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import os |
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import cv2 |
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import numpy as np |
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import torch |
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from einops import rearrange |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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from ..util import HWC3, resize_image |
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from .api import MiDaSInference |
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class MidasDetector: |
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def __init__(self, model): |
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self.model = model |
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@classmethod |
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def from_pretrained(cls, pretrained_model_or_path, model_type="dpt_hybrid", filename=None, cache_dir=None, local_files_only=False): |
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if pretrained_model_or_path == "lllyasviel/ControlNet": |
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filename = filename or "annotator/ckpts/dpt_hybrid-midas-501f0c75.pt" |
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else: |
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filename = filename or "dpt_hybrid-midas-501f0c75.pt" |
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if os.path.isdir(pretrained_model_or_path): |
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model_path = os.path.join(pretrained_model_or_path, filename) |
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else: |
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model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only) |
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model = MiDaSInference(model_type=model_type, model_path=model_path) |
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return cls(model) |
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def to(self, device): |
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self.model.to(device) |
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return self |
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def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1, depth_and_normal=False, detect_resolution=512, image_resolution=512, output_type=None): |
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device = next(iter(self.model.parameters())).device |
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if not isinstance(input_image, np.ndarray): |
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input_image = np.array(input_image, dtype=np.uint8) |
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output_type = output_type or "pil" |
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else: |
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output_type = output_type or "np" |
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input_image = HWC3(input_image) |
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input_image = resize_image(input_image, detect_resolution) |
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assert input_image.ndim == 3 |
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image_depth = input_image |
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with torch.no_grad(): |
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image_depth = torch.from_numpy(image_depth).float() |
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image_depth = image_depth.to(device) |
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image_depth = image_depth / 127.5 - 1.0 |
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w') |
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depth = self.model(image_depth)[0] |
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depth_pt = depth.clone() |
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depth_pt -= torch.min(depth_pt) |
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depth_pt /= torch.max(depth_pt) |
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depth_pt = depth_pt.cpu().numpy() |
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8) |
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if depth_and_normal: |
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depth_np = depth.cpu().numpy() |
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x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3) |
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y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3) |
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z = np.ones_like(x) * a |
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x[depth_pt < bg_th] = 0 |
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y[depth_pt < bg_th] = 0 |
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normal = np.stack([x, y, z], axis=2) |
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normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 |
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normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)[:, :, ::-1] |
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depth_image = HWC3(depth_image) |
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if depth_and_normal: |
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normal_image = HWC3(normal_image) |
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img = resize_image(input_image, image_resolution) |
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H, W, C = img.shape |
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depth_image = cv2.resize(depth_image, (W, H), interpolation=cv2.INTER_LINEAR) |
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if depth_and_normal: |
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normal_image = cv2.resize(normal_image, (W, H), interpolation=cv2.INTER_LINEAR) |
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if output_type == "pil": |
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depth_image = Image.fromarray(depth_image) |
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if depth_and_normal: |
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normal_image = Image.fromarray(normal_image) |
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if depth_and_normal: |
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return depth_image, normal_image |
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else: |
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return depth_image |
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