mirror of
https://github.com/vladmandic/automatic
synced 2026-08-25 22:20:46 +02:00
Vladimir Mandic
40 lines
1.5 KiB
Python
40 lines
1.5 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, GLPNForDepthEstimation
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from modules import devices
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from modules.shared import opts
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class GLPNDetector:
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def __init__(self, model=None, processor=None):
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self.model = model
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self.processor = processor
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def __call__(self, input_image=None):
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from modules.control.processors import cache_dir
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if self.processor is None:
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self.processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir)
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if self.model is None:
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self.model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir)
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self.model.to(devices.device)
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with devices.inference_context():
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inputs = self.processor(images=input_image, return_tensors="pt")
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inputs.to(devices.device)
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outputs = self.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 = 255 - (output * 255 / np.max(output)).astype("uint8")
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if opts.control_move_processor:
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self.model.to('cpu')
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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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