Spaces:
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 | |
| from .api import MiDaSInference | |
| class MidasDetector: | |
| def __init__(self, model): | |
| self.model = model | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, model_type="dpt_hybrid", filename="dpt_hybrid-midas-501f0c75.pt"): | |
| subfolder = "annotator/ckpts" if pretrained_model_or_path == "lllyasviel/ControlNet" else '' | |
| model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder) | |
| model = MiDaSInference(model_type=model_type, model_path=model_path) | |
| return cls(model) | |
| def to(self, device): | |
| self.model.to(device) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| image_depth = detected_map | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float() | |
| image_depth = image_depth.to(self.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) | |
| depth_image = remove_pad(depth_image) | |
| if depth_and_normal: | |
| normal_image = remove_pad(normal_image) | |
| 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 | |