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---
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license: apache-2.0
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---
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This is the Offical weights of ConFiDeNet
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```python
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from PIL import Image
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import torch
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from transformers import ConFiDeNetForDepthEstimation, ConFiDeNetImageProcessor
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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image = Image.open("<Image Path>").convert("RGB")
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print(image.size)
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# image.save("image.jpg")
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image_processor = ConFiDeNetImageProcessor.from_pretrained("<Weight-Path>")
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model = ConFiDeNetForDepthEstimation.from_pretrained("<Weigh-Path>").to(device)
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inputs = image_processor(images=image, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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post_processed_output = image_processor.post_process_depth_estimation(
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outputs, target_sizes=[(image.height, image.width)],
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)
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depth = post_processed_output[0]["predicted_depth_uint16"].detach().cpu().numpy()
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depth = Image.fromarray(depth, mode="I;16")
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depth.save("depth.png")
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``` |
