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import argparse |
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import cv2 |
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import glob |
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import numpy as np |
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import open3d as o3d |
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import os |
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from PIL import Image |
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import torch |
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from depth_anything_v2.dpt import DepthAnythingV2 |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg']) |
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parser.add_argument('--load-from', default='', type=str) |
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parser.add_argument('--max-depth', default=20, type=float) |
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parser.add_argument('--img-path', type=str) |
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parser.add_argument('--outdir', type=str, default='./vis_pointcloud') |
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args = parser.parse_args() |
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FL = 715.0873 |
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FY = 784 * 0.6 |
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FX = 784 * 0.6 |
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NYU_DATA = False |
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FINAL_HEIGHT = 518 |
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FINAL_WIDTH = 518 |
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DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' |
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model_configs = { |
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'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, |
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'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, |
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'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, |
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'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} |
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} |
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depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth}) |
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depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu')) |
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depth_anything = depth_anything.to(DEVICE).eval() |
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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 = glob.glob(os.path.join(args.img_path, '**/*'), recursive=True) |
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os.makedirs(args.outdir, exist_ok=True) |
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for k, filename in enumerate(filenames): |
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print(f'Progress {k+1}/{len(filenames)}: {filename}') |
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color_image = Image.open(filename).convert('RGB') |
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image = cv2.imread(filename) |
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pred = depth_anything.infer_image(image, FINAL_HEIGHT) |
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resized_color_image = color_image.resize((FINAL_WIDTH, FINAL_HEIGHT), Image.LANCZOS) |
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resized_pred = Image.fromarray(pred).resize((FINAL_WIDTH, FINAL_HEIGHT), Image.NEAREST) |
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focal_length_x, focal_length_y = (FX, FY) if not NYU_DATA else (FL, FL) |
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x, y = np.meshgrid(np.arange(FINAL_WIDTH), np.arange(FINAL_HEIGHT)) |
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x = (x - FINAL_WIDTH / 2) / focal_length_x |
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y = (y - FINAL_HEIGHT / 2) / focal_length_y |
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z = np.array(resized_pred) |
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points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3) |
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colors = np.array(resized_color_image).reshape(-1, 3) / 255.0 |
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pcd = o3d.geometry.PointCloud() |
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pcd.points = o3d.utility.Vector3dVector(points) |
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pcd.colors = o3d.utility.Vector3dVector(colors) |
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o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd) |
