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import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from nuscenes.utils.data_classes import LidarPointCloud, Box
from nuscenes.utils.geometry_utils import view_points, box_in_image, BoxVisibility, transform_matrix
from scripts.analysis_tools.visualize.utils import color_mapping, AgentPredictionData
from scripts.analysis_tools.visualize.render.base_render import BaseRender
from pyquaternion import Quaternion
# Define a constant for camera names
CAM_NAMES_nusc = [
'CAM_FRONT_LEFT',
'CAM_FRONT',
'CAM_FRONT_RIGHT',
'CAM_BACK_RIGHT',
'CAM_BACK',
'CAM_BACK_LEFT',
]
CAM_NAMES_carla = [
'image_front',
'image_left',
'image_right',
'image_tele',
]
class CameraRender(BaseRender):
"""
Render class for Camera View
"""
def __init__(self,
figsize=(53.3333, 20),
show_gt_boxes=False,
dataset=None):
super().__init__(figsize)
self.dataset = dataset
if dataset == 'nusc':
self.cams = CAM_NAMES_nusc
elif dataset == 'carla':
self.cams = CAM_NAMES_carla
self.show_gt_boxes = show_gt_boxes
def get_axis(self, index):
"""Retrieve the corresponding axis based on the index."""
return self.axes[index//3, index % 3]
def project_to_cam(self,
agent_prediction_list,
sample_data_token,
nusc,
lidar_cs_record,
project_traj=False,
cam=None,
):
"""Project predictions to camera view."""
_, cs_record, pose_record, cam_intrinsic, imsize = self.get_image_info(
sample_data_token, nusc)
boxes = []
# import pdb;pdb.set_trace()
for agent in agent_prediction_list:
box = Box(agent.pred_center, agent.pred_dim, Quaternion(axis=(0.0, 0.0, 1.0), radians=agent.pred_yaw),
name=agent.pred_label, token='predicted')
box.is_sdc = agent.is_sdc
if project_traj:
box.pred_traj = np.zeros((agent.pred_traj_max.shape[0]+1, 3))
box.pred_traj[:, 0] = agent.pred_center[0]
box.pred_traj[:, 1] = agent.pred_center[1]
box.pred_traj[:, 2] = agent.pred_center[2] - \
agent.pred_dim[2]/2
box.pred_traj[1:, :2] += agent.pred_traj_max[:, :2]
box.pred_traj = (Quaternion(
lidar_cs_record['rotation']).rotation_matrix @ box.pred_traj.T).T
box.pred_traj += np.array(
lidar_cs_record['translation'])[None, :]
box.rotate(Quaternion(lidar_cs_record['rotation']))
box.translate(np.array(lidar_cs_record['translation']))
boxes.append(box)
# Make list of Box objects including coord system transforms.
box_list = []
tr_id_list = []
for i, box in enumerate(boxes):
# Move box to sensor coord system.
box.translate(-np.array(cs_record['translation']))
box.rotate(Quaternion(cs_record['rotation']).inverse)
if project_traj:
box.pred_traj += -np.array(cs_record['translation'])[None, :]
box.pred_traj = (Quaternion(
cs_record['rotation']).inverse.rotation_matrix @ box.pred_traj.T).T
tr_id = agent_prediction_list[i].pred_track_id
if box.is_sdc and cam == 'CAM_FRONT':
box_list.append(box)
if not box_in_image(box, cam_intrinsic, imsize):
continue
box_list.append(box)
tr_id_list.append(tr_id)
return box_list, tr_id_list, cam_intrinsic, imsize
def render_image_data(self, sample_token, nusc):
"""Load and annotate image based on the provided path."""
sample = nusc.get('sample', sample_token)
for i, cam in enumerate(self.cams):
sample_data_token = sample['data'][cam]
data_path, _, _, _, _ = self.get_image_info(
sample_data_token, nusc)
image = self.load_image(data_path, cam)
self.update_image(image, i, cam)
def render_image_data_carla(self, sample_token, version):
"""Load and annotate image based on the provided path."""
for i, cam in enumerate(self.cams):
scene_token, frame_idstr = sample_token.split('_frame_')
data_path = f'data/carla/{version}/val/{scene_token}/{cam}/{frame_idstr}.png'
image = self.load_image(data_path, cam)
self.update_image(image, i, cam)
def load_image(self, data_path, cam):
"""Update the axis of the plot with the provided image."""
image = np.array(Image.open(data_path))
font = cv2.FONT_HERSHEY_SIMPLEX
org = (50, 60)
fontScale = 2
color = (0, 0, 0)
thickness = 4
return cv2.putText(image, cam, org, font, fontScale, color, thickness, cv2.LINE_AA)
def update_image(self, image, index, cam):
"""Render image data for each camera."""
ax = self.get_axis(index)
ax.imshow(image)
plt.axis('off')
ax.axis('off')
ax.grid(False)
def render_pred_track_bbox(self, predicted_agent_list, sample_token, nusc):
"""Render bounding box for predicted tracks."""
sample = nusc.get('sample', sample_token)
lidar_cs_record = nusc.get('calibrated_sensor', nusc.get(
'sample_data', sample['data']['LIDAR_TOP'])['calibrated_sensor_token'])
for i, cam in enumerate(self.cams):
sample_data_token = sample['data'][cam]
box_list, tr_id_list, camera_intrinsic, imsize = self.project_to_cam(
predicted_agent_list, sample_data_token, nusc, lidar_cs_record)
for j, box in enumerate(box_list):
if box.is_sdc:
continue
tr_id = tr_id_list[j]
if tr_id is None:
tr_id = 0
c = color_mapping[tr_id % len(color_mapping)]
box.render(
self.axes[i//3, i % 3], view=camera_intrinsic, normalize=True, colors=(c, c, c))
# plot gt
if self.show_gt_boxes:
data_path, boxes, camera_intrinsic = nusc.get_sample_data(
sample_data_token, selected_anntokens=sample['anns'])
for j, box in enumerate(boxes):
c = [0, 1, 0]
box.render(
self.axes[i//3, i % 3], view=camera_intrinsic, normalize=True, colors=(c, c, c))
self.axes[i//3, i % 3].set_xlim(0, imsize[0])
self.axes[i//3, i % 3].set_ylim(imsize[1], 0)
def render_pred_traj(self, predicted_agent_list, sample_token, nusc, render_sdc=False, points_per_step=10):
"""Render predicted trajectories."""
sample = nusc.get('sample', sample_token)
lidar_cs_record = nusc.get('calibrated_sensor', nusc.get(
'sample_data', sample['data']['LIDAR_TOP'])['calibrated_sensor_token'])
for i, cam in enumerate(self.cams):
sample_data_token = sample['data'][cam]
box_list, tr_id_list, camera_intrinsic, imsize = self.project_to_cam(
predicted_agent_list, sample_data_token, nusc, lidar_cs_record, project_traj=True, cam=cam)
for j, box in enumerate(box_list):
traj_points = box.pred_traj[:, :3]
total_steps = (len(traj_points)-1) * points_per_step + 1
total_xy = np.zeros((total_steps, 3))
for k in range(total_steps-1):
unit_vec = traj_points[k//points_per_step +
1] - traj_points[k//points_per_step]
total_xy[k] = (k/points_per_step - k//points_per_step) * \
unit_vec + traj_points[k//points_per_step]
in_range_mask = total_xy[:, 2] > 0.1
traj_points = view_points(
total_xy.T, camera_intrinsic, normalize=True)[:2, :]
traj_points = traj_points[:2, in_range_mask]
if box.is_sdc:
if render_sdc:
self.axes[i//3, i % 3].scatter(
traj_points[0], traj_points[1], color=(1, 0.5, 0), s=150)
else:
continue
else:
tr_id = tr_id_list[j]
if tr_id is None:
tr_id = 0
c = color_mapping[tr_id % len(color_mapping)]
self.axes[i//3, i %
3].scatter(traj_points[0], traj_points[1], color=c, s=15)
self.axes[i//3, i % 3].set_xlim(0, imsize[0])
self.axes[i//3, i % 3].set_ylim(imsize[1], 0)
def get_image_info(self, sample_data_token, nusc):
"""Retrieve image information."""
sd_record = nusc.get('sample_data', sample_data_token)
cs_record = nusc.get('calibrated_sensor',
sd_record['calibrated_sensor_token'])
sensor_record = nusc.get('sensor', cs_record['sensor_token'])
pose_record = nusc.get('ego_pose', sd_record['ego_pose_token'])
data_path = nusc.get_sample_data_path(sample_data_token)
if sensor_record['modality'] == 'camera':
cam_intrinsic = np.array(cs_record['camera_intrinsic'])
imsize = (sd_record['width'], sd_record['height'])
else:
cam_intrinsic = None
imsize = None
return data_path, cs_record, pose_record, cam_intrinsic, imsize
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