原模型检测时候只有点云的检测框，本文主要是将demo文件中的pcd_demo.py中的代码，将点云检测出的3d框投影到图像上面显示。 文章来源：https://www.toymoban.com/news/detail-639768.html
# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
# import sys
# sys.path
# sys.path.append('D:\Aware_model\mmdetection3d\mmdet3d')
import os
import sys
dir_mytest = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, dir_mytest)
from mmdet3d.apis import inference_detector, init_model, show_result_meshlab
os.environ['CUDA_LAUNCH_B LOCKING'] = '1' # 下面老是报错 shape 不一致
import numpy as np
import cv2
def project_velo2rgb(velo, calib):
T = np.zeros([4, 4], dtype=np.float32)
T[:3, :] = calib['Tr_velo2cam']
T[3, 3] = 1
R = np.zeros([4, 4], dtype=np.float32)
R[:3, :3] = calib['R0']
R[3, 3] = 1
num = len(velo)
projections = np.zeros((num, 8, 2), dtype=np.int32)
for i in range(len(velo)):
box3d = np.ones([8, 4], dtype=np.float32)
box3d[:, :3] = velo[i]
M = np.dot(calib['P2'], R)
M = np.dot(M, T)
box2d = np.dot(M, box3d.T)
box2d = box2d[:2, :].T / box2d[2, :].reshape(8, 1)
projections[i] = box2d
return projections
def draw_rgb_projections(image, projections, color=(255, 255, 255), thickness=2, darker=1):
img = image.copy() * darker
num = len(projections)
forward_color = (255, 255, 0)
for n in range(num):
qs = projections[n]
for k in range(0, 4):
i, j = k, (k + 1) % 4
cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.LINE_AA)
i, j = k + 4, (k + 1) % 4 + 4
cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.LINE_AA)
i, j = k, k + 4
cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[3, 0], qs[3, 1]), (qs[7, 0], qs[7, 1]), forward_color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[7, 0], qs[7, 1]), (qs[6, 0], qs[6, 1]), forward_color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[6, 0], qs[6, 1]), (qs[2, 0], qs[2, 1]), forward_color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[2, 0], qs[2, 1]), (qs[3, 0], qs[3, 1]), forward_color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[3, 0], qs[3, 1]), (qs[6, 0], qs[6, 1]), forward_color, thickness, cv2.LINE_AA)
cv2.line(img, (qs[2, 0], qs[2, 1]), (qs[7, 0], qs[7, 1]), forward_color, thickness, cv2.LINE_AA)
return img
def load_kitti_calib(calib_file):
"""
load projection matrix
"""
with open(calib_file) as fi:
lines = fi.readlines()
assert (len(lines) == 8)
obj = lines[0].strip().split(' ')[1:]
P0 = np.array(obj, dtype=np.float32)
obj = lines[1].strip().split(' ')[1:]
P1 = np.array(obj, dtype=np.float32)
obj = lines[2].strip().split(' ')[1:]
P2 = np.array(obj, dtype=np.float32)
obj = lines[3].strip().split(' ')[1:]
P3 = np.array(obj, dtype=np.float32)
obj = lines[4].strip().split(' ')[1:]
R0 = np.array(obj, dtype=np.float32)
obj = lines[5].strip().split(' ')[1:]
Tr_velo_to_cam = np.array(obj, dtype=np.float32)
obj = lines[6].strip().split(' ')[1:]
Tr_imu_to_velo = np.array(obj, dtype=np.float32)
return {'P2': P2.reshape(3, 4),
'R0': R0.reshape(3, 3),
'Tr_velo2cam': Tr_velo_to_cam.reshape(3, 4)}
def box3d_cam_to_velo(box3d,tr):
tx, ty, tz, l, w, h, ry = [float(i) for i in box3d] #都是激光坐标系下的参数
cam = np.ones([3, 1])
cam[0] = tx
cam[1] = ty
cam[2] = tz
t_lidar = cam.reshape(1,3)#摄像头下转点云坐标系
Box = np.array([[-l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2],
[w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2],
[0, 0, 0, 0, h, h, h, h]])
rotMat = np.array([
[np.cos(ry), -np.sin(ry), 0.0],
[np.sin(ry), np.cos(ry), 0.0],
[0.0, 0.0, 1.0]])
velo_box = np.dot(rotMat, Box)
print(np.tile(t_lidar, (8, 1)).T)
print(velo_box)
cornerPosInVelo = velo_box + np.tile(t_lidar, (8, 1)).T
box3d_corner = cornerPosInVelo.transpose()
return box3d_corner.astype(np.float32)
def load_kitti_label(box, Tr):
gt_boxes3d_corner=[]
for j in range(len(box)):
box3d_corner = box3d_cam_to_velo(box[j], Tr)
gt_boxes3d_corner.append(box3d_corner)
gt_boxes3d_corner = np.array(gt_boxes3d_corner).reshape(-1, 8, 3)
return gt_boxes3d_corner
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--pcd', default=r'D:\Aware_model\mmdetection3d\data\kitti\training\velodyne\000016.bin',
help='Point cloud file')
parser.add_argument('--config',
default=r'D:\Aware_model\mmdetection3d\work_dirs\hv_pointpillars_secfpn_6x8_160e_kitti-3d-car\hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py',
help='Config file')
parser.add_argument('--checkpoint',
default=r'D:\Aware_model\mmdetection3d\checkpoints\hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth',
help='Checkpoint file')
parser.add_argument('--img', default=r'D:\Aware_model\mmdetection3d\data\kitti\training\image_2\000016.png')
parser.add_argument('--calib', default=r'D:\Aware_model\mmdetection3d\data\kitti\training\calib\000016.txt')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--score-thr', type=float, default=0.0, help='bbox score threshold')
parser.add_argument(
'--out-dir', type=str, default='demo', help='dir to save results')
parser.add_argument(
'--show',
action='store_true',
help='show online visualization results')
parser.add_argument(
'--snapshot',
action='store_true',
help='whether to save online visualization results')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
# test a single image
result, data = inference_detector(model, args.pcd)
# show the results
show_result_meshlab(
data,
result,
args.out_dir,
args.score_thr,
# show=args.show,
show=True,
snapshot=args.snapshot,
task='det')
lidar = data['points'][0][0].cpu().numpy()
image = cv2.imread(args.img)
calib = load_kitti_calib(args.calib)
gt_box3d = result[0]['boxes_3d'].tensor.numpy()
# 在激光坐标系下预测框的八个顶点
gt_box3d = load_kitti_label(gt_box3d, calib['Tr_velo2cam'])
# view in point cloud，可视化
gt_3dTo2D = project_velo2rgb(gt_box3d, calib)
img_with_box = draw_rgb_projections(image, gt_3dTo2D, color=(0, 0, 255), thickness=1)
cv2.imwrite('img_with_box.png', img_with_box)
cv2.imshow('img_with_box.png', img_with_box)
cv2.waitKey(0) 
文章来源地址https://www.toymoban.com/news/detail-639768.html
原作者：https://blog.csdn.net/suiyingy
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