From 6bb8835d00a4afa0d5501b5eede147f94133a7fa Mon Sep 17 00:00:00 2001 From: hofee <64160135+GitHofee@users.noreply.github.com> Date: Wed, 15 Jan 2025 14:42:54 +0800 Subject: [PATCH] inference finish --- .gitignore | 168 + README.md | 5 + app_cad.py | 36 + app_inference.py | 8 + beans/pointcloud.py | 327 + combine_all_pts.py | 18 + configs/cad_close_loop_config.yaml | 28 + configs/cad_open_loop_config.yaml | 43 + configs/inference_config.yaml | 18 + debug/cad_for_init_reg.obj | 1153465 ++++++++++++++ depth_to_ee.json | 26 + hand_eye.json | 30 + load_normal.py | 49 + runners/cad_close_loop_new.py | 340 + runners/cad_close_loop_online_reg_strategy.py | 347 + runners/cad_close_loop_strategy.py | 242 + runners/cad_open_loop_strategy.py | 224 + runners/calibration.py | 475 + runners/inference.py | 182 + utils/communicate_util.py | 41 + utils/control_util.py | 284 + utils/data_load.py | 384 + utils/inference_util.py | 0 utils/pose_util.py | 164 + utils/preprocess_util.py | 247 + utils/pts_util.py | 308 + utils/reconstruction_util.py | 205 + utils/render_util.py | 45 + utils/view_util.py | 132 + utils/vis.py | 172 + 30 files changed, 1158013 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 app_cad.py create mode 100644 app_inference.py create mode 100644 beans/pointcloud.py create mode 100644 combine_all_pts.py create mode 100644 configs/cad_close_loop_config.yaml create mode 100644 configs/cad_open_loop_config.yaml create mode 100644 configs/inference_config.yaml create mode 100644 debug/cad_for_init_reg.obj create mode 100644 depth_to_ee.json create mode 100644 hand_eye.json create mode 100644 load_normal.py create mode 100644 runners/cad_close_loop_new.py create mode 100644 runners/cad_close_loop_online_reg_strategy.py create mode 100644 runners/cad_close_loop_strategy.py create mode 100644 runners/cad_open_loop_strategy.py create mode 100644 runners/calibration.py create mode 100644 runners/inference.py create mode 100644 utils/communicate_util.py create mode 100644 utils/control_util.py create mode 100644 utils/data_load.py create mode 100644 utils/inference_util.py create mode 100644 utils/pose_util.py create mode 100644 utils/preprocess_util.py create mode 100644 utils/pts_util.py create mode 100644 utils/reconstruction_util.py create mode 100644 utils/render_util.py create mode 100644 utils/view_util.py create mode 100644 utils/vis.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ca10b21 --- /dev/null +++ b/.gitignore @@ -0,0 +1,168 @@ +# ---> Python +# Byte-compiled / optimized / DLL files +test/ +output/ +__pycache__/ +*.py[cod] +*$py.class +test_output/ +test* +temp* +# C extensions +*.so +*.txt +experiments/ +temp_output/ +# 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/ + diff --git a/README.md b/README.md new file mode 100644 index 0000000..8142610 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +nbv reconstruction controll franka +cd ../.. +cd frankapy +bash bash_scripts/start_yan1.sh -i localhost +ptb run cad_cl \ No newline at end of file diff --git a/app_cad.py b/app_cad.py new file mode 100644 index 0000000..587ff55 --- /dev/null +++ b/app_cad.py @@ -0,0 +1,36 @@ +from PytorchBoot.application import PytorchBootApplication +from runners.cad_close_loop_online_reg_strategy import CADCloseLoopOnlineRegStrategyRunner + +@PytorchBootApplication("cad_register") +class AppCADRegister: + @staticmethod + def start(): + CADCloseLoopOnlineRegStrategyRunner("configs/cad_close_loop_config.yaml").register() + +@PytorchBootApplication("cad_render") +class AppCADRender: + @staticmethod + def start(): + CADCloseLoopOnlineRegStrategyRunner("configs/cad_close_loop_config.yaml").render_data() + +@PytorchBootApplication("cad_preprocess") +class AppCADPreprocess: + @staticmethod + def start(): + CADCloseLoopOnlineRegStrategyRunner("configs/cad_close_loop_config.yaml").preprocess_data() + +@PytorchBootApplication("cad_init_obj") +class AppCADPreprocess: + @staticmethod + def start(): + runner = CADCloseLoopOnlineRegStrategyRunner("configs/cad_close_loop_config.yaml") + runner.register() + runner.render_data() + runner.preprocess_data() + +@PytorchBootApplication("cad_cl") +class AppCADCloseLoopOnlineRegStrategy: + @staticmethod + def start(): + CADCloseLoopOnlineRegStrategyRunner("configs/cad_close_loop_config.yaml").run() + \ No newline at end of file diff --git a/app_inference.py b/app_inference.py new file mode 100644 index 0000000..105c587 --- /dev/null +++ b/app_inference.py @@ -0,0 +1,8 @@ +from PytorchBoot.application import PytorchBootApplication +from runners.inference import InferenceRunner + +@PytorchBootApplication("inference") +class AppInference: + @staticmethod + def start(): + InferenceRunner("configs/inference_config.yaml").run() \ No newline at end of file diff --git a/beans/pointcloud.py b/beans/pointcloud.py new file mode 100644 index 0000000..bcccb09 --- /dev/null +++ b/beans/pointcloud.py @@ -0,0 +1,327 @@ +import numpy as np +import torch + + +def rotation_6d_to_matrix_numpy(d6): + a1, a2 = d6[:3], d6[3:] + b1 = a1 / np.linalg.norm(a1) + b2 = a2 - np.dot(b1, a2) * b1 + b2 = b2 / np.linalg.norm(b2) + b3 = np.cross(b1, b2) + return np.stack((b1, b2, b3), axis=-2) + +class Pointcloud: + def __init__(self, points, camera=None, name="unknown", parent=None, from_parent_index=None): + self.points = points # Nx3 numpy array + if camera is not None and camera.shape == (9,): + rotation_matrix = rotation_6d_to_matrix_numpy(camera) + translation = camera[6:] + camera = np.eye(4) + camera[:3, :3] = rotation_matrix + camera[:3, 3] = translation + self.camera = camera # 4x4 numpy array + self.current_transform = np.eye(4) + self.transform_history = {} + self.original_points = points.copy() + self.name = name + self.parent = parent + self.from_parent_index = from_parent_index + + # --------- basic --------- + def __getitem__(self, item): + return self.points[item] + + def __len__(self): + return len(self.points) + + def __repr__(self): + return f"Pointcloud({self.points})" + + def __str__(self): + return f"Pointcloud with {len(self.points)} points. \n\tCenter: {np.mean(self.points, axis=0)} \n\tTransform history: {list(self.transform_history.keys())}" + + def __eq__(self, other): + return np.array_equal(self.points, other.points) + + def __ne__(self, other): + return not np.array_equal(self.points, other.points) + + def __contains__(self, item): + return item in self.points + + def __iter__(self): + return iter + + def concat(self, other): + return Pointcloud(np.concatenate([self.points, other.points], axis=0)) + + # --------- downsample --------- + def voxel_downsample(self, voxel_size): + voxel_indices = np.floor(self.points / voxel_size).astype(np.int32) + _, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True) + idx_sort = np.argsort(inverse) + idx_unique = idx_sort[np.cumsum(counts)-counts] + downsampled_points = self.points[idx_unique] + return Pointcloud( + downsampled_points, + name=self.name+'(voxel downsampled with voxel_size='+str(voxel_size)+')', + parent=self.parent, + from_parent_index=idx_unique + ) + + def random_downsample(self, num_points): + if self.points.shape[0] == 0: + downsampled_points = self.points + idx = np.array([]) + else: + idx = np.random.choice(len(self.points), num_points, replace=True) + downsampled_points = self.points[idx] + return Pointcloud( + downsampled_points, + name=self.name+'(random downsampled with num_points='+str(num_points)+')', + parent=self.parent, + from_parent_index=idx + ) + + def fps_downsample(self, num_points): + N = self.points.shape[0] + mask = np.zeros(N, dtype=bool) + + sampled_indices = np.zeros(num_points, dtype=int) + sampled_indices[0] = np.random.randint(0, N) + distances = np.linalg.norm(self.points - self.points[sampled_indices[0]], axis=1) + for i in range(1, num_points): + farthest_index = np.argmax(distances) + sampled_indices[i] = farthest_index + mask[farthest_index] = True + + new_distances = np.linalg.norm(self.points - self.points[farthest_index], axis=1) + distances = np.minimum(distances, new_distances) + + sampled_points = self.points[sampled_indices] + return Pointcloud( + sampled_points, + name=self.name+'(fps downsampled with num_points='+str(num_points)+')', + parent=self.parent, + from_parent_index=sampled_indices + ) + # --------- transform --------- + def transform(self, transform_matrix, name=None): + self.points = np.dot(self.points, transform_matrix[:3, :3].T) + transform_matrix[:3, 3] + self.current_transform = np.dot(self.current_transform, transform_matrix) + if name is None: + name = f"transform_{len(self.transform_history)}" + self.transform_history[name] = transform_matrix + return self + + def translate(self, translation, name=None): + transform_matrix = np.eye(4) + transform_matrix[:3, 3] = translation + self.transform(transform_matrix, name) + return self + + def rotate(self, rotation, name=None): + transform_matrix = np.eye(4) + if rotation.shape == (3, 3): + rotation_matrix = rotation + elif rotation.shape == (6,): + rotation_matrix = rotation_6d_to_matrix_numpy(rotation) + transform_matrix[:3, :3] = rotation_matrix + self.transform(transform_matrix, name) + return self + + def scale(self, scale_factor, name=None): + transform_matrix = np.eye(4) + transform_matrix[:3, :3] = scale_factor * np.eye(3) + self.transform(transform_matrix, name) + return self + + def same_transform(self, other): + return np.allclose(self.current_transform, other.current_transform) + + def print_transform_history(self): + print(f"Transform history of {self.name}:") + for name, transform_matrix in self.transform_history.items(): + print(f"\t-{name}:") + for i in range(4): + print(f"\t\t{transform_matrix[i]}") + + # --------- tensor --------- + def get_batchlized_points(self, batch_size=1): + return torch.tensor(self.points).unsqueeze(0).repeat(batch_size, 1, 1) + + # --------- visualize --------- + def visualize(self, point_size=1, color=None): + import plotly.graph_objects as go + fig = go.Figure() + if color is None: + if self.name is not None: + hash_value = hash(self.name) + r = (hash_value & 0xFF) / 255.0 + g = ((hash_value >> 8) & 0xFF) / 255.0 + b = ((hash_value >> 16) & 0xFF) / 255.0 + color = f'rgb({int(r*255)},{int(g*255)},{int(b*255)})' + else: + color = "gray" + + + if self.points is not None: + fig.add_trace(go.Scatter3d( + x=self.points[:, 0], y=self.points[:, 1], z=self.points[:, 2], + mode='markers', marker=dict(size=point_size, color=color, opacity=0.5), name=self.name + )) + + if self.camera is not None: + origin = self.camera[:3, 3] + z_axis = self.camera[:3, 2] + fig.add_trace(go.Cone( + x=[origin[0]], y=[origin[1]], z=[origin[2]], + u=[z_axis[0]], v=[z_axis[1]], w=[z_axis[2]], + colorscale="blues", + sizemode="absolute", sizeref=0.05, anchor="tail", showscale=False + )) + + title = self.name + fig.update_layout( + title=title, + scene=dict( + xaxis_title='X', + yaxis_title='Y', + zaxis_title='Z' + ), + margin=dict(l=0, r=0, b=0, t=40), + scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25)) + ) + fig.show() + + # --------- save and load --------- + def save(self, file_path): + np.save(file_path, self.points) + + def savetxt(self, file_path): + np.savetxt(file_path, self.points) + + def load(self, file_path): + self.points = np.load(file_path) + + def loadtxt(self, file_path): + self.points = np.loadtxt(file_path) + + +class PointcloudGroup: + def __init__(self, pointclouds: list[Pointcloud] = [], name="unknown"): + self.pointclouds = pointclouds + self.name = name + + # --------- basic --------- + def __getitem__(self, item): + return self.pointclouds[item] + + def __len__(self): + return len(self.pointclouds) + + def __repr__(self): + return f"PointcloudGroup({self.pointclouds})" + + def __str__(self): + return f"PointcloudGroup with {len(self.pointclouds)} pointclouds." + + def __eq__(self, other): + return np.array_equal(self.pointclouds, other.pointclouds) + + def __ne__(self, other): + return not np.array_equal(self.pointclouds, other.pointclouds) + + def __contains__(self, item): + return item in self.pointclouds + + def __iter__(self): + return iter + + def __add__(self, pointcloud: Pointcloud): + new_group = PointcloudGroup(self.name) + new_group.pointclouds = self.pointclouds.copy() + new_group.pointclouds.append(pointcloud) + return new_group + + def __iadd__(self, pointcloud: Pointcloud): + self.pointclouds.append(pointcloud) + return self + + def add(self, pointcloud: Pointcloud): + self.pointclouds.append(pointcloud) + + def concat(self, other): + new_group = PointcloudGroup(self.name) + new_group.pointclouds = self.pointclouds.copy() + new_group.pointclouds.extend(other.pointclouds) + return new_group + + # --------- merge --------- + def merge_pointclouds(self, name="unknown"): + points = np.concatenate([pointcloud.points for pointcloud in self.pointclouds], axis=0) + return Pointcloud(points, name=name) + + # --------- transform --------- + def transform(self, transform_matrix, name="unknown_transform"): + for pointcloud in self.pointclouds: + pointcloud.transform(transform_matrix, name) + + def translate(self, translation, name="unknown_translate"): + transform_matrix = np.eye(4) + transform_matrix[:3, 3] = translation + self.transform(transform_matrix, name) + + def rotate(self, rotation_matrix, name="unknown_ratate"): + transform_matrix = np.eye(4) + transform_matrix[:3, :3] = rotation_matrix + self.transform(transform_matrix, name) + + def scale(self, scale_factor, name="unknown_scale"): + transform_matrix = np.eye(4) + transform_matrix[:3, :3] = scale_factor * np.eye(3) + self.transform(transform_matrix, name) + + # --------- visualize --------- + def visualize(self, point_size=1, color=None): + import plotly.graph_objects as go + fig = go.Figure() + for pointcloud in self.pointclouds: + if color is None: + if pointcloud.name is not None: + hash_value = hash(pointcloud.name) + r = (hash_value & 0xFF) / 255.0 + g = ((hash_value >> 8) & 0xFF) / 255.0 + b = ((hash_value >> 16) & 0xFF) / 255.0 + color = f'rgb({int(r*255)},{int(g*255)},{int(b*255)})' + else: + color = "gray" + + if pointcloud.points is not None: + fig.add_trace(go.Scatter3d( + x=pointcloud.points[:, 0], y=pointcloud.points[:, 1], z=pointcloud.points[:, 2], + mode='markers', marker=dict(size=point_size, color=color, opacity=0.5), name=pointcloud.name + )) + + if pointcloud.camera is not None: + origin = pointcloud.camera[:3, 3] + z_axis = pointcloud.camera[:3, 2] + fig.add_trace(go.Cone( + x=[origin[0]], y=[origin[1]], z=[origin[2]], + u=[z_axis[0]], v=[z_axis[1]], w=[z_axis[2]], + colorscale="blues", + sizemode="absolute", sizeref=0.05, anchor="tail", showscale=False + )) + + title = self.name + fig.update_layout( + title=title, + scene=dict( + xaxis_title='X', + yaxis_title='Y', + zaxis_title='Z' + ), + margin=dict(l=0, r=0, b=0, t=40), + scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25)) + ) + fig.show() \ No newline at end of file diff --git a/combine_all_pts.py b/combine_all_pts.py new file mode 100644 index 0000000..93fe08d --- /dev/null +++ b/combine_all_pts.py @@ -0,0 +1,18 @@ +import numpy as np +import os +from utils.vis import visualizeUtil +if __name__ == "__main__": + root = r"output" + visualizeUtil.save_all_cam_pos_and_cam_axis(root,"2box", "/home/yan20/nbv_rec/project/franka_control/output/temp") + # pts_dir_path = "/home/yan20/nbv_rec/project/franka_control/output/2box/pts" + # pts_dir = os.listdir(pts_dir_path) + # pts_list = [] + # for i in range(len(pts_dir)): + # pts_path = os.path.join(pts_dir_path, pts_dir[i]) + # pts = np.load(pts_path) + # if pts.shape[0] != 0: + # pts_list.append(pts) + # combined_pts = np.vstack(pts_list) + # path = "/home/yan20/nbv_rec/project/franka_control" + # np.savetxt(os.path.join(path, "combined_pts2.txt"), combined_pts) + \ No newline at end of file diff --git a/configs/cad_close_loop_config.yaml b/configs/cad_close_loop_config.yaml new file mode 100644 index 0000000..f9fb0f2 --- /dev/null +++ b/configs/cad_close_loop_config.yaml @@ -0,0 +1,28 @@ + +runner: + general: + seed: 1 + device: cpu + cuda_visible_devices: "0,1,2,3,4,5,6,7" + + experiment: + name: debug + root_dir: "experiments" + + generate: + blender_bin_path: /home/yan20/nbv_rec/project/blender_app/blender-4.0.2-linux-x64/blender + generator_script_path: /home/yan20/Desktop/nbv_rec/project/blender_app/new_data_generator.py + model_dir: "/home/yan20/Desktop/nbv_rec/data/models" + output_dir: /home/yan20/nbv_rec/project/franka_control/output + table_model_path: "/home/yan20/Desktop/nbv_rec/data/table.obj" + model_start_idx: 0 + voxel_size: 0.005 + object_name: "2box" + + + reconstruct: + scan_points_threshold: 10 + max_shot_view_num: 50 + min_shot_new_pts_num: 10 + min_coverage_increase: 0.001 + \ No newline at end of file diff --git a/configs/cad_open_loop_config.yaml b/configs/cad_open_loop_config.yaml new file mode 100644 index 0000000..597349c --- /dev/null +++ b/configs/cad_open_loop_config.yaml @@ -0,0 +1,43 @@ + +runner: + general: + seed: 1 + device: cpu + cuda_visible_devices: "0,1,2,3,4,5,6,7" + + experiment: + name: debug + root_dir: "experiments" + + generate: + blender_bin_path: /home/yan20/Desktop/nbv_rec/project/blender_app/blender-4.2.2-linux-x64/blender + generator_script_path: /home/yan20/Desktop/nbv_rec/project/blender_app/data_generator.py + model_dir: "/home/yan20/Desktop/nbv_rec/data/models" + table_model_path: "/home/yan20/Desktop/nbv_rec/data/table.obj" + model_start_idx: 0 + voxel_size: 0.002 + max_view: 512 + min_view: 128 + max_diag: 0.7 + min_diag: 0.01 + random_view_ratio: 0 + min_cam_table_included_degree: 20 + obj_name: "bear" + light_and_camera_config: + Camera: + near_plane: 0.01 + far_plane: 5 + fov_vertical: 25 + resolution: [640,400] + eye_distance: 0.15 + eye_angle: 25 + Light: + location: [0,0,3.5] + orientation: [0,0,0] + power: 150 + + reconstruct: + soft_overlap_threshold: 0.3 + hard_overlap_threshold: 0.6 + scan_points_threshold: 10 + \ No newline at end of file diff --git a/configs/inference_config.yaml b/configs/inference_config.yaml new file mode 100644 index 0000000..a87161e --- /dev/null +++ b/configs/inference_config.yaml @@ -0,0 +1,18 @@ + +runner: + general: + seed: 0 + device: cuda + cuda_visible_devices: "0,1,2,3,4,5,6,7" + + experiment: + name: debug + root_dir: "experiments" + + inference: + server_url: "http://localhost:17748/inference" + max_iter: 50 + max_fail: 5 + max_no_new_pts: 5 + min_delta_pts_num: 10 + \ No newline at end of file diff --git a/debug/cad_for_init_reg.obj b/debug/cad_for_init_reg.obj new file mode 100644 index 0000000..9fa5fcd --- /dev/null +++ b/debug/cad_for_init_reg.obj @@ -0,0 +1,1153465 @@ +# https://github.com/mikedh/trimesh +v -0.00778118 -0.13614850 -0.01306641 +v -0.00802701 -0.13599242 -0.01273140 +v -0.00810359 -0.13560208 -0.01275595 +v -0.00814425 -0.13577839 -0.01240015 +v -0.00812674 -0.13626321 -0.01198090 +v -0.00816210 -0.13593003 -0.01208870 +v -0.00811924 -0.13635464 -0.01157264 +v -0.00816488 -0.13589137 -0.01170174 +v -0.00805566 -0.13539176 -0.01301513 +v -0.00805759 -0.13661419 -0.01190140 +v -0.00810076 -0.13633254 -0.01061417 +v -0.00815128 -0.13577339 -0.01057361 +v -0.00817461 -0.13546543 -0.01198171 +v -0.00813594 -0.13522673 -0.01276743 +v -0.00816400 -0.13538198 -0.01144216 +v -0.00804175 -0.13672661 -0.01158940 +v -0.00816716 -0.13562219 -0.01016671 +v -0.00815613 -0.13533634 -0.01094309 +v -0.00816567 -0.13502305 -0.01249992 +v -0.00813182 -0.13610251 -0.01004516 +v 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[ + 0.9999336226554764, + -0.007709353551098916, + -0.008565016580406759, + -0.04860572455833323 + ], + [ + 0.008687669832192671, + 0.01601977456164321, + 0.9998339079342421, + -0.02169476997180904 + ], + [ + 0.0, + 0.0, + 0.0, + 1.0 + ] +] \ No newline at end of file diff --git a/hand_eye.json b/hand_eye.json new file mode 100644 index 0000000..0ca0d31 --- /dev/null +++ b/hand_eye.json @@ -0,0 +1,30 @@ +{ + "rotation": [ + [ + -0.018963923592112186, + -0.9996228744230772, + 0.019861483633843654 + ], + [ + 0.9967174555711164, + -0.017337556373989038, + 0.07908048367845485 + ], + [ + -0.07870631081325144, + 0.02129596368149753, + 0.9966703560200054 + ] + ], + "translation": [ + [ + 0.026586589276512175 + ], + [ + -0.03359567525195615 + ], + [ + -0.020136829941901187 + ] + ] +} \ No newline at end of file diff --git a/load_normal.py b/load_normal.py new file mode 100644 index 0000000..e212bea --- /dev/null +++ b/load_normal.py @@ -0,0 +1,49 @@ +import cv2 +import os +import numpy as np + +def load_normal(path, binocular=False, left_only=False): + if binocular and not left_only: + normal_path_L = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + "_L.png" + ) + normal_image_L = cv2.imread(normal_path_L, cv2.IMREAD_UNCHANGED) + normal_path_R = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + "_R.png" + ) + normal_image_R = cv2.imread(normal_path_R, cv2.IMREAD_UNCHANGED) + normalized_normal_image_L = normal_image_L / 255.0 * 2.0 - 1.0 + normalized_normal_image_R = normal_image_R / 255.0 * 2.0 - 1.0 + return normalized_normal_image_L, normalized_normal_image_R + else: + if binocular and left_only: + normal_path = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + "_L.png" + ) + else: + normal_path = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + ".png" + ) + normal_image = cv2.imread(normal_path, cv2.IMREAD_UNCHANGED) + normalized_normal_image = normal_image / 255.0 * 2.0 - 1.0 + return normalized_normal_image + +def show_rgb(event, x, y, flags, param): + if event == cv2.EVENT_MOUSEMOVE: + pixel_value = param[y, x] + print(f"RGB at ({x},{y}): {pixel_value}") + +if __name__ == "__main__": + path = "/Users/hofee/temp/1" + normal_image = load_normal(path, binocular=True, left_only=True) + display_image = ((normal_image + 1.0) / 2.0 * 255).astype(np.uint8) + + cv2.namedWindow("Normal Image") + cv2.setMouseCallback("Normal Image", show_rgb, param=display_image) + + while True: + cv2.imshow("Normal Image", display_image) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + + cv2.destroyAllWindows() diff --git a/runners/cad_close_loop_new.py b/runners/cad_close_loop_new.py new file mode 100644 index 0000000..b7b4dc4 --- /dev/null +++ b/runners/cad_close_loop_new.py @@ -0,0 +1,340 @@ +import os +import time +import trimesh +import tempfile +import subprocess +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 +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.reconstruction_util import ReconstructionUtil +from utils.preprocess_util import save_scene_data +from utils.data_load import DataLoadUtil +from utils.view_util import ViewUtil + +class PointCloud: + def __init__(points, camera, name): + pass + +class PointCloudGroup: + def __init__(point_clouds, name): + pass + +@stereotype.runner("temp") +class CADCloseLoopOnlineRegStrategyRunner(Runner): + + def __init__(self, config_path: str): + super().__init__(config_path) + self.load_experiment("cad_strategy") + + self.generate_config = ConfigManager.get("runner", "generate") + self.reconstruct_config = ConfigManager.get("runner", "reconstruct") + + self.output_dir = self.generate_config["output_dir"] + self.model_dir = self.generate_config["model_dir"] + self.object_name = self.generate_config["object_name"] + self.blender_bin_path = self.generate_config["blender_bin_path"] + self.generator_script_path = self.generate_config["generator_script_path"] + self.voxel_size = self.generate_config["voxel_size"] + + self.max_shot_view_num = self.reconstruct_config["max_shot_view_num"] + self.min_shot_new_pts_num = self.reconstruct_config["min_shot_new_pts_num"] + self.min_coverage_increase = self.reconstruct_config["min_coverage_increase"] + self.scan_points_threshold = self.reconstruct_config["scan_points_threshold"] + + 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 split_scan_pts_and_obj_pts(self, world_pts, z_threshold=0): + scan_pts = world_pts[world_pts[:, 2] < z_threshold] + obj_pts = world_pts[world_pts[:, 2] >= z_threshold] + return scan_pts, obj_pts + + def loop_scan(self, first_cam_to_real_world): + view_pts_list = [] + first_view_data = CommunicateUtil.get_view_data(init=True) + ControlUtil.absolute_rotate_display_table(90) + first_pts = ViewUtil.get_pts(first_view_data) + first_real_world_pts = PtsUtil.transform_point_cloud( + first_pts, first_cam_to_real_world + ) + _, first_splitted_real_world_pts = self.split_scan_pts_and_obj_pts( + first_real_world_pts + ) + view_pts_list.append(first_splitted_real_world_pts) + shot_num = 4 + for i in range(shot_num-1): + view_data = CommunicateUtil.get_view_data() + if i != shot_num - 2: + ControlUtil.absolute_rotate_display_table(90) + time.sleep(0.5) + if view_data is None: + Log.error("No view data received") + continue + view_pts = ViewUtil.get_pts(view_data) + + real_world_pts = PtsUtil.transform_point_cloud( + view_pts, first_cam_to_real_world + ) + _, splitted_real_world_pts = self.split_scan_pts_and_obj_pts( + real_world_pts + ) + view_pts_list.append(splitted_real_world_pts) + + return view_pts_list + + + def register(self): + ControlUtil.connect_robot() + """ init robot """ + Log.info("start init") + ControlUtil.init() + first_cam_to_real_world = ControlUtil.get_pose() + """ loop shooting """ + Log.info("start loop shooting") + view_pts_list = self.loop_scan(first_cam_to_real_world) + """ register """ + Log.info("start register") + pts = np.vstack(view_pts_list) + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + if not os.path.exists(os.path.join(self.output_dir, self.object_name)): + os.makedirs(os.path.join(self.output_dir, self.object_name)) + scene_dir = os.path.join(self.output_dir, self.object_name) + model_path = os.path.join(self.model_dir, self.object_name, "mesh.obj") + cad_model = trimesh.load(model_path) + real_world_to_cad = PtsUtil.register(pts, cad_model) + cad_to_real_world = np.linalg.inv(real_world_to_cad) + Log.success("finish init and register") + real_world_to_blender_world = np.eye(4) + real_world_to_blender_world[:3, 3] = np.asarray([0, 0, 0.9215]) + cad_model_real_world: trimesh.Trimesh = cad_model.apply_transform( + cad_to_real_world + ) + cad_model_real_world.export(os.path.join(scene_dir, "mesh.obj")) + #downsampled_pts = PtsUtil.voxel_downsample_point_cloud(pts, self.voxel_size) + np.savetxt(os.path.join(scene_dir, "pts_for_init_reg.txt"), pts) + return cad_to_real_world + + def render_data(self): + scene_dir = os.path.join(self.output_dir, self.object_name) + result = subprocess.run( + [ + self.blender_bin_path, + "-b", + "-P", + self.generator_script_path, + "--", + scene_dir, + ], + capture_output=True, + text=True, + ) + print(result) + + def preprocess_data(self): + save_scene_data(self.output_dir, self.object_name, file_type="npy") + + def get_scan_points_indices(self, scan_points, mask, object_mask_label= (0, 255, 0, 255), cam_intrinsic = None, cam_extrinsic = None): + scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1)))) + points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3] + points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T + points_image_homogeneous /= points_image_homogeneous[:, 2:] + pixel_x = points_image_homogeneous[:, 0].astype(int) + pixel_y = points_image_homogeneous[:, 1].astype(int) + h, w = mask.shape[:2] + valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h) + mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]] + selected_points_indices = np.where((mask_colors != object_mask_label).any(axis=-1))[0] + selected_points_indices = np.where(valid_indices)[0][selected_points_indices] + return selected_points_indices + + def run_one_model(self, model_name): + scene_dir = os.path.join(self.output_dir, model_name) + ControlUtil.connect_robot() + """ init robot """ + Log.info("start init") + ControlUtil.init() + first_cam_to_real_world = ControlUtil.get_pose() + """ loop shooting """ + Log.info("start loop shooting") + view_pts_list = self.loop_scan(first_cam_to_real_world) + """ register """ + cad_path = os.path.join(scene_dir, "mesh.obj") + cad_model = trimesh.load(cad_path) + Log.info("start register") + init_pts = np.vstack(view_pts_list) + real_world_to_cad = PtsUtil.register(init_pts, cad_model) + curr_cad_to_real_world = np.linalg.inv(real_world_to_cad) + + # np.savetxt(os.path.join("/home/yan20/nbv_rec/project/franka_control/debug", "pts_for_init_reg.txt"), init_pts) + # debug_cad = cad_model.apply_transform(curr_cad_to_real_world) + # debug_cad.export(os.path.join("/home/yan20/nbv_rec/project/franka_control/debug", "cad_for_init_reg.obj")) + + + pts_dir = os.path.join(scene_dir, "pts") + sample_view_pts_list = [] + + frame_num = len(os.listdir(pts_dir)) + + for frame_idx in range(frame_num): + pts_path = os.path.join(scene_dir, "pts", f"{frame_idx}.npy") + + point_cloud = np.load(pts_path) + if point_cloud.shape[0] != 0: + sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud( + point_cloud, self.voxel_size + ) + sample_view_pts_list.append(sampled_point_cloud) + else: + sample_view_pts_list.append(np.zeros((0, 3))) + + + """ close-loop online registery strategy """ + scanned_pts = PtsUtil.voxel_downsample_point_cloud(init_pts, voxel_size=self.voxel_size) + shot_pts_list = [] + + last_coverage = 0 + Log.info("start close-loop control") + cnt = 0 + mask_list = [] + cam_to_cad_list = [] + cam_R_to_cad_list = [] + shot_view_idx_list = [] + scan_points_path = os.path.join(self.output_dir, self.object_name, "scan_points.txt") + display_table_scan_points = np.loadtxt(scan_points_path) + for i in range(frame_num): + path = DataLoadUtil.get_path(self.output_dir, self.object_name, i) + mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True) + mask_list.append((mask_L, mask_R)) + cam_info = DataLoadUtil.load_cam_info(path, binocular=True) + cam_to_cad = cam_info["cam_to_world"] + cam_to_cad_list.append(cam_to_cad) + cam_R_to_cad = cam_info["cam_to_world_R"] + cam_R_to_cad_list.append(cam_R_to_cad) + + selected_view = [] + while True: + import ipdb; ipdb.set_trace() + history_indices = [] + scan_points_idx_list = [] + + for i in range(frame_num): + cam_to_cad = cam_to_cad_list[i] + cam_R_to_cad = cam_R_to_cad_list[i] + curr_cam_L_to_world = curr_cad_to_real_world @ cam_to_cad + curr_cam_R_to_world = curr_cad_to_real_world @ cam_R_to_cad + scan_points_indices_L = self.get_scan_points_indices(display_table_scan_points, mask_list[i][0], cam_intrinsic=cam_info["cam_intrinsic"], cam_extrinsic=curr_cam_L_to_world) + scan_points_indices_R = self.get_scan_points_indices(display_table_scan_points, mask_list[i][1], cam_intrinsic=cam_info["cam_intrinsic"], cam_extrinsic=curr_cam_R_to_world) + scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R) + scan_points_idx_list.append(scan_points_indices) + + for shot_view_idx in shot_view_idx_list: + history_indices.append(scan_points_idx_list[shot_view_idx]) + cad_scanned_pts = PtsUtil.transform_point_cloud(scanned_pts, np.linalg.inv(curr_cad_to_real_world)) + next_best_view, next_best_coverage, next_best_covered_num = ( + ReconstructionUtil.compute_next_best_view_with_overlap( + cad_scanned_pts, + sample_view_pts_list, + selected_view, + history_indices, + scan_points_idx_list, + threshold=self.voxel_size, + overlap_area_threshold=25, + scan_points_threshold=self.scan_points_threshold, + ) + ) + if next_best_view is None: + Log.warning("No next best view found") + selected_view.append(next_best_view) + nbv_path = DataLoadUtil.get_path(self.output_dir, self.object_name, next_best_view) + nbv_cam_info = DataLoadUtil.load_cam_info(nbv_path, binocular=True) + nbv_cam_to_cad = nbv_cam_info["cam_to_world_O"] + nbv_cam_to_world = curr_cad_to_real_world @ nbv_cam_to_cad + target_camera_pose = nbv_cam_to_world @ ControlUtil.CAMERA_CORRECTION + ControlUtil.move_to(target_camera_pose) + ''' get world pts ''' + time.sleep(0.5) + view_data = CommunicateUtil.get_view_data() + if view_data is None: + Log.error("No view data received") + continue + shot_view_idx_list.append(next_best_view) + cam_shot_pts = ViewUtil.get_pts(view_data) + world_shot_pts = PtsUtil.transform_point_cloud( + cam_shot_pts, first_cam_to_real_world + ) + _, world_splitted_shot_pts = self.split_scan_pts_and_obj_pts( + world_shot_pts + ) + shot_pts_list.append(world_splitted_shot_pts) + + debug_dir = os.path.join(scene_dir, "debug") + if not os.path.exists(debug_dir): + os.makedirs(debug_dir) + + last_scanned_pts_num = scanned_pts.shape[0] + import ipdb;ipdb.set_trace() + new_scanned_pts = PtsUtil.voxel_downsample_point_cloud( + np.vstack([scanned_pts, world_splitted_shot_pts]), self.voxel_size + ) + # last_real_world_to_cad = real_world_to_cad + # real_world_to_cad = PtsUtil.register_fine(new_scanned_pts, cad_model) + # # rot distance of two rotation matrix + # rot_dist = np.arccos( + # (np.trace(real_world_to_cad[:3, :3].T @ last_real_world_to_cad[:3, :3]) - 1) / 2 + # ) + # print(f"-----rot dist: {rot_dist}") + curr_cad_to_real_world = np.linalg.inv(real_world_to_cad) + + cad_splitted_shot_pts = PtsUtil.transform_point_cloud(world_splitted_shot_pts, real_world_to_cad) + np.savetxt(os.path.join(debug_dir, f"shot_pts_{cnt}.txt"), world_splitted_shot_pts) + np.savetxt(os.path.join(debug_dir, f"render_pts_{cnt}.txt"), sample_view_pts_list[next_best_view]) + + np.savetxt(os.path.join(debug_dir, f"reg_scanned_pts_{cnt}.txt"), new_scanned_pts) + cad_pts = cad_model.vertices + world_cad_pts = PtsUtil.transform_point_cloud(cad_pts, curr_cad_to_real_world) + np.savetxt(os.path.join(debug_dir, f"world_cad_pts_{cnt}.txt"), world_cad_pts) + #import ipdb; ipdb.set_trace() + + new_scanned_pts_num = new_scanned_pts.shape[0] + scanned_pts = new_scanned_pts + Log.info( + f"Next Best cover pts: {next_best_covered_num}, Best coverage: {next_best_coverage}" + ) + + coverage_rate_increase = next_best_coverage - last_coverage + if coverage_rate_increase < self.min_coverage_increase: + Log.info(f"Coverage rate = {coverage_rate_increase} < {self.min_coverage_increase}, stop scanning") + # break + last_coverage = next_best_coverage + + new_added_pts_num = new_scanned_pts_num - last_scanned_pts_num + if new_added_pts_num < self.min_shot_new_pts_num: + Log.info(f"New added pts num = {new_added_pts_num} < {self.min_shot_new_pts_num}") + #ipdb.set_trace() + if len(shot_pts_list) >= self.max_shot_view_num: + Log.info(f"Scanned view num = {len(shot_pts_list)} >= {self.max_shot_view_num}, stop scanning") + #break + cnt += 1 + + Log.success("[Part 4/4] finish close-loop control") + + + def run(self): + self.run_one_model(self.object_name) + +# ---------------------------- test ---------------------------- # +if __name__ == "__main__": + + model_path = r"/home/yan20/nbv_rec/data/models/workpiece_1/mesh.obj" + model = trimesh.load(model_path) diff --git a/runners/cad_close_loop_online_reg_strategy.py b/runners/cad_close_loop_online_reg_strategy.py new file mode 100644 index 0000000..ef2959b --- /dev/null +++ b/runners/cad_close_loop_online_reg_strategy.py @@ -0,0 +1,347 @@ +import os +import time +import trimesh +import tempfile +import subprocess +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 +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.reconstruction_util import ReconstructionUtil +from utils.preprocess_util import save_scene_data +from utils.data_load import DataLoadUtil +from utils.view_util import ViewUtil + +from beans.pointcloud import Pointcloud,PointcloudGroup + + +@stereotype.runner("CAD_close_loop_online_reg_strategy_runner") +class CADCloseLoopOnlineRegStrategyRunner(Runner): + + def __init__(self, config_path: str): + super().__init__(config_path) + self.load_experiment("cad_strategy") + + self.generate_config = ConfigManager.get("runner", "generate") + self.reconstruct_config = ConfigManager.get("runner", "reconstruct") + + self.output_dir = self.generate_config["output_dir"] + self.model_dir = self.generate_config["model_dir"] + self.object_name = self.generate_config["object_name"] + self.blender_bin_path = self.generate_config["blender_bin_path"] + self.generator_script_path = self.generate_config["generator_script_path"] + self.voxel_size = self.generate_config["voxel_size"] + + self.max_shot_view_num = self.reconstruct_config["max_shot_view_num"] + self.min_shot_new_pts_num = self.reconstruct_config["min_shot_new_pts_num"] + self.min_coverage_increase = self.reconstruct_config["min_coverage_increase"] + self.scan_points_threshold = self.reconstruct_config["scan_points_threshold"] + + 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 split_scan_pts_and_obj_pts(self, world_pts, z_threshold=0): + scan_pts = world_pts[world_pts[:, 2] < z_threshold] + obj_pts = world_pts[world_pts[:, 2] >= z_threshold] + return scan_pts, obj_pts + + def loop_scan(self, first_cam_to_real_world): + loop_scan_pcl_group = PointcloudGroup(name="loop_scan_pcl_group") + first_view_data = CommunicateUtil.get_view_data(init=True) + ControlUtil.absolute_rotate_display_table(90) + first_pts = ViewUtil.get_pts(first_view_data) + + first_real_world_pts = PtsUtil.transform_point_cloud( + first_pts, first_cam_to_real_world + ) + _, first_splitted_real_world_pts = self.split_scan_pts_and_obj_pts( + first_real_world_pts + ) + first_real_pcl = Pointcloud(first_splitted_real_world_pts, first_cam_to_real_world, name='first_real_world_pts') + loop_scan_pcl_group.add(first_real_pcl) + shot_num = 4 + for i in range(shot_num-1): + view_data = CommunicateUtil.get_view_data() + if i != shot_num - 2: + ControlUtil.absolute_rotate_display_table(90) + time.sleep(0.5) + if view_data is None: + Log.error("No view data received") + continue + view_pts = ViewUtil.get_pts(view_data) + + real_world_pts = PtsUtil.transform_point_cloud( + view_pts, first_cam_to_real_world + ) + _, splitted_real_world_pts = self.split_scan_pts_and_obj_pts( + real_world_pts + ) + loop_scan_real_world_pcl = Pointcloud(splitted_real_world_pts, None, name=f'loop_scan_real_world_pcl_{(i+1)*90} degree') + loop_scan_pcl_group.add(loop_scan_real_world_pcl) + + #loop_scan_pcl_group.visualize() + ControlUtil.absolute_rotate_display_table(90) + return loop_scan_pcl_group + + + def register(self): + ControlUtil.connect_robot() + """ init robot """ + Log.info("start init") + ControlUtil.init() + first_cam_to_real_world = ControlUtil.get_pose() + """ loop shooting """ + Log.info("start loop shooting") + loop_scan_pcl_group = self.loop_scan(first_cam_to_real_world) + """ register """ + Log.info("start register") + merged_world_scan_loop_pcl = loop_scan_pcl_group.merge_pointclouds(name="merged_world_scan_loop_pts") + pts = merged_world_scan_loop_pcl.points + if not os.path.exists(self.output_dir): + os.makedirs(self.output_dir) + if not os.path.exists(os.path.join(self.output_dir, self.object_name)): + os.makedirs(os.path.join(self.output_dir, self.object_name)) + scene_dir = os.path.join(self.output_dir, self.object_name) + model_path = os.path.join(self.model_dir, self.object_name, "mesh.obj") + cad_model = trimesh.load(model_path) + real_world_to_cad = PtsUtil.register(pts, cad_model) + cad_to_real_world = np.linalg.inv(real_world_to_cad) + Log.success("finish init and register") + real_world_to_blender_world = np.eye(4) + real_world_to_blender_world[:3, 3] = np.asarray([0, 0, 0.9215]) + cad_model_real_world: trimesh.Trimesh = cad_model.apply_transform( + cad_to_real_world + ) + cad_model_real_world.export(os.path.join(scene_dir, "mesh.obj")) + #downsampled_pts = PtsUtil.voxel_downsample_point_cloud(pts, self.voxel_size) + np.savetxt(os.path.join(scene_dir, "pts_for_init_reg.txt"), pts) + return cad_to_real_world + + def render_data(self): + scene_dir = os.path.join(self.output_dir, self.object_name) + result = subprocess.run( + [ + self.blender_bin_path, + "-b", + "-P", + self.generator_script_path, + "--", + scene_dir, + ], + capture_output=True, + text=True, + ) + print(result) + + def preprocess_data(self): + save_scene_data(self.output_dir, self.object_name, file_type="npy") + + def get_scan_points_indices(self, scan_points, mask, object_mask_label= (0, 255, 0, 255), cam_intrinsic = None, cam_extrinsic = None): + scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1)))) + points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3] + points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T + points_image_homogeneous /= points_image_homogeneous[:, 2:] + pixel_x = points_image_homogeneous[:, 0].astype(int) + pixel_y = points_image_homogeneous[:, 1].astype(int) + h, w = mask.shape[:2] + valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h) + mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]] + selected_points_indices = np.where((mask_colors != object_mask_label).any(axis=-1))[0] + selected_points_indices = np.where(valid_indices)[0][selected_points_indices] + return selected_points_indices + + def run_one_model(self, model_name): + scene_dir = os.path.join(self.output_dir, model_name) + ControlUtil.connect_robot() + """ init robot """ + Log.info("start init") + ControlUtil.init() + first_cam_to_real_world = ControlUtil.get_pose() + """ loop shooting """ + Log.info("start loop shooting") + loop_scan_pcl_group = self.loop_scan(first_cam_to_real_world) + """ register """ + cad_path = os.path.join(scene_dir, "mesh.obj") + cad_model = trimesh.load(cad_path) + Log.info("start register") + merged_world_scan_loop_pcl = loop_scan_pcl_group.merge_pointclouds(name="merged_world_scan_loop_pts") + real_world_to_cad = PtsUtil.register(merged_world_scan_loop_pcl.points, cad_model) + curr_cad_to_real_world = np.linalg.inv(real_world_to_cad) + + # np.savetxt(os.path.join("/home/yan20/nbv_rec/project/franka_control/debug", "pts_for_init_reg.txt"), init_pts) + # debug_cad = cad_model.apply_transform(curr_cad_to_real_world) + # debug_cad.export(os.path.join("/home/yan20/nbv_rec/project/franka_control/debug", "cad_for_init_reg.obj")) + + + pts_dir = os.path.join(scene_dir, "pts") + sample_view_pts_list = [] + + frame_num = len(os.listdir(pts_dir)) + + for frame_idx in range(frame_num): + pts_path = os.path.join(scene_dir, "pts", f"{frame_idx}.npy") + + point_cloud = np.load(pts_path) + if point_cloud.shape[0] != 0: + sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud( + point_cloud, self.voxel_size + ) + sample_view_pts_list.append(sampled_point_cloud) + else: + sample_view_pts_list.append(np.zeros((0, 3))) + + + """ close-loop online registery strategy """ + + scanned_pcl = merged_world_scan_loop_pcl.voxel_downsample(self.voxel_size) + scanned_pts = scanned_pcl.points + shot_pcl_group = PointcloudGroup(name="world_shot_pcl_group") + + last_coverage = 0 + Log.info("start close-loop control") + cnt = 0 + mask_list = [] + cam_to_cad_list = [] + cam_R_to_cad_list = [] + shot_view_idx_list = [] + scan_points_path = os.path.join(self.output_dir, self.object_name, "scan_points.txt") + display_table_scan_points = np.loadtxt(scan_points_path) + for i in range(frame_num): + path = DataLoadUtil.get_path(self.output_dir, self.object_name, i) + mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True) + mask_list.append((mask_L, mask_R)) + cam_info = DataLoadUtil.load_cam_info(path, binocular=True) + cam_to_cad = cam_info["cam_to_world"] + cam_to_cad_list.append(cam_to_cad) + cam_R_to_cad = cam_info["cam_to_world_R"] + cam_R_to_cad_list.append(cam_R_to_cad) + + selected_view = [] + while True: + #import ipdb; ipdb.set_trace() + history_indices = [] + scan_points_idx_list = [] + + for i in range(frame_num): + cam_to_cad = cam_to_cad_list[i] + cam_R_to_cad = cam_R_to_cad_list[i] + curr_cam_L_to_world = curr_cad_to_real_world @ cam_to_cad + curr_cam_R_to_world = curr_cad_to_real_world @ cam_R_to_cad + scan_points_indices_L = self.get_scan_points_indices(display_table_scan_points, mask_list[i][0], cam_intrinsic=cam_info["cam_intrinsic"], cam_extrinsic=curr_cam_L_to_world) + scan_points_indices_R = self.get_scan_points_indices(display_table_scan_points, mask_list[i][1], cam_intrinsic=cam_info["cam_intrinsic"], cam_extrinsic=curr_cam_R_to_world) + scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R) + scan_points_idx_list.append(scan_points_indices) + + for shot_view_idx in shot_view_idx_list: + history_indices.append(scan_points_idx_list[shot_view_idx]) + cad_scanned_pts = PtsUtil.transform_point_cloud(scanned_pts, np.linalg.inv(curr_cad_to_real_world)) + next_best_view, next_best_coverage, next_best_covered_num = ( + ReconstructionUtil.compute_next_best_view_with_overlap( + cad_scanned_pts, + sample_view_pts_list, + selected_view, + history_indices, + scan_points_idx_list, + threshold=self.voxel_size, + overlap_area_threshold=10, + scan_points_threshold=self.scan_points_threshold, + ) + ) + if next_best_view is None: + Log.warning("No next best view found") + selected_view.append(next_best_view) + nbv_path = DataLoadUtil.get_path(self.output_dir, self.object_name, next_best_view) + nbv_cam_info = DataLoadUtil.load_cam_info(nbv_path, binocular=True) + nbv_cam_to_cad = nbv_cam_info["cam_to_world_O"] + nbv_cam_to_world = curr_cad_to_real_world @ nbv_cam_to_cad + target_camera_pose = nbv_cam_to_world @ ControlUtil.CAMERA_CORRECTION + ControlUtil.move_to(target_camera_pose) + ''' get world pts ''' + time.sleep(0.5) + view_data = CommunicateUtil.get_view_data() + if view_data is None: + Log.error("No view data received") + continue + shot_view_idx_list.append(next_best_view) + cam_shot_pts = ViewUtil.get_pts(view_data) + world_shot_pts = PtsUtil.transform_point_cloud( + cam_shot_pts, first_cam_to_real_world + ) + _, world_splitted_shot_pts = self.split_scan_pts_and_obj_pts( + world_shot_pts + ) + world_shot_pcl = Pointcloud(world_splitted_shot_pts, nbv_cam_to_world, name=f"world_shot_pts_{cnt}") + cad_render_pcl = Pointcloud(sample_view_pts_list[next_best_view], nbv_cam_to_world, name=f"cad_render_pts_{cnt}") + world_render_pcl = cad_render_pcl.transform(curr_cad_to_real_world) + shot_pcl_group.add(world_shot_pcl) + render_shot_group = PointcloudGroup(pointclouds=[world_render_pcl, world_shot_pcl],name="render_shot_group") + import ipdb; ipdb.set_trace() + render_shot_group.visualize() + debug_dir = os.path.join(scene_dir, "debug") + if not os.path.exists(debug_dir): + os.makedirs(debug_dir) + + last_scanned_pts_num = scanned_pts.shape[0] + new_scanned_pts = PtsUtil.voxel_downsample_point_cloud( + np.vstack([scanned_pts, world_splitted_shot_pts]), self.voxel_size + ) + # last_real_world_to_cad = real_world_to_cad + # real_world_to_cad = PtsUtil.register_fine(new_scanned_pts, cad_model) + # # rot distance of two rotation matrix + # rot_dist = np.arccos( + # (np.trace(real_world_to_cad[:3, :3].T @ last_real_world_to_cad[:3, :3]) - 1) / 2 + # ) + # print(f"-----rot dist: {rot_dist}") + curr_cad_to_real_world = np.linalg.inv(real_world_to_cad) + + cad_splitted_shot_pts = PtsUtil.transform_point_cloud(world_splitted_shot_pts, real_world_to_cad) + np.savetxt(os.path.join(debug_dir, f"shot_pts_{cnt}.txt"), cad_splitted_shot_pts) + np.savetxt(os.path.join(debug_dir, f"render_pts_{cnt}.txt"), sample_view_pts_list[next_best_view]) + + np.savetxt(os.path.join(debug_dir, f"reg_scanned_pts_{cnt}.txt"), new_scanned_pts) + cad_pts = cad_model.vertices + world_cad_pts = PtsUtil.transform_point_cloud(cad_pts, curr_cad_to_real_world) + np.savetxt(os.path.join(debug_dir, f"world_cad_pts_{cnt}.txt"), world_cad_pts) + #import ipdb; ipdb.set_trace() + + new_scanned_pts_num = new_scanned_pts.shape[0] + scanned_pts = new_scanned_pts + Log.info( + f"Next Best cover pts: {next_best_covered_num}, Best coverage: {next_best_coverage}" + ) + + coverage_rate_increase = next_best_coverage - last_coverage + if coverage_rate_increase < self.min_coverage_increase: + Log.info(f"Coverage rate = {coverage_rate_increase} < {self.min_coverage_increase}, stop scanning") + # break + last_coverage = next_best_coverage + + new_added_pts_num = new_scanned_pts_num - last_scanned_pts_num + if new_added_pts_num < self.min_shot_new_pts_num: + Log.info(f"New added pts num = {new_added_pts_num} < {self.min_shot_new_pts_num}") + #ipdb.set_trace() + if len(shot_pcl_group) >= self.max_shot_view_num: + Log.info(f"Scanned view num = {len(shot_pcl_group)} >= {self.max_shot_view_num}, stop scanning") + #break + cnt += 1 + + Log.success("[Part 4/4] finish close-loop control") + + + def run(self): + self.run_one_model(self.object_name) + +# ---------------------------- test ---------------------------- # +if __name__ == "__main__": + + model_path = r"/home/yan20/nbv_rec/data/models/workpiece_1/mesh.obj" + model = trimesh.load(model_path) diff --git a/runners/cad_close_loop_strategy.py b/runners/cad_close_loop_strategy.py new file mode 100644 index 0000000..400220c --- /dev/null +++ b/runners/cad_close_loop_strategy.py @@ -0,0 +1,242 @@ +import os +import time +import trimesh +import tempfile +import subprocess +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 +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.reconstruction_util import ReconstructionUtil +from utils.preprocess_util import save_scene_data +from utils.data_load import DataLoadUtil +from utils.view_util import ViewUtil + + +@stereotype.runner("CAD_close_loop_strategy_runner") +class CADCloseLoopStrategyRunner(Runner): + + def __init__(self, config_path: str): + super().__init__(config_path) + self.load_experiment("cad_strategy") + self.generate_config = ConfigManager.get("runner", "generate") + self.reconstruct_config = ConfigManager.get("runner", "reconstruct") + self.blender_bin_path = self.generate_config["blender_bin_path"] + self.generator_script_path = self.generate_config["generator_script_path"] + self.model_dir = self.generate_config["model_dir"] + self.voxel_size = self.generate_config["voxel_size"] + + self.max_shot_view_num = self.reconstruct_config["max_shot_view_num"] + self.min_shot_new_pts_num = self.reconstruct_config["min_shot_new_pts_num"] + self.min_coverage_increase = self.reconstruct_config["min_coverage_increase"] + self.scan_points_threshold = self.reconstruct_config["scan_points_threshold"] + + 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 split_scan_pts_and_obj_pts(self, world_pts, z_threshold=0): + scan_pts = world_pts[world_pts[:, 2] < z_threshold] + obj_pts = world_pts[world_pts[:, 2] >= z_threshold] + return scan_pts, obj_pts + + def run_one_model(self, model_name): + time_code = time.strftime("%Y%m%d%H%M%S", time.localtime()) + temp_dir = "/home/yan20/nbv_rec/project/franka_control/temp_output_" + time_code + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + ControlUtil.connect_robot() + """ init robot """ + Log.info("[Part 1/5] start init and register") + ControlUtil.init() + + """ load CAD model """ + model_path = os.path.join(self.model_dir, model_name, "mesh.obj") + temp_name = "cad_model_world" + cad_model = trimesh.load(model_path) + """ take first view """ + Log.info("[Part 1/5] take first view data") + view_data = CommunicateUtil.get_view_data(init=True) + first_cam_pts = ViewUtil.get_pts(view_data) + first_cam_to_real_world = ControlUtil.get_pose() + first_real_world_pts = PtsUtil.transform_point_cloud( + first_cam_pts, first_cam_to_real_world + ) + _, first_splitted_real_world_pts = self.split_scan_pts_and_obj_pts( + first_real_world_pts + ) + + debug_dir = os.path.join(temp_dir, "debug") + if not os.path.exists(debug_dir): + os.makedirs(debug_dir) + + np.savetxt(os.path.join(debug_dir,"FIRST_shot.txt"), first_splitted_real_world_pts) + # np.savetxt(f"first_real_pts_{model_name}.txt", first_splitted_real_world_pts) + """ register """ + Log.info("[Part 1/4] do registeration") + real_world_to_cad = PtsUtil.register(first_splitted_real_world_pts, cad_model) + cad_to_real_world = np.linalg.inv(real_world_to_cad) + Log.success("[Part 1/4] finish init and register") + real_world_to_blender_world = np.eye(4) + real_world_to_blender_world[:3, 3] = np.asarray([0, 0, 0.9215]) + cad_model_real_world: trimesh.Trimesh = cad_model.apply_transform( + cad_to_real_world + ) + cad_model_real_world.export( + os.path.join(temp_dir, f"real_world_{temp_name}.obj") + ) + cad_model_blender_world: trimesh.Trimesh = cad_model.apply_transform( + real_world_to_blender_world + ) + + with tempfile.TemporaryDirectory() as temp_dir: + temp_dir = "/home/yan20/nbv_rec/project/franka_control/temp_output_" + time_code + cad_model_blender_world.export(os.path.join(temp_dir, f"{temp_name}.obj")) + """ sample view """ + Log.info("[Part 2/4] start running renderer") + subprocess.run( + [ + self.blender_bin_path, + "-b", + "-P", + self.generator_script_path, + "--", + temp_dir, + ], + capture_output=True, + text=True, + ) + Log.success("[Part 2/4] finish running renderer") + + """ preprocess """ + Log.info("[Part 3/4] start preprocessing data") + save_scene_data(temp_dir, temp_name) + Log.success("[Part 3/4] finish preprocessing data") + + pts_dir = os.path.join(temp_dir, temp_name, "pts") + sample_view_pts_list = [] + scan_points_idx_list = [] + frame_num = len(os.listdir(pts_dir)) + + for frame_idx in range(frame_num): + pts_path = os.path.join(temp_dir, temp_name, "pts", f"{frame_idx}.txt") + idx_path = os.path.join( + temp_dir, temp_name, "scan_points_indices", f"{frame_idx}.npy" + ) + point_cloud = np.loadtxt(pts_path) + if point_cloud.shape[0] != 0: + sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud( + point_cloud, self.voxel_size + ) + indices = np.load(idx_path) + try: + len(indices) + except: + indices = np.array([indices]) + sample_view_pts_list.append(sampled_point_cloud) + scan_points_idx_list.append(indices) + + """ close-loop strategy """ + scanned_pts = PtsUtil.voxel_downsample_point_cloud( + first_splitted_real_world_pts, self.voxel_size + ) + shot_pts_list = [first_splitted_real_world_pts] + history_indices = [] + last_coverage = 0 + + + + Log.info("[Part 4/4] start close-loop control") + cnt = 0 + selected_view = [] + while True: + #import ipdb; ipdb.set_trace() + next_best_view, next_best_coverage, next_best_covered_num = ( + ReconstructionUtil.compute_next_best_view_with_overlap( + scanned_pts, + sample_view_pts_list, + selected_view, + history_indices, + scan_points_idx_list, + threshold=self.voxel_size, + overlap_area_threshold=50, + scan_points_threshold=self.scan_points_threshold, + ) + ) + selected_view.append(next_best_view) + nbv_path = DataLoadUtil.get_path(temp_dir, temp_name, next_best_view) + nbv_cam_info = DataLoadUtil.load_cam_info(nbv_path, binocular=True) + nbv_cam_to_world = nbv_cam_info["cam_to_world_O"] + ControlUtil.move_to(nbv_cam_to_world) + ''' get world pts ''' + time.sleep(0.5) + view_data = CommunicateUtil.get_view_data() + if view_data is None: + Log.error("No view data received") + continue + cam_shot_pts = ViewUtil.get_pts(view_data) + world_shot_pts = PtsUtil.transform_point_cloud( + cam_shot_pts, first_cam_to_real_world + ) + _, world_splitted_shot_pts = self.split_scan_pts_and_obj_pts( + world_shot_pts + ) + shot_pts_list.append(world_splitted_shot_pts) + + + np.savetxt(os.path.join(debug_dir, f"shot_pts_{cnt}.txt"), world_splitted_shot_pts) + np.savetxt(os.path.join(debug_dir, f"render_pts_{cnt}.txt"), sample_view_pts_list[next_best_view]) + #real_world_to_cad = PtsUtil.register(first_splitted_real_world_pts, cad_model) + #import ipdb; ipdb.set_trace() + last_scanned_pts_num = scanned_pts.shape[0] + new_scanned_pts = PtsUtil.voxel_downsample_point_cloud( + np.vstack([scanned_pts, world_splitted_shot_pts]), self.voxel_size + ) + new_scanned_pts_num = new_scanned_pts.shape[0] + history_indices.append(scan_points_idx_list[next_best_view]) + scanned_pts = new_scanned_pts + Log.info( + f"Next Best cover pts: {next_best_covered_num}, Best coverage: {next_best_coverage}" + ) + + coverage_rate_increase = next_best_coverage - last_coverage + if coverage_rate_increase < self.min_coverage_increase: + Log.info(f"Coverage rate = {coverage_rate_increase} < {self.min_coverage_increase}, stop scanning") + # break + last_coverage = next_best_coverage + + new_added_pts_num = new_scanned_pts_num - last_scanned_pts_num + if new_added_pts_num < self.min_shot_new_pts_num: + Log.info(f"New added pts num = {new_added_pts_num} < {self.min_shot_new_pts_num}") + #ipdb.set_trace() + if len(shot_pts_list) >= self.max_shot_view_num: + Log.info(f"Scanned view num = {len(shot_pts_list)} >= {self.max_shot_view_num}, stop scanning") + #break + cnt += 1 + + Log.success("[Part 4/4] finish close-loop control") + + + def run(self): + total = len(os.listdir(self.model_dir)) + model_start_idx = self.generate_config["model_start_idx"] + count_object = model_start_idx + # for model_name in os.listdir(self.model_dir[model_start_idx:]): + # Log.info(f"[{count_object}/{total}]Processing {model_name}") + # self.run_one_model(model_name) + # Log.success(f"[{count_object}/{total}]Finished processing {model_name}") + self.run_one_model("box_1") + +# ---------------------------- test ---------------------------- # +if __name__ == "__main__": + + model_path = r"C:\Users\hofee\Downloads\mesh.obj" + model = trimesh.load(model_path) diff --git a/runners/cad_open_loop_strategy.py b/runners/cad_open_loop_strategy.py new file mode 100644 index 0000000..cb158ed --- /dev/null +++ b/runners/cad_open_loop_strategy.py @@ -0,0 +1,224 @@ +import os +import time +import trimesh +import tempfile +import subprocess +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 +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.reconstruction_util import ReconstructionUtil +from utils.preprocess_util import save_scene_data +from utils.data_load import DataLoadUtil +from utils.view_util import ViewUtil + + +@stereotype.runner("CAD_open_loop_strategy_runner") +class CADOpenLoopStrategyRunner(Runner): + + def __init__(self, config_path: str): + super().__init__(config_path) + self.load_experiment("cad_open_loop_strategy") + self.status_info = { + "status_manager": status_manager, + "app_name": "cad", + "runner_name": "CAD_open_loop_strategy_runner" + } + self.generate_config = ConfigManager.get("runner", "generate") + self.reconstruct_config = ConfigManager.get("runner", "reconstruct") + self.blender_bin_path = self.generate_config["blender_bin_path"] + self.generator_script_path = self.generate_config["generator_script_path"] + self.model_dir = self.generate_config["model_dir"] + self.voxel_size = self.generate_config["voxel_size"] + self.max_view = self.generate_config["max_view"] + self.min_view = self.generate_config["min_view"] + self.max_diag = self.generate_config["max_diag"] + self.min_diag = self.generate_config["min_diag"] + self.min_cam_table_included_degree = self.generate_config["min_cam_table_included_degree"] + self.random_view_ratio = self.generate_config["random_view_ratio"] + + self.soft_overlap_threshold = self.reconstruct_config["soft_overlap_threshold"] + self.hard_overlap_threshold = self.reconstruct_config["hard_overlap_threshold"] + self.scan_points_threshold = self.reconstruct_config["scan_points_threshold"] + + 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 split_scan_pts_and_obj_pts(self, world_pts, z_threshold = 0): + scan_pts = world_pts[world_pts[:,2] < z_threshold] + obj_pts = world_pts[world_pts[:,2] >= z_threshold] + return scan_pts, obj_pts + + def run_one_model(self, model_name): + temp_dir = "/home/yan20/nbv_rec/project/franka_control/temp_output" + result = dict() + + shot_pts_list = [] + ControlUtil.connect_robot() + ''' init robot ''' + Log.info("[Part 1/5] start init and register") + ControlUtil.init() + + ''' load CAD model ''' + model_path = os.path.join(self.model_dir, model_name,"mesh.ply") + temp_name = "cad_model_world" + cad_model = trimesh.load(model_path) + ''' take first view ''' + Log.info("[Part 1/5] take first view data") + view_data = CommunicateUtil.get_view_data(init=True) + first_cam_pts = ViewUtil.get_pts(view_data) + first_cam_to_real_world = ControlUtil.get_pose() + first_real_world_pts = PtsUtil.transform_point_cloud(first_cam_pts, first_cam_to_real_world) + _, first_splitted_real_world_pts = self.split_scan_pts_and_obj_pts(first_real_world_pts) + np.savetxt(f"first_real_pts_{model_name}.txt", first_splitted_real_world_pts) + ''' register ''' + Log.info("[Part 1/5] do registeration") + real_world_to_cad = PtsUtil.register(first_splitted_real_world_pts, cad_model) + cad_to_real_world = np.linalg.inv(real_world_to_cad) + Log.success("[Part 1/5] finish init and register") + real_world_to_blender_world = np.eye(4) + real_world_to_blender_world[:3, 3] = np.asarray([0, 0, 0.9215]) + cad_model_real_world:trimesh.Trimesh = cad_model.apply_transform(cad_to_real_world) + cad_model_real_world.export(os.path.join(temp_dir, f"real_world_{temp_name}.obj")) + cad_model_blender_world:trimesh.Trimesh = cad_model.apply_transform(real_world_to_blender_world) + + with tempfile.TemporaryDirectory() as temp_dir: + temp_dir = "/home/yan20/nbv_rec/project/franka_control/temp_output" + cad_model_blender_world.export(os.path.join(temp_dir, f"{temp_name}.obj")) + scene_dir = os.path.join(temp_dir, temp_name) + ''' sample view ''' + Log.info("[Part 2/5] start running renderer") + subprocess.run([ + self.blender_bin_path, '-b', '-P', self.generator_script_path, '--', temp_dir + ], capture_output=True, text=True) + Log.success("[Part 2/5] finish running renderer") + + + world_model_points = np.loadtxt(os.path.join(scene_dir, "points_and_normals.txt"))[:,:3] + ''' preprocess ''' + Log.info("[Part 3/5] start preprocessing data") + save_scene_data(temp_dir, temp_name) + Log.success("[Part 3/5] finish preprocessing data") + + pts_dir = os.path.join(temp_dir,temp_name,"pts") + sample_view_pts_list = [] + scan_points_idx_list = [] + frame_num = len(os.listdir(pts_dir)) + + for frame_idx in range(frame_num): + pts_path = os.path.join(temp_dir,temp_name, "pts", f"{frame_idx}.txt") + idx_path = os.path.join(temp_dir,temp_name, "scan_points_indices", f"{frame_idx}.npy") + point_cloud = np.loadtxt(pts_path) + if point_cloud.shape[0] != 0: + sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud, self.voxel_size) + indices = np.load(idx_path) + try: + len(indices) + except: + indices = np.array([indices]) + sample_view_pts_list.append(sampled_point_cloud) + scan_points_idx_list.append(indices) + + ''' generate strategy ''' + + Log.info("[Part 4/5] start generating strategy") + limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence_with_overlap( + world_model_points, sample_view_pts_list, + scan_points_indices_list = scan_points_idx_list, + init_view=0, + threshold=self.voxel_size, + soft_overlap_threshold = self.soft_overlap_threshold, + hard_overlap_threshold = self.hard_overlap_threshold, + scan_points_threshold = self.scan_points_threshold, + status_info=self.status_info + ) + Log.success("[Part 4/5] finish generating strategy") + + ''' extract cam_to_world sequence ''' + cam_to_world_seq = [] + coveraget_rate_seq = [] + render_pts = [] + idx_seq = [] + for idx, coverage_rate in limited_useful_view: + path = DataLoadUtil.get_path(temp_dir, temp_name, idx) + cam_info = DataLoadUtil.load_cam_info(path, binocular=True) + cam_to_world_seq.append(cam_info["cam_to_world_O"]) + coveraget_rate_seq.append(coverage_rate) + idx_seq.append(idx) + render_pts.append(sample_view_pts_list[idx]) + + Log.info("[Part 5/5] start running robot") + ''' take best seq view ''' + #import ipdb; ipdb.set_trace() + target_scanned_pts = np.concatenate(sample_view_pts_list) + voxel_downsampled_target_scanned_pts = PtsUtil.voxel_downsample_point_cloud(target_scanned_pts, self.voxel_size) + result = dict() + gt_scanned_pts = np.concatenate(render_pts, axis=0) + voxel_down_sampled_gt_scanned_pts = PtsUtil.voxel_downsample_point_cloud(gt_scanned_pts, self.voxel_size) + result["gt_final_coverage_rate_cad"] = ReconstructionUtil.compute_coverage_rate(voxel_downsampled_target_scanned_pts, voxel_down_sampled_gt_scanned_pts, self.voxel_size) + step = 1 + result["real_coverage_rate_seq"] = [] + for cam_to_world in cam_to_world_seq: + try: + ControlUtil.move_to(cam_to_world) + ''' get world pts ''' + time.sleep(0.5) + view_data = CommunicateUtil.get_view_data() + if view_data is None: + Log.error("Failed to get view data") + continue + cam_pts = ViewUtil.get_pts(view_data) + shot_pts_list.append(cam_pts) + scanned_pts = np.concatenate(shot_pts_list, axis=0) + voxel_down_sampled_scanned_pts = PtsUtil.voxel_downsample_point_cloud(scanned_pts, self.voxel_size) + voxel_down_sampled_scanned_pts_world = PtsUtil.transform_point_cloud(voxel_down_sampled_scanned_pts, first_cam_to_real_world) + curr_CR = ReconstructionUtil.compute_coverage_rate(voxel_downsampled_target_scanned_pts, voxel_down_sampled_scanned_pts_world, self.voxel_size) + Log.success(f"(step {step}/{len(cam_to_world_seq)}) current coverage: {curr_CR} | gt coverage: {result['gt_final_coverage_rate_cad']}") + result["real_final_coverage_rate"] = curr_CR + result["real_coverage_rate_seq"].append(curr_CR) + step += 1 + except Exception as e: + Log.error(f"Failed to move to {cam_to_world}") + Log.error(e) + + #import ipdb;ipdb.set_trace() + + for idx in range(len(shot_pts_list)): + if not os.path.exists(os.path.join(temp_dir, temp_name, "shot_pts")): + os.makedirs(os.path.join(temp_dir, temp_name, "shot_pts")) + if not os.path.exists(os.path.join(temp_dir, temp_name, "render_pts")): + os.makedirs(os.path.join(temp_dir, temp_name, "render_pts")) + shot_pts = PtsUtil.transform_point_cloud(shot_pts_list[idx], first_cam_to_real_world) + np.savetxt(os.path.join(temp_dir, temp_name, "shot_pts", f"{idx}.txt"), shot_pts) + np.savetxt(os.path.join(temp_dir, temp_name, "render_pts", f"{idx}.txt"), render_pts[idx]) + + + Log.success("[Part 5/5] finish running robot") + + Log.debug(result) + + def run(self): + total = len(os.listdir(self.model_dir)) + model_start_idx = self.generate_config["model_start_idx"] + count_object = model_start_idx + for model_name in os.listdir(self.model_dir[model_start_idx:]): + Log.info(f"[{count_object}/{total}]Processing {model_name}") + self.run_one_model(model_name) + Log.success(f"[{count_object}/{total}]Finished processing {model_name}") + + +# ---------------------------- test ---------------------------- # +if __name__ == "__main__": + + model_path = r"C:\Users\hofee\Downloads\mesh.obj" + model = trimesh.load(model_path) + \ No newline at end of file diff --git a/runners/calibration.py b/runners/calibration.py new file mode 100644 index 0000000..2624f37 --- /dev/null +++ b/runners/calibration.py @@ -0,0 +1,475 @@ +import cv2 +import numpy as np +import open3d as o3d +import sys, os +sys.path.append(os.path.join(os.path.dirname(__file__), "..")) +from utils.control_util import ControlUtil +from utils.communicate_util import CommunicateUtil +import pickle +from tqdm import tqdm +import json +import pyrealsense2 as rs + +# marker parameters +MARKER_TYPE = "aruco" +MARKER_INFO = {"id": 582, "size": 0.1} + +# view parameters +VIEW_NUM = 6 +LOOK_AT_PT = np.array([0.5, 0, 0.2]) +TOP_DOWN_DIST = [0.3, 0.45, 0.55] +TOP_DOWN_DIRECTION = np.array([0., 0., -1.]) +RADIUS = [0.25, 0.2, 0.1] +INIT_GRIPPER_POSE = np.array([ + [1, 0, 0, 0.56], + [0, -1, 0, 0], + [0, 0, -1, 0.6], + [0, 0, 0, 1] +]) + +DEFAULT_EE_TO_BASE = np.array([ + [0, -1, 0, 0], + [1, 0, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] +]) + + + +class HandEyeCalibration: + """ + A class to perform hand-eye calibration for a robotic system using various camera types and markers. + Attributes: + marker_to_cam (list): List to store marker to camera transformation matrices. + end_effector_to_base (list): List to store end-effector to base transformation matrices. + marker_type (str): Type of marker used for calibration (default is "aruco"). + marker_info (dict): Information about the marker, including its ID and size. + camera (object): Camera object used for capturing images. + intrinsics (dict): Intrinsic parameters of the camera. + dist (optional): Distortion coefficients of the camera. + marker_solver (object): Solver object for detecting and solving marker poses. + camera_type (str): Type of camera used (default is "RealSense"). "RealSense" and "Inspire" are supported. + Methods: + get_marker_to_cam_pose(color_image, visualize=False): + Gets the pose of the marker relative to the camera from a color image. + capture_single_frame(): + Captures a single frame from the camera and gets the current end-effector pose. + capture_frames(view_num, look_at_pt, top_down_dist, top_down_direrction, radius, visualize=False): + Captures multiple frames by moving the end-effector to different poses and records the marker and end-effector poses. + get_hand_eye(marker_to_cam_poses, end_effector_to_base_poses): + Computes the hand-eye calibration matrix using the captured marker and end-effector poses. + calibrate(view_num, look_at_pt, top_down_dist, top_down_direrction, radius): + Performs the full calibration process by capturing frames and computing the hand-eye calibration matrix. + """ + def __init__(self, dist=None, marker_type="aruco", marker_info={"id": 0, "size": 0.1}, camera_type="RealSense"): + self.marker_to_cam = [] + self.end_effector_to_base = [] + self.marker_type = marker_type + self.marker_info = marker_info + ControlUtil.connect_robot() + ControlUtil.franka_reset() + + if camera_type == "RealSense": + self.camera = RealSenseCamera() + elif camera_type == "Inspire": + self.camera = InspireCamera() + else: + assert False, "Not implemented yet" + + self.intrinsics = self.camera.get_color_intrinsics() + self.dist = dist + self.marker_type = marker_type + if self.marker_type == "aruco": + self.marker_solver = ArcuoMarkerSolver(self.intrinsics, dist, marker_info) + self.camera_type = camera_type + + + + + def get_marker_to_cam_pose(self, color_image, visualize=False): + res = self.marker_solver.get_marker_to_cam_pose(color_image, visualize) + return res + + + def capture_single_frame(self): + color_image = self.camera.get_color_image() + end_effector_pose = ControlUtil.get_curr_gripper_to_base_pose() + return color_image, end_effector_pose + + + def capture_frames(self, view_num, look_at_pt, top_down_dist, top_down_direrction, radius, visualize=False): + circle_num = len(radius) + poses = [] + for i in range(circle_num): + reverse_order = i % 2 == 1 + pose_generator = EndEffectorPoseGenerator(view_num, look_at_pt, top_down_dist[i], top_down_direrction, radius[i], reverse_order) + poses += [pose @ DEFAULT_EE_TO_BASE for pose in pose_generator.poses] + if visualize == True: + pose_visualizer = PoseVisualizer(poses) + pose_visualizer.visualize() + pbar = tqdm(total=len(poses), desc="Capturing frames") + for pose in poses: + pbar.update(1) + ControlUtil.set_gripper_pose(pose) + color_image, end_effector_pose = self.capture_single_frame() + if color_image is None: + print("Failed to capture the frame") + continue + res = self.get_marker_to_cam_pose(color_image, visualize=True) + if res is None: + print("Failed to get marker pose") + continue + else: + marker_to_cam_pose = np.eye(4) + rvec = res["rvec"] + tvec = res["tvec"] + rot_mat = cv2.Rodrigues(rvec)[0] + marker_to_cam_pose[:3, :3] = rot_mat + marker_to_cam_pose[:3, 3] = tvec + self.marker_to_cam.append(marker_to_cam_pose) + self.end_effector_to_base.append(end_effector_pose) + + + def get_hand_eye(self, marker_to_cam_poses, end_effector_to_base_poses): + ############################## Debug ################################ + # with open("marker_to_cam_poses.pkl", "wb") as f: + # pickle.dump(marker_to_cam_poses, f) + # with open("end_effector_to_base_poses.pkl", "wb") as f: + # pickle.dump(end_effector_to_base_poses, f) + # with open("marker_to_cam_poses.pkl", "rb") as f: + # marker_to_cam_poses = pickle.load(f) + # with open("end_effector_to_base_poses.pkl", "rb") as f: + # end_effector_to_base_poses = pickle.load(f) + ##################################################################### + R_marker_to_cam = [] + t_marker_to_cam = [] + R_end_effector_to_base = [] + t_end_effector_to_base = [] + for index, marker_to_cam_pose in enumerate(marker_to_cam_poses): + if marker_to_cam_pose is None: + continue + R_marker_to_cam.append(marker_to_cam_pose[:3, :3]) + t_marker_to_cam.append(marker_to_cam_pose[:3, 3]) + R_end_effector_to_base.append(end_effector_to_base_poses[index][:3, :3]) + t_end_effector_to_base.append(end_effector_to_base_poses[index][:3, 3]) + if len(R_marker_to_cam) < 3: + print("Not enough data to calibrate") + return None + R_marker_to_cam = np.array(R_marker_to_cam) + t_marker_to_cam = np.array(t_marker_to_cam) + R_end_effector_to_base = np.array(R_end_effector_to_base) + t_end_effector_to_base = np.array(t_end_effector_to_base) + from ipdb import set_trace; set_trace() + hand_eye = cv2.calibrateHandEye(R_end_effector_to_base, t_end_effector_to_base, R_marker_to_cam, t_marker_to_cam, cv2.CALIB_HAND_EYE_TSAI) + cv2.destroyAllWindows() + return hand_eye + + + def calibrate(self, view_num, look_at_pt, top_down_dist, top_down_direrction, radius): + self.capture_frames(view_num, look_at_pt, top_down_dist, top_down_direrction, radius) + marker_to_cam = np.array(self.marker_to_cam) + end_effector_to_base = np.array(self.end_effector_to_base) + hand_eye = self.get_hand_eye(marker_to_cam, end_effector_to_base) + # hand_eye = self.get_hand_eye(None, None) + return hand_eye + + +class ArcuoMarkerSolver: + def __init__(self, intrinsics, dist=None, marker_info={"id": 0, "size": 0.1}): + self.intrinsics = intrinsics + self.dist = dist + self.marker_info = marker_info + + def solve_arcuo_marker_pose(self, color_image): + self.res = [] + aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_ARUCO_ORIGINAL) + aruco_params = cv2.aruco.DetectorParameters() + cv2.imshow("color_image", color_image) + cv2.waitKey(500) + corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(color_image, aruco_dict, parameters=aruco_params) + if ids is None: + return None + for i in range(len(ids)): + rvec, tvec, _ = cv2.aruco.estimatePoseSingleMarkers(corners[i], self.marker_info["size"], self.intrinsics, self.dist) + self.res.append({ + 'rvec': rvec, + 'tvec': tvec, + 'corners': corners[i], + 'ids': ids[i] + }) + for res in self.res: + if res["ids"][0] == self.marker_info["id"]: + return res + return None + + + def get_marker_to_cam_pose(self, color_image, visualize=False): + res = self.solve_arcuo_marker_pose(color_image) + if visualize and res is not None: + self.visualize_res(color_image, res) + return res + + + def visualize_res(self, color_image, res): + rvec = res["rvec"] + tvec = res["tvec"] + corners = res["corners"] + ids = res["ids"] + aruco_frame = cv2.drawFrameAxes(color_image, self.intrinsics, self.dist, rvec, tvec, 0.05) + aruco_frame = cv2.aruco.drawDetectedMarkers(aruco_frame, (corners, ), ids) + cv2.imshow("aruco_frame", aruco_frame) + cv2.waitKey(500) + + +class EndEffectorPoseGenerator: + def __init__(self, view_num, look_at_pt, top_down_dist, top_down_direrction, radius, reverse_order=False): + self.view_num = view_num + self.look_at_pt = look_at_pt + self.top_down_dist = top_down_dist + self.top_down_direrction = top_down_direrction + self.radius = radius + self.poses = self.generate_poses(reverse_order) + + def generate_poses(self, reverse_order=False): + poses = [] + for i in range(self.view_num): + order = i if not reverse_order else self.view_num - i - 1 + pose = self.get_pose(order) + poses.append(pose) + return poses + + + def get_pose(self, i): + angle = 0.8 * np.pi * i / self.view_num + 0.7 * np.pi + start_point_x = self.look_at_pt[0] + self.radius * np.cos(angle) + start_point_y = self.look_at_pt[1] + self.radius * np.sin(angle) + start_point_z = self.look_at_pt[2] + self.top_down_dist + + look_at_vector = self.look_at_pt - np.array([start_point_x, start_point_y, start_point_z]) + look_at_vector = look_at_vector / np.linalg.norm(look_at_vector) + up_vector = self.top_down_direrction + noise = np.random.uniform(0, 0.1, up_vector.shape) + right_vector = np.cross(look_at_vector, up_vector + noise) + right_vector = right_vector / np.linalg.norm(right_vector) + up_vector = np.cross(look_at_vector, right_vector) + up_vector = up_vector / np.linalg.norm(up_vector) + + pose = np.eye(4) + pose[:3, :3] = self.get_rotation(right_vector, up_vector, look_at_vector) + pose[:3, 3] = np.array([start_point_x, start_point_y, start_point_z]) + return pose + + + def get_rotation(self, right_vector, up_vector, look_at_vector): + rotation = np.eye(3) + rotation[:, 0] = right_vector + rotation[:, 1] = up_vector + rotation[:, 2] = look_at_vector + return rotation + + +class PoseVisualizer: + def __init__(self, poses): + self.poses = poses + + def visualize(self): + + vis = o3d.visualization.Visualizer() + vis.create_window() + + for pose in self.poses: + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.05) + mesh_frame.transform(pose) + vis.add_geometry(mesh_frame) + print("Press q to close the window") + vis.run() + + +class CAMERA: + def __init__(self): + self.color_intrinsics = None + self.depth_intrinsics = None + self.color = None + self.depth = None + + def get_color_intrinsics(self): + pass + + def get_depth_intrinsics(self): + pass + + def get_color_image(self): + pass + + def get_depth_image(self): + pass + + +class RealSenseCamera(CAMERA): + def __init__(self): + super().__init__() + self.pipeline = rs.pipeline() + config = rs.config() + config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30) + config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30) + align = rs.align(rs.stream.color) + self.pipeline.start(config) + self.color_intrinsics = self.get_intrinsics("color") + self.depth_intrinsics = self.get_intrinsics("depth") + + def get_color_intrinsics(self): + return self.color_intrinsics + + def get_depth_intrinsics(self): + return self.depth_intrinsics + + def get_color_image(self): + frames = self.pipeline.wait_for_frames() + color_frame = frames.get_color_frame() + color_image = np.asanyarray(color_frame.get_data()) + return color_image + + def get_depth_image(self): + frames = self.pipeline.wait_for_frames() + depth_frame = frames.get_depth_frame() + depth_image = np.asanyarray(depth_frame.get_data()) + return depth_image + + def get_intrinsics(self, camra_type): + if camra_type == "color": + profile = self.pipeline.get_active_profile() + color_profile = rs.video_stream_profile(profile.get_stream(rs.stream.color)) + color_intrinsics = color_profile.get_intrinsics() + K = [[color_intrinsics.fx, 0, color_intrinsics.ppx], [0, color_intrinsics.fy, color_intrinsics.ppy], [0, 0, 1]] + return np.array(K) + elif camra_type == "depth": + profile = self.pipeline.get_active_profile() + depth_profile = rs.video_stream_profile(profile.get_stream(rs.stream.depth)) + depth_intrinsics = depth_profile.get_intrinsics() + K = [[depth_intrinsics.fx, 0, depth_intrinsics.ppx], [0, depth_intrinsics.fy, depth_intrinsics.ppy], [0, 0, 1]] + return np.array(K) + + +class InspireCamera(CAMERA): + def __init__(self): + super().__init__() + ControlUtil.set_gripper_pose(INIT_GRIPPER_POSE) + view_data = CommunicateUtil.get_view_data(init=True) + self.color_intrinsics = self.get_intrinsics(view_data["color_intrinsics"], view_data["color_image"]) + self.depth_intrinsics = self.get_intrinsics(view_data["depth_intrinsics"], view_data["depth_image"]) + + def get_color_intrinsics(self): + return self.color_intrinsics + + def get_depth_intrinsics(self): + return self.depth_intrinsics + + def get_color_image(self): + view_data = CommunicateUtil.get_view_data() + if view_data is None: + return None + else: + color_image = view_data["color_image"] + return color_image + + def get_depth_image(self): + view_data = CommunicateUtil.get_view_data() + if view_data is None: + return None + else: + depth_image = view_data["depth_image"] + return depth_image + + def get_intrinsics(self, intrinsics, image): + height = image.shape[0] + width = image.shape[1] + scale_h = height / intrinsics["height"] + scale_w = width / intrinsics["width"] + fx = intrinsics["fx"] * scale_w + fy = intrinsics["fy"] * scale_h + cx = intrinsics["cx"] * scale_w + cy = intrinsics["cy"] * scale_h + K = np.array([ + [fx, 0, cx], + [ 0, fy, cy], + [ 0, 0, 1] + ]) + return K + + +def example_view_generator(): + pose_generator = EndEffectorPoseGenerator(VIEW_NUM, LOOK_AT_PT, TOP_DOWN_DIST, TOP_DOWN_DIRECTION, RADIUS) + base_pose = np.eye(4) + test_pose = np.array([ + [1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0.2], + [0, 0, 0, 1] + ]) + defalut_ee2base = np.array([ + [0, -1, 0, 0], + [1, 0, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + poses = [pose @ defalut_ee2base for pose in pose_generator.poses] + [base_pose, test_pose] + visualizer = PoseVisualizer(poses) + visualizer.visualize() + + +def example_calibration(): + # ControlUtil.connect_robot() + # ControlUtil.franka_reset() + # ControlUtil.set_gripper_pose(INIT_GRIPPER_POSE) + # view_data = CommunicateUtil.get_view_data(init=True) + # intrinsics = get_intrinsics(view_data["color_intrinsics"], view_data["color_image"]) + calibration = HandEyeCalibration( + # intrinsics=intrinsics, + dist=None, + marker_type=MARKER_TYPE, + marker_info=MARKER_INFO, + camera_type="Inspire" + ) + hand_eye = calibration.calibrate( + view_num=VIEW_NUM, + look_at_pt=LOOK_AT_PT, + top_down_dist=TOP_DOWN_DIST, + top_down_direrction=TOP_DOWN_DIRECTION, + radius=RADIUS + ) + print(hand_eye) + + res = { + 'rotation': hand_eye[0].tolist(), + 'translation': hand_eye[1].tolist() + } + with open("hand_eye.json", "w") as f: + json.dump(res, f, indent=4) + print("Hand eye calibration finished") + return res + + +def get_depth_to_end_effector_pose(calibration_res_path): + with open(calibration_res_path, "r") as f: + data = json.load(f) + rotation = np.array(data["rotation"]) + translation = np.array(data["translation"]) + color_to_ee = np.eye(4) + color_to_ee[:3, :3] = rotation + color_to_ee[:3, 3:] = translation + ControlUtil.connect_robot() + ControlUtil.franka_reset() + ControlUtil.set_gripper_pose(INIT_GRIPPER_POSE) + view_data = CommunicateUtil.get_view_data(init=True) + depth_to_color = np.array(view_data["depth_to_color"]) + depth_to_ee = color_to_ee @ depth_to_color + print(depth_to_ee) + with open("depth_to_ee.json", "w") as f: + json.dump(depth_to_ee.tolist(), f, indent=4) + + +if __name__ == "__main__": + res = example_calibration() + get_depth_to_end_effector_pose("hand_eye.json") + # example_view_generator() + \ No newline at end of file diff --git a/runners/inference.py b/runners/inference.py new file mode 100644 index 0000000..42c05fe --- /dev/null +++ b/runners/inference.py @@ -0,0 +1,182 @@ +import os +import numpy as np +import requests + +from PytorchBoot.runners import Runner +from PytorchBoot.config import ConfigManager +import PytorchBoot.stereotype as stereotype +from PytorchBoot.utils.log_util import Log + +from utils.pose_util import PoseUtil +from utils.control_util import ControlUtil +from utils.communicate_util import CommunicateUtil +from utils.pts_util import PtsUtil +from utils.view_util import ViewUtil +from scipy.spatial.transform import Rotation as R + +@stereotype.runner("inference_runner") +class InferenceRunner(Runner): + + def __init__(self, config_path: str): + super().__init__(config_path) + self.load_experiment("inference") + self.inference_config = ConfigManager.get("runner", "inference") + self.server_url = self.inference_config["server_url"] + self.max_iter = self.inference_config["max_iter"] + self.max_fail = self.inference_config["max_fail"] + self.max_no_new_pts = self.inference_config["max_no_new_pts"] + self.min_delta_pts_num = self.inference_config["min_delta_pts_num"] + + def check_stop(self, cnt, fail, no_new_pts): + if cnt > self.max_iter: + return True + if fail > self.max_fail: + return True + if no_new_pts > self.max_no_new_pts: + return True + return False + + def split_scan_pts_and_obj_pts(self, world_pts, z_threshold=0.005): + scan_pts = world_pts[world_pts[:, 2] < z_threshold] + obj_pts = world_pts[world_pts[:, 2] >= z_threshold] + return scan_pts, obj_pts + + def run(self): + ControlUtil.connect_robot() + ControlUtil.init() + scanned_pts_list = [] + scanned_n_to_world_pose = [] + cnt = 0 + fail = 0 + no_new_pts = 0 + + view_data = CommunicateUtil.get_view_data(init=True) + first_cam_to_real_world = ControlUtil.get_pose() + if view_data is None: + Log.error("No view data received") + fail += 1 + return + cam_shot_pts = ViewUtil.get_pts(view_data) + + # ########################################### DEBUG ########################################### + # sensor_pts = PtsUtil.transform_point_cloud(cam_shot_pts, np.linalg.inv(ControlUtil.CAMERA_TO_LEFT_CAMERA)) + # np.savetxt('/home/yan20/Downloads/left_pts_0.txt', cam_shot_pts) + # np.savetxt('/home/yan20/Downloads/sensor_pts_0.txt', sensor_pts) + # ############################################################################################# + + world_shot_pts = PtsUtil.transform_point_cloud( + cam_shot_pts, first_cam_to_real_world + ) + #import ipdb; ipdb.set_trace() + _, world_splitted_shot_pts = self.split_scan_pts_and_obj_pts( + world_shot_pts + ) + curr_pts = world_splitted_shot_pts + curr_pose = first_cam_to_real_world + curr_pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(curr_pose[:3,:3]) + curr_pose_9d = np.concatenate([curr_pose_6d, curr_pose[:3, 3]], axis=0) + scanned_pts_list.append(curr_pts.tolist()) + scanned_n_to_world_pose.append(curr_pose_9d.tolist()) + combined_pts = np.concatenate(scanned_pts_list, axis=0) + downsampled_combined_pts = PtsUtil.voxel_downsample_point_cloud(combined_pts, 0.003) + last_downsampled_combined_pts_num = downsampled_combined_pts.shape[0] + Log.info(f"First downsampled combined pts: {last_downsampled_combined_pts_num}") + + ####################################### DEBUG ####################################### + # scan_count = 0 + # save_path = "/home/yan20/Downloads/pts" + # if not os.path.exists(save_path): + # os.makedirs(save_path) + ##################################################################################### + + while not self.check_stop(cnt, fail, no_new_pts): + + data = { + "scanned_pts": scanned_pts_list, + "scanned_n_to_world_pose_9d": scanned_n_to_world_pose + } + # pts = np.array(data['scanned_pts'][-1]) + # np.savetxt(f'{save_path}/pts_{scan_count}.txt', pts) + # scan_count += 1 + response = requests.post(self.server_url, json=data) + result = response.json() + pred_pose_9d = np.array(result["pred_pose_9d"]) + pred_rot_6d = pred_pose_9d[0, :6] + pred_trans = pred_pose_9d[0, 6:] + pred_rot_mat = PoseUtil.rotation_6d_to_matrix_numpy(pred_rot_6d) + pred_pose = np.eye(4) + pred_pose[:3, :3] = pred_rot_mat + pred_pose[:3, 3] = pred_trans + target_camera_pose = pred_pose @ ControlUtil.CAMERA_CORRECTION + + ControlUtil.move_to(target_camera_pose) + cnt += 1 + + view_data = CommunicateUtil.get_view_data() + if view_data is None: + Log.error("No view data received") + fail += 1 + continue + cam_shot_pts = ViewUtil.get_pts(view_data) + left_cam_to_first_left_cam = ViewUtil.get_camera_pose(view_data) + curr_pose = first_cam_to_real_world @ left_cam_to_first_left_cam @ np.linalg.inv(ControlUtil.CAMERA_CORRECTION) + # curr_pose = pred_pose + # curr_pose = first_cam_to_real_world @ ViewUtil.get_camera_pose(view_data) + print('pred_pose:', pred_pose) + print('curr_pose:', curr_pose) + + ##################################### DEBUG ##################################### + # print(curr_pose) + # rot = R.from_matrix(curr_pose[:3, :3]) + # quat_xyzw = rot.as_quat() + # translation = curr_pose[:3, 3] + # print(quat_xyzw, translation) + # # from ipdb import set_trace; set_trace() + ################################################################################# + + world_shot_pts = PtsUtil.transform_point_cloud( + cam_shot_pts, first_cam_to_real_world + ) + _, world_splitted_shot_pts = self.split_scan_pts_and_obj_pts( + world_shot_pts + ) + curr_pts = world_splitted_shot_pts + import ipdb; ipdb.set_trace() + from utils.vis import visualizeUtil + visualizeUtil.visualize_pts_and_camera(world_splitted_shot_pts,pred_pose) + curr_pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(curr_pose[:3,:3]) + curr_pose_9d = np.concatenate([curr_pose_6d, curr_pose[:3, 3]], axis=0) + scanned_pts_list.append(curr_pts.tolist()) + scanned_n_to_world_pose.append(curr_pose_9d.tolist()) + combined_pts = np.concatenate(scanned_pts_list, axis=0) + downsampled_combined_pts = PtsUtil.voxel_downsample_point_cloud(combined_pts, 0.003) + + curr_downsampled_combined_pts_num = downsampled_combined_pts.shape[0] + Log.info(f"Downsampled combined pts: {curr_downsampled_combined_pts_num}") + if curr_downsampled_combined_pts_num < last_downsampled_combined_pts_num + self.min_delta_pts_num: + no_new_pts += 1 + Log.info(f"No new points, cnt: {cnt}, fail: {fail}, no_new_pts: {no_new_pts}") + continue + Log.success("Inference finished") + # self.save_inference_result(scanned_pts_list, downsampled_combined_pts) + + def create_experiment(self, backup_name=None): + super().create_experiment(backup_name) + self.inference_result_dir = os.path.join(self.experiment_path, "inference_result") + os.makedirs(self.inference_result_dir) + + def load_experiment(self, backup_name=None): + super().load_experiment(backup_name) + self.inference_result_dir = os.path.join(self.experiment_path, "inference_result") + + def save_inference_result(self, scanned_pts_list, downsampled_combined_pts): + import time + dir_name = time.strftime("inference_result_%Y_%m_%d_%Hh%Mm%Ss", time.localtime()) + dir_path = os.path.join(self.inference_result_dir, dir_name) + for i in range(len(scanned_pts_list)): + np.savetxt(os.path.join(dir_path, f"{i}.txt"), np.array(scanned_pts_list[i])) + + np.savetxt(os.path.join(dir_path, "downsampled_combined_pts.txt"), np.array(downsampled_combined_pts)) + + Log.success("Inference result saved") + \ No newline at end of file diff --git a/utils/communicate_util.py b/utils/communicate_util.py new file mode 100644 index 0000000..cc2c54c --- /dev/null +++ b/utils/communicate_util.py @@ -0,0 +1,41 @@ +import requests +import numpy as np + +class CommunicateUtil: + VIEW_HOST = "192.168.1.2:7999" #"10.7.250.52:7999" ## + INFERENCE_HOST = "127.0.0.1" + INFERENCE_PORT = 5000 + + def get_view_data(init = False) -> dict: + params = {} + params["create_scanner"] = init + response = requests.get(f"http://{CommunicateUtil.VIEW_HOST}/api/data", json=params) + data = response.json() + + if not data["success"]: + print(f"Failed to get view data") + return None + + image_id = data["image_id"] + depth_image = np.array(data["depth_image"], dtype=np.uint16) + color_image = np.array(data["color_image"]).astype(np.uint8) + depth_intrinsics = data["depth_intrinsics"] + depth_extrinsics = np.array(data["depth_extrinsics"]) + color_intrinsics = data["color_intrinsics"] + depth_to_color = np.array(data["depth_to_color"]) + view_data = { + "image_id": image_id, + "depth_image": depth_image, + "depth_intrinsics": depth_intrinsics, + "depth_extrinsics": depth_extrinsics, + "color_image": color_image, + "color_intrinsics": color_intrinsics, + "depth_to_color": depth_to_color + } + return view_data + + def get_inference_data(view_data:dict) -> dict: + data = {} + return data + + \ No newline at end of file diff --git a/utils/control_util.py b/utils/control_util.py new file mode 100644 index 0000000..f82fd64 --- /dev/null +++ b/utils/control_util.py @@ -0,0 +1,284 @@ +import numpy as np +from frankapy import FrankaArm +from autolab_core import RigidTransform +from scipy.spatial.transform import Rotation as R +import serial +import time + +class ControlUtil: + + __fa:FrankaArm = None + __ser: serial.Serial = None + curr_rotation = 0 + + # BASE_TO_WORLD:np.ndarray = np.asarray([ + # [1, 0, 0, -0.68834619], + # [0, 1, 0, -0.01785319], + # [0, 0, 1, -0.11924244], + # [0, 0, 0, 1] + # ]) + + # BASELINE_DIS = 0.1 + # LEFT_TO_RIGHT_ANGLE = -25.0 + # CAMERA_TO_LEFT_CAMERA:np.ndarray = np.asarray([ + # [1, 0, 0, BASELINE_DIS/2 * np.cos(np.radians(-LEFT_TO_RIGHT_ANGLE/2))], + # [0, 1, 0, 0], + # [0, 0, 1, BASELINE_DIS/2 * np.sin(np.radians(-LEFT_TO_RIGHT_ANGLE/2))], + # [0, 0, 0, 1] + # ]) + # CAMERA_TO_LEFT_CAMERA[:3, :3] = R.from_euler('y', LEFT_TO_RIGHT_ANGLE/2, degrees=True).as_matrix() + + CAMERA_CORRECTION_ANGLE = -13 + CAMERA_CORRECTION:np.ndarray = np.eye(4) + CAMERA_CORRECTION[:3, :3] = R.from_euler('y', CAMERA_CORRECTION_ANGLE, degrees=True).as_matrix() + + BASE_TO_WORLD:np.ndarray = np.asarray([ + [1, 0, 0, -0.49602172], + [0, 1, 0, 0.06688724], + [0, 0, 1, -0.2488704 ], + [0, 0, 0, 1] + ]) + + + CAMERA_TO_GRIPPER:np.ndarray = np.asarray( + [[-0.00757086, -0.99984193, 0.01608569, 0.02644546], + [ 0.99993362, -0.00770935, -0.00856502, -0.04860572], + [ 0.00868767, 0.01601977, 0.99983391, -0.02169477], + [ 0., 0. , 0. , 1. , ] + ]) + + INIT_GRIPPER_POSE:np.ndarray = np.asarray( + [[0.41869126 ,0.87596275 , 0.23951774 , 0.36005292], + [ 0.70787907 ,-0.4800251 , 0.51813998 ,-0.26499909], + [ 0.56884584, -0.04739109 ,-0.82107382 ,0.66881103], + [ 0. , 0. , 0. , 1. ]]) + + # INIT_GRIPPER_POSE:np.ndarray = np.asarray( + # [[1 ,0 , 0 , 0.5], + # [ 0 ,-1 , 0 ,0], + # [ 0, 0 ,-1 ,0.6], + # [ 0. , 0. , 0. , 1. ]]) + + INIT_JOINTS:np.ndarray = np.asarray([-1.64987928,-0.73557812,-0.02676473,-2.08365335,0.01794927,1.5182423,0.73276058]) + + @staticmethod + def connect_robot(): + if ControlUtil.__fa is None: + ControlUtil.__fa = FrankaArm(robot_num=1) + if ControlUtil.__ser is None: + ControlUtil.__ser = serial.Serial(port="/dev/ttyUSB0", baudrate=115200) + + @staticmethod + def franka_reset() -> None: + #ControlUtil.__fa.goto_joints(ControlUtil.INIT_JOINTS, duration=5, use_impedance=False, ignore_errors=False) + ControlUtil.__fa.reset_joints() + + @staticmethod + def init() -> None: + ControlUtil.franka_reset() + ControlUtil.set_gripper_pose(ControlUtil.INIT_GRIPPER_POSE) + + @staticmethod + def get_pose() -> np.ndarray: + gripper_to_base = ControlUtil.get_curr_gripper_to_base_pose() + cam_to_world = ControlUtil.BASE_TO_WORLD @ gripper_to_base @ ControlUtil.CAMERA_TO_GRIPPER + return cam_to_world + + @staticmethod + def set_pose(cam_to_world: np.ndarray) -> None: + gripper_to_base = ControlUtil.solve_gripper_to_base(cam_to_world) + ControlUtil.set_gripper_pose(gripper_to_base) + + @staticmethod + def rotate_display_table(degree): + turn_directions = { + "left": 1, + "right": 0 + } + delta_degree = degree - ControlUtil.curr_rotation + ControlUtil.curr_rotation = degree + print(f"Table rotated {ControlUtil.curr_rotation} degree") + + if delta_degree >= 0: + turn_angle = delta_degree + turn_direction = turn_directions["left"] + else: + turn_angle = -delta_degree + turn_direction = turn_directions["right"] + write_len = ControlUtil.__ser.write(f"CT+TRUNSINGLE({turn_direction},{turn_angle});".encode('utf-8')) + print(f"send command: CT+TRUNSINGLE({turn_direction},{turn_angle});") + + return delta_degree + + + @staticmethod + def get_curr_gripper_to_base_pose() -> np.ndarray: + return ControlUtil.__fa.get_pose().matrix + + @staticmethod + def get_curr_joints() -> np.ndarray: + return ControlUtil.__fa.get_joints() + + @staticmethod + def set_gripper_pose(gripper_to_base: np.ndarray) -> None: + gripper_to_base = RigidTransform(rotation=gripper_to_base[:3, :3], translation=gripper_to_base[:3, 3], from_frame="franka_tool", to_frame="world") + ControlUtil.__fa.goto_pose(gripper_to_base, duration=6, use_impedance=False, ignore_errors=False, ignore_virtual_walls=True) + + @staticmethod + def solve_gripper_to_base(cam_to_world: np.ndarray) -> np.ndarray: + return np.linalg.inv(ControlUtil.BASE_TO_WORLD) @ cam_to_world @ np.linalg.inv(ControlUtil.CAMERA_TO_GRIPPER) + + @staticmethod + def sovle_cam_to_world(gripper_to_base: np.ndarray) -> np.ndarray: + return ControlUtil.BASE_TO_WORLD @ gripper_to_base @ ControlUtil.CAMERA_TO_GRIPPER + + @staticmethod + def check_limit(new_cam_to_world): + if new_cam_to_world[0,3] > 0 or new_cam_to_world[1,3] > 0: + # if new_cam_to_world[0,3] > 0: + return False + x = abs(new_cam_to_world[0,3]) + y = abs(new_cam_to_world[1,3]) + + tan_y_x = y/x + min_angle = 20 / 180 * np.pi + max_angle = 50 / 180 * np.pi + if tan_y_x < np.tan(min_angle) or tan_y_x > np.tan(max_angle): + return False + + return True + + @staticmethod + def solve_display_table_rot_and_cam_to_world(cam_to_world: np.ndarray) -> tuple: + if ControlUtil.check_limit(cam_to_world): + return 0, cam_to_world + else: + min_display_table_rot = 180 + min_new_cam_to_world = None + for display_table_rot in np.linspace(0.1,360, 1800): + new_world_to_world = ControlUtil.get_z_axis_rot_mat(display_table_rot) + new_cam_to_new_world = cam_to_world + new_cam_to_world = new_world_to_world @ new_cam_to_new_world + + if ControlUtil.check_limit(new_cam_to_world): + if display_table_rot < min_display_table_rot: + min_display_table_rot, min_new_cam_to_world = display_table_rot, new_cam_to_world + if abs(display_table_rot - 360) < min_display_table_rot: + min_display_table_rot, min_new_cam_to_world = display_table_rot - 360, new_cam_to_world + + if min_new_cam_to_world is None: + raise ValueError("No valid display table rotation found") + + return min_display_table_rot, min_new_cam_to_world + + @staticmethod + def get_z_axis_rot_mat(degree): + radian = np.radians(degree) + return np.array([ + [np.cos(radian), -np.sin(radian), 0, 0], + [np.sin(radian), np.cos(radian), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + + @staticmethod + def get_gripper_to_base_axis_angle(gripper_to_base: np.ndarray) -> bool: + rot_mat = gripper_to_base[:3,:3] + gripper_z_axis = rot_mat[:,2] + base_x_axis = np.array([1,0,0]) + angle = np.arccos(np.dot(gripper_z_axis, base_x_axis)) + return angle + + @staticmethod + def move_to(pose: np.ndarray): + rot_degree, cam_to_world = ControlUtil.solve_display_table_rot_and_cam_to_world(pose) + + delta_degree = ControlUtil.rotate_display_table(rot_degree) + exec_time = abs(delta_degree)/9 + start_time = time.time() + ControlUtil.set_pose(cam_to_world) + end_time = time.time() + print(f"Move to pose with rotation {rot_degree} degree, exec time: {end_time - start_time}|exec time: {exec_time}") + if end_time - start_time < exec_time: + time.sleep(exec_time - (end_time - start_time)) + + @staticmethod + def absolute_rotate_display_table(degree): + exec_time = abs(degree)/9 + write_len = ControlUtil.__ser.write(f"CT+TRUNSINGLE({0},{degree});".encode('utf-8')) + print(f"send command: CT+TRUNSINGLE({0},{degree});") + time.sleep(exec_time) + +# ----------- Debug Test ------------- + +if __name__ == "__main__": + ControlUtil.connect_robot() + #ControlUtil.franka_reset() + print(ControlUtil.get_curr_gripper_to_base_pose()) + # ControlUtil.rotate_display_table(180) + # ControlUtil.init() + # ControlUtil.franka_reset() + + # def main_test(): + # print(ControlUtil.get_curr_gripper_to_base_pose()) + # ControlUtil.init() + + # def rotate_back(rotation): + # ControlUtil.rotate_display_table(-rotation) + + # #main_test() + # import sys; sys.path.append("/home/yan20/nbv_rec/project/franka_control") + # from utils.communicate_util import CommunicateUtil + # import ipdb + # ControlUtil.init() + # view_data_0 = CommunicateUtil.get_view_data(init=True) + # depth_extrinsics_0 = view_data_0["depth_extrinsics"] + # cam_to_world_0 = ControlUtil.get_pose() + # print("cam_extrinsics_0") + # print(depth_extrinsics_0) + # print("cam_to_world_0") + # print(cam_to_world_0) + + # ipdb.set_trace() + # TEST_POSE:np.ndarray = np.asarray([ + # [ 0.46532393, 0.62171798, 0.63002284, 0.30230963], + # [ 0.43205618, -0.78075723, 0.45136491, -0.29127173], + # [ 0.77251656, 0.06217437, -0.63193429, 0.559957 ], + # [ 0. , 0. , 0. , 1. ], + # ]) + # TEST_POSE_CAM_TO_WORLD = ControlUtil.BASE_TO_WORLD @ TEST_POSE @ ControlUtil.CAMERA_TO_GRIPPER + # ControlUtil.move_to(TEST_POSE_CAM_TO_WORLD) + # view_data_1 = CommunicateUtil.get_view_data() + # depth_extrinsics_1 = view_data_1["depth_extrinsics"] + # depth_extrinsics_1[:3,3] = depth_extrinsics_1[:3,3] / 1000 + # cam_to_world_1 = ControlUtil.get_pose() + # print("cam_extrinsics_1") + # print(depth_extrinsics_1) + # print("cam_to_world_1") + # print(TEST_POSE_CAM_TO_WORLD) + # actual_cam_to_world_1 = cam_to_world_0 @ depth_extrinsics_1 + # print("actual_cam_to_world_1") + # print(actual_cam_to_world_1) + # ipdb.set_trace() + # TEST_POSE_2:np.ndarray = np.asarray( + # [[ 0.74398544, -0.61922696, 0.251049, 0.47000935], + # [-0.47287207, -0.75338888, -0.45692666, 0.20843903], + # [ 0.47207883 , 0.22123272, -0.85334192, 0.57863381], + # [ 0. , 0. , 0. , 1. , ]] + # ) + # TEST_POSE_CAM_TO_WORLD_2 = ControlUtil.BASE_TO_WORLD @ TEST_POSE_2 @ ControlUtil.CAMERA_TO_GRIPPER + + # #ControlUtil.move_to(TEST_POSE_CAM_TO_WORLD_2) + # ControlUtil.set_pose(TEST_POSE_CAM_TO_WORLD_2) + # view_data_2 = CommunicateUtil.get_view_data() + # depth_extrinsics_2 = view_data_2["depth_extrinsics"] + # depth_extrinsics_2[:3,3] = depth_extrinsics_2[:3,3] / 1000 + # cam_to_world_2 = ControlUtil.get_pose() + # print("cam_extrinsics_2") + # print(depth_extrinsics_2) + # print("cam_to_world_2") + # print(TEST_POSE_CAM_TO_WORLD_2) + # actual_cam_to_world_2 = cam_to_world_0 @ depth_extrinsics_2 + # print("actual_cam_to_world_2") + # print(actual_cam_to_world_2) + # ipdb.set_trace() \ No newline at end of file diff --git a/utils/data_load.py b/utils/data_load.py new file mode 100644 index 0000000..ccd3ad4 --- /dev/null +++ b/utils/data_load.py @@ -0,0 +1,384 @@ +import os +import numpy as np +import json +import cv2 +import trimesh +import torch +import OpenEXR +import Imath +from utils.pts_util import PtsUtil + + +class DataLoadUtil: + TABLE_POSITION = np.asarray([0, 0, -0.1]) + + @staticmethod + def get_display_table_info(root, scene_name): + scene_info = DataLoadUtil.load_scene_info(root, scene_name) + display_table_info = scene_info["display_table"] + return display_table_info + + @staticmethod + def get_display_table_top(root, scene_name): + # display_table_height = DataLoadUtil.get_display_table_info(root, scene_name)[ + # "height" + # ] + display_table_height = 0.1 #FIXME + display_table_top = DataLoadUtil.TABLE_POSITION + np.asarray( + [0, 0, display_table_height] + ) + return display_table_top + + @staticmethod + def get_path(root, scene_name, frame_idx): + path = os.path.join(root, scene_name, f"{frame_idx}") + return path + + @staticmethod + def get_label_num(root, scene_name): + label_dir = os.path.join(root, scene_name, "label") + return len(os.listdir(label_dir)) + + @staticmethod + def get_label_path(root, scene_name, seq_idx): + label_dir = os.path.join(root, scene_name, "label") + if not os.path.exists(label_dir): + os.makedirs(label_dir) + path = os.path.join(label_dir, f"{seq_idx}.json") + return path + + @staticmethod + def get_label_path_old(root, scene_name): + path = os.path.join(root, scene_name, "label.json") + return path + + @staticmethod + def get_scene_seq_length(root, scene_name): + camera_params_path = os.path.join(root, scene_name, "camera_params") + return len(os.listdir(camera_params_path)) + + @staticmethod + def load_mesh_at(model_dir, object_name, world_object_pose): + model_path = os.path.join(model_dir, object_name, "mesh.obj") + mesh = trimesh.load(model_path) + mesh.apply_transform(world_object_pose) + return mesh + + @staticmethod + def get_bbox_diag(model_dir, object_name): + model_path = os.path.join(model_dir, object_name, "mesh.obj") + mesh = trimesh.load(model_path) + bbox = mesh.bounding_box.extents + diagonal_length = np.linalg.norm(bbox) + return diagonal_length + + @staticmethod + def load_scene_info(root, scene_name): + scene_info_path = os.path.join(root, scene_name, "scene_info.json") + with open(scene_info_path, "r") as f: + scene_info = json.load(f) + return scene_info + + @staticmethod + def load_target_pts_num_dict(root, scene_name): + target_pts_num_path = os.path.join(root, scene_name, "target_pts_num.json") + with open(target_pts_num_path, "r") as f: + target_pts_num_dict = json.load(f) + return target_pts_num_dict + + @staticmethod + def load_depth(path, min_depth=0.01, max_depth=5.0, binocular=False): + + def load_depth_from_real_path(real_path, min_depth, max_depth): + depth = cv2.imread(real_path, cv2.IMREAD_UNCHANGED) + depth = depth.astype(np.float32) / 65535.0 + min_depth = min_depth + max_depth = max_depth + depth_meters = min_depth + (max_depth - min_depth) * depth + return depth_meters + + if binocular: + depth_path_L = os.path.join( + os.path.dirname(path), "depth", os.path.basename(path) + "_L.png" + ) + depth_path_R = os.path.join( + os.path.dirname(path), "depth", os.path.basename(path) + "_R.png" + ) + depth_meters_L = load_depth_from_real_path( + depth_path_L, min_depth, max_depth + ) + depth_meters_R = load_depth_from_real_path( + depth_path_R, min_depth, max_depth + ) + return depth_meters_L, depth_meters_R + else: + depth_path = os.path.join( + os.path.dirname(path), "depth", os.path.basename(path) + ".png" + ) + depth_meters = load_depth_from_real_path(depth_path, min_depth, max_depth) + return depth_meters + + @staticmethod + def load_seg(path, binocular=False, left_only=False): + if binocular and not left_only: + + def clean_mask(mask_image): + green = [0, 255, 0, 255] + red = [255, 0, 0, 255] + threshold = 2 + mask_image = np.where( + np.abs(mask_image - green) <= threshold, green, mask_image + ) + mask_image = np.where( + np.abs(mask_image - red) <= threshold, red, mask_image + ) + return mask_image + + mask_path_L = os.path.join( + os.path.dirname(path), "mask", os.path.basename(path) + "_L.png" + ) + mask_image_L = clean_mask(cv2.imread(mask_path_L, cv2.IMREAD_UNCHANGED)) + mask_path_R = os.path.join( + os.path.dirname(path), "mask", os.path.basename(path) + "_R.png" + ) + mask_image_R = clean_mask(cv2.imread(mask_path_R, cv2.IMREAD_UNCHANGED)) + return mask_image_L, mask_image_R + else: + if binocular and left_only: + mask_path = os.path.join( + os.path.dirname(path), "mask", os.path.basename(path) + "_L.png" + ) + else: + mask_path = os.path.join( + os.path.dirname(path), "mask", os.path.basename(path) + ".png" + ) + mask_image = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED) + return mask_image + + @staticmethod + def load_exr_image(file_path): + exr_file = OpenEXR.InputFile(file_path) + header = exr_file.header() + dw = header['dataWindow'] + width = dw.max.x - dw.min.x + 1 + height = dw.max.y - dw.min.y + 1 + float_channels = ['R', 'G', 'B'] + img_data = [] + for channel in float_channels: + channel_data = exr_file.channel(channel) + img_data.append(np.frombuffer(channel_data, dtype=np.float16).reshape((height, width))) + img = np.stack(img_data, axis=-1) + return img + + @staticmethod + def load_normal(path, binocular=False, left_only=False, file_type="exr"): + if binocular and not left_only: + normal_path_L = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + f"_L.{file_type}" + ) + normal_image_L = DataLoadUtil.load_exr_image(normal_path_L) + + normal_path_R = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + f"_R.{file_type}" + ) + normal_image_R = DataLoadUtil.load_exr_image(normal_path_R) + normalized_normal_image_L = normal_image_L * 2.0 - 1.0 + normalized_normal_image_R = normal_image_R * 2.0 - 1.0 + return normalized_normal_image_L, normalized_normal_image_R + else: + if binocular and left_only: + normal_path = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + f"_L.{file_type}" + ) + else: + normal_path = os.path.join( + os.path.dirname(path), "normal", os.path.basename(path) + f".{file_type}" + ) + normal_image = DataLoadUtil.load_exr_image(normal_path) + normalized_normal_image = normal_image * 2.0 - 1.0 + return normalized_normal_image + + @staticmethod + def load_label(path): + with open(path, "r") as f: + label_data = json.load(f) + return label_data + + @staticmethod + def load_from_preprocessed_pts(path, file_type="npy"): + npy_path = os.path.join( + os.path.dirname(path), "pts", os.path.basename(path) + "." + file_type + ) + if file_type == "txt": + pts = np.loadtxt(npy_path) + else: + pts = np.load(npy_path) + return pts + + @staticmethod + def cam_pose_transformation(cam_pose_before): + offset = np.asarray([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) + cam_pose_after = cam_pose_before @ offset + return cam_pose_after + + @staticmethod + def load_cam_info(path, binocular=False, display_table_as_world_space_origin=True): + scene_dir = os.path.dirname(path) + root_dir = os.path.dirname(scene_dir) + scene_name = os.path.basename(scene_dir) + camera_params_path = os.path.join( + os.path.dirname(path), "camera_params", os.path.basename(path) + ".json" + ) + with open(camera_params_path, "r") as f: + label_data = json.load(f) + cam_to_world = np.asarray(label_data["extrinsic"]) + cam_to_world = DataLoadUtil.cam_pose_transformation(cam_to_world) + + if display_table_as_world_space_origin: + world_to_display_table = np.eye(4) + world_to_display_table[:3, 3] = -DataLoadUtil.get_display_table_top( + root_dir, scene_name + ) + cam_to_world = np.dot(world_to_display_table, cam_to_world) + cam_intrinsic = np.asarray(label_data["intrinsic"]) + cam_info = { + "cam_to_world": cam_to_world, + "cam_intrinsic": cam_intrinsic, + "far_plane": label_data["far_plane"], + "near_plane": label_data["near_plane"], + } + if binocular: + cam_to_world_R = np.asarray(label_data["extrinsic_R"]) + cam_to_world_R = DataLoadUtil.cam_pose_transformation(cam_to_world_R) + cam_to_world_O = np.asarray(label_data["extrinsic_cam_object"]) + cam_to_world_O = DataLoadUtil.cam_pose_transformation(cam_to_world_O) + if display_table_as_world_space_origin: + cam_to_world_O = np.dot(world_to_display_table, cam_to_world_O) + cam_to_world_R = np.dot(world_to_display_table, cam_to_world_R) + cam_info["cam_to_world_O"] = cam_to_world_O + cam_info["cam_to_world_R"] = cam_to_world_R + return cam_info + + @staticmethod + def get_real_cam_O_from_cam_L( + cam_L, cam_O_to_cam_L, scene_path, display_table_as_world_space_origin=True + ): + root_dir = os.path.dirname(scene_path) + scene_name = os.path.basename(scene_path) + if isinstance(cam_L, torch.Tensor): + cam_L = cam_L.cpu().numpy() + nO_to_display_table_pose = cam_L @ cam_O_to_cam_L + if display_table_as_world_space_origin: + display_table_to_world = np.eye(4) + display_table_to_world[:3, 3] = DataLoadUtil.get_display_table_top( + root_dir, scene_name + ) + nO_to_world_pose = np.dot(display_table_to_world, nO_to_display_table_pose) + nO_to_world_pose = DataLoadUtil.cam_pose_transformation(nO_to_world_pose) + return nO_to_world_pose + + @staticmethod + def get_target_point_cloud( + depth, cam_intrinsic, cam_extrinsic, mask, target_mask_label=(0, 255, 0, 255), require_full_points=False + ): + h, w = depth.shape + i, j = np.meshgrid(np.arange(w), np.arange(h), indexing="xy") + + z = depth + x = (i - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + mask = mask.reshape(-1, 4) + + target_mask = (mask == target_mask_label).all(axis=-1) + + target_points_camera = points_camera[target_mask] + target_points_camera_aug = np.concatenate( + [target_points_camera, np.ones((target_points_camera.shape[0], 1))], axis=-1 + ) + + target_points_world = np.dot(cam_extrinsic, target_points_camera_aug.T).T[:, :3] + data = { + "points_world": target_points_world, + "points_camera": target_points_camera, + } + return data + + @staticmethod + def get_point_cloud(depth, cam_intrinsic, cam_extrinsic): + h, w = depth.shape + i, j = np.meshgrid(np.arange(w), np.arange(h), indexing="xy") + + z = depth + x = (i - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + points_camera_aug = np.concatenate( + [points_camera, np.ones((points_camera.shape[0], 1))], axis=-1 + ) + + points_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3] + return {"points_world": points_world, "points_camera": points_camera} + + @staticmethod + def get_target_point_cloud_world_from_path( + path, + binocular=False, + random_downsample_N=65536, + voxel_size=0.005, + target_mask_label=(0, 255, 0, 255), + display_table_mask_label=(0, 0, 255, 255), + get_display_table_pts=False, + require_normal=False, + ): + cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular) + if binocular: + depth_L, depth_R = DataLoadUtil.load_depth( + path, cam_info["near_plane"], cam_info["far_plane"], binocular=True + ) + mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True) + point_cloud_L = DataLoadUtil.get_target_point_cloud( + depth_L, + cam_info["cam_intrinsic"], + cam_info["cam_to_world"], + mask_L, + target_mask_label, + )["points_world"] + point_cloud_R = DataLoadUtil.get_target_point_cloud( + depth_R, + cam_info["cam_intrinsic"], + cam_info["cam_to_world_R"], + mask_R, + target_mask_label, + )["points_world"] + point_cloud_L = PtsUtil.random_downsample_point_cloud( + point_cloud_L, random_downsample_N + ) + point_cloud_R = PtsUtil.random_downsample_point_cloud( + point_cloud_R, random_downsample_N + ) + overlap_points = PtsUtil.get_overlapping_points( + point_cloud_L, point_cloud_R, voxel_size + ) + return overlap_points + else: + depth = DataLoadUtil.load_depth( + path, cam_info["near_plane"], cam_info["far_plane"] + ) + mask = DataLoadUtil.load_seg(path) + point_cloud = DataLoadUtil.get_target_point_cloud( + depth, cam_info["cam_intrinsic"], cam_info["cam_to_world"], mask + )["points_world"] + return point_cloud + + @staticmethod + def load_points_normals(root, scene_name, display_table_as_world_space_origin=True): + points_path = os.path.join(root, scene_name, "points_and_normals.txt") + points_normals = np.loadtxt(points_path) + if display_table_as_world_space_origin: + points_normals[:, :3] = points_normals[ + :, :3 + ] - DataLoadUtil.get_display_table_top(root, scene_name) + return points_normals diff --git a/utils/inference_util.py b/utils/inference_util.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/pose_util.py b/utils/pose_util.py new file mode 100644 index 0000000..ee61001 --- /dev/null +++ b/utils/pose_util.py @@ -0,0 +1,164 @@ +import numpy as np + +class PoseUtil: + ROTATION = 1 + TRANSLATION = 2 + SCALE = 3 + + @staticmethod + def get_uniform_translation(trans_m_min, trans_m_max, trans_unit, debug=False): + if isinstance(trans_m_min, list): + x_min, y_min, z_min = trans_m_min + x_max, y_max, z_max = trans_m_max + else: + x_min, y_min, z_min = trans_m_min, trans_m_min, trans_m_min + x_max, y_max, z_max = trans_m_max, trans_m_max, trans_m_max + + x = np.random.uniform(x_min, x_max) + y = np.random.uniform(y_min, y_max) + z = np.random.uniform(z_min, z_max) + translation = np.array([x, y, z]) + if trans_unit == "cm": + translation = translation / 100 + if debug: + print("uniform translation:", translation) + return translation + + @staticmethod + def get_uniform_rotation(rot_degree_min=0, rot_degree_max=180, debug=False): + axis = np.random.randn(3) + axis /= np.linalg.norm(axis) + theta = np.random.uniform( + rot_degree_min / 180 * np.pi, rot_degree_max / 180 * np.pi + ) + + K = np.array( + [[0, -axis[2], axis[1]], [axis[2], 0, -axis[0]], [-axis[1], axis[0], 0]] + ) + R = np.eye(3) + np.sin(theta) * K + (1 - np.cos(theta)) * (K @ K) + if debug: + print("uniform rotation:", theta * 180 / np.pi) + return R + + @staticmethod + def get_uniform_pose( + trans_min, trans_max, rot_min=0, rot_max=180, trans_unit="cm", debug=False + ): + translation = PoseUtil.get_uniform_translation( + trans_min, trans_max, trans_unit, debug + ) + rotation = PoseUtil.get_uniform_rotation(rot_min, rot_max, debug) + pose = np.eye(4) + pose[:3, :3] = rotation + pose[:3, 3] = translation + return pose + + @staticmethod + def get_n_uniform_pose( + trans_min, + trans_max, + rot_min=0, + rot_max=180, + n=1, + trans_unit="cm", + fix=None, + contain_canonical=True, + debug=False, + ): + if fix == PoseUtil.ROTATION: + translations = np.zeros((n, 3)) + for i in range(n): + translations[i] = PoseUtil.get_uniform_translation( + trans_min, trans_max, trans_unit, debug + ) + if contain_canonical: + translations[0] = np.zeros(3) + rotations = PoseUtil.get_uniform_rotation(rot_min, rot_max, debug) + elif fix == PoseUtil.TRANSLATION: + rotations = np.zeros((n, 3, 3)) + for i in range(n): + rotations[i] = PoseUtil.get_uniform_rotation(rot_min, rot_max, debug) + if contain_canonical: + rotations[0] = np.eye(3) + translations = PoseUtil.get_uniform_translation( + trans_min, trans_max, trans_unit, debug + ) + else: + translations = np.zeros((n, 3)) + rotations = np.zeros((n, 3, 3)) + for i in range(n): + translations[i] = PoseUtil.get_uniform_translation( + trans_min, trans_max, trans_unit, debug + ) + for i in range(n): + rotations[i] = PoseUtil.get_uniform_rotation(rot_min, rot_max, debug) + if contain_canonical: + translations[0] = np.zeros(3) + rotations[0] = np.eye(3) + + pose = np.eye(4, 4, k=0)[np.newaxis, :].repeat(n, axis=0) + pose[:, :3, :3] = rotations + pose[:, :3, 3] = translations + + return pose + + @staticmethod + def get_n_uniform_pose_batch( + trans_min, + trans_max, + rot_min=0, + rot_max=180, + n=1, + batch_size=1, + trans_unit="cm", + fix=None, + contain_canonical=False, + debug=False, + ): + + batch_poses = [] + for i in range(batch_size): + pose = PoseUtil.get_n_uniform_pose( + trans_min, + trans_max, + rot_min, + rot_max, + n, + trans_unit, + fix, + contain_canonical, + debug, + ) + batch_poses.append(pose) + pose_batch = np.stack(batch_poses, axis=0) + return pose_batch + + @staticmethod + def get_uniform_scale(scale_min, scale_max, debug=False): + if isinstance(scale_min, list): + x_min, y_min, z_min = scale_min + x_max, y_max, z_max = scale_max + else: + x_min, y_min, z_min = scale_min, scale_min, scale_min + x_max, y_max, z_max = scale_max, scale_max, scale_max + + x = np.random.uniform(x_min, x_max) + y = np.random.uniform(y_min, y_max) + z = np.random.uniform(z_min, z_max) + scale = np.array([x, y, z]) + if debug: + print("uniform scale:", scale) + return scale + + @staticmethod + def rotation_6d_to_matrix_numpy(d6): + a1, a2 = d6[:3], d6[3:] + b1 = a1 / np.linalg.norm(a1) + b2 = a2 - np.dot(b1, a2) * b1 + b2 = b2 / np.linalg.norm(b2) + b3 = np.cross(b1, b2) + return np.stack((b1, b2, b3), axis=-2) + + @staticmethod + def matrix_to_rotation_6d_numpy(matrix): + return np.copy(matrix[:2, :]).reshape((6,)) \ No newline at end of file diff --git a/utils/preprocess_util.py b/utils/preprocess_util.py new file mode 100644 index 0000000..6f8e883 --- /dev/null +++ b/utils/preprocess_util.py @@ -0,0 +1,247 @@ +import os +import numpy as np +import time +import sys +np.random.seed(0) +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from concurrent.futures import ThreadPoolExecutor, as_completed +from utils.reconstruction_util import ReconstructionUtil +from utils.data_load import DataLoadUtil +from utils.pts_util import PtsUtil +from PytorchBoot.utils.log_util import Log + +def save_np_pts(path, pts: np.ndarray, file_type="txt"): + if file_type == "txt": + np.savetxt(path, pts) + else: + np.save(path, pts) + +def save_target_points(root, scene, frame_idx, target_points: np.ndarray, file_type="txt"): + #import ipdb;ipdb.set_trace() + pts_path = os.path.join(root,scene, "pts", f"{frame_idx}.{file_type}") + if not os.path.exists(os.path.join(root,scene, "pts")): + os.makedirs(os.path.join(root,scene, "pts")) + save_np_pts(pts_path, target_points, file_type) + +def save_scan_points_indices(root, scene, frame_idx, scan_points_indices: np.ndarray, file_type="txt"): + file_type="npy" + indices_path = os.path.join(root,scene, "scan_points_indices", f"{frame_idx}.{file_type}") + if not os.path.exists(os.path.join(root,scene, "scan_points_indices")): + os.makedirs(os.path.join(root,scene, "scan_points_indices")) + save_np_pts(indices_path, scan_points_indices, file_type) + +def save_scan_points(root, scene, scan_points: np.ndarray): + scan_points_path = os.path.join(root,scene, "scan_points.txt") + save_np_pts(scan_points_path, scan_points) + +def get_world_points(depth, mask, cam_intrinsic, cam_extrinsic, random_downsample_N): + z = depth[mask] + i, j = np.nonzero(mask) + x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + sampled_target_points = PtsUtil.random_downsample_point_cloud( + points_camera, random_downsample_N + ) + points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1) + points_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3] + + return points_world + +def get_world_points_and_normals(depth, normal, mask, cam_intrinsic, cam_extrinsic, random_downsample_N): + z = depth[mask] + i, j = np.nonzero(mask) + x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + normals_camera = normal[mask].reshape(-1, 3) + sampled_target_points, idx = PtsUtil.random_downsample_point_cloud( + points_camera, random_downsample_N, require_idx=True + ) + if len(sampled_target_points) == 0: + return np.zeros((0, 3)), np.zeros((0, 3)) + sampled_target_normals = normals_camera[idx] + + points_camera_aug = np.concatenate((sampled_target_points, np.ones((sampled_target_points.shape[0], 1))), axis=-1) + points_world = np.dot(cam_extrinsic, points_camera_aug.T).T[:, :3] + + + return points_world, sampled_target_normals + +def get_scan_points_indices(scan_points, mask, display_table_mask_label, cam_intrinsic, cam_extrinsic): + scan_points_homogeneous = np.hstack((scan_points, np.ones((scan_points.shape[0], 1)))) + points_camera = np.dot(np.linalg.inv(cam_extrinsic), scan_points_homogeneous.T).T[:, :3] + points_image_homogeneous = np.dot(cam_intrinsic, points_camera.T).T + points_image_homogeneous /= points_image_homogeneous[:, 2:] + pixel_x = points_image_homogeneous[:, 0].astype(int) + pixel_y = points_image_homogeneous[:, 1].astype(int) + h, w = mask.shape[:2] + valid_indices = (pixel_x >= 0) & (pixel_x < w) & (pixel_y >= 0) & (pixel_y < h) + mask_colors = mask[pixel_y[valid_indices], pixel_x[valid_indices]] + selected_points_indices = np.where((mask_colors == display_table_mask_label).all(axis=-1))[0] + selected_points_indices = np.where(valid_indices)[0][selected_points_indices] + return selected_points_indices + +def save_scene_data(root, scene, file_type="npy"): + + ''' configuration ''' + target_mask_label = (0, 255, 0, 255) + display_table_mask_label=(0, 0, 255, 255) + random_downsample_N = 32768 + voxel_size=0.002 + filter_degree = 90 + min_z = 0.25 + max_z = 0.5 + + ''' scan points ''' + display_table_info = DataLoadUtil.get_display_table_info(root, scene) + # radius = display_table_info["radius"] #FIXME + radius = 0.25 + + scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius)) + + ''' read frame data(depth|mask|normal) ''' + frame_num = DataLoadUtil.get_scene_seq_length(root, scene) + for frame_id in range(frame_num): + Log.info(f"frame({frame_id}/{frame_num})]Processing {scene} frame {frame_id}") + path = DataLoadUtil.get_path(root, scene, frame_id) + cam_info = DataLoadUtil.load_cam_info(path, binocular=True) + depth_L, depth_R = DataLoadUtil.load_depth( + path, cam_info["near_plane"], + cam_info["far_plane"], + binocular=True + ) + mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True) + normal_L = DataLoadUtil.load_normal(path, binocular=True, left_only=True) + + ''' target points ''' + mask_img_L = mask_L + mask_img_R = mask_R + + target_mask_img_L = (mask_L == target_mask_label).all(axis=-1) + target_mask_img_R = (mask_R == target_mask_label).all(axis=-1) + + + sampled_target_points_L, sampled_target_normals_L = get_world_points_and_normals(depth_L, normal_L, target_mask_img_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"], random_downsample_N) + sampled_target_points_R = get_world_points(depth_R, target_mask_img_R, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"], random_downsample_N) + + + has_points = sampled_target_points_L.shape[0] > 0 and sampled_target_points_R.shape[0] > 0 + if has_points: + target_points, overlap_idx = PtsUtil.get_overlapping_points( + sampled_target_points_L, sampled_target_points_R, voxel_size, require_idx=True + ) + target_normals = sampled_target_normals_L[overlap_idx] + + if has_points: + has_points = target_points.shape[0] > 0 + + if has_points: + target_points, target_normals = PtsUtil.filter_points( + target_points, target_normals, cam_info["cam_to_world"], theta_limit = filter_degree, z_range=(min_z, max_z) + ) + + + ''' scan points indices ''' + # scan_points_indices_L = get_scan_points_indices(scan_points, mask_img_L, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"]) + # scan_points_indices_R = get_scan_points_indices(scan_points, mask_img_R, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"]) + # scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R) + + if not has_points: + target_points = np.zeros((0, 3)) + target_normals = np.zeros((0, 3)) + + save_target_points(root, scene, frame_id, target_points, file_type=file_type) + #save_scan_points_indices(root, scene, frame_id, scan_points_indices, file_type=file_type) + + save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess + +# def process_frame(frame_id, root, scene, scan_points, file_type, target_mask_label, display_table_mask_label, random_downsample_N, voxel_size, filter_degree, min_z, max_z): +# Log.info(f"[frame({frame_id})]Processing {scene} frame {frame_id}") +# path = DataLoadUtil.get_path(root, scene, frame_id) +# cam_info = DataLoadUtil.load_cam_info(path, binocular=True) +# depth_L, depth_R = DataLoadUtil.load_depth( +# path, cam_info["near_plane"], +# cam_info["far_plane"], +# binocular=True +# ) +# mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True) + +# target_mask_img_L = (mask_L == target_mask_label).all(axis=-1) +# target_mask_img_R = (mask_R == target_mask_label).all(axis=-1) + +# sampled_target_points_L = get_world_points(depth_L, target_mask_img_L, cam_info["cam_intrinsic"], cam_info["cam_to_world"], random_downsample_N) +# sampled_target_points_R = get_world_points(depth_R, target_mask_img_R, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"], random_downsample_N) + +# has_points = sampled_target_points_L.shape[0] > 0 and sampled_target_points_R.shape[0] > 0 +# target_points = np.zeros((0, 3)) + +# if has_points: +# target_points = PtsUtil.get_overlapping_points(sampled_target_points_L, sampled_target_points_R, voxel_size) + +# if has_points and target_points.shape[0] > 0: +# points_normals = DataLoadUtil.load_points_normals(root, scene, display_table_as_world_space_origin=True) +# target_points = PtsUtil.filter_points( +# target_points, points_normals, cam_info["cam_to_world"], voxel_size=0.002, theta=filter_degree, z_range=(min_z, max_z) +# ) + +# scan_points_indices_L = get_scan_points_indices(scan_points, mask_L, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world"]) +# scan_points_indices_R = get_scan_points_indices(scan_points, mask_R, display_table_mask_label, cam_info["cam_intrinsic"], cam_info["cam_to_world_R"]) +# scan_points_indices = np.intersect1d(scan_points_indices_L, scan_points_indices_R) + +# save_target_points(root, scene, frame_id, target_points, file_type=file_type) +# save_scan_points_indices(root, scene, frame_id, scan_points_indices, file_type=file_type) + +# def save_scene_data_multithread(root, scene, file_type="txt"): +# target_mask_label = (0, 255, 0, 255) +# display_table_mask_label = (0, 0, 255, 255) +# random_downsample_N = 32768 +# voxel_size = 0.002 +# filter_degree = 75 +# min_z = 0.2 +# max_z = 0.5 + +# display_table_info = DataLoadUtil.get_display_table_info(root, scene) +# radius = display_table_info["radius"] +# scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0, display_table_radius=radius)) +# frame_num = DataLoadUtil.get_scene_seq_length(root, scene) + +# with ThreadPoolExecutor() as executor: +# futures = {executor.submit(process_frame, frame_id, root, scene, scan_points, file_type, target_mask_label, display_table_mask_label, random_downsample_N, voxel_size, filter_degree, min_z, max_z): frame_id for frame_id in range(frame_num)} + +# for future in as_completed(futures): +# frame_id = futures[future] +# try: +# future.result() +# except Exception as e: +# Log.error(f"Error processing frame {frame_id}: {e}") + +# save_scan_points(root, scene, scan_points) # The "done" flag of scene preprocess + + + +if __name__ == "__main__": + #root = "/media/hofee/repository/new_data_with_normal" + root = r"/home/yan20/nbv_rec/project/franka_control/temp_debug" + # list_path = r"/media/hofee/repository/full_list.txt" + # scene_list = [] + + # with open(list_path, "r") as f: + # for line in f: + # scene_list.append(line.strip()) + scene_list = os.listdir(root) + from_idx = 0 # 1000 + to_idx = 1 # 1500 + + + cnt = 0 + import time + total = to_idx - from_idx + for scene in scene_list[from_idx:to_idx]: + start = time.time() + save_scene_data(root, scene, file_type="npy") + cnt+=1 + end = time.time() + print(f"Time cost: {end-start}") diff --git a/utils/pts_util.py b/utils/pts_util.py new file mode 100644 index 0000000..71d4727 --- /dev/null +++ b/utils/pts_util.py @@ -0,0 +1,308 @@ +import numpy as np +import open3d as o3d +import torch +import trimesh +from scipy.spatial import cKDTree +from utils.pose_util import PoseUtil + + +class PtsUtil: + + @staticmethod + def voxel_downsample_point_cloud_old(point_cloud, voxel_size=0.005): + o3d_pc = o3d.geometry.PointCloud() + o3d_pc.points = o3d.utility.Vector3dVector(point_cloud) + downsampled_pc = o3d_pc.voxel_down_sample(voxel_size) + return np.asarray(downsampled_pc.points) + + @staticmethod + def voxel_downsample_point_cloud(point_cloud, voxel_size=0.005, require_idx=False): + voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32) + if require_idx: + _, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True) + idx_sort = np.argsort(inverse) + idx_unique = idx_sort[np.cumsum(counts)-counts] + downsampled_points = point_cloud[idx_unique] + return downsampled_points, idx_unique + else: + unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True) + return unique_voxels[0]*voxel_size + + @staticmethod + def random_downsample_point_cloud(point_cloud, num_points, require_idx=False): + if point_cloud.shape[0] == 0: + if require_idx: + return point_cloud, np.array([]) + return point_cloud + idx = np.random.choice(len(point_cloud), num_points, replace=True) + if require_idx: + return point_cloud[idx], idx + return point_cloud[idx] + + @staticmethod + def fps_downsample_point_cloud(point_cloud, num_points, require_idx=False): + N = point_cloud.shape[0] + mask = np.zeros(N, dtype=bool) + + sampled_indices = np.zeros(num_points, dtype=int) + sampled_indices[0] = np.random.randint(0, N) + distances = np.linalg.norm( + point_cloud - point_cloud[sampled_indices[0]], axis=1 + ) + for i in range(1, num_points): + farthest_index = np.argmax(distances) + sampled_indices[i] = farthest_index + mask[farthest_index] = True + + new_distances = np.linalg.norm( + point_cloud - point_cloud[farthest_index], axis=1 + ) + distances = np.minimum(distances, new_distances) + + sampled_points = point_cloud[sampled_indices] + if require_idx: + return sampled_points, sampled_indices + return sampled_points + + @staticmethod + def random_downsample_point_cloud_tensor(point_cloud, num_points): + idx = torch.randint(0, len(point_cloud), (num_points,)) + return point_cloud[idx] + + @staticmethod + def voxelize_points(points, voxel_size): + voxel_indices = np.floor(points / voxel_size).astype(np.int32) + unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True) + return unique_voxels + + @staticmethod + def transform_point_cloud(points, pose_mat): + points_h = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) + points_h = np.dot(pose_mat, points_h.T).T + return points_h[:, :3] + + @staticmethod + def get_overlapping_points( + point_cloud_L, point_cloud_R, voxel_size=0.005, require_idx=False + ): + voxels_L, indices_L = PtsUtil.voxelize_points(point_cloud_L, voxel_size) + voxels_R, _ = PtsUtil.voxelize_points(point_cloud_R, voxel_size) + + voxel_indices_L = voxels_L.view([("", voxels_L.dtype)] * 3) + voxel_indices_R = voxels_R.view([("", voxels_R.dtype)] * 3) + overlapping_voxels = np.intersect1d(voxel_indices_L, voxel_indices_R) + mask_L = np.isin( + indices_L, np.where(np.isin(voxel_indices_L, overlapping_voxels))[0] + ) + overlapping_points = point_cloud_L[mask_L] + if require_idx: + return overlapping_points, mask_L + return overlapping_points + + @staticmethod + def filter_points(points, normals, cam_pose, theta_limit=45, z_range=(0.2, 0.45)): + + """ filter with normal """ + normals_normalized = normals / np.linalg.norm(normals, axis=1, keepdims=True) + cos_theta = np.dot(normals_normalized, np.array([0, 0, 1])) + theta = np.arccos(cos_theta) * 180 / np.pi + idx = theta < theta_limit + filtered_sampled_points = points[idx] + filtered_normals = normals[idx] + + """ filter with z range """ + points_cam = PtsUtil.transform_point_cloud(filtered_sampled_points, np.linalg.inv(cam_pose)) + idx = (points_cam[:, 2] > z_range[0]) & (points_cam[:, 2] < z_range[1]) + z_filtered_points = filtered_sampled_points[idx] + z_filtered_normals = filtered_normals[idx] + return z_filtered_points[:, :3], z_filtered_normals + + + @staticmethod + def multi_scale_icp( + source, target, voxel_size_range, init_transformation=None, steps=20 + ): + pipreg = o3d.pipelines.registration + + if init_transformation is not None: + current_transformation = init_transformation + else: + current_transformation = np.identity(4) + cnt = 0 + best_score = 1e10 + best_reg = None + voxel_sizes = [] + for i in range(steps): + voxel_sizes.append( + voxel_size_range[0] + + i * (voxel_size_range[1] - voxel_size_range[0]) / steps + ) + + for voxel_size in voxel_sizes: + radius_normal = voxel_size * 2 + source_downsampled = source.voxel_down_sample(voxel_size) + source_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + target_downsampled = target.voxel_down_sample(voxel_size) + target_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + + reg_icp = pipreg.registration_icp( + source_downsampled, + target_downsampled, + voxel_size * 2, + current_transformation, + pipreg.TransformationEstimationPointToPlane(), + pipreg.ICPConvergenceCriteria(max_iteration=500), + ) + cnt += 1 + if reg_icp.fitness == 0: + score = 1e10 + else: + score = reg_icp.inlier_rmse / reg_icp.fitness + + if score < best_score: + best_score = score + best_reg = reg_icp + + return best_reg, best_score + + @staticmethod + def multi_scale_ransac(source_downsampled, target_downsampled, source_fpfh, model_fpfh, voxel_size_range, steps=20): + pipreg = o3d.pipelines.registration + cnt = 0 + best_score = 1e10 + best_reg = None + voxel_sizes = [] + for i in range(steps): + voxel_sizes.append( + voxel_size_range[0] + + i * (voxel_size_range[1] - voxel_size_range[0]) / steps + ) + + for voxel_size in voxel_sizes: + reg_ransac = pipreg.registration_ransac_based_on_feature_matching( + source_downsampled, + target_downsampled, + source_fpfh, + model_fpfh, + mutual_filter=True, + max_correspondence_distance=voxel_size*2, + estimation_method=pipreg.TransformationEstimationPointToPoint(False), + ransac_n=4, + checkers=[pipreg.CorrespondenceCheckerBasedOnEdgeLength(0.9)], + criteria=pipreg.RANSACConvergenceCriteria(8000000, 500), + ) + cnt += 1 + if reg_ransac.fitness == 0: + score = 1e10 + else: + score = reg_ransac.inlier_rmse / reg_ransac.fitness + if score < best_score: + best_score = score + best_reg = reg_ransac + + return best_reg, best_score + + @staticmethod + def register(pcl: np.ndarray, model: trimesh.Trimesh, voxel_size=0.01): + radius_normal = voxel_size * 2 + pipreg = o3d.pipelines.registration + model_pcd = o3d.geometry.PointCloud() + model_pcd.points = o3d.utility.Vector3dVector(model.vertices) + model_downsampled = model_pcd.voxel_down_sample(voxel_size) + model_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + model_fpfh = pipreg.compute_fpfh_feature( + model_downsampled, + o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=100), + ) + + source_pcd = o3d.geometry.PointCloud() + source_pcd.points = o3d.utility.Vector3dVector(pcl) + source_downsampled = source_pcd.voxel_down_sample(voxel_size) + source_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + source_fpfh = pipreg.compute_fpfh_feature( + source_downsampled, + o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=100), + ) + + reg_ransac, ransac_best_score = PtsUtil.multi_scale_ransac( + source_downsampled, + model_downsampled, + source_fpfh, + model_fpfh, + voxel_size_range=(0.03, 0.005), + steps=3, + ) + reg_icp, icp_best_score = PtsUtil.multi_scale_icp( + source_downsampled, + model_downsampled, + voxel_size_range=(0.02, 0.001), + init_transformation=reg_ransac.transformation, + steps=50, + ) + return reg_icp.transformation + + @staticmethod + def register_fine(pcl: np.ndarray, model: trimesh.Trimesh, voxel_size=0.01): + radius_normal = voxel_size * 2 + pipreg = o3d.pipelines.registration + model_pcd = o3d.geometry.PointCloud() + model_pcd.points = o3d.utility.Vector3dVector(model.vertices) + model_downsampled = model_pcd.voxel_down_sample(voxel_size) + model_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + source_pcd = o3d.geometry.PointCloud() + source_pcd.points = o3d.utility.Vector3dVector(pcl) + source_downsampled = source_pcd.voxel_down_sample(voxel_size) + source_downsampled.estimate_normals( + search_param=o3d.geometry.KDTreeSearchParamHybrid( + radius=radius_normal, max_nn=30 + ) + ) + reg_icp, icp_best_score = PtsUtil.multi_scale_icp( + source_downsampled, + model_downsampled, + voxel_size_range=(0.02, 0.001), + init_transformation=np.eye(4), + steps=50, + ) + return reg_icp.transformation + @staticmethod + def get_pts_from_depth(depth, cam_intrinsic, cam_extrinsic): + h, w = depth.shape + i, j = np.meshgrid(np.arange(w), np.arange(h), indexing="xy") + + z = depth + x = (i - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + mask = mask.reshape(-1, 4) + points_camera = np.concatenate( + [points_camera, np.ones((points_camera.shape[0], 1))], axis=-1 + ) + + points_world = np.dot(cam_extrinsic, points_camera.T).T[:, :3] + data = { + "points_world": points_world, + "points_camera": points_camera, + } + return data \ No newline at end of file diff --git a/utils/reconstruction_util.py b/utils/reconstruction_util.py new file mode 100644 index 0000000..b4ea984 --- /dev/null +++ b/utils/reconstruction_util.py @@ -0,0 +1,205 @@ +import numpy as np +from scipy.spatial import cKDTree +from utils.pts_util import PtsUtil + +class ReconstructionUtil: + + @staticmethod + def compute_coverage_rate(target_point_cloud, combined_point_cloud, threshold=0.01): + kdtree = cKDTree(combined_point_cloud) + distances, _ = kdtree.query(target_point_cloud) + covered_points_num = np.sum(distances < threshold*2) + coverage_rate = covered_points_num / target_point_cloud.shape[0] + return coverage_rate, covered_points_num + + @staticmethod + def check_overlap(new_point_cloud, combined_point_cloud, overlap_area_threshold=25, voxel_size=0.01): + kdtree = cKDTree(combined_point_cloud) + distances, _ = kdtree.query(new_point_cloud) + overlapping_points = np.sum(distances < voxel_size*2) + cm = 0.01 + voxel_size_cm = voxel_size / cm + overlap_area = overlapping_points * voxel_size_cm * voxel_size_cm + return overlap_area > overlap_area_threshold + + + @staticmethod + def get_new_added_points(old_combined_pts, new_pts, threshold=0.005): + if old_combined_pts.size == 0: + return new_pts + if new_pts.size == 0: + return np.array([]) + + tree = cKDTree(old_combined_pts) + distances, _ = tree.query(new_pts, k=1) + new_added_points = new_pts[distances > threshold] + return new_added_points + + @staticmethod + def compute_next_best_view_sequence_with_overlap(target_point_cloud, point_cloud_list, scan_points_indices_list, threshold=0.01, soft_overlap_threshold=0.5, hard_overlap_threshold=0.7, init_view = 0, scan_points_threshold=5, status_info=None): + selected_views = [init_view] + combined_point_cloud = point_cloud_list[init_view] + history_indices = [scan_points_indices_list[init_view]] + + max_rec_pts = np.vstack(point_cloud_list) + downsampled_max_rec_pts = PtsUtil.voxel_downsample_point_cloud(max_rec_pts, threshold) + + max_rec_pts_num = downsampled_max_rec_pts.shape[0] + max_real_rec_pts_coverage, _ = ReconstructionUtil.compute_coverage_rate(target_point_cloud, downsampled_max_rec_pts, threshold) + + new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, combined_point_cloud, threshold) + current_coverage = new_coverage + current_covered_num = new_covered_num + + remaining_views = list(range(len(point_cloud_list))) + view_sequence = [(init_view, current_coverage)] + cnt_processed_view = 0 + remaining_views.remove(init_view) + curr_rec_pts_num = combined_point_cloud.shape[0] + + while remaining_views: + best_view = None + best_coverage_increase = -1 + best_combined_point_cloud = None + best_covered_num = 0 + + for view_index in remaining_views: + if point_cloud_list[view_index].shape[0] == 0: + continue + if selected_views: + new_scan_points_indices = scan_points_indices_list[view_index] + if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold): + overlap_threshold = hard_overlap_threshold + else: + overlap_threshold = soft_overlap_threshold + overlap_rate = ReconstructionUtil.compute_overlap_rate(point_cloud_list[view_index],combined_point_cloud, threshold) + if overlap_rate < overlap_threshold: + continue + new_combined_point_cloud = np.vstack([combined_point_cloud, point_cloud_list[view_index]]) + new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold) + new_coverage, new_covered_num = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold) + + coverage_increase = new_coverage - current_coverage + if coverage_increase > best_coverage_increase: + best_coverage_increase = coverage_increase + best_view = view_index + best_covered_num = new_covered_num + best_combined_point_cloud = new_downsampled_combined_point_cloud + + + if best_view is not None: + if best_coverage_increase <=1e-3 or best_covered_num - current_covered_num <= 5: + break + + selected_views.append(best_view) + best_rec_pts_num = best_combined_point_cloud.shape[0] + print(f"Current rec pts num: {curr_rec_pts_num}, Best rec pts num: {best_rec_pts_num}, Best cover pts: {best_covered_num}, Max rec pts num: {max_rec_pts_num}") + print(f"Current coverage: {current_coverage}, Best coverage increase: {best_coverage_increase}, Max Real coverage: {max_real_rec_pts_coverage}") + current_covered_num = best_covered_num + curr_rec_pts_num = best_rec_pts_num + combined_point_cloud = best_combined_point_cloud + remaining_views.remove(best_view) + history_indices.append(scan_points_indices_list[best_view]) + current_coverage += best_coverage_increase + cnt_processed_view += 1 + if status_info is not None: + sm = status_info["status_manager"] + app_name = status_info["app_name"] + runner_name = status_info["runner_name"] + sm.set_status(app_name, runner_name, "current coverage", current_coverage) + sm.set_progress(app_name, runner_name, "processed view", cnt_processed_view, len(point_cloud_list)) + + view_sequence.append((best_view, current_coverage)) + + else: + break + if status_info is not None: + sm = status_info["status_manager"] + app_name = status_info["app_name"] + runner_name = status_info["runner_name"] + sm.set_progress(app_name, runner_name, "processed view", len(point_cloud_list), len(point_cloud_list)) + return view_sequence, remaining_views, combined_point_cloud + + @staticmethod + def compute_next_best_view_with_overlap(scanned_pts, point_cloud_list, selected_view, history_indices, scan_points_indices_list, threshold=0.01, overlap_area_threshold=25, scan_points_threshold=5): + max_rec_pts = np.vstack(point_cloud_list) + downsampled_max_rec_pts = PtsUtil.voxel_downsample_point_cloud(max_rec_pts, threshold) + best_view = None + new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(scanned_pts,threshold) + best_coverage,_ = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold) + best_covered_num = new_downsampled_combined_point_cloud.shape[0] + #import ipdb; ipdb.set_trace() + for view in range(len(point_cloud_list)): + print(f"Processing view {view}/{len(point_cloud_list)}: {best_coverage},{best_covered_num}") + if point_cloud_list[view].shape[0] == 0: + continue + if view in selected_view: + continue + + new_scan_points_indices = scan_points_indices_list[view] + + # if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold): + # curr_overlap_area_threshold = overlap_area_threshold + # else: + # curr_overlap_area_threshold = overlap_area_threshold * 0.8 + + # if not ReconstructionUtil.check_overlap(point_cloud_list[view], scanned_pts, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=threshold): + # continue + + + new_combined_point_cloud = np.vstack([scanned_pts ,point_cloud_list[view]]) + new_downsampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(new_combined_point_cloud,threshold) + + new_coverage, _ = ReconstructionUtil.compute_coverage_rate(downsampled_max_rec_pts, new_downsampled_combined_point_cloud, threshold) + new_covered_num = new_downsampled_combined_point_cloud.shape[0] + print("the new coverage_rate: ", new_coverage, " the new covered num: ", new_covered_num, " the fake covered num:" , _) + if new_coverage > best_coverage : + #import ipdb; ipdb.set_trace() + best_coverage = new_coverage + best_covered_num = new_covered_num + best_view = view + + return best_view, best_coverage, best_covered_num + + @staticmethod + def generate_scan_points(display_table_top, display_table_radius, min_distance=0.03, max_points_num = 500, max_attempts = 1000): + points = [] + attempts = 0 + while len(points) < max_points_num and attempts < max_attempts: + angle = np.random.uniform(0, 2 * np.pi) + r = np.random.uniform(0, display_table_radius) + x = r * np.cos(angle) + y = r * np.sin(angle) + z = display_table_top + new_point = (x, y, z) + if all(np.linalg.norm(np.array(new_point) - np.array(existing_point)) >= min_distance for existing_point in points): + points.append(new_point) + attempts += 1 + return points + + @staticmethod + def compute_covered_scan_points(scan_points, point_cloud, threshold=0.01): + + tree = cKDTree(point_cloud) + covered_points = [] + indices = [] + for i, scan_point in enumerate(scan_points): + if tree.query_ball_point(scan_point, threshold): + covered_points.append(scan_point) + indices.append(i) + return covered_points, indices + + @staticmethod + def check_scan_points_overlap(history_indices, indices2, threshold=5): + try: + if len(indices2) == 0: + return False + for indices1 in history_indices: + if len(set(indices1).intersection(set(indices2))) >= threshold: + return True + except Exception as e: + print(e) + import ipdb; ipdb.set_trace() + return False + + \ No newline at end of file diff --git a/utils/render_util.py b/utils/render_util.py new file mode 100644 index 0000000..c17814c --- /dev/null +++ b/utils/render_util.py @@ -0,0 +1,45 @@ + +import os +import json +import subprocess +import tempfile +import shutil +from utils.data_load import DataLoadUtil +from utils.pts_util import PtsUtil +class RenderUtil: + + @staticmethod + def render_pts(cam_pose, object_name, 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 = DataLoadUtil.get_real_cam_O_from_cam_L(cam_pose, nO_to_nL_pose, scene_path=scene_path) + with tempfile.TemporaryDirectory() as temp_dir: + params = { + "cam_pose": nO_to_world_pose.tolist(), + "object_name": 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) + + filtered_point_cloud = PtsUtil.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 = PtsUtil.get_overlapping_points(point_cloud_L, point_cloud_R) + + + return filtered_point_cloud, full_scene_point_cloud \ No newline at end of file diff --git a/utils/view_util.py b/utils/view_util.py new file mode 100644 index 0000000..c3e349a --- /dev/null +++ b/utils/view_util.py @@ -0,0 +1,132 @@ +import numpy as np +from scipy.spatial.transform import Rotation as R +from dataclasses import dataclass + + +@dataclass +class CameraIntrinsics: + width: int + height: int + fx: float + fy: float + cx: float + cy: float + + @property + def intrinsic_matrix(self): + return np.array([[self.fx, 0, self.cx], [0, self.fy, self.cy], [0, 0, 1]]) + + +@dataclass +class CameraExtrinsics: + def __init__(self, rotation: np.ndarray, translation: np.ndarray, rot_type: str): + """ + rotation: 3x3 rotation matrix or 1x3 euler angles or 1x4 quaternion + translation: 1x3 or 3x1 translation vector + rot_type: "mat", "euler_xyz", "quat_xyzw" + """ + assert rot_type in ["mat", "euler_xyz", "quat_xyzw"] + if rot_type == "mat": + self._rot = R.from_matrix(rotation) + elif rot_type == "euler_xyz": + self._rot = R.from_euler('xyz', rotation, degrees=True) + elif rot_type == "quat_xyzw": + self._rot = R.from_quat(rotation) + self._translation = translation + + @property + def extrinsic_matrix(self): + return np.vstack([np.hstack([self._rot.as_matrix(), self._translation.reshape(3, 1)]), [0, 0, 0, 1]]) + + @property + def rotation_euler_xyz(self): + return self._rot.as_euler('xyz', degrees=True) + + @property + def rotation_quat_xyzw(self): + return self._rot.as_quat() + + @property + def rotation_matrix(self): + return self._rot.as_matrix() + + @property + def translation(self): + return self._translation + + +@dataclass +class CameraData: + def __init__(self, depth_image: np.ndarray, image_id: int, intrinsics: CameraIntrinsics, extrinsics: CameraExtrinsics): + self._depth_image = depth_image + self._image_id = image_id + self._intrinsics = intrinsics + self._extrinsics = extrinsics + + @property + def depth_image(self): + return self._depth_image + + @property + def image_id(self): + return self._image_id + + @property + def intrinsics(self): + return self._intrinsics.intrinsic_matrix + + @property + def extrinsics(self): + return self._extrinsics.extrinsic_matrix + + @property + def projection_matrix(self): + return self.intrinsics @ self.extrinsics[:3, :4] + + @property + def pts_camera(self): + height, width = self.depth_image.shape + v, u = np.indices((height, width)) + points = np.vstack([u.flatten(), v.flatten(), np.ones_like(u.flatten())]) # 3xN + points = np.linalg.inv(self.intrinsics) @ points # 3xN + points = points.T # Nx3 + points = points * self.depth_image.flatten()[:, np.newaxis] # Nx3 + points = points[points[:, 2] > 0] # Nx3 + return points + + @property + def pts_world(self): + homogeneous_pts = np.hstack([self.pts_camera, np.ones((self.pts_camera.shape[0], 1))]) # Nx4 + pts_world = self.extrinsics @ homogeneous_pts.T # 4xN + return pts_world[:3, :].T + +class ViewUtil: + def get_pts(view_data): + image_id = view_data["image_id"] + depth_intrinsics = view_data["depth_intrinsics"] + depth_extrinsics = view_data["depth_extrinsics"] + depth_image = np.array(view_data["depth_image"], dtype=np.uint16) + if image_id is None: + return None + else: + camera_intrinsics = CameraIntrinsics( + depth_intrinsics['width'], + depth_intrinsics['height'], + depth_intrinsics['fx'], + depth_intrinsics['fy'], + depth_intrinsics['cx'], + depth_intrinsics['cy'] + ) + camera_extrinsics = CameraExtrinsics( + depth_extrinsics[:3, :3], + depth_extrinsics[:3, 3], + rot_type="mat" + ) + camera_data = CameraData(depth_image, image_id, camera_intrinsics, camera_extrinsics) + pts = camera_data.pts_world + return pts/1000 + + def get_camera_pose(view_data, translation_scale=0.001): + depth_extrinsics = view_data["depth_extrinsics"] + depth_extrinsics[:3, 3] *= translation_scale + return depth_extrinsics \ No newline at end of file diff --git a/utils/vis.py b/utils/vis.py new file mode 100644 index 0000000..4641e4f --- /dev/null +++ b/utils/vis.py @@ -0,0 +1,172 @@ +import numpy as np +import matplotlib.pyplot as plt +import sys +import os +import trimesh + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from utils.data_load import DataLoadUtil +from utils.pts_util import PtsUtil + +class visualizeUtil: + + @staticmethod + def save_all_cam_pos_and_cam_axis(root, scene, output_dir): + length = DataLoadUtil.get_scene_seq_length(root, scene) + all_cam_pos = [] + all_cam_axis = [] + for i in range(length): + path = DataLoadUtil.get_path(root, scene, i) + cam_info = DataLoadUtil.load_cam_info(path, binocular=True) + cam_pose = cam_info["cam_to_world"] + cam_pos = cam_pose[:3, 3] + cam_axis = cam_pose[:3, 2] + + num_samples = 10 + sample_points = [cam_pos + 0.02*t * cam_axis for t in range(num_samples)] + sample_points = np.array(sample_points) + + all_cam_pos.append(cam_pos) + all_cam_axis.append(sample_points) + + all_cam_pos = np.array(all_cam_pos) + all_cam_axis = np.array(all_cam_axis).reshape(-1, 3) + np.savetxt(os.path.join(output_dir, "all_cam_pos.txt"), all_cam_pos) + np.savetxt(os.path.join(output_dir, "all_cam_axis.txt"), all_cam_axis) + + @staticmethod + def save_all_combined_pts(root, scene, output_dir): + length = DataLoadUtil.get_scene_seq_length(root, scene) + all_combined_pts = [] + for i in range(length): + path = DataLoadUtil.get_path(root, scene, i) + pts = DataLoadUtil.load_from_preprocessed_pts(path,"npy") + if pts.shape[0] == 0: + continue + all_combined_pts.append(pts) + all_combined_pts = np.vstack(all_combined_pts) + downsampled_all_pts = PtsUtil.voxel_downsample_point_cloud(all_combined_pts, 0.001) + np.savetxt(os.path.join(output_dir, "all_combined_pts.txt"), downsampled_all_pts) + + @staticmethod + def save_target_mesh_at_world_space( + root, model_dir, scene_name, display_table_as_world_space_origin=True + ): + scene_info = DataLoadUtil.load_scene_info(root, scene_name) + target_name = scene_info["target_name"] + transformation = scene_info[target_name] + if display_table_as_world_space_origin: + location = transformation["location"] - DataLoadUtil.get_display_table_top( + root, scene_name + ) + else: + location = transformation["location"] + rotation_euler = transformation["rotation_euler"] + pose_mat = trimesh.transformations.euler_matrix(*rotation_euler) + pose_mat[:3, 3] = location + + mesh = DataLoadUtil.load_mesh_at(model_dir, target_name, pose_mat) + mesh_dir = os.path.join(root, scene_name, "mesh") + if not os.path.exists(mesh_dir): + os.makedirs(mesh_dir) + model_path = os.path.join(mesh_dir, "world_target_mesh.obj") + mesh.export(model_path) + + @staticmethod + def save_points_and_normals(root, scene, frame_idx, output_dir, binocular=False): + target_mask_label = (0, 255, 0, 255) + path = DataLoadUtil.get_path(root, scene, frame_idx) + cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular, display_table_as_world_space_origin=False) + depth = DataLoadUtil.load_depth( + path, cam_info["near_plane"], + cam_info["far_plane"], + binocular=binocular, + ) + if isinstance(depth, tuple): + depth = depth[0] + + mask = DataLoadUtil.load_seg(path, binocular=binocular, left_only=True) + normal = DataLoadUtil.load_normal(path, binocular=binocular, left_only=True) + ''' target points ''' + if mask is None: + target_mask_img = np.ones_like(depth, dtype=bool) + else: + target_mask_img = (mask == target_mask_label).all(axis=-1) + cam_intrinsic = cam_info["cam_intrinsic"] + z = depth[target_mask_img] + i, j = np.nonzero(target_mask_img) + x = (j - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0] + y = (i - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1] + + random_downsample_N = 1000 + + points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3) + normal_camera = normal[target_mask_img].reshape(-1, 3) + sampled_target_points, idx = PtsUtil.random_downsample_point_cloud( + points_camera, random_downsample_N, require_idx=True + ) + if len(sampled_target_points) == 0: + print("No target points") + + + sampled_normal_camera = normal_camera[idx] + sampled_visualized_normal = [] + sampled_normal_camera[:, 2] = -sampled_normal_camera[:, 2] + sampled_normal_camera[:, 1] = -sampled_normal_camera[:, 1] + num_samples = 10 + for i in range(len(sampled_target_points)): + sampled_visualized_normal.append([sampled_target_points[i] + 0.02*t * sampled_normal_camera[i] for t in range(num_samples)]) + + sampled_visualized_normal = np.array(sampled_visualized_normal).reshape(-1, 3) + np.savetxt(os.path.join(output_dir, "target_pts.txt"), sampled_target_points) + np.savetxt(os.path.join(output_dir, "target_normal.txt"), sampled_visualized_normal) + + @staticmethod + def visualize_pts_and_camera(pts, camera): + import plotly.graph_objects as go + fig = go.Figure() + if pts is not None: + fig.add_trace(go.Scatter3d( + x=pts[:, 0], y=pts[:, 1], z=pts[:, 2], + mode='markers', marker=dict(size=1, color='gray', opacity=0.5), name='Input Points' + )) + colors = ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance', + 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg'] + + color = colors[0] + origin_candidate = camera[:3, 3] + z_axis_candidate = camera[:3, 2] + fig.add_trace(go.Cone( + x=[origin_candidate[0]], y=[origin_candidate[1]], z=[origin_candidate[2]], + u=[z_axis_candidate[0]], v=[z_axis_candidate[1]], w=[z_axis_candidate[2]], + colorscale=color, + sizemode="absolute", sizeref=0.1, anchor="tail", showscale=False + )) + + fig.update_layout( + title="Clustered Poses and Input Points", + scene=dict( + xaxis_title='X', + yaxis_title='Y', + zaxis_title='Z' + ), + margin=dict(l=0, r=0, b=0, t=40), + scene_camera=dict(eye=dict(x=1.25, y=1.25, z=1.25)) + ) + + fig.show() + + +# ------ Debug ------ + +if __name__ == "__main__": + root = r"/home/yan20/nbv_rec/project/franka_control/output" + model_dir = r"H:\\AI\\Datasets\\scaled_object_box_meshes" + scene = "box_1" + output_dir = f"/home/yan20/nbv_rec/project/franka_control/output/{scene}/visualize" + if not os.path.exists(output_dir): + os.makedirs(output_dir) + visualizeUtil.save_all_cam_pos_and_cam_axis(root, scene, output_dir) + visualizeUtil.save_all_combined_pts(root, scene, output_dir) + #visualizeUtil.save_target_mesh_at_world_space(root, model_dir, scene) + #visualizeUtil.save_points_and_normals(root, scene,"10", output_dir, binocular=True) \ No newline at end of file