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做钢材销售客户哪里去开发网站,增城低价网站建设,企业网站开发php,手机网银怎么开通总览 输入#xff1a;环绕Object拍摄的RGB视频 输出#xff1a;自定义相机路径的渲染视频#xff08;包含渐变效果#xff09; 实现过程 首先#xff0c;编译3DGS的C代码#xff0c;并跑通convert.py、train.py和render.py。教程如下#xff1a; github网址#xf…总览 输入环绕Object拍摄的RGB视频 输出自定义相机路径的渲染视频包含渐变效果 实现过程 首先编译3DGS的C代码并跑通convert.py、train.py和render.py。教程如下 github网址https://github.com/graphdeco-inria/gaussian-splatting新手教程https://www.youtube.com/watch?vUXtuigy_wYc训练自己的视频数据-教程https://www.youtube.com/watch?vwuKhEGCt6ks 在掌握训练自己的视频后可以生成一组input图像对应的render图像但点云和参数都是固定的如 渲染的scaling_modifier参数固定为1.0。渲染时使用的点云 始终是train得到的完整点云。 因此我们为了得到上面视频中的渐变效果需要调整这两个地方scaling_modifier参数和点云采样。 1 调整scaling_modifier参数 修改render.py中的调用的render函数向里面传入scaling_modifier参数。

对每一帧进行渲染

for idx, view in enumerate(tqdm(views, descRendering progress)):rendering render(view, gaussians, pipeline, background, scaling_modifierscaling_modifier)[render]进入该render函数将scaling_modifier传入GaussianRasterizationSettings方法中。 def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier 1.0, override_color None):# Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) meansscreenspace_points torch.zeros_like(pc.get_xyz, dtypepc.get_xyz.dtype, requires_gradTrue, devicecuda) 0try:screenspace_points.retain_grad()except:pass# Set up rasterization configurationtanfovx math.tan(viewpoint_camera.FoVx * 0.5)tanfovy math.tan(viewpoint_camera.FoVy * 0.5)raster_settings GaussianRasterizationSettings(image_heightint(viewpoint_camera.image_height),image_widthint(viewpoint_camera.image_width),tanfovxtanfovx,tanfovytanfovy,bgbg_color,scale_modifierscaling_modifier,viewmatrixviewpoint_camera.world_view_transform,projmatrixviewpoint_camera.full_proj_transform,sh_degreepc.active_sh_degree,camposviewpoint_camera.camera_center,prefilteredFalse,debugpipe.debug)通过上面的方式即可对每一帧在不同的scaling_modifier下进行渲染该参数在SIBR Viewer中也可以修改修改位置如下 如下左图为scaling_modifier0.01、右图为scaling_modifier1.0
2 点云采样 为了实现视频一开始由中心物体向四周扩散的渐变效果我们需要通过点云采样的方式实现点云数量渐变式增多 具体步骤如下 计算原始点云中所有点的密度大小。以密度最大的点作为中心点计算每个点到该点的距离得到升序排序后的索引。根据该索引生成渐变式的点云。 对应在render.py中添加如下代码 def get_indices(model_path, iteration):path os.path.join(model_path, pointcloud, iteration str(iteration), point_cloud.ply)plydata PlyData.read(path)xyz np.stack((np.asarray(plydata[vertex][x]),np.asarray(plydata[vertex][y]),np.asarray(plydata[vertex][z])), axis1)# 定义邻域半径neighbor_radius 0.1 # 例如这里假设邻域半径为0.1# 使用最近邻算法查找每个点的邻域内的点的数量nbrs NearestNeighbors(radiusneighbor_radius, algorithmauto).fit(xyz)densities nbrs.radius_neighbors(xyz, return_distanceFalse)# 使用最近邻算法查找每个点的邻域内的点的数量nbrs NearestNeighbors(radiusneighbor_radius, algorithmauto).fit(xyz)densities nbrs.radius_neighbors(xyz, return_distanceFalse)# 计算每个点的密度point_cloud_density np.array([len(density) for density in densities])# 确定渲染顺序start_idx np.argmax(point_cloud_density)start_point xyz[start_idx]# 根据与起始点的距离对点云进行排序distances np.linalg.norm(xyz - start_point, axis1)sorted_indices np.argsort(distances)return sorted_indices 在render_set函数中调用get_indices函数 def render_set(model_path, name, iteration, views, gaussians, pipeline, background, scene):render_path os.path.join(modelpath, name, ours{}.format(iteration), renders)gts_path os.path.join(modelpath, name, ours{}.format(iteration), gt)makedirs(render_path, exist_okTrue)# makedirs(gts_path, exist_okTrue)### 计算点的渲染顺序sorted_indices get_indices(model_path, iteration)# 对给定的images.bin(相机外参)一帧帧图片进行渲染for idx, view in enumerate(tqdm(views, descRendering progress)):# 修改点云切片if idx120:indices sorted_indices[:(len(sorted_indices)//120 * idx)]scene.change_pc_indice(indicesindices)scaling_modifier 0.01elif scaling_modifier1:scaling_modifier 0.01else:scaling_modifier 1rendering render(view, gaussians, pipeline, background, scaling_modifierscaling_modifier)[render]torchvision.utils.save_image(rendering, os.path.join(render_path, {0:05d}.format(idx) .png))最后运行render.py即可得到最后的渲染视频包含渐变效果。 3 自定义环绕Object的相机路径 render.py使用的相机外参和内参分别存储在images.bin和cameras.bin中。 cameras.bin(内参) 该文件解析(read_intrinsics_binary函数)后得到如下key-value(int-Camera对象)组成的字典。 {…, 1: Camera(id1, modelPINHOLE, width1332, height876, paramsarray([1035.49659905, 1034.97186374, 666. , 438.])),…}images.bin(外参) 该文件解析(read_extrinsics_binary函数)后得到如下key-value(int-Image对象)组成的字典 {…, 263: Image(id263, qvecarray([-0.15935236, -0.46899572, 0.35922958, 0.79095129]), tvecarray([-0.68604342, -0.24766367, 1.17531395]), camera_id1, nameIMG_6597.jpg, xysarray([[ 826.85421273, 3.56521302],[ 791.22610197, 6.24990826],[1318.28015465, 6.96729477],…,[1041.33873779, 316.22219915],[ 737.99930832, 487.77142606],[ 649.78058365, 72.14452395]]), point3D_idsarray([ -1, -1, 75770, …, -1, -1, -1])) ,…}在不考虑测试集的时候我们不会使用该字典的xys和point3D_ids相机外参仅由qvec和tvec构成。 修改images.bin(外参) 为了生成自定义的相机路径我们仅需修改images.bin中每一帧的qvec和tvec。核心代码如下

读取相机内外参

images read_extrinsics_binary(../C4879_4/sparse/0/images_original.bin) qvecs, tvecs get_qvec_tvec(../C4879_4/sparse/0/images_original.bin) # 获取qvecs, tvecsqvecs np.array(qvecs) tvecs np.array(tvecs) mean_x tvecs[:,0].sum() / len(tvecs) mean_y tvecs[:,1].sum() / len(tvecs) mean_z tvecs[:,2].sum() / len(tvecs) print(mean_x,mean_y,mean_z) #################################以二维平面中的一个圆的轨迹为例############################

定义圆形轨迹的参数

radius 1.0 # 圆的半径 num_poses len(qvecs) # 生成的外参数量 center np.array([mean_x,mean_y,mean_z]) # 圆心坐标# 生成沿着圆形轨迹的外参 poses [] for i in range(num_poses):angle 2 * np.pi * i / num_poses # 在圆上均匀分布的角度position center np.array([radius * np.cos(angle), radius * np.sin(angle), 0]) # 根据角度计算位置q R.from_euler(xyz, [0, angle, 0]).as_quat() # 根据角度计算旋转四元数tvec position # 平移向量即为位置poses.append((q, tvec))new_images {} for i in range(len(images)):new_images[i1] Image(idimages[i1].id, qvecnp.array(poses[i][0]), tvecnp.array(poses[i][1]),camera_idimages[i1].camera_id, name{:03d}.format(i), xysimages[i1].xys, point3D_idsimages[i1].point3D_ids)# 写入相机内外参 write_images_binary(new_images, ../C4879_4/sparse/0/images.bin)使用到的依赖库和函数 import numpy as np import struct import collections from PIL import Image from scipy.spatial.transform import Rotation import pandas as pd from scipy.spatial.transform import Rotation as RCameraModel collections.namedtuple(CameraModel, [model_id, model_name, num_params]) Camera collections.namedtuple(Camera, [id, model, width, height, params]) BaseImage collections.namedtuple(Image, [id, qvec, tvec, camera_id, name, xys, point3D_ids]) Point3D collections.namedtuple(Point3D, [id, xyz, rgb, error, image_ids, point2D_idxs]) CAMERA_MODELS {CameraModel(model_id0, model_nameSIMPLE_PINHOLE, num_params3),CameraModel(model_id1, model_namePINHOLE, num_params4),CameraModel(model_id2, model_nameSIMPLE_RADIAL, num_params4),CameraModel(model_id3, model_nameRADIAL, num_params5),CameraModel(model_id4, model_nameOPENCV, num_params8),CameraModel(model_id5, model_nameOPENCV_FISHEYE, num_params8),CameraModel(model_id6, model_nameFULL_OPENCV, num_params12),CameraModel(model_id7, model_nameFOV, num_params5),CameraModel(model_id8, model_nameSIMPLE_RADIAL_FISHEYE, num_params4),CameraModel(model_id9, model_nameRADIAL_FISHEYE, num_params5),CameraModel(model_id10, model_nameTHIN_PRISM_FISHEYE, num_params12) } CAMERA_MODEL_IDS dict([(camera_model.model_id, camera_model)for camera_model in CAMERA_MODELS])def qvec2rotmat(qvec):return np.array([[1 - 2 * qvec[2]2 - 2 * qvec[3]2,2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],2 * qvec[3] * qvec[1] 2 * qvec[0] * qvec[2]],[2 * qvec[1] * qvec[2] 2 * qvec[0] * qvec[3],1 - 2 * qvec[1]2 - 2 * qvec[3]2,2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],[2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],2 * qvec[2] * qvec[3] 2 * qvec[0] * qvec[1],1 - 2 * qvec[1]2 - 2 * qvec[2]2]])def rotmat2qvec®:Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz R.flatK np.array([[Rxx - Ryy - Rzz, 0, 0, 0],[Ryx Rxy, Ryy - Rxx - Rzz, 0, 0],[Rzx Rxz, Rzy Ryz, Rzz - Rxx - Ryy, 0],[Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx Ryy Rzz]]) / 3.0eigvals, eigvecs np.linalg.eigh(K)qvec eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]if qvec[0] 0:qvec * -1return qvecclass Image(BaseImage):def qvec2rotmat(self):return qvec2rotmat(self.qvec)def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character):Read and unpack the next bytes from a binary file.:param fid::param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.:param endian_character: Any of {, , , , !}:return: Tuple of read and unpacked values.data fid.read(num_bytes)return struct.unpack(endian_character format_char_sequence, data)def read_extrinsics_binary(path_to_model_file):see: src/base/reconstruction.ccvoid Reconstruction::ReadImagesBinary(const std::string path)void Reconstruction::WriteImagesBinary(const std::string path)images {}with open(path_to_model_file, rb) as fid:num_reg_images read_next_bytes(fid, 8, Q)[0]for i in range(num_reg_images):binary_image_properties read_next_bytes(fid, num_bytes64, format_char_sequenceidddddddi)image_id binary_image_properties[0]qvec np.array(binary_image_properties[1:5])tvec np.array(binary_image_properties[5:8])camera_id binary_image_properties[8]image_name current_char read_next_bytes(fid, 1, c)[0]while current_char ! b\x00: # look for the ASCII 0 entryimage_name current_char.decode(utf-8)current_char read_next_bytes(fid, 1, c)[0]num_points2D read_next_bytes(fid, num_bytes8,format_char_sequenceQ)[0]x_y_id_s read_next_bytes(fid, num_bytes24*num_points2D,format_char_sequenceddq*num_points2D)xys np.column_stack([tuple(map(float, x_y_id_s[0::3])),tuple(map(float, x_y_id_s[1::3]))])point3D_ids np.array(tuple(map(int, x_y_id_s[2::3])))images[image_id] Image(idimage_id, qvecqvec, tvectvec,camera_idcamera_id, nameimage_name,xysxys, point3D_idspoint3D_ids)# if i3:# breakreturn imagesdef write_next_bytes(fid, data, format_char_sequence, endian_character):pack and write to a binary file.:param fid::param data: data to send, if multiple elements are sent at the same time,they should be encapsuled either in a list or a tuple:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.should be the same length as the data list or tuple:param endian_character: Any of {, , , , !}if isinstance(data, (list, tuple)):bytes struct.pack(endian_character format_char_sequence, *data)else:bytes struct.pack(endian_character format_char_sequence, data)fid.write(bytes)def write_images_binary(images, path_to_model_file):see: src/colmap/scene/reconstruction.ccvoid Reconstruction::ReadImagesBinary(const std::string path)void Reconstruction::WriteImagesBinary(const std::string path)with open(path_to_model_file, wb) as fid:write_next_bytes(fid, len(images), Q)for i, img in images.items():write_next_bytes(fid, img.id, i)tmp_qvec [q*1.01 for q in img.qvec.tolist()]write_next_bytes(fid, tmp_qvec, dddd)tmp_tvec [v*1.02 for v in img.tvec.tolist()]write_next_bytes(fid, tmp_tvec, ddd)write_next_bytes(fid, img.camera_id, i)for char in img.name:write_next_bytes(fid, char.encode(utf-8), c)write_next_bytes(fid, b\x00, c)write_next_bytes(fid, len(img.point3D_ids), Q)for xy, p3d_id in zip(np.zeros_like(img.xys), np.zeros_like(img.point3D_ids)):write_next_bytes(fid, [*xy, p3d_id], ddq)def get_qvec_tvec(path_to_model_file):qvecs []tvecs []with open(path_to_model_file, rb) as fid:num_reg_images read_next_bytes(fid, 8, Q)[0]for i in range(num_reg_images):binary_image_properties read_next_bytes(fid, num_bytes64, format_char_sequenceidddddddi)image_id binary_image_properties[0]qvec np.array(binary_image_properties[1:5])qvecs.append(qvec)tvec np.array(binary_image_properties[5:8])tvecs.append(tvec)camera_id binary_image_properties[8]image_name current_char read_next_bytes(fid, 1, c)[0]while current_char ! b\x00: # look for the ASCII 0 entryimage_name current_char.decode(utf-8)current_char read_next_bytes(fid, 1, c)[0]num_points2D read_next_bytes(fid, num_bytes8,format_char_sequenceQ)[0]x_y_id_s read_next_bytes(fid, num_bytes24*num_points2D,format_char_sequenceddq*num_points2D)xys np.column_stack([tuple(map(float, x_y_id_s[0::3])),tuple(map(float, x_y_id_s[1::3]))])point3D_ids np.array(tuple(map(int, x_y_id_s[2::3])))return qvecs, tvecs