tools/visualize_trajectory.py

import os
import argparse
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

import pdb

class CameraPoseVisualizer:
    def __init__(self, xlim, ylim, zlim):
        self.fig = plt.figure(figsize=(18, 7))
        self.ax = self.fig.add_subplot(projection='3d')
        self.plotly_data = None  # plotly data traces
        self.ax.set_aspect("auto")
        self.ax.set_xlim(xlim)
        self.ax.set_ylim(ylim)
        self.ax.set_zlim(zlim)
        self.ax.set_xlabel('x')
        self.ax.set_ylabel('y')
        self.ax.set_zlabel('z')
        
        # self.ax.view_init(elev=30, azim=-90)
        print('initialize camera pose visualizer')

    def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=9/16, base_xval=1, zval=3):
        vertex_std = np.array([[0, 0, 0, 1],
                               [base_xval, -base_xval * hw_ratio, zval, 1],
                               [base_xval, base_xval * hw_ratio, zval, 1],
                               [-base_xval, base_xval * hw_ratio, zval, 1],
                               [-base_xval, -base_xval * hw_ratio, zval, 1]])
        vertex_transformed = vertex_std @ extrinsic.T
        meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]],
                            [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]],
                            [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]],
                            [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]],
                            [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]]

        color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map)

        self.ax.add_collection3d(
            Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35))

    def colorbar(self, max_frame_length):
        cmap = mpl.cm.rainbow
        norm = mpl.colors.Normalize(vmin=0, vmax=max_frame_length)
        self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical', label='Frame Indexes')

    def show(self, save_path=None):
        plt.title('Camera Trajectory')
        plt.show()
        if save_path is not None:
            plt.savefig(save_path)


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--pose_file_path', required=True)
    parser.add_argument('--hw_ratio', default=9/16, type=float)
    parser.add_argument('--base_xval', type=float, default=1.0)
    parser.add_argument('--zval', type=float, default=2.0)
    parser.add_argument('--use_exact_fx', action='store_true')
    parser.add_argument('--relative_c2w', action='store_true')
    parser.add_argument('--x_min', type=float, default=-2)
    parser.add_argument('--x_max', type=float, default=2)
    parser.add_argument('--y_min', type=float, default=-2)
    parser.add_argument('--y_max', type=float, default=2)
    parser.add_argument('--z_min', type=float, default=-2)
    parser.add_argument('--z_max', type=float, default=2)
    parser.add_argument('--save_path', type=str, default='./assets/cam_trajectory/')
    return parser.parse_args()


# def get_c2w(w2cs, transform_matrix, relative_c2w):
#     if relative_c2w:
#         target_cam_c2w = np.array([
#             [1, 0, 0, 0],
#             [0, 1, 0, 0],
#             [0, 0, 1, 0],
#             [0, 0, 0, 1]
#         ])
#         pdb.set_trace()
#         abs2rel = target_cam_c2w @ w2cs[0]
#         ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
#         pdb.set_trace()
#         camera_positions = np.asarray([c2w[:3, 3] for c2w in ret_poses])        # [n_frame, 3]
#         position_distances = [camera_positions[i] - camera_positions[i - 1] for i in range(1, len(camera_positions))]
#         xyz_max = np.max(camera_positions, axis=0)
#         xyz_min = np.min(camera_positions, axis=0)
#         xyz_ranges = xyz_max - xyz_min           # [3, ]
#         max_range = np.max(xyz_ranges)
#         expected_xyz_ranges = 1
#         pdb.set_trace()
#         scale_ratio = expected_xyz_ranges / max_range
#         scaled_position_distances = [dis * scale_ratio for dis in position_distances]      # [n_frame - 1]
#         scaled_camera_positions = [camera_positions[0], ]
#         scaled_camera_positions.extend([camera_positions[0] + np.sum(np.asarray(scaled_position_distances[:i]), axis=0)
#                                         for i in range(1, len(camera_positions))])
#         pdb.set_trace()
#         ret_poses = [np.concatenate((np.concatenate((ori_pose[:3, :3], cam_position[:, None]), axis=1), np.asarray([0, 0, 0, 1])[None]), axis=0)
#                      for ori_pose, cam_position in zip(ret_poses, scaled_camera_positions)]
#         pdb.set_trace()
#     else:
#         ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
#     ret_poses = [transform_matrix @ x for x in ret_poses]
#     return np.array(ret_poses, dtype=np.float32)


def get_c2w(w2cs, transform_matrix, relative_c2w):
    if relative_c2w:
        target_cam_c2w = np.array([
            [1, 0, 0, 0],
            [0, 1, 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1]
        ])
        abs2rel = target_cam_c2w @ w2cs[0]
        ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
        # camera_positions = np.asarray([c2w[:3, 3] for c2w in ret_poses])        # [n_frame, 3]        
        # ret_poses = [np.concatenate((np.concatenate((ori_pose[:3, :3], cam_position[:, None]), axis=1), np.asarray([0, 0, 0, 1])[None]), axis=0)
        #              for ori_pose, cam_position in zip(ret_poses, camera_positions)]
    else:
        ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
    ret_poses = [transform_matrix @ x for x in ret_poses]
    return np.array(ret_poses, dtype=np.float32)


if __name__ == '__main__':
    args = get_args()
    with open(args.pose_file_path, 'r') as f:
        poses = f.readlines()
    w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[6:]]).reshape(3, 4) for pose in poses]
    fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]]
    num_frames = len(w2cs)
    transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) # For World Coordinates!
    # transform_matrix = np.asarray([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]).reshape(4, 4) # For Camera Coordinates!
    last_row = np.zeros((1, 4))
    last_row[0, -1] = 1.0
    w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
    c2ws = get_c2w(w2cs, transform_matrix, args.relative_c2w)

    visualizer = CameraPoseVisualizer([args.x_min, args.x_max], [args.y_min, args.y_max], [args.z_min, args.z_max])
    for frame_idx, c2w in enumerate(c2ws):
        visualizer.extrinsic2pyramid(c2w, frame_idx / num_frames, hw_ratio=args.hw_ratio, base_xval=args.base_xval,
                                     zval=(fxs[frame_idx] if args.use_exact_fx else args.zval))

    visualizer.colorbar(num_frames)
    pose_file_name = args.pose_file_path.split('/')[-1].split('.')[0]
    
    os.makedirs(args.save_path, exist_ok=True)
    save_path = os.path.join(args.save_path, pose_file_name+'.png')
    visualizer.show(save_path)