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import gradio as gr |
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import cv2 |
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import matplotlib |
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import numpy as np |
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import os |
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from PIL import Image |
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import spaces |
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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 huggingface_hub import hf_hub_download |
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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 '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 = 'vitg' |
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model = DepthAnythingV2(**model_configs[encoder]) |
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filepath = hf_hub_download(repo_id="LiheYoung/Depth-Anything-V2-Checkpoints", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model") |
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state_dict = torch.load(filepath, 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](), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details.""" |
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@spaces.GPU |
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def predict_depth(image): |
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return model.infer_image(image).cpu() |
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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.numpy().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.numpy().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 = os.listdir('examples') |
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example_files.sort() |
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example_files = [os.path.join('examples', filename) for filename in example_files] |
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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(share=True) |
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