mirror of
https://github.com/vladmandic/automatic
synced 2026-08-29 08:31:00 +02:00
Vladimir Mandic
36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
from PIL import Image
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import numpy as np
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import torch
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from transformers import AutoImageProcessor, DPTForDepthEstimation
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from modules import devices
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image_processor: AutoImageProcessor = None
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dpt_model: DPTForDepthEstimation = None
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class DPTDetector:
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def __call__(self, input_image=None):
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global image_processor, dpt_model # pylint: disable=global-statement
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from modules.control.processors import cache_dir
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if image_processor is None:
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image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large", cache_dir=cache_dir)
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if dpt_model is None:
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dpt_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", cache_dir=cache_dir)
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with devices.inference_context():
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inputs = image_processor(images=input_image, return_tensors="pt")
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outputs = dpt_model(**inputs)
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predicted_depth = outputs.predicted_depth
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prediction = torch.nn.functional.interpolate(
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predicted_depth.unsqueeze(1),
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size=input_image.size[::-1],
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mode="bicubic",
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align_corners=False,
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)
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output = prediction.squeeze().cpu().numpy()
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formatted = (output * 255 / np.max(output)).astype("uint8")
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depth = Image.fromarray(formatted)
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depth = depth.convert('RGB')
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return depth
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