A Python package for statistical analysis of labeled data.
Project description
lblStatistic
基于标签的指标统计
Usage
评估每个类别的最优置信度阈值
from lblStatistic import StatisticMatrix
# 获取检测的最优置信度阈值
stm = StatisticMatrix(
pred_dir=["datasets/coco128/jsons"], # list[str|Path]
gt_dir=["datasets/coco128/xml1"], # list[str|Path]
dst_dir=Path("test_lbl_statistic"), # 输出的保存根目录地址
project="detection", # 实验项目名称, 用于生成保存文件夹
plot=True, # 是否生成可视化结果
classes=classes, # 类别名列表或者字典(编码与name的映射关系)
pred_suffix=".json", # 预测结果文件后缀, Literal['.txt', '.json', '.xml']
gt_suffix=".xml", # 可选类型分别表示yolo、labelme、pascalvoc的格式
verbose=False, # 是否输出每个类别的详细信息
chinese=False # 可以直接指定中文路径
)
stm()
超参数:
pred_dir: 预测结果文件夹路径, 可以是多个文件夹路径的列表gt_dir: 真实标注文件夹路径, 可以是多个文件夹路径的列表dst_dir: 输出的保存根目录地址project: 实验项目名称, 用于生成保存文件夹plot: 是否生成可视化结果classes: 类别名列表或者字典(编码与name的映射关系)pred_suffix: 预测结果文件后缀, Literal['.txt', '.json', '.xml']分别表示yolo、labelme、pascalvoc的格式gt_suffix: 同上, 可配置不同的后缀verbose: 是否输出每个类别的详细信息chinese: 可以直接指定中文路径
运行时参数: 无
使用自定义IOS/IOU匹配规则统计指标
from lblStatistic import StatisticConfusion
stc = StatisticConfusion(
src_gt=["/Users/elfindan/datasets/coco128/jsons"],
src_pred=["/Users/elfindan/datasets/coco128/xml1"],
dst_dir=Path("test_lbl_statistic"),
project='inference',
use_ios=True,
classes=classes,
chinese=True,
gt_suffix='.json',
pred_suffix='.xml',
use_fpfn=False,
conf=0.,
ios_thresh=0.1,
iou_thresh=0.5,
filter_category=[],
difficult_filter=True,
)
stc()
超参数:
- src_gt (list[str]): 标签文件路径, 支持多个数据子集,需要指定到数据子集的标签文件存放路径
- src_pred (list[str]): 推理结果文件路径, 支持多个数据子集,需要指定到数据子集的推理结果文件存放路径
- dst_dir (str): 预测结果保存目录
- project (str, optional): 实验名称, defaults to 'inference'.
- use_ios (bool, optional): 是否使用IOS计算, defaults to True, False表示使用IOU计算
- classes (str, List[str]): 类别文件路径, defaults to 'classes.txt'.
- chinese (bool, optional): 是否使用中文类别, 可以指定中文字体文件的路径, defaults to True
- gt_suffix (str, optional): 标签文件后缀名, defaults to '.txt'.
- pred_suffix (str, optional): 推理结果文件后缀名, defaults to '.json'.
- use_fpfn (bool, optional): 是否保存FPFN的图片, defaults to False.
- conf (float, List[float]): 置信度阈值, defaults to 0.0.
- ios_thresh (float, optional): IOS阈值, defaults to 0.5.
- iou_thresh (float, optional): IOU阈值, defaults to 0.5.
- filter_category (List[str], optional): 过滤类别, defaults to [].
- difficult_filter (bool, optional): 是否过滤困难样本, defaults to True.
运行时参数:
- max_workers (int, optional): 并行处理的线程数, 默认是根据系统自动配置.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
lblstatistic-1.0.3.tar.gz
(32.5 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file lblstatistic-1.0.3.tar.gz.
File metadata
- Download URL: lblstatistic-1.0.3.tar.gz
- Upload date:
- Size: 32.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6e44521a01c04ca22fe25ab3362eba9c91db47eb1e37a51f2883ce0eb74fab3e
|
|
| MD5 |
06dd4ad5cc061a09143ef911945dafd1
|
|
| BLAKE2b-256 |
f1fee6135886a8b35f0a75ca622623d8e50539d6e64781a5b745038ed417a581
|
File details
Details for the file lblstatistic-1.0.3-py3-none-any.whl.
File metadata
- Download URL: lblstatistic-1.0.3-py3-none-any.whl
- Upload date:
- Size: 34.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c43ebf81f80b91e14fe94dbc8d4389452e7739e7afadebcf397998c6ead2c02d
|
|
| MD5 |
e05b695926f8af3a60a4c91c0447899c
|
|
| BLAKE2b-256 |
eff469ff7a61aebb7d39aafda135494c37495b92b4b36bb3b31c40176c77474d
|