| 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 .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth |
| from .zoedepth.models.zoedepth_nk.zoedepth_nk_v1 import ZoeDepthNK |
| from .zoedepth.utils.config import get_config |
|
|
|
|
| class ZoeDetector: |
| def __init__(self, model): |
| self.model = model |
|
|
| @classmethod |
| def from_pretrained(cls, pretrained_model_or_path, model_type="zoedepth", filename=None, cache_dir=None, local_files_only=False): |
| filename = filename or "ZoeD_M12_N.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) |
| |
| conf = get_config(model_type, "infer") |
| model_cls = ZoeDepth if model_type == "zoedepth" else ZoeDepthNK |
| model = model_cls.build_from_config(conf) |
| model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))['model']) |
| model.eval() |
|
|
| return cls(model) |
|
|
| def to(self, device): |
| self.model.to(device) |
| return self |
| |
| def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type=None, gamma_corrected=False): |
| 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().to(device) |
| 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 |
|
|
| if gamma_corrected: |
| depth = np.power(depth, 2.2) |
| depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8) |
|
|
| detected_map = depth_image |
| detected_map = HWC3(detected_map) |
| |
| img = resize_image(input_image, image_resolution) |
| H, W, C = img.shape |
|
|
| detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) |
| |
| if output_type == "pil": |
| detected_map = Image.fromarray(detected_map) |
| |
| return detected_map |
|
|
