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update calculating pts_num in inference.py

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This commit is contained in:
hofee 2024-11-07 19:42:44 +08:00
parent 6a608ea74b
commit 493639287e
2 changed files with 11 additions and 15 deletions
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8
configs/local/view_generate_config.yaml
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@ -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

18
runners/inferencer.py
View File

@ -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