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| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from ..util import HWC3, resize_image | |
| from .api import MiDaSInference | |
| class MidasDetector: | |
| def __init__(self, model): | |
| self.model = model | |
| def from_pretrained(cls, pretrained_model_or_path, model_type="dpt_hybrid", filename=None, cache_dir=None, local_files_only=False): | |
| if pretrained_model_or_path == "lllyasviel/ControlNet": | |
| filename = filename or "annotator/ckpts/dpt_hybrid-midas-501f0c75.pt" | |
| else: | |
| filename = filename or "dpt_hybrid-midas-501f0c75.pt" | |
| if os.path.isdir(pretrained_model_or_path): | |
| model_path = os.path.join(pretrained_model_or_path, filename) | |
| else: | |
| model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only) | |
| model = MiDaSInference(model_type=model_type, model_path=model_path) | |
| return cls(model) | |
| def to(self, device): | |
| self.model.to(device) | |
| return self | |
| def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, image_resolution=512, output_type=None): | |
| device = next(iter(self.model.parameters())).device | |
| if not isinstance(input_image, np.ndarray): | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| output_type = output_type or "pil" | |
| else: | |
| output_type = output_type or "np" | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| assert input_image.ndim == 3 | |
| image_depth = input_image | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float() | |
| image_depth = image_depth.to(device) | |
| image_depth = image_depth / 127.5 - 1.0 | |
| image_depth = rearrange(image_depth, 'h w c -> 1 c h w') | |
| depth = self.model(image_depth)[0] | |
| 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) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| depth_image = cv2.resize(depth_image, (W, H), interpolation=cv2.INTER_LINEAR) | |
| if depth_and_normal: | |
| normal_image = cv2.resize(normal_image, (W, H), interpolation=cv2.INTER_LINEAR) | |
| 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 | |