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| import argparse | |
| import cv2 | |
| import numpy as np | |
| import os | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms import Compose | |
| from tqdm import tqdm | |
| from depth_anything.dpt import DepthAnything | |
| from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--img-path', type=str) | |
| parser.add_argument('--outdir', type=str, default='./vis_depth') | |
| parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl']) | |
| parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction') | |
| parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette') | |
| args = parser.parse_args() | |
| margin_width = 50 | |
| caption_height = 60 | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| font_scale = 1 | |
| font_thickness = 2 | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(args.encoder)).to(DEVICE).eval() | |
| total_params = sum(param.numel() for param in depth_anything.parameters()) | |
| print('Total parameters: {:.2f}M'.format(total_params / 1e6)) | |
| transform = Compose([ | |
| Resize( | |
| width=518, | |
| height=518, | |
| resize_target=False, | |
| keep_aspect_ratio=True, | |
| ensure_multiple_of=14, | |
| resize_method='lower_bound', | |
| image_interpolation_method=cv2.INTER_CUBIC, | |
| ), | |
| NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| PrepareForNet(), | |
| ]) | |
| if os.path.isfile(args.img_path): | |
| if args.img_path.endswith('txt'): | |
| with open(args.img_path, 'r') as f: | |
| filenames = f.read().splitlines() | |
| else: | |
| filenames = [args.img_path] | |
| else: | |
| filenames = os.listdir(args.img_path) | |
| filenames = [os.path.join(args.img_path, filename) for filename in filenames if not filename.startswith('.')] | |
| filenames.sort() | |
| os.makedirs(args.outdir, exist_ok=True) | |
| for filename in tqdm(filenames): | |
| raw_image = cv2.imread(filename) | |
| image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 | |
| h, w = image.shape[:2] | |
| image = transform({'image': image})['image'] | |
| image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) | |
| with torch.no_grad(): | |
| depth = depth_anything(image) | |
| depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] | |
| depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 | |
| depth = depth.cpu().numpy().astype(np.uint8) | |
| if args.grayscale: | |
| depth = np.repeat(depth[..., np.newaxis], 3, axis=-1) | |
| else: | |
| depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO) | |
| filename = os.path.basename(filename) | |
| if args.pred_only: | |
| cv2.imwrite(os.path.join(args.outdir, filename[:filename.rfind('.')] + '_depth.png'), depth) | |
| else: | |
| split_region = np.ones((raw_image.shape[0], margin_width, 3), dtype=np.uint8) * 255 | |
| combined_results = cv2.hconcat([raw_image, split_region, depth]) | |
| caption_space = np.ones((caption_height, combined_results.shape[1], 3), dtype=np.uint8) * 255 | |
| captions = ['Raw image', 'Depth Anything'] | |
| segment_width = w + margin_width | |
| for i, caption in enumerate(captions): | |
| # Calculate text size | |
| text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0] | |
| # Calculate x-coordinate to center the text | |
| text_x = int((segment_width * i) + (w - text_size[0]) / 2) | |
| # Add text caption | |
| cv2.putText(caption_space, caption, (text_x, 40), font, font_scale, (0, 0, 0), font_thickness) | |
| final_result = cv2.vconcat([caption_space, combined_results]) | |
| cv2.imwrite(os.path.join(args.outdir, filename[:filename.rfind('.')] + '_img_depth.png'), final_result) | |