mirror of
https://github.com/huchenlei/Depth-Anything.git
synced 2026-01-26 15:29:46 +00:00
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
import argparse
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import cv2
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import numpy as np
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import os
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import torch
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import torch.nn.functional as F
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from torchvision.transforms import Compose
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from tqdm import tqdm
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from depth_anything.dpt import DepthAnything
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from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--img-path', type=str)
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parser.add_argument('--outdir', type=str, default='./vis_depth')
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parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl'])
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args = parser.parse_args()
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margin_width = 50
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caption_height = 60
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font = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 1
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font_thickness = 2
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(args.encoder)).to(DEVICE)
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total_params = sum(param.numel() for param in depth_anything.parameters())
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print('Total parameters: {:.2f}M'.format(total_params / 1e6))
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depth_anything.eval()
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transform = Compose([
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Resize(
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width=518,
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height=518,
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resize_target=False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method='lower_bound',
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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])
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if os.path.isfile(args.img_path):
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if args.img_path.endswith('txt'):
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with open(args.img_path, 'r') as f:
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filenames = f.read().splitlines()
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else:
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filenames = [args.img_path]
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else:
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filenames = os.listdir(args.img_path)
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filenames = [os.path.join(args.img_path, filename) for filename in filenames]
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filenames.sort()
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for filename in tqdm(filenames):
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raw_image = cv2.imread(filename)
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image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
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h, w = image.shape[:2]
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image = transform({'image': image})['image']
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image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
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with torch.no_grad():
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depth = depth_anything(image)
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depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth = depth.cpu().numpy().astype(np.uint8)
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depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
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os.makedirs(args.outdir, exist_ok=True)
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filename = os.path.basename(filename)
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split_region = np.ones((raw_image.shape[0], margin_width, 3), dtype=np.uint8) * 255
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combined_results = cv2.hconcat([raw_image, split_region, depth_color])
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caption_space = np.ones((caption_height, combined_results.shape[1], 3), dtype=np.uint8) * 255
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captions = ['Raw image', 'Depth Anything']
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segment_width = w + margin_width
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for i, caption in enumerate(captions):
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# Calculate text size
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text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0]
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# Calculate x-coordinate to center the text
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text_x = int((segment_width * i) + (w - text_size[0]) / 2)
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# Add text caption
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cv2.putText(caption_space, caption, (text_x, 40), font, font_scale, (0, 0, 0), font_thickness)
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final_result = cv2.vconcat([caption_space, combined_results])
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cv2.imwrite(os.path.join(args.outdir, filename[:filename.find('.')] + '_img_depth.png'), final_result)
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