add inference

core/evaluation.py

@@ -1,43 +1,14 @@
 import torch
-import os
-import json
 import numpy as np
-import subprocess
-import tempfile
-from utils.data_load import DataLoadUtil
 from utils.reconstruction import ReconstructionUtil
 from utils.pose import PoseUtil
 from utils.pts import PtsUtil
+from utils.render import RenderUtil
 import PytorchBoot.stereotype as stereotype
 import PytorchBoot.namespace as namespace
 from PytorchBoot.utils.log_util import Log
 
-def render_pts(cam_pose, scene_path,script_path, model_points_normals, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None):
-    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")
-        
-        point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
-        cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
-        sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=filter_degree)
-        return sampled_point_cloud
+
 
 @stereotype.evaluation_method("pose_diff")
 class PoseDiff:
@@ -110,7 +81,7 @@ class ConverageRateIncrease:
                 filter_degree = filter_degree_list[idx]
                 nO_to_nL_pose = nO_to_nL_pose_batch[idx]
                 try:
-                    new_pts = render_pts(pred_pose, scene_path, self.renderer_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=nO_to_nL_pose)
+                    new_pts, _ = RenderUtil.render_pts(pred_pose, scene_path, self.renderer_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=nO_to_nL_pose)
                     pred_cr = self.compute_coverage_rate(scanned_view_pts, new_pts, down_sampled_model_pts, threshold=voxel_threshold)
                 except Exception as e:
                     Log.warning(f"Error in scene {scene_path}, {e}")