predict_depth(image):
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return model.infer_image(image)
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with gr.Blocks(css=css) as demo:
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gr.Markdown(title)
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gr.Markdown(description)
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gr.Markdown("### Depth Prediction demo")
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with gr.Row():
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input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
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depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
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submit = gr.Button(value="Compute Depth")
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gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
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raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",)
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cmap = matplotlib.colormaps.get_cmap('Spectral_r')
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def on_submit(image):
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original_image = image.copy()
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h, w = image.shape[:2]
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depth = predict_depth(image[:, :, ::-1])
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raw_depth = Image.fromarray(depth.astype('uint16'))
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tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
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raw_depth.save(tmp_raw_depth.name)
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth = depth.astype(np.uint8)
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colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
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gray_depth = Image.fromarray(depth)
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tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
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gray_depth.save(tmp_gray_depth.name)
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return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
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submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file])
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example_files = glob.glob('assets/examples/*')
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examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
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if __name__ == '__main__':
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demo.queue().launch() |