Visualize RGBD in Open3D

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README.md

@@ -1,7 +1,7 @@
 ## Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
 
 This software project accompanies the research paper:
-**[Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073)**, 
+**[Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073)**,
 *Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, and Vladlen Koltun*.
 
 ![](data/depth-pro-teaser.jpg)
@@ -57,18 +57,24 @@ focallength_px = prediction["focallength_px"]  # Focal length in pixels.
 ```
 
 
-### Evaluation (boundary metrics) 
+### Evaluation (boundary metrics)
 
 Our boundary metrics can be found under `eval/boundary_metrics.py` and used as follows:
 
 ```python
 # for a depth-based dataset
 boundary_f1 = SI_boundary_F1(predicted_depth, target_depth)
 
-# for a mask-based dataset (image matting / segmentation) 
+# for a mask-based dataset (image matting / segmentation)
 boundary_recall = SI_boundary_Recall(predicted_depth, target_mask)
 ```
 
+### Visualization
+
+```bash
+python src/tools/visualize_rgbd.py --input_depth output/depth.npz --input_image data/rgb.jpeg --focal_length 1500.0
+```
+
 
 ## Citation
 

src/tools/visualize_rgbd.py

@@ -0,0 +1,64 @@
+import argparse
+import os
+import numpy as np
+from PIL import Image
+import open3d as o3d
+
+import plotly.graph_objects as go
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Visualize depth maps")
+    parser.add_argument("--input_depth", type=str, required=True, help="Path to the input depth map (npz file)")
+    parser.add_argument("--input_image", type=str, required=True, help="Path to the input image (png file)")
+    parser.add_argument("--focal_length", type=float, default=1500.0, help="Focal length of the camera")
+    return parser.parse_args()
+
+
+def visualize_depth_with_open3d(depth, image_rgb, focal_length=1500.0):
+    # Convert numpy arrays to Open3D Image objects
+    color_image = o3d.geometry.Image(image_rgb)
+    depth_image = o3d.geometry.Image(depth)
+
+    rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
+        color_image, depth_image,
+        depth_scale=1.0,  # Adjust this value based on your depth scale
+        depth_trunc=100.0,  # Adjust this value based on your depth range
+        convert_rgb_to_intensity=False
+    )
+
+    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
+        rgbd,
+        o3d.camera.PinholeCameraIntrinsic(
+            width=depth.shape[1],
+            height=depth.shape[0],
+            fx=focal_length,
+            fy=focal_length,
+            cx=depth.shape[1] / 2,
+            cy=depth.shape[0] / 2
+        )
+    )
+
+    # flip the orientation, so it looks upright, not upside-down
+    pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
+
+    # Create coordinate frame
+    coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1, origin=[0, 0, 0])
+    # Visualize the point cloud with coordinate frame
+    # Set a smaller point size for the point cloud
+    o3d.visualization.draw_geometries(
+        [pcd, coordinate_frame], point_show_normal=True)  # Reduced point size to 1
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # load depth map
+    with np.load(args.input_depth) as data:
+        depth = data["depth"]
+
+    # load image
+    img_pil = Image.open(args.input_image).convert("RGB")
+    image = np.array(img_pil)
+
+    visualize_depth_with_open3d(depth, image, focal_length=args.focal_length)