add inference

utils/pts.py

@@ -1,5 +1,6 @@
 import numpy as np
 import open3d as o3d
+import torch
 
 class PtsUtil:
 
@@ -19,4 +20,9 @@ class PtsUtil:
     @staticmethod
     def random_downsample_point_cloud(point_cloud, num_points):
         idx = np.random.choice(len(point_cloud), num_points, replace=True)
+        return point_cloud[idx]
+    
+    @staticmethod
+    def random_downsample_point_cloud_tensor(point_cloud, num_points):
+        idx = torch.randint(0, len(point_cloud), (num_points,))
         return point_cloud[idx]

utils/render.py

@@ -0,0 +1,51 @@
+
+import os
+import json
+import subprocess
+import tempfile
+from utils.data_load import DataLoadUtil
+from utils.reconstruction import ReconstructionUtil
+from utils.pts import PtsUtil
+class RenderUtil:
+
+    @staticmethod
+    def render_pts(cam_pose, scene_path,script_path, model_points_normals, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None, require_full_scene=False):
+        nO_to_world_pose = cam_pose.cpu().numpy()  @ nO_to_nL_pose
+        nO_to_world_pose = DataLoadUtil.cam_pose_transformation(nO_to_world_pose)
+        
+        
+        with tempfile.TemporaryDirectory() as temp_dir:
+            params = {
+                "cam_pose": nO_to_world_pose.tolist(),
+                "scene_path": scene_path
+            }
+            params_data_path = os.path.join(temp_dir, "params.json")
+            with open(params_data_path, 'w') as f:
+                json.dump(params, f)
+            result = subprocess.run([
+                'blender', '-b', '-P', script_path, '--', temp_dir
+            ], capture_output=True, text=True)
+            if result.returncode != 0:
+                print("Blender script failed:")
+                print(result.stderr)
+                return None
+            path = os.path.join(temp_dir, "tmp")
+            # ------  Debug Start ------
+            import ipdb;ipdb.set_trace()
+            # ------  Debug End ------
+            
+            point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
+            cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
+            filtered_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=filter_degree)
+            
+            full_scene_point_cloud = None
+            if require_full_scene:
+                depth_L, depth_R = DataLoadUtil.load_depth(path, cam_params['near_plane'], cam_params['far_plane'], binocular=True)
+                point_cloud_L = DataLoadUtil.get_point_cloud(depth_L, cam_params['cam_intrinsic'], cam_params['cam_to_world'])['points_world']
+                point_cloud_R = DataLoadUtil.get_point_cloud(depth_R, cam_params['cam_intrinsic'], cam_params['cam_to_world_R'])['points_world']
+            
+                point_cloud_L = PtsUtil.random_downsample_point_cloud(point_cloud_L, 65536)
+                point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, 65536)
+                full_scene_point_cloud = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R)
+
+            return filtered_point_cloud, full_scene_point_cloud