| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from .api import MiDaSInference | |
| class MidasDetector: | |
| def __init__(self): | |
| self.model = MiDaSInference(model_type="dpt_hybrid").cuda() | |
| def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1): | |
| assert input_image.ndim == 3 | |
| image_depth = input_image | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float().cuda() | |
| 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) | |
| 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) | |
| return depth_image, normal_image | |