| """ |
| MiDaS implementation using HuggingFace transformers for PyTorch 2.7 compatibility. |
| """ |
| import numpy as np |
| import torch |
| import cv2 |
| from PIL import Image |
| from typing import Union |
|
|
| |
| from ..util import HWC3, common_input_validate, resize_image_with_pad |
|
|
|
|
| class MidasDetector: |
| |
| def __init__(self, model_name="Intel/dpt-large"): |
| from transformers import DPTForDepthEstimation, DPTImageProcessor |
| |
| self.model_name = model_name |
| self.processor = DPTImageProcessor.from_pretrained(model_name) |
| self.model = DPTForDepthEstimation.from_pretrained(model_name) |
| self.device = "cpu" |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_or_path=None, model_type="dpt_hybrid", filename="dpt_hybrid-midas-501f0c75.pt"): |
| |
| model_mapping = { |
| "dpt_large": "Intel/dpt-large", |
| "dpt_hybrid": "Intel/dpt-hybrid-midas", |
| "midas_v21": "Intel/dpt-large", |
| "midas_v21_small": "Intel/dpt-large" |
| } |
| |
| |
| if filename and isinstance(filename, str): |
| if "dpt_large" in filename.lower(): |
| model_name = "Intel/dpt-large" |
| elif "dpt_hybrid" in filename.lower(): |
| model_name = "Intel/dpt-hybrid-midas" |
| else: |
| model_name = model_mapping.get(model_type, "Intel/dpt-large") |
| else: |
| model_name = model_mapping.get(model_type, "Intel/dpt-large") |
| |
| return cls(model_name) |
|
|
| def to(self, device): |
| self.model = self.model.to(device) |
| self.device = device |
| return self |
|
|
| def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) |
| detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) |
| |
| |
| pil_image = Image.fromarray(detected_map.astype(np.uint8)) |
| |
| |
| with torch.no_grad(): |
| inputs = self.processor(images=pil_image, return_tensors="pt") |
| inputs = {k: v.to(self.device) for k, v in inputs.items()} |
| |
| outputs = self.model(**inputs) |
| depth = outputs.predicted_depth |
| |
| |
| depth = torch.nn.functional.interpolate( |
| depth.unsqueeze(1), |
| size=(detected_map.shape[0], detected_map.shape[1]), |
| mode="bicubic", |
| align_corners=False, |
| ).squeeze() |
| |
| depth_pt = depth.clone() |
| depth_pt -= torch.min(depth_pt) |
| depth_pt /= torch.max(depth_pt) |
| depth_pt = depth_pt.cpu().numpy() |
| depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8) |
|
|
| if depth_and_normal: |
| depth_np = depth.cpu().numpy() |
| x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3) |
| y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3) |
| z = np.ones_like(x) * a |
| x[depth_pt < bg_th] = 0 |
| y[depth_pt < bg_th] = 0 |
| normal = np.stack([x, y, z], axis=2) |
| normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 |
| normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1] |
| |
| depth_image = HWC3(depth_image) |
| if depth_and_normal: |
| normal_image = HWC3(normal_image) |
|
|
| depth_image = remove_pad(depth_image) |
| if depth_and_normal: |
| normal_image = remove_pad(normal_image) |
| |
| if output_type == "pil": |
| depth_image = Image.fromarray(depth_image) |
| if depth_and_normal: |
| normal_image = Image.fromarray(normal_image) |
| |
| if depth_and_normal: |
| return depth_image, normal_image |
| else: |
| return depth_image |
