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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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parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction') |
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parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette') |
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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).eval() |
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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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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 if not filename.startswith('.')] |
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filenames.sort() |
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os.makedirs(args.outdir, exist_ok=True) |
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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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if args.grayscale: |
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depth = np.repeat(depth[..., np.newaxis], 3, axis=-1) |
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else: |
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depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO) |
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filename = os.path.basename(filename) |
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if args.pred_only: |
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cv2.imwrite(os.path.join(args.outdir, filename[:filename.rfind('.')] + '_depth.png'), depth) |
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else: |
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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]) |
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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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text_size = cv2.getTextSize(caption, font, font_scale, font_thickness)[0] |
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text_x = int((segment_width * i) + (w - text_size[0]) / 2) |
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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.rfind('.')] + '_img_depth.png'), final_result) |
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