mirror of https://github.com/vladmandic/automatic
49 lines
2.3 KiB
Python
49 lines
2.3 KiB
Python
import cv2
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import torch
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image
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from modules import devices, masking
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from modules.shared import opts
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class DepthAnythingV2Detector:
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def __init__(self, model, processor):
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self.model = model
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self.processor = processor
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@classmethod
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def from_pretrained(cls, pretrained_model_or_path="depth-anything/Depth-Anything-V2-Small-hf", cache_dir=None, local_files_only=False):
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from transformers import AutoImageProcessor, AutoModelForDepthEstimation
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processor = AutoImageProcessor.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only)
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model = AutoModelForDepthEstimation.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only, use_safetensors=True).to(devices.device).eval()
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return cls(model, processor)
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def __call__(self, image, color_map="none", output_type="pil"):
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self.model.to(devices.device)
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if isinstance(image, Image.Image):
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image = np.array(image)
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h, w = image.shape[:2]
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(image_rgb)
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inputs = self.processor(images=pil_image, return_tensors="pt")
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inputs = {k: v.to(devices.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
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with devices.inference_context():
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outputs = self.model(**inputs)
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predicted_depth = outputs.predicted_depth
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depth = F.interpolate(predicted_depth.unsqueeze(1), size=(h, w), mode="bicubic", align_corners=False).squeeze()
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if opts.control_move_processor:
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self.model.to("cpu")
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depth = depth - depth.min()
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depth_max = depth.max()
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if depth_max > 0:
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depth = depth / depth_max
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depth = (depth * 255.0).clamp(0, 255).to(torch.uint8).cpu().numpy()
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if color_map != "none":
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colormap_key = color_map if color_map in masking.COLORMAP else "inferno"
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depth = cv2.applyColorMap(depth, masking.COLORMAP.index(colormap_key))[:, :, ::-1]
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if output_type == "pil":
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mode = "RGB" if depth.ndim == 3 else "L"
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depth = Image.fromarray(depth, mode=mode)
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return depth
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