debug

inference.py

@@ -37,13 +37,16 @@ def PCL(mask, depth):
     assert type(depth) == np.ndarray
     rgb_mask = np.zeros((mask.shape[0], mask.shape[1], 3)).astype("uint8")
     rgb_mask[mask] = (255, 0, 0)
-    print(mask.sum())
+    print(np.unique(rgb_mask))
     depth_o3d = o3d.geometry.Image(depth)
     image_o3d = o3d.geometry.Image(rgb_mask)
+    print(len(depth_o3d))
+    print(len(image_o3d))
     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
+    print(len(pcd))
     points = np.asarray(pcd.points)
     colors = np.asarray(pcd.colors)
     print(np.unique(colors, axis=0))
@@ -163,7 +166,7 @@ class DepthPredictor:
     def generate_obj_masks2(self, image, masks, cube_size, n_samples, min_depth, max_depth):
         # Generate a point cloud
         depth = self.predict(image)
-        depth = map_image_range(depth, min_depth, max_depth)
+        #depth = map_image_range(depth, min_depth, max_depth)
         image = np.array(image)
         mesh = o3d.geometry.TriangleMesh()
         # Create cubes and add them to the mesh