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| """ | |
| Born out of Depth Anything V1 Issue 36 | |
| Make sure you have the necessary libraries installed. | |
| Code by @1ssb | |
| This script processes a set of images to generate depth maps and corresponding point clouds. | |
| The resulting point clouds are saved in the specified output directory. | |
| Usage: | |
| python script.py --encoder vitl --load-from path_to_model --max-depth 20 --img-path path_to_images --outdir output_directory --focal-length-x 470.4 --focal-length-y 470.4 | |
| Arguments: | |
| --encoder: Model encoder to use. Choices are ['vits', 'vitb', 'vitl', 'vitg']. | |
| --load-from: Path to the pre-trained model weights. | |
| --max-depth: Maximum depth value for the depth map. | |
| --img-path: Path to the input image or directory containing images. | |
| --outdir: Directory to save the output point clouds. | |
| --focal-length-x: Focal length along the x-axis. | |
| --focal-length-y: Focal length along the y-axis. | |
| """ | |
| 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 | |
| def main(): | |
| # Parse command-line arguments | |
| parser = argparse.ArgumentParser(description='Generate depth maps and point clouds from images.') | |
| parser.add_argument('--encoder', default='vitl', type=str, choices=['vits', 'vitb', 'vitl', 'vitg'], | |
| help='Model encoder to use.') | |
| parser.add_argument('--load-from', default='', type=str, required=True, | |
| help='Path to the pre-trained model weights.') | |
| parser.add_argument('--max-depth', default=20, type=float, | |
| help='Maximum depth value for the depth map.') | |
| parser.add_argument('--img-path', type=str, required=True, | |
| help='Path to the input image or directory containing images.') | |
| parser.add_argument('--outdir', type=str, default='./vis_pointcloud', | |
| help='Directory to save the output point clouds.') | |
| parser.add_argument('--focal-length-x', default=470.4, type=float, | |
| help='Focal length along the x-axis.') | |
| parser.add_argument('--focal-length-y', default=470.4, type=float, | |
| help='Focal length along the y-axis.') | |
| args = parser.parse_args() | |
| # Determine the device to use (CUDA, MPS, or CPU) | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' | |
| # Model configuration based on the chosen encoder | |
| 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]} | |
| } | |
| # Initialize the DepthAnythingV2 model with the specified configuration | |
| 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() | |
| # Get the list of image files to process | |
| 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) | |
| # Create the output directory if it doesn't exist | |
| os.makedirs(args.outdir, exist_ok=True) | |
| # Process each image file | |
| for k, filename in enumerate(filenames): | |
| print(f'Processing {k+1}/{len(filenames)}: {filename}') | |
| # Load the image | |
| color_image = Image.open(filename).convert('RGB') | |
| width, height = color_image.size | |
| # Read the image using OpenCV | |
| image = cv2.imread(filename) | |
| pred = depth_anything.infer_image(image, height) | |
| # Resize depth prediction to match the original image size | |
| resized_pred = Image.fromarray(pred).resize((width, height), Image.NEAREST) | |
| # Generate mesh grid and calculate point cloud coordinates | |
| x, y = np.meshgrid(np.arange(width), np.arange(height)) | |
| x = (x - width / 2) / args.focal_length_x | |
| y = (y - height / 2) / args.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(color_image).reshape(-1, 3) / 255.0 | |
| # Create the point cloud and save it to the output directory | |
| 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) | |
| if __name__ == '__main__': | |
| main() | |