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Configuration error
Configuration error
| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from PIL import Image | |
| from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME, DEPTH_ANYTHING_MODEL_NAME | |
| from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth | |
| from .zoedepth.models.zoedepth_anything.zoedepth_v1 import ZoeDepth as ZoeDepthAnything | |
| from .zoedepth.utils.config import get_config | |
| class ZoeDetector: | |
| def __init__(self, model): | |
| self.model = model | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="ZoeD_M12_N.pt"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename) | |
| conf = get_config("zoedepth", "infer") | |
| model = ZoeDepth.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) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| image_depth = input_image | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float().to(self.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 | |
| depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8) | |
| detected_map = remove_pad(HWC3(depth_image)) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |
| class ZoeDepthAnythingDetector: | |
| def __init__(self, model): | |
| self.model = model | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=DEPTH_ANYTHING_MODEL_NAME, filename="depth_anything_metric_depth_indoor.pt"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder="checkpoints_metric_depth", repo_type="space") | |
| conf = get_config("zoedepth", "infer") | |
| model = ZoeDepthAnything.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) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| image_depth = input_image | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float().to(self.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 | |
| depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8) | |
| detected_map = remove_pad(HWC3(depth_image)) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map |