配置kitti_object_vis没成功，用kitti_object_vis的一些函数加上matplotlib进行可视化

import numpy as np
import matplotlib.pyplot as plt


import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def roty(t):
    """ Rotation about the y-axis. """
    c = np.cos(t)
    s = np.sin(t)
    return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])

def compute_box_3d(obj):
    """ Takes an object and a projection matrix (P) and projects the 3d
        bounding box into the image plane.
        Returns:
            corners_3d: (8,3) array in in rect camera coord.
    """
    # compute rotational matrix around yaw axis
    R = roty(obj.ry)

    # 3d bounding box dimensions
    l = obj.l
    w = obj.w
    h = obj.h

    # 3d bounding box corners
    x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
    y_corners = [0, 0, 0, 0, -h, -h, -h, -h]
    z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]


    corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
    # print corners_3d.shape
    corners_3d[0, :] = corners_3d[0, :] + obj.t[0]
    corners_3d[1, :] = corners_3d[1, :] + obj.t[1]
    corners_3d[2, :] = corners_3d[2, :] + obj.t[2]

    return np.transpose(corners_3d)

def read_label(label_filename):
    lines = [line.rstrip() for line in open(label_filename)]
    objects = [Object3d(line) for line in lines if line[:8]!="DontCare"]
    return objects

def choice_points(points,k=int(1e4)):
    selected_indices = np.random.choice(len(points), k, replace=False)
    return points[selected_indices]
def inverse_rigid_trans(Tr):
    """ Inverse a rigid body transform matrix (3x4 as [R|t])
        [R'|-R't; 0|1]
    """
    inv_Tr = np.zeros_like(Tr)  # 3x4
    inv_Tr[0:3, 0:3] = np.transpose(Tr[0:3, 0:3])
    inv_Tr[0:3, 3] = np.dot(-np.transpose(Tr[0:3, 0:3]), Tr[0:3, 3])
    return inv_Tr

class Calibration(object):
    """ Calibration matrices and utils
        3d XYZ in <label>.txt are in rect camera coord.
        2d box xy are in image2 coord
        Points in <lidar>.bin are in Velodyne coord.

        y_image2 = P^2_rect * x_rect
        y_image2 = P^2_rect * R0_rect * Tr_velo_to_cam * x_velo
        x_ref = Tr_velo_to_cam * x_velo
        x_rect = R0_rect * x_ref

        P^2_rect = [f^2_u,  0,      c^2_u,  -f^2_u b^2_x;
                    0,      f^2_v,  c^2_v,  -f^2_v b^2_y;
                    0,      0,      1,      0]
                 = K * [1|t]

        image2 coord:
         ----> x-axis (u)
        |
        |
        v y-axis (v)

        velodyne coord:
        front x, left y, up z

        rect/ref camera coord:
        right x, down y, front z

        Ref (KITTI paper): http://www.cvlibs.net/publications/Geiger2013IJRR.pdf

        TODO(rqi): do matrix multiplication only once for each projection.
    """

    def __init__(self, calib_filepath, from_video=False):
        if from_video:
            calibs = self.read_calib_from_video(calib_filepath)
        else:
            calibs = self.read_calib_file(calib_filepath)
        # Projection matrix from rect camera coord to image2 coord
        self.P = calibs["P2"]
        self.P = np.reshape(self.P, [3, 4])
        # Rigid transform from Velodyne coord to reference camera coord
        self.V2C = calibs["Tr_velo_to_cam"]
        self.V2C = np.reshape(self.V2C, [3, 4])
        self.C2V = inverse_rigid_trans(self.V2C)
        # Rotation from reference camera coord to rect camera coord
        self.R0 = calibs["R0_rect"]
        self.R0 = np.reshape(self.R0, [3, 3])

        # Camera intrinsics and extrinsics
        self.c_u = self.P[0, 2]
        self.c_v = self.P[1, 2]
        self.f_u = self.P[0, 0]
        self.f_v = self.P[1, 1]
        self.b_x = self.P[0, 3] / (-self.f_u)  # relative
        self.b_y = self.P[1, 3] / (-self.f_v)

    def read_calib_file(self, filepath):
        """ Read in a calibration file and parse into a dictionary.
        Ref: https://github.com/utiasSTARS/pykitti/blob/master/pykitti/utils.py
        """
        data = {}
        with open(filepath, "r") as f:
            for line in f.readlines():
                line = line.rstrip()
                if len(line) == 0:
                    continue
                key, value = line.split(":", 1)
                # The only non-float values in these files are dates, which
                # we don't care about anyway
                try:
                    data[key] = np.array([float(x) for x in value.split()])
                except ValueError:
                    pass

        return data

    def read_calib_from_video(self, calib_root_dir):
        """ Read calibration for camera 2 from video calib files.
            there are calib_cam_to_cam and calib_velo_to_cam under the calib_root_dir
        """
        data = {}
        cam2cam = self.read_calib_file(
            os.path.join(calib_root_dir, "calib_cam_to_cam.txt")
        )
        velo2cam = self.read_calib_file(
            os.path.join(calib_root_dir, "calib_velo_to_cam.txt")
        )
        Tr_velo_to_cam = np.zeros((3, 4))
        Tr_velo_to_cam[0:3, 0:3] = np.reshape(velo2cam["R"], [3, 3])
        Tr_velo_to_cam[:, 3] = velo2cam["T"]
        data["Tr_velo_to_cam"] = np.reshape(Tr_velo_to_cam, [12])
        data["R0_rect"] = cam2cam["R_rect_00"]
        data["P2"] = cam2cam["P_rect_02"]
        return data

    def cart2hom(self, pts_3d):
        """ Input: nx3 points in Cartesian
            Oupput: nx4 points in Homogeneous by pending 1
        """
        n = pts_3d.shape[0]
        pts_3d_hom = np.hstack((pts_3d, np.ones((n, 1))))
        return pts_3d_hom

    # ===========================
    # ------- 3d to 3d ----------
    # ===========================
    def project_velo_to_ref(self, pts_3d_velo):
        pts_3d_velo = self.cart2hom(pts_3d_velo)  # nx4
        return np.dot(pts_3d_velo, np.transpose(self.V2C))

    def project_ref_to_velo(self, pts_3d_ref):
        pts_3d_ref = self.cart2hom(pts_3d_ref)  # nx4
        return np.dot(pts_3d_ref, np.transpose(self.C2V))

    def project_rect_to_ref(self, pts_3d_rect):
        """ Input and Output are nx3 points """
        return np.transpose(np.dot(np.linalg.inv(self.R0), np.transpose(pts_3d_rect)))

    def project_ref_to_rect(self, pts_3d_ref):
        """ Input and Output are nx3 points """
        return np.transpose(np.dot(self.R0, np.transpose(pts_3d_ref)))

    def project_rect_to_velo(self, pts_3d_rect):
        """ Input: nx3 points in rect camera coord.
            Output: nx3 points in velodyne coord.
        """
        pts_3d_ref = self.project_rect_to_ref(pts_3d_rect)
        return self.project_ref_to_velo(pts_3d_ref)

    def project_velo_to_rect(self, pts_3d_velo):
        pts_3d_ref = self.project_velo_to_ref(pts_3d_velo)
        return self.project_ref_to_rect(pts_3d_ref)

    # ===========================
    # ------- 3d to 2d ----------
    # ===========================
    def project_rect_to_image(self, pts_3d_rect):
        """ Input: nx3 points in rect camera coord.
            Output: nx2 points in image2 coord.
        """
        pts_3d_rect = self.cart2hom(pts_3d_rect)
        pts_2d = np.dot(pts_3d_rect, np.transpose(self.P))  # nx3
        pts_2d[:, 0] /= pts_2d[:, 2]
        pts_2d[:, 1] /= pts_2d[:, 2]
        return pts_2d[:, 0:2]

    def project_velo_to_image(self, pts_3d_velo):
        """ Input: nx3 points in velodyne coord.
            Output: nx2 points in image2 coord.
        """
        pts_3d_rect = self.project_velo_to_rect(pts_3d_velo)
        return self.project_rect_to_image(pts_3d_rect)

    def project_8p_to_4p(self, pts_2d):
        x0 = np.min(pts_2d[:, 0])
        x1 = np.max(pts_2d[:, 0])
        y0 = np.min(pts_2d[:, 1])
        y1 = np.max(pts_2d[:, 1])
        x0 = max(0, x0)
        # x1 = min(x1, proj.image_width)
        y0 = max(0, y0)
        # y1 = min(y1, proj.image_height)
        return np.array([x0, y0, x1, y1])

    def project_velo_to_4p(self, pts_3d_velo):
        """ Input: nx3 points in velodyne coord.
            Output: 4 points in image2 coord.
        """
        pts_2d_velo = self.project_velo_to_image(pts_3d_velo)
        return self.project_8p_to_4p(pts_2d_velo)

    # ===========================
    # ------- 2d to 3d ----------
    # ===========================
    def project_image_to_rect(self, uv_depth):
        """ Input: nx3 first two channels are uv, 3rd channel
                   is depth in rect camera coord.
            Output: nx3 points in rect camera coord.
        """
        n = uv_depth.shape[0]
        x = ((uv_depth[:, 0] - self.c_u) * uv_depth[:, 2]) / self.f_u + self.b_x
        y = ((uv_depth[:, 1] - self.c_v) * uv_depth[:, 2]) / self.f_v + self.b_y
        pts_3d_rect = np.zeros((n, 3))
        pts_3d_rect[:, 0] = x
        pts_3d_rect[:, 1] = y
        pts_3d_rect[:, 2] = uv_depth[:, 2]
        return pts_3d_rect

    def project_image_to_velo(self, uv_depth):
        pts_3d_rect = self.project_image_to_rect(uv_depth)
        return self.project_rect_to_velo(pts_3d_rect)

    def project_depth_to_velo(self, depth, constraint_box=True):
        depth_pt3d = get_depth_pt3d(depth)
        depth_UVDepth = np.zeros_like(depth_pt3d)
        depth_UVDepth[:, 0] = depth_pt3d[:, 1]
        depth_UVDepth[:, 1] = depth_pt3d[:, 0]
        depth_UVDepth[:, 2] = depth_pt3d[:, 2]
        # print("depth_pt3d:",depth_UVDepth.shape)
        depth_pc_velo = self.project_image_to_velo(depth_UVDepth)
        # print("dep_pc_velo:",depth_pc_velo.shape)
        if constraint_box:
            depth_box_fov_inds = (
                (depth_pc_velo[:, 0] < cbox[0][1])
                & (depth_pc_velo[:, 0] >= cbox[0][0])
                & (depth_pc_velo[:, 1] < cbox[1][1])
                & (depth_pc_velo[:, 1] >= cbox[1][0])
                & (depth_pc_velo[:, 2] < cbox[2][1])
                & (depth_pc_velo[:, 2] >= cbox[2][0])
            )
            depth_pc_velo = depth_pc_velo[depth_box_fov_inds]
        return depth_pc_velo

class Object3d(object):
    """ 3d object label """

    def __init__(self, label_file_line):
        data = label_file_line.split(" ")
        data[1:] = [float(x) for x in data[1:]]

        # extract label, truncation, occlusion
        self.type = data[0]  # 'Car', 'Pedestrian', ...
        self.truncation = data[1]  # truncated pixel ratio [0..1]
        self.occlusion = int(
            data[2]
        )  # 0=visible, 1=partly occluded, 2=fully occluded, 3=unknown
        self.alpha = data[3]  # object observation angle [-pi..pi]

        # extract 2d bounding box in 0-based coordinates
        self.xmin = data[4]  # left
        self.ymin = data[5]  # top
        self.xmax = data[6]  # right
        self.ymax = data[7]  # bottom
        self.box2d = np.array([self.xmin, self.ymin, self.xmax, self.ymax])

        # extract 3d bounding box information
        self.h = data[8]  # box height
        self.w = data[9]  # box width
        self.l = data[10]  # box length (in meters)
        self.t = (data[11], data[12], data[13])  # location (x,y,z) in camera coord.
        self.ry = data[14]  # yaw angle (around Y-axis in camera coordinates) [-pi..pi]

    def estimate_diffculty(self):
        """ Function that estimate difficulty to detect the object as defined in kitti website"""
        # height of the bounding box
        bb_height = np.abs(self.xmax - self.xmin)

        if bb_height >= 40 and self.occlusion == 0 and self.truncation <= 0.15:
            return "Easy"
        elif bb_height >= 25 and self.occlusion in [0, 1] and self.truncation <= 0.30:
            return "Moderate"
        elif (
            bb_height >= 25 and self.occlusion in [0, 1, 2] and self.truncation <= 0.50
        ):
            return "Hard"
        else:
            return "Unknown"

    def print_object(self):
        print(
            "Type, truncation, occlusion, alpha: %s, %d, %d, %f"
            % (self.type, self.truncation, self.occlusion, self.alpha)
        )
        print(
            "2d bbox (x0,y0,x1,y1): %f, %f, %f, %f"
            % (self.xmin, self.ymin, self.xmax, self.ymax)
        )
        print("3d bbox h,w,l: %f, %f, %f" % (self.h, self.w, self.l))
        print(
            "3d bbox location, ry: (%f, %f, %f), %f"
            % (self.t[0], self.t[1], self.t[2], self.ry)
        )
        print("Difficulty of estimation: {}".format(self.estimate_diffculty()))

def load_velo_scan(velo_filename, dtype=np.float32, n_vec=4):
    scan = np.fromfile(velo_filename, dtype=dtype)
    scan = scan.reshape((-1, n_vec))
    return scan
    
def draw_gt_boxes3d(    gt_boxes3d,    ax,    color=(0, 1, 0),    line_width=2,    draw_text=True,    text_scale=(1, 1, 1),    color_list=None,    label=""):
    """ Draw 3D bounding boxes
    Args:
        gt_boxes3d: numpy array (n,8,3) for XYZs of the box corners
        ax: matplotlib axes handler
        color: RGB value tuple in range (0,1), box line color
        line_width: box line width
        draw_text: boolean, if true, write box indices beside boxes
        text_scale: three number tuple
        color_list: a list of RGB tuple, if not None, overwrite color.
        label: label to display beside the boxes
    Returns:
        ax: updated axes
    """
    num = len(gt_boxes3d)
    # print(gt_boxes3d)
    for n in range(num):
        b = gt_boxes3d[n]
        # b[:, [0,1, 2]] = b[:, [2,0, 1]]
        # b[:, [0,1, 2]] = b[:, [0,2,1]]
        ## b[:, [0,1, 2]] = b[:, [1,0,2]]
        ## b[:, [0,1, 2]] = b[:, [1,2,0]]
        ##b[:, [0,1, 2]] = b[:, [2,1,0]]
        if color_list is not None:
            color = color_list[n]
        if draw_text:
            ax.text(
                b[4, 0],
                b[4, 1],
                b[4, 2],
                label,
                color=color,
                fontsize=10,
                ha='center',
                va='center',
            )
        for k in range(0, 4):
            i, j = k, (k + 1) % 4
            ax.plot(
                [b[i, 0], b[j, 0]],
                [b[i, 1], b[j, 1]],
                [b[i, 2], b[j, 2]],
                color=color,
                linewidth=line_width,
            )

            i, j = k + 4, (k + 1) % 4 + 4
            ax.plot(
                [b[i, 0], b[j, 0]],
                [b[i, 1], b[j, 1]],
                [b[i, 2], b[j, 2]],
                color=color,
                linewidth=line_width,
            )

            i, j = k, k + 4
            ax.plot(
                [b[i, 0], b[j, 0]],
                [b[i, 1], b[j, 1]],
                [b[i, 2], b[j, 2]],
                color=color,
                linewidth=line_width,
            )

    return ax


color_dt={"Car":(0,1,0),"Pedestrian":(0,1,1),"Cyclist":(1,1,0)}
def MyDraw():
    points=load_velo_scan(lidar_path)
    objs=read_label(label_path)
    box_corners = [compute_box_3d(obj) for obj in objs]
    calib = Calibration(calib_path)
    box_corners =[ calib.project_rect_to_velo(box_corner) for box_corner in box_corners]
    if reduce_points:
        points=choice_points(points)   
    xyz = points[:, :3]
    color = points[:, 3]
    min_xyz = np.min(xyz, axis=0)
    max_xyz = np.max(xyz, axis=0)
    print(max_xyz)
    fig = plt.figure(figsize=(15, 15))  
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(xyz[:, 0], xyz[:, 1], xyz[:, 2], c=color, cmap='jet', s=1,alpha=points[:, 3]) 
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')

    # 设置坐标轴范围
    # ax.set_xlim(min_xyz[0], max_xyz[0])
    # ax.set_ylim(min_xyz[1], max_xyz[1])
    # ax.set_zlim(min_xyz[2], max_xyz[2])
    ax.set_xlim(-70, 70)
    ax.set_ylim(-70, 70)
    ax.set_zlim(-70, 70)

    ax=draw_gt_boxes3d(ax=ax,gt_boxes3d=box_corners,color_list=[color_dt[obj.type] for obj in objs])

    plt.axis('off')
    plt.grid(False)
    plt.tight_layout()
    plt.show()
    # if save_name:
    #     plt.savefig(save_name)  
num="004139"
lidar_path = "/mnt/kitti/training/velodyne/%s.bin"%(num)
label_path= "/mnt/kitti/training/label_2/%s.txt"%(num)
calib_path="/mnt/kitti/training/calib/%s.txt"%(num)
reduce_points=True
MyDraw()