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import gradio as gr |
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import cv2 |
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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 torch.nn.functional as F |
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from torchvision.transforms import Compose, Normalize |
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import tempfile |
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from gradio_imageslider import ImageSlider |
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from iebins.networks.NewCRFDepth import NewCRFDepth |
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from iebins.util.transfrom import Resize, NormalizeImage, PrepareForNet |
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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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""" |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = NewCRFDepth(version='large07', inv_depth=False, |
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max_depth=10).to(DEVICE).eval() |
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model = torch.nn.DataParallel(model) |
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checkpoint = torch.load('checkpoints/nyu_L.pth', |
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map_location=torch.device(DEVICE)) |
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model.load_state_dict(checkpoint['model']) |
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title = "# IEBins: Iterative Elastic Bins for Monocular Depth Estimation" |
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description = """Demo for **IEBins: Iterative Elastic Bins for Monocular Depth Estimation**. |
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Please refer to the [paper](https://arxiv.org/abs/2309.14137), [github](https://github.com/ShuweiShao/IEBins), or [poster](https://nips.cc/media/PosterPDFs/NeurIPS%202023/70695.png?t=1701662442.5228624) for more details.""" |
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transform = Compose([ |
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Resize( |
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width=518, |
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height=518, |
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resize_target=False, |
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keep_aspect_ratio=True, |
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ensure_multiple_of=14, |
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resize_method='lower_bound', |
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image_interpolation_method=cv2.INTER_CUBIC, |
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), |
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
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PrepareForNet(), |
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]) |
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@spaces.GPU |
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@torch.no_grad() |
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def predict_depth(model, image): |
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return model(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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with gr.Row(): |
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input_image = gr.Image(label="Input Image", |
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type='numpy', elem_id='img-display-input') |
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depth_image_slider = ImageSlider( |
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label="Depth Map with Slider View", elem_id='img-display-output', position=0.5,) |
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raw_file = gr.File( |
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label="16-bit raw depth (can be considered as disparity)") |
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submit = gr.Button("Submit") |
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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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image = np.asarray(image, dtype=np.float32) / 255.0 |
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image = torch.from_numpy(image.transpose((2, 0, 1))) |
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image = Normalize(mean=[0.485, 0.456, 0.406], std=[ |
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0.229, 0.224, 0.225])(image) |
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image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) |
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depth = predict_depth(model, image) |
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depth = F.interpolate(depth[None], (h, w), |
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mode='bilinear', align_corners=False)[0, 0] |
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raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16')) |
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tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) |
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raw_depth.save(tmp.name) |
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 |
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depth = depth.cpu().numpy().astype(np.uint8) |
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colored_depth = cv2.applyColorMap( |
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depth, cv2.COLORMAP_INFERNO)[:, :, ::-1] |
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return [(original_image, colored_depth), tmp.name] |
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submit.click(on_submit, inputs=[input_image], outputs=[ |
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depth_image_slider, 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) |
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for filename in example_files] |
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examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[ |
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depth_image_slider, raw_file], fn=on_submit, cache_examples=False) |
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if __name__ == '__main__': |
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demo.queue().launch() |
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