From 493639287e0ba297d56f99cba1c4b9af2d99d5d2 Mon Sep 17 00:00:00 2001
From: hofee <64160135+GitHofee@users.noreply.github.com>
Date: Thu, 7 Nov 2024 19:42:44 +0800
Subject: [PATCH] update calculating pts_num in inference.py

---
 configs/local/view_generate_config.yaml |  8 ++++----
 runners/inferencer.py                   | 18 +++++++-----------
 2 files changed, 11 insertions(+), 15 deletions(-)

diff --git a/configs/local/view_generate_config.yaml b/configs/local/view_generate_config.yaml
index 803bae8..4396de7 100644
--- a/configs/local/view_generate_config.yaml
+++ b/configs/local/view_generate_config.yaml
@@ -8,11 +8,11 @@ runner:
     root_dir: experiments
   generate:
     port: 5002
-    from: 0
-    to: -1 # -1 means all
-    object_dir: C:\\Document\\Datasets\\ball_meshes
+    from: 1
+    to: 50 # -1 means all
+    object_dir: C:\\Document\\Datasets\\scaled_object_meshes
     table_model_path: C:\\Document\\Datasets\\table.obj
-    output_dir: C:\\Document\\Datasets\\debug_ball_generate_view
+    output_dir: C:\\Document\\Datasets\\debug_generate_view
     binocular_vision: true
     plane_size: 10
     max_views: 512
diff --git a/runners/inferencer.py b/runners/inferencer.py
index 0ba8a4f..238da0b 100644
--- a/runners/inferencer.py
+++ b/runners/inferencer.py
@@ -128,7 +128,7 @@ class Inferencer(Runner):
         retry = 0
         pred_cr_seq = [last_pred_cr]
         success = 0
-        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], 0.002)
+        last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], 0.002).shape[0]
         import time
         while len(pred_cr_seq) < max_iter and retry < max_retry:
             start_time = time.time()
@@ -151,7 +151,7 @@ class Inferencer(Runner):
                     curr_overlap_area_threshold = overlap_area_threshold * 0.5  
 
                 downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
-                overlap, new_added_pts_num = ReconstructionUtil.check_overlap(downsampled_new_target_pts, down_sampled_model_pts, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
+                overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, down_sampled_model_pts, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
                 if not overlap:
                     retry += 1
                     retry_overlap_pose.append(pred_pose.cpu().numpy().tolist())
@@ -175,27 +175,22 @@ class Inferencer(Runner):
                 continue
             
             start_time = time.time()
-            pred_cr, covered_pts_num = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
+            pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
             end_time = time.time()
             print(f"Time taken for coverage rate computation: {end_time - start_time} seconds")
             print(pred_cr, last_pred_cr, " max: ", data["seq_max_coverage_rate"])
             if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
                 print("max coverage rate reached!: ", pred_cr)
                 success += 1
-            if pred_cr <= last_pred_cr + cr_increase_threshold:
-                retry += 1
-                retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
-                continue
+        
 
             retry = 0
             pred_cr_seq.append(pred_cr)
             scanned_view_pts.append(new_target_pts)
-            down_sampled_new_pts_world = PtsUtil.random_downsample_point_cloud(new_target_pts, input_pts_N)
             
-            new_pts = down_sampled_new_pts_world
             input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)]
             
-            combined_scanned_pts = np.concatenate([input_data["combined_scanned_pts"][0].cpu().numpy(), new_pts], axis=0)
+            combined_scanned_pts = np.vstack(scanned_view_pts)
             voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, 0.002)
             random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
             input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
@@ -204,8 +199,9 @@ class Inferencer(Runner):
                 break
             last_pred_cr = pred_cr
             pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
-            if pts_num - last_pts_num < 10:
+            if pts_num - last_pts_num < 10 and pred_cr < data["seq_max_coverage_rate"] - 1e-3:
                 retry += 1
+                retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
                 print("delta pts num < 10:", pts_num, last_pts_num)
             last_pts_num = pts_num