Spaces:
Running
on
Zero
Running
on
Zero
| # Born out of Depth Anything V1 Issue 36 | |
| # Make sure you have the necessary libraries | |
| # Code by @1ssb | |
| import argparse | |
| import cv2 | |
| import glob | |
| import numpy as np | |
| import open3d as o3d | |
| import os | |
| from PIL import Image | |
| import torch | |
| from depth_anything_v2.dpt import DepthAnythingV2 | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg']) | |
| parser.add_argument('--load-from', default='', type=str) | |
| parser.add_argument('--max-depth', default=20, type=float) | |
| parser.add_argument('--img-path', type=str) | |
| parser.add_argument('--outdir', type=str, default='./vis_pointcloud') | |
| args = parser.parse_args() | |
| # Global settings | |
| FL = 715.0873 | |
| FY = 784 * 0.6 | |
| FX = 784 * 0.6 | |
| NYU_DATA = False | |
| FINAL_HEIGHT = 518 | |
| FINAL_WIDTH = 518 | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' | |
| model_configs = { | |
| 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, | |
| 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, | |
| 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, | |
| 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} | |
| } | |
| depth_anything = DepthAnythingV2(**{**model_configs[args.encoder], 'max_depth': args.max_depth}) | |
| depth_anything.load_state_dict(torch.load(args.load_from, map_location='cpu')) | |
| depth_anything = depth_anything.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) | |
| os.makedirs(args.outdir, exist_ok=True) | |
| for k, filename in enumerate(filenames): | |
| print(f'Progress {k+1}/{len(filenames)}: {filename}') | |
| color_image = Image.open(filename).convert('RGB') | |
| image = cv2.imread(filename) | |
| pred = depth_anything.infer_image(image, FINAL_HEIGHT) | |
| # Resize color image and depth to final size | |
| resized_color_image = color_image.resize((FINAL_WIDTH, FINAL_HEIGHT), Image.LANCZOS) | |
| resized_pred = Image.fromarray(pred).resize((FINAL_WIDTH, FINAL_HEIGHT), Image.NEAREST) | |
| focal_length_x, focal_length_y = (FX, FY) if not NYU_DATA else (FL, FL) | |
| x, y = np.meshgrid(np.arange(FINAL_WIDTH), np.arange(FINAL_HEIGHT)) | |
| x = (x - FINAL_WIDTH / 2) / focal_length_x | |
| y = (y - FINAL_HEIGHT / 2) / focal_length_y | |
| z = np.array(resized_pred) | |
| points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3) | |
| colors = np.array(resized_color_image).reshape(-1, 3) / 255.0 | |
| pcd = o3d.geometry.PointCloud() | |
| pcd.points = o3d.utility.Vector3dVector(points) | |
| pcd.colors = o3d.utility.Vector3dVector(colors) | |
| o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd) |