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【pytorch】3D Gaussian：数据处理_distcuda2

作者：weixin_40725706 | 2024-07-16 10:24:16

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distcuda2

Camera

先看一下最终的json格式：

[
	{
		"id": 0,
		 "img_name": "00001",
		 "width": 1959,
		 "height": 1090,
		 "position": 
		 	 [-3.0089893469241797, -0.11086489695181866, -3.7527640949141428],
		 "rotation": [
			 [0.876134201218856, 0.06925962026449776, 0.47706599800804744],
			 [-0.04747421839895102, 0.9972110940209488, -0.057586739349882114], 
			 [-0.4797239414934443, 0.027805376500959853, 0.8769787916452908]
		 ],
		 "fy": 1164.6601287484507,
		 "fx": 1159.5880733038064
	 }
 ]
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数据信息

在这里插入图片描述

image.bin: 图像相关

for each image:

image_id + qvec(shape:(4, )) + tvec(shape:(3, )) + camera_id

    with open(path_to_model_file, "rb") as fid:
        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_reg_images):
            binary_image_properties = read_next_bytes(
                fid, num_bytes=64, format_char_sequence="idddddddi")
            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 entry
                image_name += current_char.decode("utf-8")
                current_char = read_next_bytes(fid, 1, "c")[0]
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num_points2D 点云数量 eg.11661

x_y_id_s 读取点云坐标

xys整理转换->[x, y]

point3D_ids:3D点云 z轴坐标

            num_points2D = read_next_bytes(fid, num_bytes=8,
                                           format_char_sequence="Q")[0]
            x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
                                       format_char_sequence="ddq"*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])))
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返回图像信息：

            images[image_id] = Image(
                id=image_id, qvec=qvec, tvec=tvec,
                camera_id=camera_id, name=image_name,
                xys=xys, point3D_ids=point3D_ids)
    return images
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cameras.bin: 相机内参

camera_id + model_id + model_name（相机模型）

def read_intrinsics_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::WriteCamerasBinary(const std::string& path)
        void Reconstruction::ReadCamerasBinary(const std::string& path)
    """
    cameras = {}
    with open(path_to_model_file, "rb") as fid:
        num_cameras = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_cameras):
            camera_properties = read_next_bytes(
                fid, num_bytes=24, format_char_sequence="iiQQ")
            camera_id = camera_properties[0]
            model_id = camera_properties[1]
            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
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CAMERA_MODELS = {
    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
    CameraModel(model_id=7, model_name="FOV", num_params=5),
    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
}
CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model)
                         for camera_model in CAMERA_MODELS])
CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
                           for camera_model in CAMERA_MODELS])
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width:1090, height:1959

num_params:4 相机参数量

params:(1159.5880733038061, 1164.6601287484507, 979.5, 545.0)

分别对应：focal_length_x focal_length_y

            width = camera_properties[2]
            height = camera_properties[3]
            num_params = CAMERA_MODEL_IDS[model_id].num_params
            params = read_next_bytes(fid, num_bytes=8*num_params,
                                     format_char_sequence="d"*num_params)
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返回相机：

            cameras[camera_id] = Camera(id=camera_id,
                                        model=model_name,
                                        width=width,
                                        height=height,
                                        params=np.array(params))
        assert len(cameras) == num_cameras
    return cameras
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在这里插入图片描述

转换处理

readColmapCameras：结合外参读取cam_Info

每帧对应一个cam_Info

一个相机内参但有frame个cam_Info

可能是因为object是静态？只需要一个相机在不同时刻拍下即可。

class CameraInfo(NamedTuple):
    uid: int
    R: np.array
    T: np.array
    FovY: np.array
    FovX: np.array
    image: np.array
    image_path: str
    image_name: str
    width: int
    height: int
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开始：

def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
    cam_infos = []
    for idx, key in enumerate(cam_extrinsics):
        sys.stdout.write('\r')
        # the exact output you're looking for:
        sys.stdout.write("Reading camera {}/{}".format(idx+1, len(cam_extrinsics)))
        sys.stdout.flush()

        extr = cam_extrinsics[key]
        intr = cam_intrinsics[extr.camera_id]
        height = intr.height
        width = intr.width
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由q、t得到旋转和平移矩阵：

        uid = intr.id
        R = np.transpose(qvec2rotmat(extr.qvec))
        T = np.array(extr.tvec)
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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]])
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根据相机的焦距和图像的宽/高计算视场角：

        if intr.model=="SIMPLE_PINHOLE":
            focal_length_x = intr.params[0]
            FovY = focal2fov(focal_length_x, height)
            FovX = focal2fov(focal_length_x, width)
        elif intr.model=="PINHOLE":
            focal_length_x = intr.params[0]
            focal_length_y = intr.params[1]
            FovY = focal2fov(focal_length_y, height)
            FovX = focal2fov(focal_length_x, width)
        else:
            assert False, "Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!"

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        image_path = os.path.join(images_folder, os.path.basename(extr.name))
        image_name = os.path.basename(image_path).split(".")[0] 
        # basename 提取文件名部分 split(".") "00001.jpg" 拆开 [0] 取"00001" 
        image = Image.open(image_path)
        # 读取image type：PIL 
        cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
                              image_path=image_path, image_name=image_name, width=width, height=height)
        cam_infos.append(cam_info)
    sys.stdout.write('\n')
    return cam_infos
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getNerfppNorm

    # 计算所有相机的中心点位置, 以及它到最远camera的距离
    nerf_normalization = getNerfppNorm(train_cam_infos)
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def getNerfppNorm(cam_info):
    def get_center_and_diag(cam_centers):
        cam_centers = np.hstack(cam_centers) # （3， 301）
        avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)
        center = avg_cam_center # （3， 1）
        dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True) 
        diagonal = np.max(dist) # 最远距离
        return center.flatten(), diagonal

    cam_centers = []

    for cam in cam_info:
        W2C = getWorld2View2(cam.R, cam.T)
        C2W = np.linalg.inv(W2C)
        cam_centers.append(C2W[:3, 3:4])

    center, diagonal = get_center_and_diag(cam_centers) # 所有相机的均值中心点，最远距离
    radius = diagonal * 1.1

    translate = -center

    return {"translate": translate, "radius": radius}
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W2C：world to camera view 世界坐标转局部坐标

-> 求逆：C2W 局部坐标转世界坐标

def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):
    Rt = np.zeros((4, 4))
    Rt[:3, :3] = R.transpose()
    Rt[:3, 3] = t
    Rt[3, 3] = 1.0

    C2W = np.linalg.inv(Rt)
    cam_center = C2W[:3, 3]
    cam_center = (cam_center + translate) * scale
    C2W[:3, 3] = cam_center
    Rt = np.linalg.inv(C2W)
    return np.float32(Rt)
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readColmapSceneInfo

相机信息处理完毕：

def readColmapSceneInfo(path, images, eval, llffhold=8):
    try:
        cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin")
        cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin")
        cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)
        cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)
    except:
        cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt")
        cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt")
        cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)
        cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)

    reading_dir = "images" if images == None else images
    cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))
    cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name) # 按照图片顺序排序

    if eval:
        train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]
        test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]
    else:
        train_cam_infos = cam_infos
        test_cam_infos = []
       
    # 计算所有相机的中心点位置, 以及它到最远camera的距离
    nerf_normalization = getNerfppNorm(train_cam_infos)
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point cloud

在这里插入图片描述

vertices: type=PlyElement; size=182686

positions：点的xyz坐标向量

colors：点的颜色

normals：点的法向量

def fetchPly(path):
    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)
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    ply_path = os.path.join(path, "sparse/0/points3D.ply")
    bin_path = os.path.join(path, "sparse/0/points3D.bin")
    txt_path = os.path.join(path, "sparse/0/points3D.txt")
    if not os.path.exists(ply_path):
        print("Converting point3d.bin to .ply, will happen only the first time you open the scene.")
        try:
            xyz, rgb, _ = read_points3D_binary(bin_path)
        except:
            xyz, rgb, _ = read_points3D_text(txt_path)
        storePly(ply_path, xyz, rgb)
    try:
        pcd = fetchPly(ply_path)
    except:
        pcd = None

    scene_info = SceneInfo(point_cloud=pcd,
                           train_cameras=train_cam_infos,
                           test_cameras=test_cam_infos,
                           nerf_normalization=nerf_normalization,
                           ply_path=ply_path)
    return scene_info
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至此，场景信息加载完毕

class SceneInfo(NamedTuple):
    point_cloud: BasicPointCloud
    train_cameras: list
    test_cameras: list
    nerf_normalization: dict
    ply_path: str
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Scene

上述已完成：

    def __init__(self, args : ModelParams, gaussians : GaussianModel, load_iteration=None, shuffle=True, resolution_scales=[1.0]):
        """b
        :param path: Path to colmap scene main folder.
        """
        self.model_path = args.model_path
        self.loaded_iter = None
        self.gaussians = gaussians

        if load_iteration:
            if load_iteration == -1:
                self.loaded_iter = searchForMaxIteration(os.path.join(self.model_path, "point_cloud"))
            else:
                self.loaded_iter = load_iteration
            print("Loading trained model at iteration {}".format(self.loaded_iter))

        self.train_cameras = {}
        self.test_cameras = {}

        if os.path.exists(os.path.join(args.source_path, "sparse")):
            scene_info = sceneLoadTypeCallbacks["Colmap"](args.source_path, args.images, args.eval)
        elif os.path.exists(os.path.join(args.source_path, "transforms_train.json")):
            print("Found transforms_train.json file, assuming Blender data set!")
            scene_info = sceneLoadTypeCallbacks["Blender"](args.source_path, args.white_background, args.eval)
        else:
            assert False, "Could not recognize scene type!"
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把相机相关信息存入json文件：

        if not self.loaded_iter:
            # 复制ply
            with open(scene_info.ply_path, 'rb') as src_file, open(os.path.join(self.model_path, "input.ply") , 'wb') as dest_file:
                dest_file.write(src_file.read())
            json_cams = []
            camlist = []
            if scene_info.test_cameras:
                camlist.extend(scene_info.test_cameras)
            if scene_info.train_cameras:
                camlist.extend(scene_info.train_cameras) # 一次性追加另一个序列的多个值
            # camlist len(301) 301张image对应的相机参数（fov image_path...）
            for id, cam in enumerate(camlist):
                # 解析每个camera_info对应到frame
                json_cams.append(camera_to_JSON(id, cam))
                # 写入json文件
            with open(os.path.join(self.model_path, "cameras.json"), 'w') as file:
                json.dump(json_cams, file)
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def camera_to_JSON(id, camera : Camera):
	# 旋转平移矩阵
    Rt = np.zeros((4, 4))
    Rt[:3, :3] = camera.R.transpose()
    Rt[:3, 3] = camera.T
    Rt[3, 3] = 1.0
	
	# 变换到局部坐标系
    W2C = np.linalg.inv(Rt)
    pos = W2C[:3, 3]
    rot = W2C[:3, :3]
    serializable_array_2d = [x.tolist() for x in rot]
    camera_entry = {
        'id' : id,
        'img_name' : camera.image_name,
        'width' : camera.width,
        'height' : camera.height,
        'position': pos.tolist(),
        'rotation': serializable_array_2d,
        'fy' : fov2focal(camera.FovY, camera.height),
        'fx' : fov2focal(camera.FovX, camera.width)
    }
    return camera_entry
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视场角求得焦距：

def fov2focal(fov, pixels):
    return pixels / (2 * math.tan(fov / 2))
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接着，

        
        # 随机打乱所有图片和对应cameInfo的顺序
        if shuffle:
            random.shuffle(scene_info.train_cameras)  # Multi-res consistent random shuffling
            random.shuffle(scene_info.test_cameras)  # Multi-res consistent random shuffling

        # 所有相机的中心点位置到最远camera的距离 7.49063801765442
        self.cameras_extent = scene_info.nerf_normalization["radius"]
        for resolution_scale in resolution_scales:
            print("Loading Training Cameras")
            self.train_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.train_cameras, resolution_scale, args)
            print("Loading Test Cameras")
            self.test_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.test_cameras, resolution_scale, args)

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创建 3D gaussian

        # 如果是初次训练, 则从COLMAP创建的点云中初始化每个点对应的3D gaussian, 否则直接从之前保存的模型文件中读取3D gaussian
        if self.loaded_iter:
            self.gaussians.load_ply(os.path.join(self.model_path,
                                                           "point_cloud",
                                                           "iteration_" + str(self.loaded_iter),
                                                           "point_cloud.ply"))
        else:
            self.gaussians.create_from_pcd(scene_info.point_cloud, self.cameras_extent)
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把SceneInfo中，即刚刚读取到的点云数据，传入create_from_pcd函数：

    def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
        self.spatial_lr_scale = spatial_lr_scale
        fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() # torch.Size([182686, 3])
        # rgb转化为球谐系数
        fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) # torch.Size([182686, 3])
        features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() # torch.Size([182686, 3，16])
        # 每个颜色通道有16个球谐系数

        features[:, :3, 0 ] = fused_color 
        features[:, 3:, 1:] = 0.0 

        print("Number of points at initialisation : ", fused_point_cloud.shape[0])

        # gaussian shape（scale）
        # distCUDA2 计算点云中的每个点到与其最近的N个点的平均距离的平方
        dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) # torch.Size([182686])
        scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) # 尺度缩放到log(平均距离)
        # repeat 0维复制1次，1维复制3次 ->[182686, 3]

        # gaussian rotation 初始化为0
        rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") # 旋转参数 四元组
        rots[:, 0] = 1

        # (P, 1), 每个点的不透明度, 初始化为0.1
        opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))

        # 转为参数类型
        self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) # (P, 3)
        self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) # (P, 1, 3)
        self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) # (P, 15, 3)
        self._scaling = nn.Parameter(scales.requires_grad_(True)) # (P, 3)
        self._rotation = nn.Parameter(rots.requires_grad_(True)) # (P, 4)
        self._opacity = nn.Parameter(opacities.requires_grad_(True)) # (P, 1)
        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") # (P, )   投影到2D时, 每个2D gaussian最大的半径
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完毕。

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