3D目标检测 yolo 3d目标检测数据集制作

KITTI dataset

Download dataset

• KITTI 3D Object Detection Evaluation 2017 link
• 下载四个部分，共41.4GB



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• 解压后为四部分内容（相机校准矩阵calib、RGB图像image_2、标签label_2、点云数据velodyne） 对应的testing和training数据。其中，training数据为7481张（图片和点云对应的场景）,testing数据 7518张（无label_2数据）。

Data Preprocess

Part 1 3D Point Cloud Data Croping

• clone这个利用TensorFlow实现的voxelNet，里面有数据预处理的相关内容。voxelnet
• 这个tensorflow版本的voxelnet，首先将training集里的所有点云数据进行裁剪操作，具体的就是利用calib中存放的相机校准矩阵，将3d点云的点投影到2dRGB图像中（利用Tr_velo_to_cam将3d点云坐标映射到0号3d相机坐标系中，然后利用R_rect将多个相机图像位于同一平面内，最后利用对应相机的投影矩阵P将点投影到相机的平面上），在2dRGB图像坐标系以外的点云将被移除掉。

这里有两个点需要注意一下

1. 图片读取的问题：1.6.3版本的scipy无法import imread，两种解决方法1）scipy降级 2）换库引入 from imageio import imread
2. 输出文件名，和原来保持一致，6位数字高位补0

output_name = PC_NEW_ROOT + str(frame).zfill(6) + '.bin' print(output_name) # str(frame) int -> str # zfill(6) 六位数字 高位补0

3. 想要复现voxelnet的小伙伴，这个项目中原本用c生成的部分需要删掉，然后再在自己电脑里重新生成。还有就是pip install（速度慢只能换源解决）的时候强力推荐中科大源，https://pypi.mirrors.ustc.edu.cn/simple/

• Crop.py代码详解，建议断点debug过一遍流程，过程中的矩阵中间量就很清晰了~

import numpy as np
from imageio import imread
# 换了imageio库来进行图片读取

CAM = 2 # 最终投影到2号相机上（0，1，2，3）

def load_velodyne_points(filename):
    # 读某一个.bin文件里的数据，000000.bin中，就有大概19000个点，即19000行
    # 4列表示x，y，z，intensity。intensity是回波强度，和物体与雷达的距离以及物体本身的反射率有关。
    points = np.fromfile(filename, dtype=np.float32).reshape(-1, 4)
    return points

def load_calib(calib_dir):
    # P2 * R0_rect * Tr_velo_to_cam * y
    # 最后就是利用上面这个转换式来转换

    # 按行读取并保存
    lines = open(calib_dir).readlines()
    lines = [ line.split()[1:] for line in lines ][:-1]

    # 投影矩阵 3x4矩阵
    # 这里是最终要投影到2号相机上，即左边彩色相机
    P = np.array(lines[CAM]).reshape(3,4)

    # Tr_velo_to_cam，4x4矩阵
    # 点云坐标系转换到0号相机坐标系，都是3维的哦~
    Tr_velo_to_cam = np.array(lines[5]).reshape(3,4)
    Tr_velo_to_cam = np.concatenate(  [ Tr_velo_to_cam, np.array([0,0,0,1]).reshape(1,4)  ]  , 0     )

    # R_cam_to_rect，4x4矩阵
    # 校准，将多个相机图像位于同一个平面上
    R_cam_to_rect = np.eye(4)
    R_cam_to_rect[:3,:3] = np.array(lines[4][:9]).reshape(3,3)

    # 类型转换
    P = P.astype('float32')
    Tr_velo_to_cam = Tr_velo_to_cam.astype('float32')
    R_cam_to_rect = R_cam_to_rect.astype('float32')
    return P, Tr_velo_to_cam, R_cam_to_rect

def prepare_velo_points(pts3d_raw):
    # 将反射率值，其实就是intensity，设为1
    # 并将点云数据矩阵转置，比如19000x4 变成 4x19000
    # 后期可以直接和投影矩阵相乘~
    pts3d = pts3d_raw
    # Reflectance > 0
    # 第4个元素组成的矩阵，判断intensity是否大于0 1x19000的数组，里面是true/false
    indices = pts3d[:, 3] > 0
    pts3d = pts3d[indices ,:]
    pts3d[:,3] = 1 # intensity大于0的都设为1
    return pts3d.transpose(), indices

def project_velo_points_in_img(pts3d, T_cam_velo, Rrect, Prect):
    '''将3d点云数据投影到2d图片坐标系，
       点云数据为4xN矩阵
       只保留在相机前方的点云数据的投影点'''
    # 3D points in camera reference frame.
    pts3d_cam = Rrect.dot(T_cam_velo.dot(pts3d))
    # Before projecting, keep only points with z>0
    # (points that are in fronto of the camera).
    idx = (pts3d_cam[2,:]>=0)
    pts2d_cam = Prect.dot(pts3d_cam[:,idx])
    return pts3d[:, idx], pts2d_cam/pts2d_cam[2,:], idx


def align_img_and_pc(img_dir, pc_dir, calib_dir):
    
    img = imread(img_dir)
    pts = load_velodyne_points( pc_dir )
    P, Tr_velo_to_cam, R_cam_to_rect = load_calib(calib_dir)

    pts3d, indices = prepare_velo_points(pts)
    pts3d_ori = pts3d.copy()
    reflectances = pts[indices, 3]
    pts3d, pts2d_normed, idx = project_velo_points_in_img( pts3d, Tr_velo_to_cam, R_cam_to_rect, P  )
    #print reflectances.shape, idx.shape
    reflectances = reflectances[idx]
    #print reflectances.shape, pts3d.shape, pts2d_normed.shape
    assert reflectances.shape[0] == pts3d.shape[1] == pts2d_normed.shape[1]

    rows, cols = img.shape[:2]

    points = []
    for i in range(pts2d_normed.shape[1]):
        c = int(np.round(pts2d_normed[0,i]))
        r = int(np.round(pts2d_normed[1,i]))
        # 保留在图片坐标系内的点
        if c < cols and r < rows and r > 0 and c > 0:
            color = img[r, c, :]
            point = [ pts3d[0,i], pts3d[1,i], pts3d[2,i], reflectances[i], color[0], color[1], color[2], pts2d_normed[0,i], pts2d_normed[1,i]  ]
            points.append(point)
            
    points = np.array(points)
    return points

# update the following directories
IMG_ROOT = '/home/vanessa/Desktop/voxelnet/data/KITTI/image/training/image_2/'
PC_ROOT = '/home/vanessa/Desktop/voxelnet/data/KITTI/point_cloud/training/velodyne/'
PC_NEW_ROOT = '/home/vanessa/Desktop/voxelnet/data/KITTI/point_cloud/training/velodyne_new/'
CALIB_ROOT = '/home/vanessa/Desktop/voxelnet/data/KITTI/calib/training/calib/'



for frame in range(0, 7481):
    img_dir = IMG_ROOT + '%06d.png' % frame
    pc_dir = PC_ROOT + '%06d.bin' % frame
    calib_dir = CALIB_ROOT + '%06d.txt' % frame

    points = align_img_and_pc(img_dir, pc_dir, calib_dir)
    output_name = PC_NEW_ROOT + str(frame).zfill(6) + '.bin'
    print(output_name)
    points[:,:4].astype('float32').tofile(output_name)

• 修改数据集路径，然后运行crop.py，就开始裁剪了，过程有点耗时，需要耐心等待一下。
  

Part 2 Split dataset

• voxelnet，dataset划分Split protocol

complex-yolov4的数据集划分：train 6000 val 1481 test 7518
voxelnet的数据集划分：train 3712 val 3769 test 7518
我们按照voxelnet的数据集计划分进行后续操作。

• clone后配置一下基础环境Complex-YOLOv4-Pytorch Github Link，Complex_YOLOv4这里面还有个classes_names.txt，只检测三类目标，汽车、行人和骑自行车的
• split.py，数据路径修改为你自己的路径，training的点云数据要用上一步裁剪好的新数据哦！

import os
from shutil import copyfile

import pdb


def rearrange(original_folders, target_folders, split_file):
    for _, v in original_folders.items():
        if not os.path.isdir(v):
            raise Exception('No such folder: %s' % v)

    for _, v in target_folders.items():
        if not os.path.isdir(v):
            os.makedirs(v)

    if not os.path.exists(split_file):
        raise Exception('No such split file: %s' % split_file)
    else:
        with open(split_file) as f:
            content = f.readlines()
        content = [x.strip() for x in content]

        for file in content:
            src_img = os.path.join(original_folders['IMG_ROOT'], file + '.png')
            src_pc = os.path.join(original_folders['PC_ROOT'], file + '.bin')
            src_label = os.path.join(original_folders['LABEL_ROOT'], file + '.txt')
            src_calib = os.path.join(original_folders['CALIB_ROOT'], file + '.txt')

            if (not os.path.exists(src_img)) or (not os.path.exists(src_pc)) or (not os.path.exists(src_label) or (not os.path.exists(src_calib))):
                 print('No such file: %s' % file)
            else:
                dst_img = os.path.join(target_folders['IMG_ROOT'], file + '.png')
                dst_pc = os.path.join(target_folders['PC_ROOT'], file + '.bin')
                dst_label = os.path.join(target_folders['LABEL_ROOT'], file + '.txt')
                dst_calib = os.path.join(target_folders['CALIB_ROOT'], file + '.txt')

                copyfile(src_img, dst_img)
                copyfile(src_pc, dst_pc)
                copyfile(src_label, dst_label)
                copyfile(src_calib, dst_calib)


if __name__ == '__main__':
    # 加入calib的split
    # Original folder
    original_folders = dict()
    original_folders['IMG_ROOT'] = '/home/vanessa/Desktop/voxelnet/data/KITTI/image/training/image_2/'
    original_folders['PC_ROOT'] = '/home/vanessa/Desktop/voxelnet/data/KITTI/point_cloud/training/velodyne_new/'
    original_folders['LABEL_ROOT'] = '/home/vanessa/Desktop/voxelnet/data/KITTI/label/training/label_2/'
    original_folders['CALIB_ROOT'] = '/home/vanessa/Desktop/voxelnet/data/KITTI/calib/training/calib/'

    # Modified folder
    train_folders = dict()
    train_folders['IMG_ROOT'] = './data/MD_KITTI/training/image_2/'
    train_folders['PC_ROOT'] = './data/MD_KITTI/training/velodyne/'
    train_folders['LABEL_ROOT'] = './data/MD_KITTI/training/label_2/'
    train_folders['CALIB_ROOT'] = './data/MD_KITTI/training/calib/'

    val_folders = dict()
    val_folders['IMG_ROOT'] = './data/MD_KITTI/validation/image_2/'
    val_folders['PC_ROOT'] = './data/MD_KITTI/validation/velodyne/'
    val_folders['LABEL_ROOT'] = './data/MD_KITTI/validation/label_2/'
    val_folders['CALIB_ROOT'] = './data/MD_KITTI/validation/calib/'

    # Split file
    train_split_file = '/home/vanessa/Desktop/voxelnet/data/KITTI/ImageSets/train.txt'
    val_split_file = '/home/vanessa/Desktop/voxelnet/data/KITTI/ImageSets/val.txt'

    rearrange(original_folders, train_folders, train_split_file)
    rearrange(original_folders, val_folders, val_split_file)

Dataset folder structure

• 按照如下结构构建数据集

└── dataset/    
    └── kitti/
        ├──ImageSets/
        │   ├── train.txt
        │   └── val.txt
        │   └── trainval.txt
        │   └── test.txt
        ├── training/
        │   ├── image_2/ <-- for visualization
        │   ├── calib/
        │   ├── label_2/
        │   └── velodyne/
         ├── validation/
        │   ├── image_2/ <-- for visualization
        │   ├── calib/
        │   ├── label_2/
        │   └── velodyne/
        └── testing/  
        │   ├── image_2/ <-- for visualization
        │   ├── calib/
        │   └── velodyne/ 
        └── classes_names.txt

至此，我们的KITTI数据集预处理和制作就完成啦~~~