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from PIL import Image
import numpy as np
import torch
from transformers import AutoImageProcessor, DPTForDepthEstimation
from modules import devices
from modules.shared import opts
image_processor: AutoImageProcessor = None
class DPTDetector:
def __init__(self, model=None, processor=None, model_path=None):
self.model = model
self.processor = processor
self.model_path = model_path or "Intel/dpt-large"
def __call__(self, input_image=None, model_path=None):
from modules.control.processors import cache_dir
if model_path is not None and model_path != self.model_path:
self.model_path = model_path
self.processor = None
self.model = None
if self.processor is None:
self.processor = AutoImageProcessor.from_pretrained(self.model_path, cache_dir=cache_dir)
if self.model is None:
self.model = DPTForDepthEstimation.from_pretrained(self.model_path, cache_dir=cache_dir)
self.model.to(devices.device)
with devices.inference_context():
inputs = self.processor(images=input_image, return_tensors="pt")
inputs.to(devices.device)
outputs = self.model(**inputs)
predicted_depth = outputs.predicted_depth
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=input_image.size[::-1],
mode="bicubic",
align_corners=False,
)
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
if opts.control_move_processor:
self.model.to('cpu')
depth = Image.fromarray(formatted)
depth = depth.convert('RGB')
return depth
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