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博客园 - 无左无右

mmdetection3d-1.0.0rc0 安裝 左乘和右乘,行向量和列向量 grep -rl "math\.tan" /media/data_1/everyday/2025_down --include="*.py" - 无左无右 已知相机到车的rt 4x4矩阵,求pitch和yaw角度 torch.where(condition, x, y) 是一个三元运算符:如果条件为真,取 x 的值;如果条件为假,保持 y(即原本的 weights)不变。 for decoder_idx, (cls, reg) in enumerate(zip(cls_scores, reg_preds)): log_str += ', '.join(log_items) 左乘与右乘 GridMask--随机用“网格状”的遮挡去盖住图片的一部分,迫使模型学习更鲁棒的特征。 obtain_sensor2top函数, sensor → ego_s → global → ego_lidar → lidar assert osp.exists(self.table_root), 'Database version not found: {}'.format(self.table_root) Deformable-DETR 网页绘图,无需注册 value = value.masked_fill(input_padding_mask[..., None], float(0)) DETR 点云绕不同的轴旋转可视化,roll,pitch,yaw 相机坐标系转车辆坐标系以及相反, RT矩阵,旋转变换P_cam = rot_car2cam * P_car + trans_car2cam; P_cam = rot * (P_car - trans) 连续200帧的ego的RT矩阵R_prevel2wld,shape是[200,4,4],目标的rt矩阵的R_curpt2curvels的shape是[87, 200, 4, 4], 87是目标数量, 把t11时刻的目标对齐到t0, numpy实现 vscode launch.json debug 带caffe库的工程代码 标注工具--抹除目标 ubuntu1804安装 mmdet3d 0.17.1 报错与解决 外参扰动 car_noise2cam = car2cam @ car_noise2car BEVDet-net部分 TP, FP, precision, recall bevdepth- 数据处理部分 ubuntu 硬盘挂载,重启后硬盘掉了 create_frustum 分析 (frustum = torch.stack((x_coords, y_coords, d_coords, paddings), -1)) sweep_lidar_depth = sweep_lidar_depth.reshape(batch_size * num_cams, *sweep_lidar_depth.shape[2:]) torch.where(condition, x, y) 自动驾驶,单目3D中的alpha角度
np.stack(a,axis=x), x=0,1,2
无左无右 · 2025-06-26 · via 博客园 - 无左无右
import numpy as np

a = [
    np.array([[1, 2, 3], [4, 5, 6]]),           # A0
    np.array([[10, 20, 30], [40, 50, 60]]),     # A1
    np.array([[100, 200, 300], [400, 500, 600]]),  # A2
    np.array([[7, 8, 9], [10, 11, 12]]),        # A3
    np.array([[13, 14, 15], [16, 17, 18]]),     # A4
    np.array([[21, 22, 23], [24, 25, 26]])      # A5
]  # a 是一个 list,长度为 6,每个元素 shape = (2, 3)


print("====> np.stack(a, axis=0)")
out0 = np.stack(a, axis=0)
print(out0.shape)  # (6, 2, 3)
print(out0)

print("\n====> np.stack(a, axis=1)")
out1 = np.stack(a, axis=1)
print(out1.shape)  # (2, 6, 3)
print(out1)

print("\n====> np.stack(a, axis=2)")
out2 = np.stack(a, axis=2)
print(out2.shape)  # (2, 3, 6)
print(out2)

====> np.stack(a, axis=0)
(6, 2, 3)
[[[  1   2   3]
  [  4   5   6]]

 [[ 10  20  30]
  [ 40  50  60]]

 [[100 200 300]
  [400 500 600]]

 [[  7   8   9]
  [ 10  11  12]]

 [[ 13  14  15]
  [ 16  17  18]]

 [[ 21  22  23]
  [ 24  25  26]]]

====> np.stack(a, axis=1)
(2, 6, 3)
[[[  1   2   3]
  [ 10  20  30]
  [100 200 300]
  [  7   8   9]
  [ 13  14  15]
  [ 21  22  23]]

 [[  4   5   6]
  [ 40  50  60]
  [400 500 600]
  [ 10  11  12]
  [ 16  17  18]
  [ 24  25  26]]]

====> np.stack(a, axis=2)
(2, 3, 6)
[[[  1  10 100   7  13  21]
  [  2  20 200   8  14  22]
  [  3  30 300   9  15  23]]

 [[  4  40 400  10  16  24]
  [  5  50 500  11  17  25]
  [  6  60 600  12  18  26]]]

Process finished with exit code 0