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@@ -9,4 +9,45 @@ repo_url: https://github.com/lpiccinelli-eth/UniDepth
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  This model has been pushed to the Hub using **UniDepth**:
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  - Repo: https://github.com/lpiccinelli-eth/UniDepth
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- - Docs: [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  This model has been pushed to the Hub using **UniDepth**:
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  - Repo: https://github.com/lpiccinelli-eth/UniDepth
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+
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+ ## Installation
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+
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+ First install the UniDepth package as follows:
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+ ```python
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+ !git clone -b add_hf https://github.com/NielsRogge/UniDepth.git
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+ !cd UniDepth
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+ !pip install -r requirements.txt
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+ ```
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+
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+ ## Usage
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+ Next, one can load the model and perform inference as follows:
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+ ```python
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+ from unidepth.models import UniDepthV1HF
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+ import numpy as np
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+ from PIL import Image
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+
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+ model = UniDepthV1HF.from_pretrained("nielsr/unidepth-v1-convnext-large")
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+
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+ # Move to CUDA, if any
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ model = model.to(device)
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+
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+ # Load the RGB image and the normalization will be taken care of by the model
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+ rgb = torch.from_numpy(np.array(Image.open(image_path))).permute(2, 0, 1) # C, H, W
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+
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+ predictions = model.infer(rgb)
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+
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+ # Metric Depth Estimation
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+ depth = predictions["depth"]
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+
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+ # Point Cloud in Camera Coordinate
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+ xyz = predictions["points"]
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+
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+ # Intrinsics Prediction
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+ intrinsics = predictions["intrinsics"]
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+
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+
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+ ```