fix

app.py

@@ -5,7 +5,7 @@ import cv2
 from PIL import Image
 import torch
 from inference import SegmentPredictor
-from utils import generate_PCL
+from utils import generate_PCL, PCL3
 
 
 
@@ -52,7 +52,7 @@ with block:
         print("depth reconstruction")
         image = inputs[raw_image]
         # depth reconstruction
-        fig = generate_PCL(image)
+        fig = PCL3(image)
         return {pcl_figure: fig}
     
     depth_reconstruction_btn.click(on_depth_reconstruction_btn_click, components, [pcl_figure], queue=False)

utils.py

@@ -153,4 +153,17 @@ def generate_PCL(image):
 def plot_PCL(rgb_image, depth_image):
     pcd, colors = array_PCL(rgb_image, depth_image)
     fig = px.scatter_3d(x=pcd[:, 0], y=pcd[:, 1], z=pcd[:, 2], color=colors, size_max=0.1)
-    return fig
+    return fig
+
+
+def PCL3(rgb_image, depth_image):
+    # Step 2: Create an RGBD image from the RGB and depth image
+    depth_o3d = o3d.geometry.Image(depth_image)
+    image_o3d = o3d.geometry.Image(rgb_image)
+    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(image_o3d, depth_o3d, convert_rgb_to_intensity=False)
+    # Step 3: Create a PointCloud from the RGBD image
+    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, o3d.camera.PinholeCameraIntrinsic(o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault))
+    # Step 4: Convert PointCloud data to a NumPy array
+    vis = o3d.visualization.Visualizer()
+    vis.add_geometry(pcd)
+    return vis