diff --git a/database.py b/database.py new file mode 100644 index 0000000..ec7d075 --- /dev/null +++ b/database.py @@ -0,0 +1,119 @@ +# This script is based on an original implementation by True Price. +# Created by liminghao +import sys +import numpy as np +import sqlite3 + +IS_PYTHON3 = sys.version_info[0] >= 3 + +def array_to_blob(array): + if IS_PYTHON3: + return array.tostring() + else: + return np.getbuffer(array) + +def blob_to_array(blob, dtype, shape=(-1,)): + if IS_PYTHON3: + return np.fromstring(blob, dtype=dtype).reshape(*shape) + else: + return np.frombuffer(blob, dtype=dtype).reshape(*shape) + +class COLMAPDatabase(sqlite3.Connection): + + @staticmethod + def connect(database_path): + return sqlite3.connect(database_path, factory=COLMAPDatabase) + + def __init__(self, *args, **kwargs): + super(COLMAPDatabase, self).__init__(*args, **kwargs) + + self.create_tables = lambda: self.executescript(CREATE_ALL) + self.create_cameras_table = \ + lambda: self.executescript(CREATE_CAMERAS_TABLE) + self.create_descriptors_table = \ + lambda: self.executescript(CREATE_DESCRIPTORS_TABLE) + self.create_images_table = \ + lambda: self.executescript(CREATE_IMAGES_TABLE) + self.create_two_view_geometries_table = \ + lambda: self.executescript(CREATE_TWO_VIEW_GEOMETRIES_TABLE) + self.create_keypoints_table = \ + lambda: self.executescript(CREATE_KEYPOINTS_TABLE) + self.create_matches_table = \ + lambda: self.executescript(CREATE_MATCHES_TABLE) + self.create_name_index = lambda: self.executescript(CREATE_NAME_INDEX) + + def update_camera(self, model, width, height, params, camera_id): + params = np.asarray(params, np.float64) + cursor = self.execute( + "UPDATE cameras SET model=?, width=?, height=?, params=?, prior_focal_length=True WHERE camera_id=?", + (model, width, height, array_to_blob(params),camera_id)) + return cursor.lastrowid + +def camTodatabase(): + import os + import argparse + + camModelDict = {'SIMPLE_PINHOLE': 0, + 'PINHOLE': 1, + 'SIMPLE_RADIAL': 2, + 'RADIAL': 3, + 'OPENCV': 4, + 'FULL_OPENCV': 5, + 'SIMPLE_RADIAL_FISHEYE': 6, + 'RADIAL_FISHEYE': 7, + 'OPENCV_FISHEYE': 8, + 'FOV': 9, + 'THIN_PRISM_FISHEYE': 10} + parser = argparse.ArgumentParser() + parser.add_argument("--database_path", type=str, default="database.db") + parser.add_argument("--txt_path", type=str, default="colmap/sparse_cameras.txt") + # breakpoint() + args = parser.parse_args() + if os.path.exists(args.database_path)==False: + print("ERROR: database path dosen't exist -- please check database.db.") + return + # Open the database. + db = COLMAPDatabase.connect(args.database_path) + + idList=list() + modelList=list() + widthList=list() + heightList=list() + paramsList=list() + # Update real cameras from .txt + with open(args.txt_path, "r") as cam: + lines = cam.readlines() + for i in range(0,len(lines),1): + if lines[i][0]!='#': + strLists = lines[i].split() + cameraId=int(strLists[0]) + cameraModel=camModelDict[strLists[1]] #SelectCameraModel + width=int(strLists[2]) + height=int(strLists[3]) + paramstr=np.array(strLists[4:12]) + params = paramstr.astype(np.float64) + idList.append(cameraId) + modelList.append(cameraModel) + widthList.append(width) + heightList.append(height) + paramsList.append(params) + camera_id = db.update_camera(cameraModel, width, height, params, cameraId) + + # Commit the data to the file. + db.commit() + # Read and check cameras. + rows = db.execute("SELECT * FROM cameras") + for i in range(0,len(idList),1): + camera_id, model, width, height, params, prior = next(rows) + params = blob_to_array(params, np.float64) + assert camera_id == idList[i] + assert model == modelList[i] and width == widthList[i] and height == heightList[i] + assert np.allclose(params, paramsList[i]) + + # Close database.db. + db.close() + +if __name__ == "__main__": + import sys,os + + camTodatabase() \ No newline at end of file diff --git a/scene/dataset_readers.py b/scene/dataset_readers.py index 32c23bf..d3161cc 100644 --- a/scene/dataset_readers.py +++ b/scene/dataset_readers.py @@ -332,8 +332,7 @@ def readNerfSyntheticInfo(path, white_background, eval, extension=".png"): print(f"Generating random point cloud ({num_pts})...") # We create random points inside the bounds of the synthetic Blender scenes - # xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 - xyz = np.random.random((num_pts, 3)) * 0.5 - 0.25 + xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 shs = np.random.random((num_pts, 3)) / 255.0 pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))) # storePly(ply_path, xyz, SH2RGB(shs) * 255) @@ -382,20 +381,11 @@ def readHyperDataInfos(datadir,use_bg_points,eval): video_cam_infos = copy.deepcopy(test_cam_infos) video_cam_infos.split="video" - # ply_path = os.path.join(datadir, "points.npy") - - # xyz = np.load(ply_path,allow_pickle=True) - # xyz -= train_cam_infos.scene_center - # xyz *= train_cam_infos.coord_scale - # xyz = xyz.astype(np.float32) - # shs = np.random.random((xyz.shape[0], 3)) / 255.0 - # pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((xyz.shape[0], 3))) + ply_path = os.path.join(datadir, "points3D_downsample.ply") - # ply_path = os.path.join(datadir, "points3D.ply") pcd = fetchPly(ply_path) xyz = np.array(pcd.points) - # xyz -= train_cam_infos.scene_center - # xyz *= train_cam_infos.coord_scale + pcd = pcd._replace(points=xyz) nerf_normalization = getNerfppNorm(train_cam) plot_camera_orientations(train_cam_infos, pcd.points) @@ -433,12 +423,7 @@ def format_render_poses(poses,data_infos): image_path=image_path, image_name=image_name, width=image.shape[2], height=image.shape[1], time = time, mask=None)) return cameras - # plydata = PlyData.read(path) - # vertices = plydata['vertex'] - # positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T - # colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0 - # normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T - # return BasicPointCloud(points=positions, colors=colors, normals=normals) + def add_points(pointsclouds, xyz_min, xyz_max): add_points = (np.random.random((100000, 3)))* (xyz_max-xyz_min) + xyz_min add_points = add_points.astype(np.float32) @@ -478,26 +463,8 @@ def readdynerfInfo(datadir,use_bg_points,eval): eval_index=0, ) train_cam_infos = format_infos(train_dataset,"train") - - # test_cam_infos = format_infos(test_dataset,"test") val_cam_infos = format_render_poses(test_dataset.val_poses,test_dataset) nerf_normalization = getNerfppNorm(train_cam_infos) - # create pcd - # if not os.path.exists(ply_path): - # Since this data set has no colmap data, we start with random points - # num_pts = 2000 - # print(f"Generating random point cloud ({num_pts})...") - # threshold = 3 - # xyz_max = np.array([1.5*threshold, 1.5*threshold, 1.5*threshold]) - # xyz_min = np.array([-1.5*threshold, -1.5*threshold, -3*threshold]) - # xyz_max = np.array([1.5*threshold, 1.5*threshold, 1.5*threshold]) - # xyz_min = np.array([-1.5*threshold, -1.5*threshold, -1.5*threshold]) - # We create random points inside the bounds of the synthetic Blender scenes - # xyz = (np.random.random((num_pts, 3)))* (xyz_max-xyz_min) + xyz_min - # print("point cloud initialization:",xyz.max(axis=0),xyz.min(axis=0)) - # shs = np.random.random((num_pts, 3)) / 255.0 - # pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3))) - # storePly(ply_path, xyz, SH2RGB(shs) * 255) # xyz = np.load pcd = fetchPly(ply_path) @@ -557,12 +524,9 @@ def plot_camera_orientations(cam_list, xyz): # 提取 R 和 T R = cam.R T = cam.T - # print(R,T) - # breakpoint() - # 计算相机朝向（一个单位向量） + direction = R @ np.array([0, 0, 1]) - # 绘制相机位置和朝向 ax.quiver(T[0], T[1], T[2], direction[0], direction[1], direction[2], length=1) ax.set_xlabel('X Axis') diff --git a/scene/gaussian_model.py b/scene/gaussian_model.py index ebd40b5..5225c74 100644 --- a/scene/gaussian_model.py +++ b/scene/gaussian_model.py @@ -88,8 +88,9 @@ class GaussianModel: def restore(self, model_args, training_args): (self.active_sh_degree, self._xyz, + deform_state, self._deformation_table, - self._deformation, + # self.grid, self._features_dc, self._features_rest, @@ -101,6 +102,7 @@ class GaussianModel: denom, opt_dict, self.spatial_lr_scale) = model_args + self._deformation.load_state_dict(deform_state) self.training_setup(training_args) self.xyz_gradient_accum = xyz_gradient_accum self.denom = denom diff --git a/train.py b/train.py index 6c40c43..d2c0384 100644 --- a/train.py +++ b/train.py @@ -45,6 +45,7 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i gaussians.training_setup(opt) if checkpoint: + breakpoint() (model_params, first_iter) = torch.load(checkpoint) gaussians.restore(model_params, opt)