plotly

app.py

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

utils.py

@@ -107,8 +107,10 @@ def point_cloud(rgb_image, depth_image):
     data = {'x': points[:, 0], 'y': points[:, 1], 'z': points[:, 2],
             'red': colors[:, 0], 'green': colors[:, 1], 'blue': colors[:, 2]}
     df = pd.DataFrame(data)
+    size = np.zeros(len(df))
+    size[:] = 0.01
     # Step 6: Create a 3D scatter plot using Plotly Express
-    fig = px.scatter_3d(df, x='x', y='y', z='z', color='red', size_max=0.1)
+    fig = px.scatter_3d(df, x='x', y='y', z='z', color='red', size=size)
     
     return fig