点云检测框投影到图像上（mmdetection3d）

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。

# 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_meshlabos.environ['CUDA_LAUNCH_B LOCKING'] = '1'  # 下面老是报错 shape 不一致import numpy as np
import cv2def project_velo2rgb(velo, calib):T = np.zeros([4, 4], dtype=np.float32)T[:3, :] = calib['Tr_velo2cam']T[3, 3] = 1R = np.zeros([4, 4], dtype=np.float32)R[:3, :3] = calib['R0']R[3, 3] = 1num = 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] = box2dreturn projectionsdef draw_rgb_projections(image, projections, color=(255, 255, 255), thickness=2, darker=1):img = image.copy() * darkernum = 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) % 4cv2.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 + 4cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.LINE_AA)i, j = k, k + 4cv2.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 imgdef 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] = txcam[1] = tycam[2] = tzt_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)).Tbox3d_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_cornerif __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 filemodel = init_model(args.config, args.checkpoint, device=args.device)# test a single imageresult, data = inference_detector(model, args.pcd)# show the resultsshow_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)

原作者：https://blog.csdn.net/suiyingy

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点云检测框投影到图像上（mmdetection3d）

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