Saravanan666/pypi-org huggingface dataset:
Package: camelsplit
camelsplitCamel case aware word splitting.Usagefrom camelsplit import camelsplit

# 'CamelCase' -> ['Camel', 'Case']
camelsplit('CamelCase')

# 'HTTPRequest' -> ['HTTP', 'Request']
camelsplit('HTTPRequest')

# 'IEEE 802.11ac' -> ['IEEE', ' 802.11', 'ac']
camelsplit('IEEE 802.11ac')LicenseMIT© 2018 Florian Pigorsch & contributors
Package: camely
Camelcase ConverterConvert snake_case to camelCaseScreenshotInstall$ pip install camelyUsageimportcamelyprint(camely.convert("yoginth_var"))#=> YoginthVarprint(camely.convert("this_is_variable"))#=> ThisIsVariableGet HelpThere are few ways to get help:Pleasepost questions on Stack Overflow. You can open issues with questions, as long you add a link to your Stack Overflow question.For bug reports and feature requests, open issues.For direct and quick help, you canemail me.How to contributeHave an idea? Found a bug? Seehow to contribute.Thanks!LicenseMIT
Package: camembert
CamembertConfigure middlewares and hooks for the Falcon framework easily.RequirementsPlease refer torequirements.txtfor list of requirements.List of middleware functionsConverting request to JSONRequests LoggingResponse headersContent TypeRequire HTTPSSQLAlchemy session managementInternal Server ErrorRateLimitingBlacklisted IPInstalling CamembertLocal installationClone this repository to your workspace and change directory to it.Run dependencies usingpython3 -m pip install -r requirements.txtIf you are in need to use a proxy certificate runpython3 -m pip install -r requirements.txt --cert <proxy-cert>Then runpython3 setup.py installInstallation as a PIP modulepip install camembertPypi page -CamembertExamplesPlease seeExamples.mdfor examples.
Package: camembert-bri
camembertpyCython wrapper forhttps://github.com/jts/briwith pysam integration.InstallationFrom pip repositorypipinstallcamembert-briFrom sourceconda approach (recommended)condaenvcreate-fenvironment.yml
condaactivatecamembert
pythonsetup.pyinstallpip approach# optional virtualenv:virtualenvcamembertsourcecamembert/bin/activate# Installationpipinstall-rrequirements.txt
pythonsetup.pyinstallExamplesAPIbuild read indexfromcamembertimportBrib=Bri(bam_file)b.create()retrieve readsfromcamembertimportBrib=Bri(bam_file)b.load()forreadinb.get(read_name):print(read.to_string())# read is of type pysam.AligmentSegmentCLIA command line interface is included and mimics the original bri CLI command. For usage, refer tocamembert--help
camembert<subcommand>--helpbuild read indexcamembertindexbam_file.bamretrieve readscamembertgetbam_file.bamread_name
Package: cameo
What is cameo?Cameois a high-level python library developed to aid the strain
design process in metabolic engineering projects. The library provides a
modular framework of simulation and strain design methods that targets
developers that want to develop new design algorithms and custom analysis workflows.
Furthermore, it exposes a high-level API to users that just want to
compute promising strain designs.Curious? Head over totry.cameo.bioand give it a try.Please citehttps://doi.org/10.1021/acssynbio.7b00423if you’ve used cameo in a scientific publication.InstallationUse pip to install cameo fromPyPI.$ pip install cameoIn case you downloaded or cloned the source code fromGitHubor your own fork, you can run the following to install cameo for development.$ pip install -e <path-to-cameo-repo>  # recommendedYou might need to run these commands with administrative
privileges if you’re not using a virtual environment (usingsudofor example).
Please check thedocumentationfor further details.Documentation and ExamplesDocumentation is available oncameo.bio. NumerousJupyter notebooksprovide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (try.cameo.bio).
Furthermore, course materials for a two day cell factory engineering course are availablehere.High-level API (for users)Compute strain engineering strategies for a desired product in a number
of host organisms using the high-level interface (runtime is on the order of hours).from cameo.api import design
design(product='L-Serine')OutputThe high-level API can also be called from the command line.$ cameo design vanillinFor more information run$ cameo --helpLow-level API (for developers)Find gene knockout targets using evolutionary computation.from cameo import models
from cameo.strain_design.heuristic import GeneKnockoutOptimization
from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield

model = models.bigg.e_coli_core
obj = biomass_product_coupled_yield(
    model.reactions.Biomass_Ecoli_core_w_GAM,
    model.reactions.EX_succ_e,
    model.reactions.EX_glc_e)
ko = GeneKnockoutOptimization(model=model, objective_function=obj)
ko.run(max_evaluations=50000, n=1, mutation_rate=0.15, indel_rate=0.185)OutputPredict heterologous pathways for a desired chemical.from cameo.strain_design import pathway_prediction
predictor = pathway_prediction.PathwayPredictor(model)
pathways = predictor.run(product="vanillin")OutputContributions… are very welcome! Please read theguidelinefor instructions how to contribute.
Package: cameo-eco-query
Cameo Eco Query這是一個專門用於環保查詢的服務，從enviRobot服務中拆分出來。安裝使用 pip 安裝：pip install cameo_eco_query使用方法首先，確保您已經安裝了此套件和所需的環境變數。接下來，您可以這樣使用get_event_gmap_info函數：from cameo_eco_query import get_event_gmap_info  # 請依您的模組結構調整這個 import 語句

# 範例輸入字串，包含時間、地點和事件
input_str = "下星期五晚上七點，在台北101舉行的環保會議"

# 使用預設的 'CH'（中文）語言參數
result = get_event_gmap_info(input_str)

# 或者，您也可以指定語言和提詞文檔的路徑
result_with_custom_prompt = get_event_gmap_info(input_str, lang='EN', prompt_path="your_prompt_path.txt")

print(result)
print(result_with_custom_prompt)這會回傳一個包含狀態、提示訊息、時間、地點、事件以及 Google 地圖 URL 的字典。例如：{
    "status": "success", 
    "message": "", 
    "data": {
        "time": "2023-11-10 19:00:00", 
        "location": "台北101", 
        "event": "環保會議", 
        "gmap": "https://www.google.com/maps?q=25.0339639,121.5644722"
    }
}若輸入字串中無法判讀時間資訊, 則會回傳如下：{
    "status": "error", 
    "message": "你的事件缺少具體發生時間. 請在'在台北101舉行環保會議'中適當位置加入'{事件發生時間}'", 
    "data": {
        "time": "", 
        "location": "台北101", 
        "event": "環保會議", 
        "gmap": "https://www.google.com/maps?q=25.0339639,121.5644722"
    }
}設定環境變數本服務需要以下環境變數：OPENAI_KEY: OpenAI API 金鑰GOOGLE_API_KEY: Google MAP API 金鑰您可以在.env檔案中設定這些變數。問題與反饋如有任何問題或反饋，請在GitHub Issues中提出。開發者JcXGTcW (@jcxgtcw)授權本專案使用 MIT 授權。
Package: cameo-pg2gsheet
cameo-pg2gsheet使用Docker的執行方式調整docker-compose.yaml的pg2gsheet_run及pg2gsheet_output路徑在docker-compose.yaml填入postgresql連線資訊複製src/examples.py到pg2gsheet_run目錄並調整要執行的函式輸入以下指令運行Dockerdocker-composebuildcameo-pg2gsheet
docker-composeup-dcameo-pg2gsheet執行examples.pydockerexecDockerContainerIdpython/app/run/examples.py不使用Docker的執行方式前置步驟安裝cameo-pg2gsheet套件pipinstallcameo-pg2gsheet複製src/dot_env_file_template成.env檔，並填入postgresql連線資訊範例 (另可參考src/examples.py)從單一table匯出CSVfromcameo_pg2gsheetimportexport_single_table_to_csvcsv_file_path='匯出的CSV路徑含檔名'table_name='table名稱'export_single_table_to_csv(csv_file_path,table_name)從SQL指令所join的table匯出CSVfromcameo_pg2gsheetimportexport_csv_by_sql_commandcsv_file_path='匯出的CSV路徑含檔名'sql_command='SQL指令'export_csv_by_sql_command(csv_file_path,sql_command)將CSV匯入到Google sheetfromcameo_pg2gsheetimportimport_csv_to_gsheetgoogle_api_credentials_file_path='Google API驗證檔路徑'gsheet_id='Google sheet ID'worksheet_name='worksheet名稱'csv_file_path='匯出的CSV路徑含檔名'import_csv_to_gsheet(google_api_credentials_file_path,gsheet_id,worksheet_name,csv_file_path)
Package: cameo-table
安裝(installation)pip install cameo_tableCLI 使用(CLI Usage (Chinese Only) )python -m cameo_table選擇想要的功能輸入1:挑選出指定欄位為某值的所有列輸入2:將直欄轉換為橫向資料輸入0:離開輸入1:挑選出指定欄位為某值的所有列以空白隔開 請依序輸入:要處理的檔名(路徑/url)指定值所在的欄位指定的值輸出的檔名(路徑)path/to/original/file column_to_find_value value path/to/result/file舉例 (example)原始檔案內容 (orginal file content)other_datacolumn_to_find_valuea1b2c3d1e2f3在終端機輸入以下參數 (input following arguments to terminal)> python -m cameo_table
選擇想要的功能 輸入1:挑選出指定欄位為某值的所有列 輸入2:將直欄轉換為橫向資料 輸入0:離開
1
已選功能1:挑選出指定欄位為某值的所有列
以空白隔開 請依序輸入:要處理的檔名(路徑/url) 指定值所在的欄位 指定的值 輸出的檔名(路徑)
path/to/original/file column_to_find_value 3 path/to/result/file結果檔案內容 (result file content)other_datacolumn_to_find_valuec3f3輸入2:將直欄轉換為橫向資料以空白隔開  請依序輸入:
要處理的檔名(路徑/url)轉換為欄位標題的欄位傳換為對應值的欄位輸出的檔名(路徑)path/to/original/file column_to_transform_as_column_label column_to_transform_as_value path/to/result/file舉例 (example)原始檔案內容 (orginal file content)other_datalabelvalueAa1Ab2Ac3Ba4Bb5Cb6在終端機輸入以下參數 (input following arguments to terminal)> python -m cameo_table
選擇想要的功能 輸入1:挑選出指定欄位為某值的所有列 輸入2:將直欄轉換為橫向資料 輸入0:離開
2
已選功能2:將直欄轉換為橫向資料
以空白隔開 請依序輸入:要處理的檔名(路徑/url) 轉換為欄位標題的欄位 對應值所在的欄位 輸出的檔名(路徑)
path/to/original/file label value path/to/result/file結果檔案內容 (result file content)other_dataabcA123B45C6輸入3:文件尚未完成...Python 使用 (Python usage)引入函式庫 (import module)import cameo_tablev0.2.1 舊版程式碼(old version v0.2.1 of code usage, not updated to v1.0.3 yet)cameo_table.pick_row_contain_certain_value()cameo_table.pick_rows_contain_certain_value(
    path: str or df,
    col_to_apply_on: str,
    value,
    result_path: str
)參數 (arguments):參數名(Name)類型(Type)意義(Meaning)pathstr待處理檔案的路徑(path of the file to handle with)DataFrame待處理的DataFrame(DataFrame to handle with)col_to_apply_onstr要找到某值的欄位名稱(name of the column to find value)value-在某欄要找到的值(value to be picked in the column)result_pathstr結果檔案的路徑(path to the result file)舉例 (example)原始檔案內容 (orginal file content)other_datacolumn_to_find_valuea1b2c3d1e2f3使用以下程式碼 (use following codes)import cameo_table
cameo_table.pick_rows_contain_certain_value(
    path = 'path/to/original/file',
    col_to_apply_on = 'column_to_find_value',
    value = 3,
    result_path = 'path/to/result/file')結果檔案內容 (result file content)other_datacolumn_to_find_valuec3f3cameo_table.turn_vertical_to_horizontal()cameo_table.turn_vertical_to_horizontal(
    path: str or df,
    col_to_apply_on: str,
    value,
    result_path: str
)參數 (arguments):參數名(Name)類型(Type)意義(Meaning)pathstr待處理檔案的路徑(path of the file to handle with)DataFrame待處理的DataFrame(DataFrame to handle with)col_to_apply_onstr要轉換成欄位標題的欄位(column to transform as column label)valuestr轉換為對應值的欄位(column_to_transform_as_value)result_pathstr結果檔案的路徑(path to the result file)舉例 (example)原始檔案內容 (orginal file content)other_datalabelvalueAa1Ab2Ac3Ba4Bb5Cb6使用以下程式碼 (use following codes)import cameo_table
cameo_table.turn_vertical_to_horizontal(
    path = 'path/to/original/file',
    col_to_apply_on = 'label',
    value = 'value',
    result_path = 'path/to/result/file')結果檔案內容 (result file content)other_dataabcA123B45C6
Package: cameo-txt
cameo-txtcameo-txt是一個用於將不同檔案格式（如 docx、pdf、csv、odt 等）轉換為純文本文件的 Python 庫。安裝您可以使用以下命令安裝此套件：pipinstallcameo-txt用法以下是一個簡單的例子，說明如何使用這個函數庫：from cameo_txt import convert_to_txt

# 單個檔案
result = convert_to_txt('path/to/your/file.docx')

# 多個檔案
results = convert_to_txt(['path/to/your/file1.pdf', 'path/to/your/file2.csv'])

# 保存到特定輸出資料夾
results = convert_to_txt(['path/to/your/file1.pdf', 'path/to/your/file2.csv'], output_folder='path/to/output/folder')功能cameo-txt主要提供以下功能：下載文件如果提供了URL，庫將自動下載文件並保存為臨時文件。支援多種格式支援docx、pdf、csv和odt格式的文件。您可以輕鬆添加對更多格式的支援。並行處理使用concurrent.futures並行處理多個文件，以提高效率。自動編碼檢測使用chardet自動檢測和處理不同編碼的文件。
Package: cameo-youtube-transcript
Youtube Transcript DownloaderYoutube Transcript Downloader 是一個 Python 套件，它可以簡單地從 YouTube 影片下載字幕。此工具可以透過 Python 庫的方式使用，也可以在命令列界面運行。安裝使用以下命令安裝 Youtube Transcript Downloader：python3-mpipinstallcameo-youtube-transcript套件用法在 Python 程式可以透過以下方式匯入並使用 Youtube Transcript Downloader：fromcameo_youtube_transcriptimportyoutube_transcript# 下載字幕youtube_transcript.download_transcript(url,username,output_folder)其中，url是您要下載字幕的 Youtube 影片網址，username是您的使用者名稱，output_folder是存放輸出目錄的路徑。命令列界面用法在命令列界面，可以直接運行模組：python3-myoutube_transcript命令列界面提供以下參數：urls: Youtube 影片網址（必充）username: 使用者名稱（可選，預設為 "cbh@cameo.tw"）folder: 輸出目錄的路徑（可選）範例：python3-myoutube_transcript"https://www.youtube.com/watch?v=dQw4w9WgXcQ"-u"cbh@cameo.tw"-f"/path/to/output/folder"本文件生成prompt請撰寫完整 README.md要包含安裝 python3 -m pip install youtube-transcript要包含套件用法 from youtube_transcript import youtube_transcript要包含cli用法 python3 -m youtube_transcript用繁體中文寫程式碼生成prompt-- 我要寫一個 .py
檔名: youtube_transcript.py
目的: 我要做一個可以抓取youtube字幕的抓取工具，儲存變成文字json檔案
用法: 同時要支援 cli 可以參數運用
用法: 同時要支援 import 套件可以運用呼叫
輸入參數: 一個或多個 youtube 網址
輸入參數: 使用者名稱，預設是 cbh@cameo.tw
輸入參數: 輸出檔案群.json要放到哪個folder, 預設放在 data/users/[使用者名稱]/youtube_transcript/[日期]/*.json
輸出檔案群: 根據輸入的 youtube 網址輸出類似這樣的多個 json 內容檔案
{
  "user": "cbh@cameo.tw",
  "type": "youtube_transcript",
  "time": "2023-05-21T04:21:18Z",
  "id": "23b9242f-2f62-42e8-bdbe-f65241d2ac5a",
  "file_path": "data/users/cbh_cameo_tw/youtube_transcript/2023-05-21/type_youtube_transcript_time_2023-05-21T04_21_18Z_id_23b9242f-2f62-42e8-bdbe-f65241d2ac5a.json",
  "url":"https://www.youtube.com/watch?v=HEquaCEckwg",
  "transcript": "0:00
summary of finish big by bill birlingham
0:04
written by lee schullery in quick read
0:06
narrated
0:07
by alex smith introduction
0:11
the truth is that every entrepreneur
...
"
}
例外: 如果其中一個影片URL抓不到 transcript 也沒有關係，印出抓不到的訊息，就可以忽略就好，不要跳出離開程式碼Release notespython3 -m to_pip -n youtube-transcript -v 1.0.4 youtube_transcript.py2023-05-21 14:19 Bowen 改成可以 setup.py 用 github 安裝的方法，避免pypi當站python3 -m pip install git+https://github.com/bohachu/youtube_transcript.gitpython3 -m to_pip -n youtube_transcript -v 1.0.3 youtube_transcript.py2023-05-21 13:28改名字從 youtube_transcript_downloader to youtube_transcript 避免撞名 pypipython3 -m to_pip -n youtube_transcript -v 1.0.2 youtube_transcript.py2023-05-21 13:21 Bowen新增加 README.md 完整說明
Package: came-pytorch
CAME OptimizerACL 2023 Outstanding Paper AwardConfidence-guided Adaptive Memory Efficient OptimizationThis is an official implementation ofCAMEoptimizer in the "Confidence-guided Adaptive Memory Efficient Optimization". Please cite the paper and star this repo if you find CAME useful. Thanks!Paper|Twitter|Blog|Pypi Package|zhihuMethodIn this work, we studied a confidence-guided strategy to reduce the instability of existing memory efficient optimizers. Based on this strategy, we proposed CAME to simultaneously achieve two goals: fast convergence as in traditional adaptive methods, and low memory usage as in memory-efficient methods.The pseudo code is presented in the figure with difference with Adafactor in blue fonts.Installpip install came-pytorchUsagefromcame_pytorchimportCAMEoptimizer=CAME(model.parameters(),lr=2e-4,weight_decay=1e-2,betas=(0.9,0.999,0.9999),eps=(1e-30,1e-16))Hyper-parameter TuningPre-training: Based on our experiments on BERT-Large, GPT-2 and T5, it's suitable to choose a learning rate for CAME 3-1x smaller than that for AdamW.Consider choosing $\beta_3$ between $[0.9995, 0.99995]$ if setting $\beta_1, \beta_2=0.9, 0.999$. Due to computational resource constraints, we did not explore more combinations of three betas. Different training tasks may require different combinations of optimal performance.If you have any feedback or comments regarding hyper-parameter tuning, please do not hesitate to provide them to us!ExperimentsApart from the BERT and T5 experiments shown in the paper, we conduct more and record the results here.Fine-tuning LLaMA-7BMMLUWikiTextHellaSwagTruthfulQA (MC)BoolQCOPAWSCWICAlpaca-7B40.216.7459.7638.8979.5788.0046.1549.84Alpaca-7B-CAME40.596.3859.8038.6179.0888.0049.0450.78We fine-tuned LLaMA-7B withstanford-alpaca(52k instruction-tuning dataset). To replicate our result, first register the CAME optimizer to the transformer package. Then in Alpaca training script, change the default optimizer from "adamw" to "came".Alpaca-7B and Alpaca-7B-CAME are evaluated usingInstruct-evalandlm-evaluation-harness.Pre-training GPT-2The pre-training of GPT-2 (Medium, 345M) is based onMegatron-LM. To replicate our result, add the CAME optimizer inmegatron/optimizer/__init__.pyand set theargs.optimizerto "came".Memory Usage ComparisonTo ensure a fair comparison, we set the batch size to 1 for the pre-training of GPT-2 (Medium) to examine the memory footprint of CAME and AdamW.AdamWCAMEMemory (GiB)8.777.44Citation@inproceedings{luo2023came,title={CAME: Confidence-guided Adaptive Memory Efficient Optimization},author={Luo, Yang and Ren, Xiaozhe and Zheng, Zangwei and Jiang, Zhuo and Jiang, Xin and You, Yang},booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},pages={4442--4453},year={2023}}
Package: camera4kivy
Camera4KivyYet Another Camera for Kivy2023-11-13 This repository is archived.2023/02/09 : Android users: camerax_provider has been updated to version 0.0.3OverviewInstallInstall Camera4Kivy on DesktopInstall Camera4Kivy on Androidbuildozer.spec:Run Time PermissionsInstall Camera4Kivy on iOSRun Time PermissionsExamplesTested ExamplesC4K Photo ExampleC4K QR ExampleC4K OpenCV ExampleC4K MLKit ExampleC4K TFLite ExampleTested PlatformsPreview WidgetPreview Widget Propertiesaspect_ratioletterbox_colororientationPreview Widget APIConnect Cameracamera_idmirroredfilepath_callbacksensor_resolutionsensor_rotationdefault_zoomanalyze_pixels_resolutionenable_analyze_pixelsenable_analyze_imageproxyenable_zoom_gestureenable_focus_gestureimageproxy_data_formatDisconnect CameraCapturelocationsubdirnameSelect CameraZoomPan/scrollFlashTorchFocuscamera_connectedImage analysisOverview and ExamplesUser InteractionCoordinates and image encodingAnalysis ConfigurationDebuggingPerformanceCamera BehaviorA Physical CameraResolutionSensor ResolutionCropped Sensor ResolutionPreview ResolutionCapture ResolutionAnalysis ResolutionDisplay Resolution.Camera ProviderAndroid Camera ProviderOpenCVGStreamerPicameraAVFoundationKnown BehaviorBehavior: Android .mp4 OrientationBehavior: Android .jpg Orientation.Behavior: Android armeabi-v7a build installed on an arm64-v8a deviceBehavior: Android "No supported surface combination"
Package: camera_calibration
This library takes in images of a chessboard or grid of circles in a folder and
generates camera calibration data based on the images.Some tips:Use an asymmetric grid (different number of rows and cols). Don’t worry
if you already have a grid that is symmetric, just cover a row or column with
white paper or tape.Use a grid with a white border around it.Take more images than you think you will need. Often, some images fail.To Install:Using PIP (recommended):pip install camera_calibrationUsing SetupTools:Clone the repository or download the ziprun:python setup.py installA Note on virtualenv: When using a virtual env, one must copy their cv2.so
(linux) or cv2.pyd (windows) file into the virtual environment at
Lib/site-packagesTo use within another python script:The calibration routine can be run from within another script like this:import camera_calibration as cc
cc.calibrate(dir, rows, cols, win, save, outdir, space, visualize, circles)ArgTypeUsedirstrThe directory where the image sources arerowsintThe number of internal corners on the grid verticallycolsintThe number of internal corners on the grid horizontallywinintThe window across which to look for sub-pixel cornerssaveboolWhether or not to save outputoutdirstrWhere to save outputspacefloatThe grid spacing in mmvisualizeboolWhether or not to visualize output while runningcirclesboolWhether to use a circle gridTo use as a standalone command line utility:This system can also be used from the commandline. Once the system is installed,
so long as python is on your path, it can be run very simply. For more
information, runcalibrate-camera-hfrom the commandline/terminalThanks:This is heavily based onThis Tutorial.Contributors:Michael SobreperaToni CvitanicLicense:The MIT License (MIT)Copyright (c) 2016 GTRC.Permission 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,
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Package: camera-discovery
Camera DiscoveryThis is a package to discover all onvif devices on your network.InstallationInstall the package through pip:pip install camera-discoveryExecutionTo execute the command that discover all cameras:from camera_discovery import CameraDiscovery
CameraDiscovery.ws_discovery()To execute the comand that shows information about the cameras:from camera_discovery import CameraONVIF
Class = CameraONVIF(camera_ip, user, password)
Package: camera-fusion
camera-fusionMultiple cameras correction calibration and fusion with OpenCV Python.
This package use ChAruco board to achieve accurate multi-cameras fusion.Installationpipinstallcamera-fusionDepending of your environment, you could have to compile OpenCV from source with Python bindings.QuickstartCalibrationPrint aChAruco board, for example the one in the./resourcesfolder.Measure the length of the Aruco marker and the length of the black chess square. Then start the calibration scripts.Usage examplesGenerate the lens correction calibration file for a specific camera.python3./bin/camera_calibrationGenerate homographies between multiple cameras to fuse/blend on a specific plane. If no lens correction calibration exist for the cameras, they will be generate.python3./bin/camera_fusionSimple blending methods are implemented:Blue channel to RGB blendingGray scale to RGB blendingWeighted blendingDifferenceUse casesThis project was made to create a super low-cost degree of linear polarization imager. With three cheap repurposed webcams, we achieve decent results and frame-rate.DevelopmentTest:toxBuild:makeall