|
|
| import os
|
| import cv2
|
| import numpy as np
|
| import torch
|
| from torch import nn
|
|
|
| from einops import rearrange
|
| from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
|
| from .zoedepth.utils.config import get_config
|
|
|
|
|
| class ZoeDetector(nn.Module):
|
| def __init__(self, model_path):
|
| conf = get_config("zoedepth", "infer")
|
| model = ZoeDepth.build_from_config(conf)
|
| model.load_state_dict(torch.load(model_path, map_location=model.device)['model'])
|
| model.eval()
|
| self.model = model
|
|
|
| def __call__(self, input_image):
|
| 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 / 255.0
|
| image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
|
| depth = self.model.infer(image_depth)
|
|
|
| depth = depth[0, 0].cpu().numpy()
|
|
|
| vmin = np.percentile(depth, 2)
|
| vmax = np.percentile(depth, 85)
|
|
|
| depth -= vmin
|
| depth /= vmax - vmin
|
| depth = 1.0 - depth
|
| depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
|
|
|
| return depth_image
|
|
|
