Saravanan666/pypi-org huggingface dataset:
Package: probstructs
Probabilistic StructuresProbstructsis easy to use Python wrapper for C++ libraryprobstructs. It supports Exponential Histograms, Count Min Sketch (CM-Sketch), and Exponential Count Min Sketch (ECM-Sketch).InstallationWith pip:pipinstallprobstructsFrom source:pipinstall.ClassesCountMinSketchCount–min sketch (CM sketch) is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space, at the expense of overcounting some events due to collisions.C++ documentation:https://probstructs.readthedocs.io/en/latest/classes.html#countminsketchfromprobstructsimportCountMinSketchcm_sketch=CountMinSketch(100,4)cm_sketch.inc("aaa",1)cm_sketch.inc("bbb",5)cm_sketch.inc("aaa",2)print(cm_sketch.get("aaa"))# 3print(cm_sketch.get("bbb"))# 5print(cm_sketch.get("ccc"))# 0cm_sketch=CountMinSketch(width=100,depth=4)cm_sketch.inc(key="bbb",delta=5)print(cm_sketch.get(key="bbb"))# 5ExponentialHistorgramExponential histogram (EH) is a probabilistic data structure that serves as a frequency counter for specific elements in the last N elements from stream..C++ documentation:https://probstructs.readthedocs.io/en/latest/classes.html#exponentialhistorgramfromprobstructsimportExponentialHistorgrameh=ExponentialHistorgram(1)eh.inc(1,1)print(eh.get(1,1))# 1eh.inc(1,1)print(eh.get(1,1))# 2eh.inc(2,1)print(eh.get(1,2))# 1eh=ExponentialHistorgram(window=1)eh.inc(tick=1,delta=1)print(eh.get(window=1,tick=1))# 1eh.inc(tick=1,delta=1)print(eh.get(window=1,tick=1))# 2eh.inc(tick=2,delta=1)print(eh.get(window=1,tick=2))# 1ExponentialCountMinSketchExponential count-min sketch (ECM-Sketch) combines CM-Sketch with EH to count number of different elements in the last N elements in the stream.C++ documentation:https://probstructs.readthedocs.io/en/latest/classes.html#exponentialcountminsketchfromprobstructsimportExponentialCountMinSketchecm_sketch=ExponentialCountMinSketch(100,4,8)ts=0ecm_sketch.inc("aaa",ts,1)ecm_sketch.inc("bbb",ts,4)ecm_sketch.inc("ccc",ts,8)print(ecm_sketch.get("aaa",4,ts))# 1print(ecm_sketch.get("bbb",4,ts))# 4print(ecm_sketch.get("ccc",4,ts))# 8print(ecm_sketch.get("ddd",4,ts))# 0ecm_sketch=ExponentialCountMinSketch(width=100,depth=4,window=8)ts=0ecm_sketch.inc(key="aaa",tick=ts,delta=1)ecm_sketch.inc(key="bbb",tick=ts,delta=4)ecm_sketch.inc(key="ccc",tick=ts,delta=8)print(ecm_sketch.get(key="aaa",window=4,tick=ts))# 1print(ecm_sketch.get(key="bbb",window=4,tick=ts))# 4print(ecm_sketch.get(key="ccc",window=4,tick=ts))# 8print(ecm_sketch.get(key="ddd",window=4,tick=ts))# 0Changelog0.2.0Introduce named parametersUpdate documentation to contain examples0.1.0Initial version
Package: procaine
Procaineis a REST client library for SAP AI Core API.  Reduces the pain.InstallationTo useProcaine, first install it using pip:$pipinstallprocaineUsageRun a flow:>>> from procaine import aicore

>>> auth = {"url": AUTH_URL, "clientid": CLIENT_ID, "clientsecret": CLIENT_SECRET}
>>> api = aicore.Client(AI_API_URL, auth)

>>> hello = api.create_execution("hello-world")
>>> hello
{'id': 'e96bc32ee9bf9e63', 'message': 'Execution scheduled', 'status': 'UNKNOWN', 'targetStatus': 'COMPLETED'}

>>> api.execution(hello)
{ ... 'status': 'COMPLETED', ... 'targetStatus': 'COMPLETED'}

>>> logs = api.execution_logs(hello)
>>> print(logs)
 _____________
< hello world >
 -------------
    \
     \
      \
                    ##        .
              ## ## ##       ==
           ## ## ## ##      ===
       /""""""""""""""""___/ ===
  ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ /  ===- ~~~
       \______ o          __/
        \    \        __/
          \____\______/DocumentationMore usage examples and detailed documentation could be found onhttps://procaine.readthedocs.io/
Package: procbridge
procbridge-pythonProcBridge is a super-lightweight IPC (Inter-Process Communication) protocol over TCP socket or Unix domain socket. It enables you tosend and recieve JSONbetween processes easily. ProcBridge is much like a simplified version of HTTP protocol, but only transfer JSON values.Please note that this repo is thePython implementationof ProcBridge protocol. You can find detailed introduction of ProcBridge protocol in the main repository:gongzhang/procbridge.Installationpip install procbridge==1.2.1ExampleServer Side:importprocbridgeaspbdefdelegate(method,args):# define remote methods:ifmethod=='echo':returnargselifmethod=='sum':returnsum(xforxinargs)elifmethod=='err':raiseRuntimeError("an server error")if__name__=='__main__':PORT=8000s=pb.Server('0.0.0.0',PORT,delegate)s.start(daemon=False)print("Server is on{}...".format(PORT))Client Side:importprocbridgeaspbclient=pb.Client('127.0.0.1',8000)# call remote methods:client.request("echo",123)# 123client.request("echo",['a','b','c'])# ['a', 'b', 'c']client.request("sum",[1,2,3,4])# 10
Package: procdog
Procdog is a simple command-line tool to start, stop, and check the health of
processes.It is intended to be a simple, cross-platform alternative to standard tools
like System V service scripts, Upstart, and systemd, for use in development, in
build systems and test harnesses, etc.It operates by starting a simple daemon that popen()s and monitors the process.
The daemon listens and accepts commands on a local Unix domain socket, making
it possible to check the process is running or terminate it, and to do basic
health checks. Every process is independent: There is a single Procdog daemon
for each monitored process. The daemon exits if the process terminates.Many options can be set via a configuration file.For further documentation, see:https://github.com/jlevy/procdog
Package: procdrive
procdriveProcdrive is a procedural wrapper aroundpy-easydrivewhich is itself a wrapper aroundpy-drivesdk.This library was developed to provide a practical environment for learning Python.
Procdrive aims to be simple and intuitive and a transition into more complicated language constructs
such as object orientation and exception handling.LimitationsSince Procdrive is not object-oriented, it does not provide support
for controlling multiple vehicles at the same time.
If you require this functionality you should check out the other libraries mentioned above.InstallationAs Procdrive does not have a pip repository yet, the install process is a little involved:gitclonehttps://github.com/PascalRombach/procdrive.gitcdprocdriveLinuxpython3-mpipinstall.Windowspy-mpipinstall.Code ExampleCoding in procdrive is very simple.
The following code scans the current map in and then reports the track pieces in order.fromprocdriveimport*connect()scan()set_speed(300)whileTrue:print(wait_for_track_change())
Package: proceduraldata
Thisproceduraldata storage package provides a self-contained
interfacefocused exclusively on storing and reading experimental
data, using an approachindependent of the specific measurement
framework used for instrument control.The main goals are to provide an interface that:Automatically stores a lot of metadata, including parameters that change during a measurement.Isproceduralrather thanfunctionalin terms of the API the experimenter sees, as procedural programming tends to be easier to understand for a typical experimental physicist.Uses standard Python flow-control constructs (for, while, if, etc.) for looping over setpoints.The API aims to be self-explanatory, wherever possible.In practice, the experimenter calls an explicitadd_points(<new
data points>)function to add rows to a traditional table of data
points, with user-defined columns. In the background,pdata
automatically records all changes to instrument parameterseach
timeadd_pointsis called.In addition, pdata provides useful helpers for reading back the data,
including automatically recorded instrument parameters, basic helpers
for visualization, and good export capabilities to other tools for
further analysis.Getting started/Full documentationSee thedocumentationat RTD for
instructions on getting started.MIT LicenseCopyright (c) 2016 Joonas GoveniusPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: proceeds
<body><header><address>proceeds</address></header><code>pip install proceeds</code><code>import proceedsproceeds.abundantly ({"build": [{"kind": "header","fields": {"name": "candidate"}},{"kind": "company","fields": {"name": "company 4","descriptions": [],"statuses": [{"names": [ "status 2" ],"places": [ "place 1", "place 2" ],"dates": [ "Year Month Day of Month", "Year Month Day of Month" ],"feats": ["feat 1","feat 2","feat 3"]},{"names": [ "status 1" ],"places": [],"dates": [],"feats": []}]}},{"kind": "company","fields": {"name": "company 4","descriptions": [],"statuses": [{"names": [ "status 2" ],"places": [ "place 1", "place 2" ],"dates": [ "Year Month Day of Month", "Year Month Day of Month" ],"feats": ["feat 1","feat 2","feat 3"]}]}},{"kind": "academics","fields": {"name": "academics 1",}}]})</code></body>
Package: procell
ProCellProCell is anaward winningmodeling and simulation framework designed to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events.ProCell manipulates raw data coming from flow cytometry experiments. Specifically, uses as input:a histogram of initial cell fluorescences (e.g., GFP signal in the population);the number of different sub-populations, along with their proportions;the mean and standard deviation of division time for each population;a fluorescence minimum threshold;a maximum simulation time T, expressed in hours.The output produced by ProCell is a histogram of GFP fluorescence after time T.Installing and using ProCellProCell can be easily installed with pip:pip install procellOnce installed, ProCell's new GUIdesigned by Luca Zaninican be launched by typing:python -m procell.guifrom the console.A tutorial about modeling, calibration, simulation and validation will be published soon.You can find a list of ProCell's pre-requisites here:https://docs.google.com/document/d/1OC0DDQQHAKs6sMOi7hWleWcwgU04h9RAsZzOye6dnAU/edit?usp=sharingGPU version of ProCellWe are developing a GPU-accelerated version of ProCell, able to strongly reduce the computational effort of simulations. The alpha implementation can be downloaded from:https://github.com/ericniso/cuda-pro-cellMore info about ProCellIf you need additional information about ProCell please write to:nobile@disco.unimib.it.Citing ProCellNobile M.S., Vlachou T., Spolaor S., Bossi D., Cazzaniga P., Lanfrancone L., Mauri G., Pelicci P.G., Besozzi D.: Modeling cell proliferation in human acute myeloid leukemia xenografts, Bioinformatics, 35(18):3378â€“3386, 2019Nobile M.S., Vlachou T., Spolaor S., Cazzaniga P., Mauri G., Pelicci P.G., Besozzi D.: ProCell: Investigating cell proliferation with Swarm Intelligence, Proceedings of the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2019), Certosa di Pontignano, Siena, Tuscany, Italy, 2019
Package: proces
Proces🐨 文本预处理。1 安装⚠️ 注意：本地安装仅支持 Python 的 3.6 以上版本；尽可能使用proces的最新版本。使用 pip 安装pipinstallproces-U从代码库安装gitclonehttps://github.com/Ailln/proces.gitcdproces&&pythonsetup.pyinstall2 使用fromprocesimportpreprocess# 默认会按照顺序执行，处理空白字符、大写转小写、繁体转简体、全角转半角result=preprocess("Today, 你 幹 什 麼 ！")# result: today,你干什么!# 配置 pipeline，比如只去除空白字符result=preprocess("Today, 你 幹 什 麼 ！",pipelines=["handle_blank_character"])# result: Today,你幹什麼！# 单独使用子方法fromprocesimportfilter_unusual_characters,filter_fromprocesimporthandle_blank_characterfromprocesimportuppercase_to_lowercasefromprocesimporttraditional_to_simplifiedfromprocesimportfull_angle_to_half_anglefromprocesimporthandle_substitute# 删除不常见字符result=filter_unusual_characters("【你是个恶魔😈啊�】")# result: 【你是个恶魔啊】# 也可以使用短方法 filter_result=filter_("【你是个恶魔😈啊�】")# result: 【你是个恶魔啊】# 处理空白字符result=handle_blank_character("空 白 字 符")# result: 空白字符result=handle_blank_character("空 白 字 符",",")# result: 空,白,字,符# 大写转小写result=uppercase_to_lowercase("UP to low")# result: up to low# 繁体转简体result=traditional_to_simplified("我幹什麼不干你事")# result: 我干什么不干你事# 全角转半角result=full_angle_to_half_angle("你好！")# result: 你好!# 替换一些字符result=handle_substitute("你好！/:-",r"/:-","表情")# result: 你好！表情## 敏感信息过滤fromprocesimportmask_phone,mask_address# 过滤手机号result=mask_phone("手机号 13397238231")# result: 手机号 133********# 过滤地址result=mask_address("我在浙江杭州余杭区")# result: 我在浙江杭州***3 TODOadd get all methods of preprocess装饰器4 许可
Package: procesador-de-texto
TextPreprocessorOverviewThe TextPreprocessor is a Python class designed for comprehensive text preprocessing.
This library can perform the following tasks:1. Text lematization    --> text_lematizacion()
2. Text stemming        --> text_stemming()
3. Remove links         --> remove_links()
4. Remove hastags       --> remove_hastags()
5. Remove characters    --> remove_characters()
6. Convert to lowercase --> convert_to_lowercase()
7. Remove emojis        --> remove_emojis()
8. Remove numbers       --> remove_numbers()
9. Remove stopwords     --> remove_stopwords()
10. Cloud words         --> cloudwords()
11. Frequency words     --> frecwords()InstallationEnsure you have NLTK installed. You can install NLTK via pip:pip install nltkUsage### Import the TextPreprocessor classfromtext_preprocessorimportTextPreprocessor# Initialize the preprocessor with default settingspreprocessor=TextPreprocessor()# Customize the preprocessor by setting flagspreprocessor=TextPreprocessor(text_lematizacion_flag=True,text_stemming_flag=True,remove_links=True,remove_hastags=True,remove_characters=True,convert_to_lowercase=True,remove_emojis=True,remove_numbers=True,remove_stopwords_flag=True,cloudwords=True,frecwords=True)text="Your text goes here..."processed_text=preprocessor.preprocess_text(text)Available Methodspreprocess_text(text): Preprocesses the input text based on the initialized flags.Other methods in the class can be used individually for specific preprocessing steps (e.g., remove_links, remove_stopwords, etc.).Examplestext="Hello! This is an example text with #hashtags and links: https://example.com"# Initialize preprocessorpreprocessor=TextPreprocessor(remove_links=True,remove_hashtags=True)# Preprocess textprocessed_text=preprocessor.preprocess_text(text)print(processed_text)Output:"Hello This is an example text with and links"texto="If you need to tokenize the text before removing stopwords, you might want to incorporate a tokenization function such as word_tokenize from NLTK."# lematizacionp1=pt.TextPreprocessor()doc=p1.lematizacion_text(texto)Output:"if you need to tokenize the text before remove stopword , you might want to incorporate a tokenization function such as word_tokenize from NLTK ."texto="If you need to tokenize the text before removing stopwords, you might want to incorporate a tokenization function such as word_tokenize from NLTK."# stemmingp1=pt.TextPreprocessor()doc=p1.stemming_text(texto)Output:"if you need to token the text befor remov stopword , you might want to incorpor a token function such as word_token from nltk ."LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: procesadortext
TextPreprocessorOverviewThe TextPreprocessor is a Python class designed for comprehensive text preprocessing.
This library can perform the following tasks:1. Text lematization    --> text_lematizacion()
2. Text stemming        --> text_stemming()
3. Remove links         --> remove_links()
4. Remove hastags       --> remove_hastags()
5. Remove characters    --> remove_characters()
6. Convert to lowercase --> convert_to_lowercase()
7. Remove emojis        --> remove_emojis()
8. Remove numbers       --> remove_numbers()
9. Remove stopwords     --> remove_stopwords()
10. Cloud words         --> cloudwords()
11. Frequency words     --> frecwords()InstallationEnsure you have NLTK installed. You can install NLTK via pip:pip install nltkUsage### Import the TextPreprocessor classfromtext_preprocessorimportTextPreprocessor# Initialize the preprocessor with default settingspreprocessor=TextPreprocessor()# Customize the preprocessor by setting flagspreprocessor=TextPreprocessor(text_lematizacion_flag=True,text_stemming_flag=True,remove_links=True,remove_hastags=True,remove_characters=True,convert_to_lowercase=True,remove_emojis=True,remove_numbers=True,remove_stopwords_flag=True,cloudwords=True,frecwords=True)text="Your text goes here..."processed_text=preprocessor.preprocess_text(text)Available Methodspreprocess_text(text): Preprocesses the input text based on the initialized flags.Other methods in the class can be used individually for specific preprocessing steps (e.g., remove_links, remove_stopwords, etc.).Examplestext="Hello! This is an example text with #hashtags and links: https://example.com"# Initialize preprocessorpreprocessor=TextPreprocessor(remove_links=True,remove_hashtags=True)# Preprocess textprocessed_text=preprocessor.preprocess_text(text)print(processed_text)Output:"Hello This is an example text with and links"texto="If you need to tokenize the text before removing stopwords, you might want to incorporate a tokenization function such as word_tokenize from NLTK."# lematizacionp1=pt.TextPreprocessor()doc=p1.lematizacion_text(texto)Output:"if you need to tokenize the text before remove stopword , you might want to incorporate a tokenization function such as word_tokenize from NLTK ."texto="If you need to tokenize the text before removing stopwords, you might want to incorporate a tokenization function such as word_tokenize from NLTK."# stemmingp1=pt.TextPreprocessor()doc=p1.stemming_text(texto)Output:"if you need to token the text befor remov stopword , you might want to incorpor a token function such as word_token from nltk ."LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: processador-de-imagem-ptbr
PROCESSADOR DE IMAGENS EM PORTUGUES - BRASILPacote de processamento de imagens desenvolvido com base na biblioteca scikit-image com comandos em portugues do Brasil.O pacote processador_de_imagem_ptbr e usado para:Processamento:
	-  Encontrar taxa de similaridade entre as imagens
	-  Realizar correspondencia de histograma
	-  Redimensionar imagem

Utilidades:
	-  Ler imagem
	-  Salvar imagem
	-  Plotar imagem
	-  Mostrar resultado de combinacao das imagens
	-  Plotar histogramaInstalacaoUsar o gerenciador de pacotespippara instalar o processador_de_imagenspipinstallprocessador_de_imagem_ptbrAutorGabriel CalasansLicenseMIT
Package: processador-imagem
processador_imagemDescription.
The package image_processing is used to:
Processing:
- Histogram matching
- Structural similarity
-Resize image
Utils:
-Read image
-Save image
-Plot image
-Plot result
-Plot histogramO pacote image_processing é usado para:
Processing:
- Correspondência de histograma
- Semelhança estrutural
-Redimensionar imagem
Utils:
-Ler imagens
-Gravar imagens
-Plot imagem
-Plot resultado
-Plot histogramaInstallationUse the package managerpipto install package_namepipinstallprocessador_imagemAuthorUiliamLicenseMIT
Package: processador-imagem-virtual
PROCESSADOR DE IMAGEM VIRTUALVIRTUAL IMAGE PROCESSORPORTUGUESDescricao.
O processamento da imagem do pacote e usado para:
Em processamento:
- CorrespondÃªncia de histograma
- Semelhanca estrutural
- Redimensionar imagem
Util:
- Ler imagem
- Salvar imagem
- Plotar imagem
- Resultado da trama
- Plotar histogramaENGLISHDescription.
The package image processing is used to:
Processing:
- Histogram  matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationPORTUGUESUsar o gerenciador de pacotespippara instalar processador_imagemENGLISHUse the package managerpipto install processador_imagempipinstallprocessador_imagem_virtualAuthorUbiratan MottaLicenseMIT
Package: processador-imagem-virtual-da
Processador de imagem virtual utilizando PythonBootcamp Geração Tech Unimed-BH Ciência de Dados daDiO- criamos um pacote de processamento de imagens em python, utilizando o gerenciador de pacotes pip para instalação do pacote 'processamento_de_imagem'. Posteriormente, também disponibilizamos o projeto no repositório Pypi.Funções do processador de imagem:Processamentocorrespondência de histogramasemelhança estruturalredimensionamento de imagemUtilidade:ler imagemsalvar imagemplotar imagemresultado da tramaplotar histogramaInstalação do pacote do projetoUtilize o gerenciador de pacotepippara instalar o pacote do projetopip install processador_imagem_virtualUsofrom package_name import file1_name
file1_name.my_function()

# Leitura da Imagem
imagem = io.ler_imagem("image.jpg")

# Carregando a Imagem
plot.plot_imagem(imagem)

# Combinações das Imagens
imagem_correspondente = combinacao.transferindo_histograma_dupla(imagem1, imagem2)
plot.plot_imagem(imagem_correspondente)AutorDaniel HisatuguLicençaMIT
Package: processador-imagens
processador_imagensDescription.
The processador_imagens is used to:
Processamento
- Histogram matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogramInstallationUse the package managerpipto install processador_imagenspipinstallprocessador_imagensAuthorGuilhermeLicenseMIT
Package: processador-imagens-wsc
PROCESSADOR DE IMAGENS VIRTUAIS WSCDescriÃ§Ã£oProjeto processador de imagens realizado no bootcamp GeraÃ§Ã£o Tech Unimed-BH CiÃªncia de Dados na Digital Innovation One (DIO).O processamento da imagem do pacote e usado para:Emprocessador:-Correspondenciadehistograma-Semelhancaestrutural-Redimensionarimagem

Util:-Lerimagem-Salvarimagem-Plotarimagem-Resultadodatrama-PlotarhistogramaInstalaÃ§Ã£oUtilize o gerenciador de pacotespippara instalar processador-imagens-wscpipinstallprocessador_imagens_wscAutorWillian CoutinhoLicenseMIT
Package: processamento-basico-de-imagens
Processamento bÃ¡sico de imagensO Processamento bÃ¡sico de imagens deste pacote permite:MÃ³dulo "processamento":CorrespondÃªncia de histograma;Similaridade estrutural;Redimensionar imagem;MÃ³dulo "utilidades":Ler imagem;Salvar imagem;Plotar imagem;Resultado do grÃ¡fico;Plotar histograma;InstalaÃ§Ã£o!pip install -r requirements.txtUse o gerenciador de pacotespippara processamento_basico_de_imagens!pipinstallprocessamento_basico_de_imagensExemplo de usofromprocessamento_basico_de_imagens.processingimportcombinacaocombinacao.diferencas_imagens(img1,img2)AuthorJoadir Damasceno JuniorLicenseMIT
Package: processamento-de-imagem
Processamento de ImagemO pacotede de processamento de imagem e usado para:- Transferencia de histograma.- Diferenciacao de imagem.- Redimencionamento de imagem.- Plot de imagem.- Plot de histograma.- Plot de resultados.- Salvar imagem.- Carregar imagem.InstalacaoUse o pacote de instalacaopippara a instalacao, por exemplo:pipinstallprocessamento_de_imagemAutorJhonathan AlvesLicencaMIT