| """ |
| Modern DepthAnything implementation using HuggingFace transformers. |
| Replaces legacy torch.hub.load DINOv2 backbone with transformers pipeline. |
| """ |
|
|
| import numpy as np |
| import torch |
| from PIL import Image |
| from transformers import pipeline |
|
|
| from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad |
|
|
| class DepthAnythingDetector: |
| """DepthAnything depth estimation using HuggingFace transformers.""" |
| |
| def __init__(self, model_name="LiheYoung/depth-anything-large-hf"): |
| """Initialize DepthAnything with specified model.""" |
| self.pipe = pipeline(task="depth-estimation", model=model_name) |
| self.device = "cpu" |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_or_path=None, filename="depth_anything_vitl14.pth"): |
| """Create DepthAnything from pretrained model, mapping legacy names to HuggingFace models.""" |
| |
| |
| model_mapping = { |
| "depth_anything_vitl14.pth": "LiheYoung/depth-anything-large-hf", |
| "depth_anything_vitb14.pth": "LiheYoung/depth-anything-base-hf", |
| "depth_anything_vits14.pth": "LiheYoung/depth-anything-small-hf" |
| } |
| |
| model_name = model_mapping.get(filename, "LiheYoung/depth-anything-large-hf") |
| return cls(model_name=model_name) |
| |
| def to(self, device): |
| """Move model to specified device.""" |
| self.pipe.model = self.pipe.model.to(device) |
| self.device = device |
| return self |
| |
| def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): |
| """Perform depth estimation on input image.""" |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) |
| |
| if isinstance(input_image, np.ndarray): |
| pil_image = Image.fromarray(input_image) |
| else: |
| pil_image = input_image |
| |
| with torch.no_grad(): |
| result = self.pipe(pil_image) |
| depth = result["depth"] |
| |
| if isinstance(depth, Image.Image): |
| depth_array = np.array(depth, dtype=np.float32) |
| else: |
| depth_array = np.array(depth) |
| |
| |
| depth_min = depth_array.min() |
| depth_max = depth_array.max() |
| if depth_max > depth_min: |
| depth_array = (depth_array - depth_min) / (depth_max - depth_min) * 255.0 |
| else: |
| depth_array = np.zeros_like(depth_array) |
| |
| depth_image = depth_array.astype(np.uint8) |
|
|
| detected_map = remove_pad(HWC3(depth_image)) |
| |
| if output_type == "pil": |
| detected_map = Image.fromarray(detected_map) |
| |
| return detected_map |
