.gitignore

@@ -1,162 +0,0 @@
-# ---> Python
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
-

runners/cad_strategy.py

@@ -7,10 +7,6 @@ import PytorchBoot.stereotype as stereotype
 from PytorchBoot.utils.log_util import Log
 from PytorchBoot.status import status_manager
 
-
-import sys
-
-sys.path.append("/home/user/nbv_rec/nbv_rec_control")
 from utils.control_util import ControlUtil
 from utils.communicate_util import CommunicateUtil
 from utils.pts_util import PtsUtil
@@ -105,7 +101,7 @@ class CADStrategyRunner(Runner):
             status_info=self.status_info
         )
         
-        ''' extract cam_to_world sequence  '''
+        ''' extract cam_to world sequence  '''
         cam_to_world_seq = []
         coveraget_rate_seq = []
         
@@ -136,13 +132,13 @@ class CADStrategyRunner(Runner):
             
     
 if __name__ == "__main__":
-    model_path = "/home/user/nbv_rec/data/mesh.obj"
+    model_path = "/home/yan20/nbv_rec/data/test_CAD/test_model/bear_scaled.ply"
     model = trimesh.load(model_path)
-    test_pts_L = np.loadtxt("/home/user/nbv_rec/data/cam_pts_0_L.txt")
+    test_pts_L = np.loadtxt("/home/yan20/nbv_rec/data/test_CAD/cam_pts_0_L.txt")
-    test_pts_R = np.loadtxt("/home/user/nbv_rec/data/cam_pts_0_R.txt")
+    test_pts_R = np.loadtxt("/home/yan20/nbv_rec/data/test_CAD/cam_pts_0_R.txt")
-    cad_to_cam_L = PtsUtil.register_icp(test_pts_L, model)
+    cam_to_world_L = PtsUtil.register_icp(test_pts_L, model)
-    cad_to_cam_R = PtsUtil.register_icp(test_pts_R, model)
+    cam_to_world_R = PtsUtil.register_icp(test_pts_R, model)
-    cad_pts_L = PtsUtil.transform_point_cloud(test_pts_L, cad_to_cam_L)
+    print(cam_to_world_L)
-    cad_pts_R = PtsUtil.transform_point_cloud(test_pts_R, cad_to_cam_R)
+    print("================================")
-    np.savetxt("/home/user/nbv_rec/data/cad_pts_0_L.txt", cad_pts_L)
+    print(cam_to_world_R)
-    np.savetxt("/home/user/nbv_rec/data/cad_pts_0_R.txt", cad_pts_R)
+    

runners/inference.py

@@ -1,80 +0,0 @@
-import os
-import trimesh
-import numpy as np
-from PytorchBoot.runners.runner import Runner
-from PytorchBoot.config import ConfigManager
-import PytorchBoot.stereotype as stereotype
-from PytorchBoot.utils.log_util import Log
-import PytorchBoot.namespace as namespace
-from PytorchBoot.status import status_manager
-
-from utils.control_util import ControlUtil
-from utils.communicate_util import CommunicateUtil
-from utils.pts_util import PtsUtil
-from utils.view_sample_util import ViewSampleUtil
-from utils.reconstruction_util import ReconstructionUtil
-
-
-@stereotype.runner("inferencer")
-class Inferencer(Runner):
-
-    def __init__(self, config_path: str):
-        super().__init__(config_path)
-        self.load_experiment("inferencer")
-        self.reconstruct_config = ConfigManager.get("runner", "reconstruct")
-        self.voxel_size = self.reconstruct_config["voxel_size"]
-        
-    def create_experiment(self, backup_name=None):
-        super().create_experiment(backup_name)
-    
-    def load_experiment(self, backup_name=None):
-        super().load_experiment(backup_name)
-        
-    def run_inference(self, model_name):
-        
-        ''' init robot '''
-        ControlUtil.init()
-        ''' take first view '''
-        view_data = CommunicateUtil.get_view_data()
-        first_cam_pts = None
-        first_cam_pose = None
-        combined_pts = first_cam_pts
-        input_data = {
-            "scanned_target_points_num": [first_cam_pts.shape[0]],
-            "scanned_n_to_world_pose_9d": [first_cam_pose],
-            "combined_scanned_pts": combined_pts
-        }
-        ''' enter loop '''
-        while True:
-            ''' inference '''
-            inference_result = CommunicateUtil.get_inference_data(input_data)
-            cam_to_world = inference_result["cam_to_world"]
-            ''' set pose '''
-            ControlUtil.set_pose(cam_to_world)
-            ''' take view '''
-            view_data = CommunicateUtil.get_view_data()
-            curr_cam_pts = None
-            curr_cam_pose = None
-            ''' update combined pts '''
-            combined_pts = np.concatenate([combined_pts, curr_cam_pts], axis=0)
-            combined_pts = PtsUtil.voxel_downsample_point_cloud(combined_pts, voxel_size=self.voxel_size)
-            
-            
-            ''' update input data '''
-            
-    
-    def run(self):
-        self.run_inference()
-            
-    
-if __name__ == "__main__":
-    model_path = "/home/yan20/nbv_rec/data/test_CAD/test_model/bear_scaled.ply"
-    model = trimesh.load(model_path)
-    test_pts_L = np.loadtxt("/home/yan20/nbv_rec/data/test_CAD/cam_pts_0_L.txt")
-    test_pts_R = np.loadtxt("/home/yan20/nbv_rec/data/test_CAD/cam_pts_0_R.txt")
-    cam_to_world_L = PtsUtil.register_icp(test_pts_L, model)
-    cam_to_world_R = PtsUtil.register_icp(test_pts_R, model)
-    print(cam_to_world_L)
-    print("================================")
-    print(cam_to_world_R)
-    

test.py

@@ -1,10 +0,0 @@
-import flask
-
-app = flask.Flask(__name__)
-
-@app.route('/hello')
-def hello():
-    return "Hello, World!"
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7999)

utils/communicate_util.py

@@ -4,10 +4,10 @@ class CommunicateUtil:
     INFERENCE_HOST = "127.0.0.1:5000"
     
     def get_view_data() -> dict:
-        data = {}
+        data = None
         return data
     
-    def get_inference_data(view_data:dict) -> dict:
+    def get_inference_data() -> dict:
-        data = {}
+        data = None
         return data
         

utils/control_util.py

@@ -4,7 +4,7 @@ from autolab_core import RigidTransform
 
 class ControlUtil:
     
-    #__fa = FrankaArm(robot_num=2)
+    __fa = FrankaArm(robot_num=2)
     
     BASE_TO_WORLD:np.ndarray = np.asarray([
         [1, 0, 0, -0.5],
@@ -114,7 +114,7 @@ class ControlUtil:
 # ----------- Debug Test -------------
 
 if __name__ == "__main__":
-    ControlUtil.init()
+    #ControlUtil.init()
     import time
     start = time.time()
     rot_degree, cam_to_world = ControlUtil.solve_display_table_rot_and_cam_to_world(ControlUtil.INIT_POSE)

utils/pts_util.py

@@ -102,23 +102,23 @@ class PtsUtil:
         return filtered_sampled_points[:, :3]
     
     @staticmethod
-    def register_icp(pcl: np.ndarray, model: trimesh.Trimesh, threshold=0.5) -> np.ndarray:
+    def register_icp(pcl: np.ndarray, model: trimesh.Trimesh, threshold = 0.005) -> np.ndarray:
+        """
+        Register point cloud to CAD model.
+        Returns the transformation matrix.
+        """
+
         mesh_points = np.asarray(model.vertices)
-        downsampled_mesh_points = PtsUtil.random_downsample_point_cloud(mesh_points, pcl.shape[0])
         mesh_point_cloud = o3d.geometry.PointCloud()
-        mesh_point_cloud.points = o3d.utility.Vector3dVector(downsampled_mesh_points)
+        mesh_point_cloud.points = o3d.utility.Vector3dVector(mesh_points)
-        np.savetxt("mesh_point_cloud.txt", downsampled_mesh_points)
+
         pcl_point_cloud = o3d.geometry.PointCloud()
         pcl_point_cloud.points = o3d.utility.Vector3dVector(pcl)
-        initial_transform = np.eye(4)
+
+         
         reg_icp = o3d.pipelines.registration.registration_icp(
             pcl_point_cloud, mesh_point_cloud, threshold,
-            initial_transform,
+            np.eye(4),
             o3d.pipelines.registration.TransformationEstimationPointToPoint()
         )
-        if np.allclose(reg_icp.transformation, np.eye(4)):
-            print("Registration failed. Check your initial alignment and point cloud quality.")
-        else:
-            print("Registration successful.")
-        print(reg_icp.transformation)
         return reg_icp.transformation