Saravanan666/pypi-org huggingface dataset:
Package: cvsimpleton
COMMANDS

Window
imshow          WIP, as a substitute for original imshow in OPENCV, passing in on image variable name in string type, variable name will be displayed as title. Resizable window as default.
Utility
converge        use eitehr mean or gaussian kernel to make a high pass filter, low frequency signal will be merged into average color. 
                (https://github.com/JiayouQin/Python-projects/tree/master/17%20Image%20Balancing)
list_images     return all image files in the directory including sub directories in a list
imread          identical to openCV imread but supports UTF-8 formatted string path
imwrite         identical to openCV imwrite but supports UTF-8 formatted string path
open_morph      perform open operation with given kernel
open_circle     perform open operation with circular kernel
open_rect       perform open operation with rectangular kernel
close_morph     perform close operation with given kernel
close_circle    perform close operation with circular kernel
close_rect      perform close operation with rectangular kernel
bgr_to_hsv      convert a bgr image to hsv image, if parameter 2 is true then returned image will be split into 3 channels
add_noise       add noise of given type to image (0:gaussian, 1:s&p), accepts both string and integer number

命令列表
窗口
imshow          测试中功能，替代原OPENCV功能，传入变量名称字符串，以变量名为标题显示该图像，默认可调节窗口大小。
功能
converge        使用均值或高斯卷积核作为高通滤波核，低频信号并入直方图均值。
                (https://github.com/JiayouQin/Python-projects/tree/master/17%20Image%20Balancing)
list_images     返回目录下所有子文件夹中图像到列表中
imread          与原OPENCV功能相同并支持UTF-8格式
imwrite         与原OPENCV功能相同并支持UTF-8格式
open_morph      使用指定核做开操作
open_circle     使用圆形核做开操作
open_rect       使用方形核做开操作
close_morph     使用指定核做闭操作
close_circle    使用圆形核做闭操作
close_rect      使用方形核做闭操作
bgr_to_hsv      将BGR图像转换为HSV图像，如果第二参数为True返回图像三通道分离三张图像
add_noise       给图像加入指定噪声(0:高斯, 1:s&p)，可传入字符串或整数
Package: cvslicer
CV Slicer - Python3 PackageHandling the slicing of enormously large image without compromising quality or information. Specially made for computer vision object detection.UsageTo install this package:pipinstallcvslicerTo import:importcvslicerTo use:We need to have 2 parameters ( in pixels ),The maximum size of image after sliced.The maximum size of object for detection.# 100, 100: Image CutOff Height and Width# 40, 40: Object Maximum Height and Widthimg_slicer=cvslicer.CVSlicer(img_path,100,100,40,40)# Below function gives you a 2D array with the below elements# [ image file path, [x-coordinate to shift, y-coordinate to shift]  ]print(img_slicer.slice_img())You may referherefor sample usage.OverviewSay, you have extremely large dimension images like floor plans, maps, building layout, etc. You want to use them with your object detection model. Sometimes, it might have some compatibility issues with some models or some programs might not be taking images in such an enormous dimension.There are some workarounds, ie.(1) Downsize the image dimension by losing quality.(2) Crop/Slice the image into smaller pieces.Both the above methods will cause you to lose information. Particularly for (2), if your object gets sliced in half within the borders of cropped image, it will not be detected.Thus, here comes the purpose of CV Slicer. It is a Python-3 package which is made to handle the issues mentioned in (1) and (2) above. It is made to be compatible with OpenCV.On the other hand, CV Slicer may also help those who wish to load large image in OpenCV. OpenCV limits the size of image up to a certain constant ( you may refer here:https://github.com/opencv/opencv/blob/8eba3c1e7e8975ff1d263a41a5753efaa51d54fc/modules/imgcodecs/src/loadsave.cpp#L61).To bypass that limit, you have to apply some hacks by changing the environment variable (for newer version only) or by redefining the constants and recompile the whole OpenCV package again. But if you wish to avoid or are unable to do that, you may use CV Slicer too.Concepts & DocumentationsIn brief, if we are to sliced the image below into smaller pieces, we are going to slice it into 9 smaller pieces. The 4 pieces are the main pieces. While for the 5th - 9th pieces, they are strips above the borders, their width and height are determined by the size of the object that you are detecting. Those strips are to prevent the case where your object gets sliced in half within the borders of the first 4 pieces that we sliced.For more details please referhereLicenseMIT LicenseAuthorQuek JY
Package: cvss
This Python package contains CVSS v2, v3 and v4 computation utilities and
interactive calculator (for v2 and v3 only) compatible with both Python 2 and Python 3.The library is tested on all currently-supported Python versions available
via GitHub Actions (with the exception of Python 2.7, which is EOL but
still tested against), but it is simple enough to run on even older versions.Installation# pip install cvssUsageLibraryfromcvssimportCVSS2,CVSS3,CVSS4vector='AV:L/AC:L/Au:M/C:N/I:P/A:C/E:U/RL:W/RC:ND/CDP:L/TD:H/CR:ND/IR:ND/AR:M'c=CVSS2(vector)print(vector)print(c.clean_vector())print(c.scores())print()vector='CVSS:3.0/S:C/C:H/I:H/A:N/AV:P/AC:H/PR:H/UI:R/E:H/RL:O/RC:R/CR:H/IR:X/AR:X/MAC:H/MPR:X/MUI:X/MC:L/MA:X'c=CVSS3(vector)print(vector)print(c.clean_vector())print(c.scores())print(c.severities())print()vector='CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:N'c=CVSS4(vector)print(vector)print(c.base_score)print(c.severity)Sample output:AV:L/AC:L/Au:M/C:N/I:P/A:C/E:U/RL:W/RC:ND/CDP:L/TD:H/CR:ND/IR:ND/AR:M
AV:L/AC:L/Au:M/C:N/I:P/A:C/E:U/RL:W/CDP:L/TD:H/AR:M
(5.0, 4.0, 4.6)

CVSS:3.0/S:C/C:H/I:H/A:N/AV:P/AC:H/PR:H/UI:R/E:H/RL:O/RC:R/CR:H/IR:X/AR:X/MAC:H/MPR:X/MUI:X/MC:L/MA:X
CVSS:3.0/AV:P/AC:H/PR:H/UI:R/S:C/C:H/I:H/A:N/E:H/RL:O/RC:R/CR:H/MAC:H/MC:L
(6.5, 6.0, 5.3)
('Medium', 'Medium', 'Medium')

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:N
9.9
CriticalInteractive calculatorFor interactive calculator run the following:$ cvss_calculatorFor help on the calculator options run:$ cvss_calculator --helpTestingFor extensive testing, the test vectors were generated using official
JavaScript generators andcvsslib.To run all tests using all supported versions of Python 2 and Python 3 installed:$ tox
$ tox -e py311   # Run tests using a specific version of Python
Package: cvsser
cvssercvsseris a simple library for interpreting CVSS vector strings and converting their metrics into ready-to-publish formats.>>> import cvsser
>>> import json
>>> sample = "CVSS:3.0/AV:L/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H/E:U/RL:O/RC:C"
>>> vs = cvsser.VectorString(sample)
>>> print(vs.privileges_required)
N
>>> print(json.dumps(vs.to_dict(style="verbose", parentheticals="values", include="mandatory"), indent=2))
{
  "AV": {
    "metric": "Attack Vector",
    "value": "Local (L)"
  },
  "AC": {
    "metric": "Attack Complexity",
    "value": "High (H)"
  },
  "PR": {
    "metric": "Privileges Required",
    "value": "None (N)"
  },
  "UI": {
    "metric": "User Interaction",
    "value": "Required (R)"
  },
  "S": {
    "metric": "Scope",
    "value": "Unchanged (U)"
  },
  "C": {
    "metric": "Confidentiality Impact",
    "value": "High (H)"
  },
  "I": {
    "metric": "Integrity Impact",
    "value": "High (H)"
  },
  "A": {
    "metric": "Availability Impact",
    "value": "High (H)"
  }
}
>>> print(json.dumps(vs.guide, indent=2))
{
  "v2.0": {
    "AV": {
      "name": "Access Vector",
      "values": {
        "L": "Local",
        "A": "Adjacent Network",
        "N": "Network"
      },
      "type": "Base",
      "mandatory": true
    },
    "AC": {
      "name": "Access Complexity",
      "values": {
        "H": "High",
        "M": "Medium",
        "L": "Low"
      },
      "type": "Base",
      "mandatory": true
    },
...Installing cvssercvsser is available on PyPI:$python-mpipinstallcvsser
Package: cvss-rescore
cvss_rescoreRescore cvss3 and 3.1 results from any json file based on custom rules.The ProblemCvss scoring consists of three components: Base, Temporal, and Environmental.When working with third-party dependency (SCA) vulnerabilities,
nearly every tool reports it’s scores only using the base score. This is
understandable, as the reporters of the vulnerabilities would only know about
the vulnerabilities themselves. They would have no idea how the vulnerable package
is actually used in your project. Do you have mitigating controls in place? Is it only
a test project? Is it only in a protected CI/CD pipeline? All of these factors and more
can impact the environmental score, which can lower the actual score of a vulnerability
significantly.How We Use ThisOutput-AgnosticWe use the cvss-rescore packate as a post-processor after our SCA scan has been run. Because
the cvss-rescore package can take any json format output, it is tool-agnostic. We have tested
it successfully using Dependabot and JFrog Xray, but there’s no reason
any other tool can’t be used so long as the output is json.Rules-BasedBecause we leverage the Python rule-engine package as a dependency, users can create a
rules_actions.json file in their root directory. Users can create as many rules as they need,
modifying one or more cvss vector metrics per rule.RequirementsPython 3.6 or higherA working knowledge of CVSS calcuation. You can reference the calculator athttps://www.first.org/cvss/calculator/3.1https://www.first.org/cvss/user-guidehttps://www.first.org/cvss/v3.1/examplesInstallationYou can find the package at pypi.orghttps://pypi.org/project/cvss-rescore/To install, simply run ‘pip install cvss-rescore’ from the command lineDocumentationYou can get the current documentation athttps://cvss-rescore.readthedocs.io/en/latest/Dependenciesrule-engine:https://pypi.org/project/rule-engine/cvss:https://pypi.org/project/cvss/NoteThis project has been set up using PyScaffold 4.3.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: cvsutils
cvsutilsUnofficial utility scripts for Microsoft Custom Vision Service.SetuppipinstallcvsutilsexportCVS_ENDPOINT='<endpoint>'exportCVS_TRAINING_KEY='<training_key>'exportCVS_PREDICTION_KEY='<prediction_key>'exportCVS_PREDICTION_RESOURCE_ID='<resource_id>'Those keys and endpoint information can be found in the Custom Vision's settings page.Available commands# Show a list of projectscvs_list_projects[--verbose]# Create a new projectcvs_create_project<dataset_filepath>[--project_name<name>][--domain_id<domain_id>]# Download dataset from a projectcvs_download_project<project_id><output_dir>[--filter_tag<tag_id>[<tag_id>...]]# Train a modelcvs_train_project<project_id>[--domain_id<domain_id>][--type{multiclass,multilabel}][--force]# Export a modelcvs_export_model<project_id><iteration_id>{tensorflow,coreml,onnx}[--output_filepath<filepath>]Andcvs_add_imagescvs_download_predictionscvs_evaluate_projectcvs_get_domainscvs_predict_datasetcvs_predict_imagecvs_remove_iterationcvs_show_projectcvs_validate_datasetTo see the detailed help, please run the command with "-h" option.Dataset file formatThis tool uses the SIMPLE dataset format to upload/download datasets from Custom Vision Service.For details, please see thesimpledatasetrepository.
Package: cvsxxdiff
cvsxxdiff is a collection of utilities which allow the user to graphically
review changes, track status and manage commits of files managed by a CVS
repository.Convenient options to review changes of a file as it was n versions
ago along with the respective logs are provided. When invoked in conflict
resolve mode, it works as a graphical tool to resolve conflicts. At commit
time, logs are formated based on a user defined template.Graphical output of diffs is provided by xxdiff.
Package: cvtk
cvtkcomputer vision toolkit of pytorchPyTorch 1.7+packaging# pip install --upgrade setuptools wheel# pip install --upgrade twinepythonsetup.pysdistbdist_wheel
twineuploaddist/*

rm-rfbuilddist*.egg-infoinstallationpipinstallcvtk# https://pip.pypa.io/en/latest/reference/pip_install/#gitpipinstallgit+https://github.com/flystarhe/cvtk.git@hash
pipinstallgit+https://github.com/flystarhe/cvtk.git@main
pipinstallgit+https://github.com/flystarhe/cvtk.git@v1.0
Package: cvtk-release
CVTK Python APICVTK, a Computer Vision ToolKit. The project provides Python API for CVTK. More details can be access toPypi.Installationpipinstallcvtk-releaseTest callimportcvtkcvtk.add(1,2)LicenseCVTK is provided under a BSD-style license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.
Package: cvtoolbox
项目意义本项目是一个工具箱，旨在运用该工具箱来快速的整理和训练数据项目内容编号文件函数内容1tools_0_replace_str.pyreplaceNegtiveToZero(path, old_str, new_str)替换文件中的内容2tools_1_statisitc.pyextra_box(image_path)替换文件中的内容
Package: cvtools
cvtoolsOpenCV tools for cv applications.cvtools.cv_load_imageUse opencv to load images, either in local file system or remote http servers.from cvtools import cv_load_image
cv_load_image('https://ss1.baidu.com/6ONXsjip0QIZ8tyhnq/it/u=4234657924,3898859385&fm=173&app=25&f=JPEG?w=640&h=756&s=DA202EC74623C8EE582E73620300D07F')cvtools.get_image_infoTo get image meta info really fast.from cvtools import get_image_info, verify_jpeg
with open("test.png", 'rb') as f:
    print(get_image_info(f))
    print(verify_jpeg(f))

# 'image/png', 1506, 1186
# False
Package: cvtoolss
cvtoolsComputer Vision Tool LibraryIntroductioncvtools is a helpful python library for computer vision.It provides the following functionalities.Dataset Conversion(voc to coco, bdd to coco, ...)Data Augmentation(random mirror, random sample crop, ...)Dataset Analysis(visualization, cluster analysis, ...)Image processing(crop, resize, ...)Useful utilities (iou, timer, ...)Universal IO APIsSee thedocumentationfor more features and usage.InstallationTry and start withpipinstallcvtoolssNote: There are two s at the end.or install from sourcegitclonehttps://github.com/gfjiangly/cvtools.gitcdcvtools
pipinstall.# (add "-e" if you want to develop or modify the codes)exampleconvert voc-like dataset to coco-like datasetimportcvtoolsmode='train'root='D:/data/VOCdevkit/VOC2007'# The cls parameter is a file containing categories,# one category string is one linevoc_to_coco=cvtools.VOC2COCO(root,mode=mode,cls='voc/cls.txt')voc_to_coco.convert()voc_to_coco.save_json(to_file='voc/{}.json'.format(mode))convert dota dataset to coco-like dataset.importcvtools# convert dota dataset to coco dataset format# label folderlabel_root='/media/data/DOTA/train/labelTxt/'# imgage folderimage_root='/media/data/DOTA/train/images/'dota_to_coco=cvtools.DOTA2COCO(label_root,image_root)dota_to_coco.convert()save='dota/train_dota_x1y1wh_polygen.json'dota_to_coco.save_json(save)coco-like dataset analysisimportcvtools# imgage folderimg_prefix='/media/data/DOTA/train/images'# position you save in dataset convertion.ann_file='../label_convert/dota/train_dota_x1y1wh_polygen.json'coco_analysis=cvtools.COCOAnalysis(img_prefix,ann_file)save='dota/vis_dota_whole/'coco_analysis.vis_instances(save,vis='segmentation',box_format='x1y1x2y2x3y3x4y4')# Size distribution analysis for each categorysave='size_per_cat_data.json'coco_analysis.stats_size_per_cat(save)# Average number of targets per image for each categorysave='stats_num.json'coco_analysis.stats_objs_per_img(save)# Analysis of target quantity per categorysave='objs_per_cat_data.json'coco_analysis.stats_objs_per_cat(save)save='dota/bbox_distribution/'coco_analysis.cluster_analysis(save,name_clusters=('area',))# and so on...
Package: cvtrafficregenerator
cvtrafficregeneratorCV-TrafficRegenerator is a Python library for regenerating the API events captured in CloudVector's APIShark tool with
the new additions when there are changes in the APISpecVisithttps://www.cloudvector.com/api-shark-free-observability-security-monitoring-tool/#apisharkInstallationUse the package managerpipto install foobar.pipinstallcvtrafficregeneratorUsage****************************************************************************************************CloudVectorCommunityEdition-Coverageanalysisplugin****************************************************************************************************EnterCommunityEdition(CE)hostinformat<host>:<port>:x.x.x.x:yEnteryourCommunityEdition(CE)username:sandeepCommunityEdition(CE)password:EnterabsolutepathtoOldAPISPEC:../input.jsonEnterabsolutepathtonewAPISPEC:../input1.jsoninstead of giving inputs every single time you can also alternatively create a file called my_cesetup.yaml in the path from where you are running the toolce_host:ce_username:ce_recording:input_apispec:you can have multiple such my_cesetup.yaml for different CE setup or different recordings and run them from specific paths for its corresponding reportsLicenseMIT
Package: cvu
Connectome Visualization Utility version 0.5.1Author: Roan LaPlanteDESCRIPTIONThe Connectome Visualization Utility is a free and open source software projectdesigned for the visualization of multi-modal, abstract brain networks. Tofacilitate visualization of network topology and modularity, cvu additionallyinterfaces with graph theory tools.The program is designed for scientists, but to run as a standalone application.COPYRIGHT INFORMATIONThis program strictly observes the tenets of fundamentalist Theravada Mahasistyle Buddhism.  Any use of this program in violation of these aforementionedtenets or in violation of the principles described in the Visuddhimagga Sutta isstrictly prohibited and punishable by extensive Mahayana style practice.Note that the observation of the tenets of fundamentalist Theravada Mahasistyle Buddhism and the Visuddhimagga Sutta is optional as long as the terms andconditions of the GNU GPLv3+ are upheld.DEPENDENCIESThese dependencies are satisfied by installing a scientific python distributionsuch as Canopy or anaconda:matplotlib -- plotting librarychaco -- interactive plotting librarynumpy -- math librarymayavi -- interactive 3D visualization libraryThese dependencies are *required* and must be manually installed into yourpython distribution. Typically this is as simple as typing 'pip install package'mne -- MNE python, a broad library for MNE processing that is used in CVU tomanipulate surface and parcellation files.bctpy -- a graph theory libraryOptional dependencies:nibabel -- nibabel is a neuroimaging library that is used to locate subcorticalstructures. Without nibabel, subcortical structures may not appear in anyparcellations.freesurfer -- freesurfer is a very powerful set of tools to do automaticsegmentation and parcellation. CVU calls freesurfer commands to registertractography to the structural space. Without freesurfer, tractographycannot be visualized.
Package: cvutils
This repository contains useful utilities to ease the task of creating
computer vision and image processing projects. A number of command line
tool have also been added to make the project useful for general use.REQUIREMENTScv2osmatplotlibargparseINSTALLATIONFrom sourcegitclonehttps://github.com/bikz05/cvutils.gitcdcvutilspythoninstallsetup.pyFrom PyPIpip install cvutilsNOTE– You might need root access to install the package globally
on Linux. Use thesudoprefix to solve this problem.COMMAND LINE TOOLSResizing an imageTo resize an image use the command-line toolcvutils-resize.cvutils-i<pathtoimage>-w<requiredwidth>-ht<requiredheight>The resuting image is saved in the same directory as the input image
with the suffix-resizedadded before the extension.Height (-ht) is optional and is automatically calculated if width
is set to zero.Interactive CropTo crop an image use the command-line toolcvutils-crop.cvutils-crop-i<pathtoimage>Multiple regions can also be cropped from the image using the script.Selected regions can also be undone by pressing thedkey.To save the selects, pressskey and to discard the selections
press theqkey.
Package: cv-validator
CV validatorLibrary to validate computer vision data and models.Installationpip install cv-validatorUsageExample on colab:Linkfromcv_validator.checksimport*fromcv_validator.core.dataimportDataSourcefromcv_validator.core.suiteimportBaseSuite# Create class with data informationtrain=DataSource(train_image_paths,train_labels,train_predictions,transform=None)test=DataSource(test_image_paths,test_labels,test_predictions,transform=transform)# Create suite with different checkssuite=BaseSuite(checks=[ImageSize(),ColorShift(),BrightnessCheck(need_transformed_img=True),ClassifierLabelDistribution(),MetricCheck(),MetricDiff(),MetricBySize(),MetricByRatio(),HashDuplicates(mode="exact",datasource_type="train"),])# Run checkssuite.run(task="multiclass",train=train,test=test,num_workers=4)
Package: cvxbson
cvxbsonIPCIPC stands for InterProcess Communication. It is a mechanism that allows to share
data between processes. A traditional way to do so is to use json files.
Json files are rather flexible and can be used to share data between different
programming languages. However, they are not very efficient.Here we use their binary counterpart, bson files. Bson files are much more efficient
but somewhat lack the flexibility of json files. Here we rely on thebsonpackage to read and write bson files. We are interested in parsing dictionaries
of numpy arrays, pandas and  polars dataframes as fast as possible.There might be faster ways to achieve this goal and we are open to suggestions
and pull requests.We recommend using json files to transfer configurations and small amounts of data.
Bson files can then be used to transfer large matrices. A coexistence is possible
and encouraged.Demoimportnumpyasnpfromcvx.bsonimportread_bson,write_bsondata={"A":np.random.rand(50,50),"B":np.random.rand(50)}write_bson("test.bson",data)recovered=read_bson("test.bson")assertnp.allclose(data["A"],recovered["A"])assertnp.allclose(data["B"],recovered["B"])We have also implemented the same functionality in for json files but would advise
against using it. It is much slower and less efficient.You may want to avoid the explicit construction of files.
It is possible to work directly with bson strings. We provide methods for that, too.PoetryWe assume you share already the love forPoetry.
Once you have installed poetry you can performmakeinstallto replicate the virtual environment we have defined inpyproject.tomland locked inpoetry.lock.JupyterWe installJupyterLabon fly within the aforementioned
virtual environment. Executingmakejupyterwill install and start the jupyter lab.