| import argparse |
| import cv2 |
| import glob |
| import matplotlib |
| import numpy as np |
| import os |
| import torch |
| import torch.nn.functional as F |
| from ppd.utils.set_seed import set_seed |
| from ppd.models.ppd import PixelPerfectDepth |
| |
|
|
| if __name__ == '__main__': |
| set_seed(666) |
| parser = argparse.ArgumentParser(description='Pixel-Perfect Depth') |
| parser.add_argument('--img_path', type=str, default='assets/examples/images') |
| parser.add_argument('--input_size', type=int, default=[1024, 768]) |
| parser.add_argument('--outdir', type=str, default='depth_vis') |
| parser.add_argument('--semantics_model', type=str, default='DA2', choices=['MoGe2', 'DA2']) |
| parser.add_argument('--sampling_steps', type=int, default=4) |
| parser.add_argument('--pred_only', action='store_true', help='only display/save the predicted depth (no input image)') |
| parser.add_argument('--save_npy', action='store_true', help='save raw depth prediction as .npy file (float32, unnormalized)') |
|
|
| args = parser.parse_args() |
|
|
| DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') |
|
|
| if args.semantics_model == 'MoGe2': |
| semantics_pth = 'checkpoints/moge2.pt' |
| model_pth = 'checkpoints/ppd_moge.pth' |
| else: |
| semantics_pth = 'checkpoints/depth_anything_v2_vitl.pth' |
| model_pth = 'checkpoints/ppd.pth' |
|
|
| model = PixelPerfectDepth(semantics_model=args.semantics_model, semantics_pth=semantics_pth, sampling_steps=args.sampling_steps) |
| model.load_state_dict(torch.load(model_pth, map_location='cpu'), strict=False) |
|
|
| model = model.to(DEVICE).eval() |
|
|
| 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 = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True) |
| filenames = sorted(filenames) |
|
|
| os.makedirs(args.outdir, exist_ok=True) |
|
|
| cmap = matplotlib.colormaps.get_cmap('Spectral') |
|
|
| for k, filename in enumerate(filenames): |
| print(f'Progress {k+1}/{len(filenames)}: {filename}') |
| |
| image = cv2.imread(filename) |
| H, W = image.shape[:2] |
| depth, _ = model.infer_image(image) |
| depth = F.interpolate(depth, size=(H, W), mode='bilinear', align_corners=False)[0, 0] |
| depth = depth.squeeze().cpu().numpy() |
| |
| vis_depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 |
| vis_depth = vis_depth.astype(np.uint8) |
| vis_depth = (cmap(vis_depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8) |
| |
| if args.pred_only: |
| cv2.imwrite(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), vis_depth) |
| else: |
| split_region = np.ones((image.shape[0], 50, 3), dtype=np.uint8) * 255 |
| combined_result = cv2.hconcat([image, split_region, vis_depth]) |
| cv2.imwrite(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), combined_result) |
|
|
| if args.save_npy: |
| depth_npy_dir = 'depth_npy' |
| os.makedirs(depth_npy_dir, exist_ok=True) |
| npy_path = os.path.join(depth_npy_dir, os.path.splitext(os.path.basename(filename))[0] + '.npy') |
| np.save(npy_path, depth) |
|
|
