Visualization waypoint

[azamshoaib]

@penghao-wu
Hi, Since tcp_agent is dumping the predicted waypoints, can you please share the visualization code for projecting it on the bev image.

[penghao-wu]

Hi, visualizing the waypoints in BEV should be easy as you do not need to do perspective projection. I use the following function to visualize the waypoints. If you want to visualize the waypoints in the front-view camera, you should first do the perspective transformation.

def draw_trajectory_on_ax(ax: Axes, trajectories, labels, colors, line_type='o-', transparent=True, xlim=(-70, 70), ylim=(0, 120)):
    '''
    ax: matplotlib.axes.Axes, the axis to draw trajectories on
    trajectories: List of numpy arrays of shape (num_points, 2 or 3)
    confs: List of numbers, 1 means gt
    '''
    for idx, (trajectory, label, color) in enumerate(zip(trajectories, labels, colors)):
        ax.plot(-1*trajectory[:, 1], trajectory[:, 0], line_type, label=label, color = color)
    if xlim is not None:
        ax.set_xlim(*xlim)
    if ylim is not None:
        ax.set_ylim(*ylim)
    ax.legend()

    return ax

[azamshoaib]

@penghao-wu
I have run your function but the waypoints are projecting out of the image bound.

[penghao-wu]

If you want to show them on the front-view image, you need to transform the waypoints using camera's intrinsic and extrinsic matrix first.

[azamshoaib]

For perspective I have projected the waypoints but I need to project it on BEV image. The function you give for projecting the waypoints on BEV image is projecting the waypoints out of bound.

[penghao-wu]

For the predicted waypoints, the x is to the left and the y is upwards. So you might need to adjust them according to your definition of your BEV coordinate.

[azamshoaib]

@penghao-wu

import matplotlib.pyplot as plt
from matplotlib.axes import Axes
import json
import numpy as np
def draw_trajectory_on_ax(ax: Axes, trajectories, labels, colors, line_type='o-', transparent=True, xlim=(-70, 70), ylim=(0, 120)):
    for idx, (trajectory, label, color) in enumerate(zip(trajectories, labels, colors)):
        ax.plot(-1*trajectory[:, 1], trajectory[:, 0], line_type, label=label, color=color)
    if xlim is not None:
        ax.set_xlim(*xlim)
    if ylim is not None:
        ax.set_ylim(*ylim)
    ax.legend()
img = plt.imread('bev/0763.png')
with open('meta/0763.json', 'r') as f:
    data = json.load(f)
fig, ax = plt.subplots()
ax.imshow(img, extent=[-70, 70, 0, 120]) # Set extent based on your BEV image dimensions
wp_json = np.array([data['wp_1'], data['wp_2'], data['wp_3'], data['wp_4']])
wp_json[:, 1] = -wp_json[:, 1] # Flip y
wp_json = wp_json[:, [1, 0]] # Swap x and y
trajectories = [wp_json]
draw_trajectory_on_ax(ax, trajectories, ['Waypoints'], ['r'])
plt.show()

I have written this code your visualization function. Can you please tell me where I am doing the mistake. Thank you

[penghao-wu]

Could you please share the bev image and the json file?

[azamshoaib]

@penghao-wu

Json file

{
    "speed": 5.222938537597656,
    "steer": 0.054270822865752015,
    "throttle": 0.6499840478102348,
    "brake": 0.0,
    "wp_4": [
        2.357274055480957,
        10.448390007019043
    ],
    "wp_3": [
        1.3261723518371582,
        8.031651496887207
    ],
    "wp_2": [
        0.6107930541038513,
        5.470467567443848
    ],
    "wp_1": [
        0.21580669283866882,
        2.746610641479492
    ],
    "aim": [
        0.21580669283866882,
        2.746610641479492
    ],
    "target": [
        37.07563781738281,
        31.653648376464844
    ],
    "desired_speed": 5.3593800862630205,
    "angle": 0.04991787848456755,
    "angle_last": 0.2567279621075312,
    "angle_target": 0.5501185481779519,
    "angle_final": 0.04991787848456755,
    "delta": 0.1364415486653643,
    "agent": "traj",
    "steer_ctrl": 0.08452033996582031,
    "steer_traj": 0.054270822865752015,
    "throttle_ctrl": 0.9460892677307129,
    "throttle_traj": 0.6499840478102348,
    "brake_ctrl": 0.0,
    "brake_traj": 0.0,
    "status": 0
}

[penghao-wu]

I am sorry for the previous mistake. The y should be upwards, so you should modify the Flip y line to wp_json[:, 0] = -wp_json[:, 0] # Flip x. Besides, as you want to project the trajectory onto the image, you need to translate the trajectory to make the origin at the center of the image and scale the waypoints according to the camera parameters. You can also view this as a perspective transformation and conduct the projection accordingly.