semtic kityy数据集信息和工具可视化

2023-10-30

0 sem11-21的提交测试地址

https://competitions.codalab.org/competitions/24025#learn_the_details-evaluation

1 数据集结构，instance是panoptic polarnet预处理脚本生成，用于全景分割的实例分割

在这里插入图片描述

2 github code 安装

https://github.com/PRBonn/semantic-kitti-api

conda create --name smekitty python=3.7
pip install -r requeriment.txt

3 可视化GT

python visulize.py --sequence 00 --dataset /mnt/data/1polarnet/1polarcode/Panoptic-PolarNet-main/data

q,n,b,其中00是4000多帧 ls -l | grep “^-” | wc -l

在这里插入图片描述

4 可视化预测，其中predictions目录格式如label，unint32前16是实例ID，后16是语义label

label = np.fromfile(filename, dtype=np.uint32)
label = label.reshape((-1))
self.set_label(label)

self.sem_label = label & 0xFFFF  # semantic label in lower half，与运算保留低16位，语义标签
self.inst_label = label >> 16    # instance id in upper half,向右移位16，实例标签

each point an unsigned int (32-bit) with the instance and label, which contains in the upper part (16-bits) a class-wise instance id and in the lower part (16-bits) the class
一个label对应一帧，一帧点云平均值10.4万：从11万,49万0001.bin：（118017，4）

在这里插入图片描述
20类标签，8类things

./visualize.py --sequence 11 --dataset /mnt/data/1polarnet/1polarcode/Panoptic-PolarNet-main/data --predictions /mnt/data/1polarnet/1polarcode/Panoptic-PolarNet-main/out/SemKITTI/sequences/11/predictions
增加一个维度
points = scan[:, 0:3] # get xyz

核心加载代码

auxiliary/laserscan.py

def open_label(self,filename):
	# if all goes well, open label
    label = np.fromfile(filename, dtype=np.uint32)
    label = label.reshape((-1))

    # set it
    self.set_label(label)

def set_label(label):
	if label.shape[0] == self.points.shape[0]:
      self.sem_label = label & 0xFFFF  # semantic label in lower half，与运算保留低16位，语义标签
      self.inst_label = label >> 16    # instance id in upper half,向右移位16，实例标签

标签映射 project
def do_label_projection(self):
    # only map colors to labels that exist
    mask = self.proj_idx >= 0

    # semantics
    self.proj_sem_label[mask] = self.sem_label[self.proj_idx[mask]]
    self.proj_sem_color[mask] = self.sem_color_lut[self.sem_label[self.proj_idx[mask]]]

    # instances
    self.proj_inst_label[mask] = self.inst_label[self.proj_idx[mask]]
    self.proj_inst_color[mask] = self.inst_color_lut[self.inst_label[self.proj_idx[mask]]]

# projection color with semantic labels
    self.proj_sem_label = np.zeros((self.proj_H, self.proj_W),
                                   dtype=np.int32)              # [H,W]  label
    self.proj_sem_color = np.zeros((self.proj_H, self.proj_W, 3),
                                   dtype=np.float)              # [H,W,3] color

    # projection color with instance labels
    self.proj_inst_label = np.zeros((self.proj_H, self.proj_W),
                                    dtype=np.int32)              # [H,W]  label
    self.proj_inst_color = np.zeros((self.proj_H, self.proj_W, 3),
                                    dtype=np.float)              # [H,W,3] color

本文内容由网友自发贡献，版权归原作者所有，本站不承担相应法律责任。如您发现有涉嫌抄袭侵权的内容，请联系:hwhale#tublm.com(使用前将#替换为@)