Update depth_to_pointcloud.py

metric_depth/depth_to_pointcloud.py

@@ -1,6 +1,23 @@
-# Born out of Depth Anything V1 Issue 36
-# Make sure you have the necessary libraries
-# Code by @1ssb
+"""
+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
@@ -14,38 +31,43 @@ 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')
-    
+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()
-    
-    # Global settings
-    FL = 715.0873
-    FY = 784 * 0.6
-    FX = 784 * 0.6
-    NYU_DATA = False
-    FINAL_HEIGHT = 518
-    FINAL_WIDTH = 518
-    
+
+    # 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:
@@ -54,30 +76,39 @@ if __name__ == '__main__':
             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'Progress {k+1}/{len(filenames)}: {filename}')
-        
+        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, 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
+        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(resized_color_image).reshape(-1, 3) / 255.0
-        
+        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)
+        o3d.io.write_point_cloud(os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + ".ply"), pcd)
+
+
+if __name__ == '__main__':
+    main()