Saravanan666/pypi-org huggingface dataset:
Package: cocojson
cocojsonUtility functions for COCO json annotation format. The COCO Format is definedhere.Installcocojsonis available onpypithroughpip3 install cocojsonor if you prefer, clone this repo and it can be installed throughpip3 install -e .(editable install) orpip3 install .as well.UsagePlease click into each for more details (if applicable). Links works only if you're viewing from thegithub homepage.Utility ToolsCOCO Categori-fyConvert your custom dataset into COCO categories. Usually used for testing a coco-pretrained model against a custom dataset with overlapping categories with the 80 COCO classes.python3-mcocojson.run.coco_catify-hExtract only AnnotationsGet annotations/predictions only from a COCO JSON. Usually used to generate a list of predictions for COCO evaluation.python3-mcocojson.run.pred_only-hFilter CategoriesFilter categories from COCO JSON.python3-mcocojson.run.filter_cat-hInsert Images Meta-InformationInsert any extra attributes/image meta information associated with the images into the coco json file.python3-mcocojson.run.insert_img_meta-hMap CategoriesMapping categories to a new dataset. Usually used for converting annotation labels to actual class label for training.python3-mcocojson.run.map_cat-hMatch Images between 2 COCO JSONsMatch images between a reference COCO JSON A and COCO JSON B (to be trimmed). Any images in JSON B that is not found in JSON A will be removed (along with associated annotations)python3-mcocojson.run.match_imgs-hMergeMerges multiple datasetspython3-mcocojson.run.merge-hMerge from fileMerges multiple datasetspython3-mcocojson.run.merge_from_file-hPrune IgnoresRemove images annotated with certain "ignore" category labels. This is usually used for removing rubbish images that are pointed out by annotators to ignore frame.python3-mcocojson.run.ignore_prune-hRemove EmptyRemove empty/negative images from COCO JSON, aka images without associated annotations.python3-mcocojson.run.remove_empty-hSampleSamples k images from a datasetpython3-mcocojson.run.sample-hSample by CategorySamples images from each category for given sample number(s).python3-mcocojson.run.sample_by_class-hSplitSplit up a COCO JSON file by images into N sets defined by ratio of total imagespython3-mcocojson.run.split-hSplit by Image Meta-InformationSplit up a COCO JSON file by images' meta-information/attributespython3-mcocojson.run.split_by_meta-hVisualiseVisualise annotations onto images. Best used for sanity check.python3-mcocojson.run.viz-hConvertersCVAT Video XML to COCO JSONConvert CVAT Video XML to COCO JSON whilst preserving track information.python3-mcocojson.run.cvatvid2coco-hCVAT Image XML to COCO JSONTODOCrowdHuman odgt to COCO JSONConverts CrowdHuman's odgt annotation format to COCO JSON format.python3-mcocojson.run.crowdhuman2coco-hCustom Object Detection Logging format to COCO JSONConverts Custom Object Detection Logging format to COCO JSON format.python3-mcocojson.run.log2coco-hCOCO to DarknetTODOCOCO EvalPlease usehttps://github.com/levan92/cocoapi.
Package: coco-labels-parser
Installpip install coco-labels-parserUsagefrom coco._parser import  CocoBoxesParser
json_file = coco/annotations/instances_train2014.json    #"path to Annotation file which is json format!"
save_path = coco/labels    #"where you want to save"
my_parser = CocoBoxesParser(json_file,save_path)
my_parser.get_categories_id()
my_parser.get_labels()
Package: coco-lib
coco-libCOCO dataset library. Provides serializable native Python bindings for several COCO dataset formats.Supported bindings and their corresponding modules:Object Detection:objectdetectionKeypoint Detection:keypointdetectionPanoptic Segmentation:panopticsegmentationImage Captioning:imagecaptioningInstallationcoco-libis available on PyPI:pipinstallcoco-libUsageCreating a dataset (Object Detection)>>>fromcoco_lib.commonimportInfo,Image,License>>>fromcoco_lib.objectdetectionimportObjectDetectionAnnotation,\...ObjectDetectionCategory,\...ObjectDetectionDataset>>>fromdatetimeimportdatetime>>>info=Info(# Describe the dataset...year=datetime.now().year,...version='1.0',...description='This is a test dataset',...contributor='Test',...url='https://test',...date_created=datetime.now()...)>>>mit_license=License(# Set the license...id=0,...name='MIT',...url='https://opensource.org/licenses/MIT'...)>>>images=[# Describe the images...Image(...id=0,...width=640,height=480,...file_name='test.jpg',...license=mit_license.id,...flickr_url='',...coco_url='',...date_captured=datetime.now()...),.........]>>>categories=[# Describe the categories...ObjectDetectionCategory(...id=0,...name='pedestrian',...supercategory=''...),.........]>>>annotations=[# Describe the annotations...ObjectDetectionAnnotation(...id=0,...image_id=0,...category_id=0,...segmentation=[],...area=800.0,...bbox=[300.0,100.0,20.0,40.0],...is_crowd=0...),.........]>>>dataset=ObjectDetectionDataset(# Create the dataset...info=info,...images=images,...licenses=[mit_license],...categories=categories,...annotations=annotations...)>>>dataset.save('test_dataset.json',indent=2)# Save the datasetLoading a dataset>>>fromcoco_lib.objectdetectionimportObjectDetectionDataset>>>dataset=ObjectDetectionDataset.load('test_dataset.json')# Load the dataset
Package: coco-loader
coco-loaderCOCO dataset loader.
Provides serializable native Python bindings for several COCO dataset formats.Supported bindings and their corresponding modules:Object Detection:objectdetectionKeypoint Detection:keypointdetectionPanoptic Segmentation:panopticsegmentationImage Captioning:imagecaptioningInstallationcoco-loaderis available on PyPI:pipinstallcoco-loaderUsageCreating a dataset (Object Detection)>>>fromcoco_loader.commonimportInfo,Image,License>>>fromcoco_loader.objectdetectionimportObjectDetectionAnnotation,\...ObjectDetectionCategory,\...ObjectDetectionDataset>>>fromdatetimeimportdatetime>>>info=Info(# Describe the dataset...year=datetime.now().year,...version='1.0',...description='This is a test dataset',...contributor='Test',...url='https://test',...date_created=datetime.now()...)>>>mit_license=License(# Set the license...id=0,...name='MIT',...url='https://opensource.org/licenses/MIT'...)>>>images=[# Describe the images...Image(...id=0,...width=640,height=480,...file_name='test.jpg',...license=mit_license.id,...flickr_url='',...coco_url='',...date_captured=datetime.now()...),.........]>>>categories=[# Describe the categories...ObjectDetectionCategory(...id=0,...name='pedestrian',...supercategory=''...),.........]>>>annotations=[# Describe the annotations...ObjectDetectionAnnotation(...id=0,...image_id=0,...category_id=0,...segmentation=[],...area=800.0,...bbox=[300.0,100.0,20.0,40.0],...is_crowd=0...),.........]>>>dataset=ObjectDetectionDataset(# Create the dataset...info=info,...images=images,...licenses=[mit_license],...categories=categories,...annotations=annotations...)>>>dataset.save('test_dataset.json',indent=2)# Save the datasetLoading a dataset>>>fromcoco_loader.objectdetectionimportObjectDetectionDataset>>>dataset=ObjectDetectionDataset.load('test_dataset.json')# Load the dataset
Package: coco-microsoft-bot-framework
A Toolkit to work with components in Microsoft Bot FrameworkInstallation (We recommend using a virtualenv):pip install coco-microsoft-bot-frameworkSetup:Setting Conversation Stateinapp.py, include the lines below.fromcoco_microsoft_bot_frameworkimportCoCoActivityHandlerfrombotbuilder.coreimport(BotFrameworkAdapterSettings,TurnContext,BotFrameworkAdapter,UserState,MemoryStorage,ConversationState,)MEMORY=MemoryStorage()CONVERSATION_STATE=ConversationState(MEMORY)# Create the BotBOT=MyBot(CONVERSATION_STATE)Setting Activity Handlerinbot.pyImport CoCo custom Activity Handler, then use it to create your bot.
At theon_message_activitymethod add the three lines as below, in order to
manage CoCo context during the conversation.fromcoco_microsoft_bot_frameworkimportCoCoActivityHandlerclassMyBot(CoCoActivityHandler):asyncdefon_message_activity(self,turn_context:TurnContext):ifself.is_component_active():awaitself.call_active_component(turn_context)returnActivate CoCo ComponentActivate CoCo register component, which ID is: "register_vp3".classMyBot(CoCoActivityHandler):asyncdefon_message_activity(self,turn_context:TurnContext):ifself.is_component_active():awaitself.call_active_component(turn_context)returnifintent=="register":awaitself.activate_component(turn_context,"register_vp3")else:awaitturn_context.send_activity("I don't understand.")
Package: cocomoco
CocomocoE is the Effort in staff monthsa and b are coefficients to be determinedKLOC is thousands of lines of code (NOTE:cocomoco uses LOC, do not enter kloc values)With cocomoco, to calculate the effort for 100000 lines of code using theorganic model:print(cocomoco.calculate(100000).effort)-> 302.1 (person-months).Project Development Time, Staff Size & ProductivityDevelopment TimeDTime is time for developmentc and d are constants to be determinedE is the effortWith cocomoco, to calculate the development time for 100000 lines of code using theorganic model:print(cocomoco.calculate(100000).dtime)-> 21.9 (months).Staff SizeAverage staff size can be calculated in the following way:Remember:Effort == Staff Months & Dtime == Months -> divide both cancel the months and staff remains!With cocomoco, to calculate the average staff size for 100000 lines of code using theorganic model:print(cocomoco.calculate(100000).staff)-> 14 number of average staff size.ProductivityHow many lines of code per staff month can be calculated via:With cocomoco, to calculate the staff productivity for 100000 lines of code
using theorganic model:print(cocomoco.calculate(100000).sloc_per_staff_month)-> 331 lines of code
per staff member and month.ModelsStandard ModelsModels define the coefficients a and b for typical projects.Organic2-50 KLOCstable devlittle innovationSemidetached50-300 KLOCaverage abilitiesmedium time-constraintsEmbeddedlarger 300 KLOClarge project teamcomplexinnovativesevere constraintsStandard ConstantsOrganic:a: 2.4b: 1.05c: 2.5d: 0.38Semidetached:a: 3.0b: 1.12c: 2.5d: 0.35Embedded:a: 3.6b: 1.2c: 2.5d: 0.32Intermediate ModelsIntermediate cocomo introduces cost drivers to the standard models.Product AttributesRELYRequired Software ReliabilityDATAData Base SizeCPLXProduct ComplexityComputer AttributesTIMEExecution Time ConstraintSTORMain Storage ConstraintVIRTVirtual Machine VolatilityTURNComputer Turnaround TimePersonnel AttributesACAPAnalyst CapabilityAEXPApplication ExperiencePCAPProgramming CapabilityVEXPVirtual Machine ExperienceLEXPProgramming Language ExperienceProject AttributesMODPModern Programming PracticesTOOLUse of Software ToolsSCEDRequired Development ScheduleCocomo commes with a predefined set of values in the following categories: very
low, low, nominal, high, very high, extra high.Show CaseFollowing charts are created viapython3 -m cocomoco --demo-mode:Effort 100k -> 500k LOCProducticity 100k -> 500k LOCInstallationSimple install this module via pip (pip for Python 2 is also supported)pip3 install --user cocomocoUsageAs Python Moduleimport cocomoco

result = cocomoco.calculate(100000)
print(result)As Python Executable$python3-mcocomoco--sloc<number>[--model<modelname>]ReferencesAlan Caine, Constructive Cost Model COCOMO,https://cs.uwaterloo.ca/~apidduck/se362/Lectures/cocomo.pdf
Package: coconerd
Coconerd (COde COunt for NERD)Count how many lines of code you had written during your doctoral study.Note: this package use regular expression to code the specific set of attributes in python file without importing the module, thus the code issafe(anyway, be aware of which github repo you are downloading from).Installpip install coconerdUsagecoconerd [path_or_url] -cache --clearArgumentHelppath_or_urlpath to local directory or url to github repo, multiple input concatenated by,-cachepath to cache directory--clearclear all cached source codeExamplecoconerd https://github.com/trungnt13/odin-ai,https://github.com/trungnt13/sisuaProcessing: 377files [00:00, 5727.62files/s]
https://github.com/trungnt13/sisua
 for_loop  : 891
 while_loop: 3
 if_cond   : 1199
 func_def  : 422
 class_def : 43
 lines     : 16744
https://github.com/trungnt13/odin-ai
 for_loop  : 3841
 while_loop: 57
 if_cond   : 6130
 func_def  : 3260
 class_def : 575
 lines     : 83649
---Total:
 for_loop  : 4732
 while_loop: 60
 if_cond   : 7329
 func_def  : 3682
 class_def : 618
 lines     : 100393
Package: coconet-binning
Citation (Work in progress)Arisdakessian C., Nigro O., Steward G., Poisson G., Belcaid M.
CoCoNet: An Efficient Deep Learning Tool for Viral Metagenome BinningDescriptionCoCoNet (Composition and Coverage Network) is a binning method for viral metagenomes. It leverages deep learning to abstract the modeling of the k-mer composition and the coverage for binning contigs assembled form viral metagenomic data. Specifically, our method uses a neural network to learn from the metagenomic data a flexible function for predicting the probability that any pair of contigs originated from the same genome. These probabilities are subsequently combined to infer bins, or clusters representing the species present in the sequenced samples. Our approach was specifically designed for diverse viral metagenomes, such as those found in environmental samples (e.g., oceans, soil, etc.).InstallInstall latest PyPi release (recommended)pip3install--usernumpypip3install--usercoconet-binningFor more installation options, see thedocumentationBasic usageCoCoNet is available as the command line program. For a list of all the options, open a terminal and run:coconetrun-hFor more details, please see the documentation onReadTheDocsChecking the installationA test dataset is provided in this repository in tests/sim_data. To quickly verify the installation worked, you can simply download the repository and run the test command as follows:gitclonehttps://github.com/Puumanamana/CoCoNetcdCoCoNetmaketestContributeIssue Tracker:githubSource Code:github
Package: coconut
Coconut (coconut-lang.org) is a variant ofPythonthatadds on top of Python syntaxnew features for simple, elegant, Pythonicfunctional programming.Coconut is developed onGitHuband hosted onPyPI. Installing Coconut is as easy as opening a command prompt and entering:pip install coconutTo help you get started, check out these links for more information about Coconut:Tutorial: If you’re new to Coconut, a good place to start is Coconut’stutorial.Documentation: If you’re looking for info about a specific feature, check out Coconut’sdocumentation.Online Interpreter: If you want to try Coconut in your browser, check out Coconut’sonline interpreter.FAQ: If you have general questions about Coconut—like who Coconut is built for and whether or not you should use it—Coconut’s frequently asked questions are often the best place to start.Create a New Issue: If you’re having a problem with Coconut, creating a new issue detailing the problem will allow it to be addressed as soon as possible.Gitter: For any questions, concerns, or comments about anything Coconut-related, ask around at Coconut’s Gitter, a GitHub-integrated chat room for Coconut developers.Releases: Want to know what’s been added in recent Coconut versions? Check out the release log for all the new features and fixes.CreditsContributorsThis project exists thanks to all the people who contribute!Become a contributor.BackersThank you to all our backers!Become a backer.SponsorsSupport Coconut by becoming a sponsor. Your logo will show up here with a link to your website.Become a sponsor.
Package: coconut-ai
coconut-aiAboutCoconut AIis aPython librarythat allows you to useAUTOMATIC1111/Stable Diffusion Web APIandLLama 2, togenerate images from text,images to imagesortext to text.The library is available onPyPy:https://pypi.org/project/coconut-ai/.Getting StartedPrerequisitesPythonv3.10PipenvAUTOMATIC1111/Stable Diffusion Web UIv1.6.0Installationpipenvinstallcoconut-aiStable Diffusion GuideFor a complete example, see thecoconut-ai-examplerepository.Install Stable Diffusion Web UIAUTOMATIC1111/Stable Diffusion Web UI installation guide# Clone the AUTOMATIC1111/Stable Diffusion Web UI repositorygitclonehttps://github.com/AUTOMATIC1111/stable-diffusion-webui.git# Go to AUTOMATIC1111/Stable Diffusion Web UI source codecdstable-diffusion-webui# For Windows users, run the following command for starting the server./webui-user.bat--api--nowebui# For Linux/MacOS users, run the following command for starting the server./webui.sh--api--nowebuiStable Diffusion UsageNote:Before using the library, you must start the Stable Diffusion Web UI server.# Go to your Python project/source codecd../python-project
pipenvinstallcoconut-aiCreate a file namedmain.pywith the following content (minimal example):importosimportcoconut_aiif__name__=='__main__':print("Coconut AI")output_path=os.path.abspath("output.png")os.makedirs(os.path.dirname(output_path),exist_ok=True)coconut_ai.text_to_image({"input_text":"Coconut","output_path":output_path,"steps":5})Run the script:pipenvrunpythonmain.pyLLama 2 GuideInstall LLama 2 ModelFor a complete example, see thecoconut-ai-examplerepository.Download the modelllama-2-7b.Q5_K_M.ggufmanually.All available models can be found on theHugging Facewebsite.LLama 2 Usage# Go to your Python project/source codecd../python-project
pipenvinstallcoconut-aiCreate a file namedmain.pywith the following content (minimal example):importosimportcoconut_aiif__name__=='__main__':print("Coconut AI")# Replace with the model path for the llama-2-7b.Q5_K_M.gguf model downloaded manuallymodel_path=os.path.abspath("data/llama-2-7b.Q5_K_M.gguf")print(coconut_ai.text_to_text({'input_text':"Generate a list of 5 funny dog names.",'max_tokens':1000,'model_path':model_path}))Run the script:pipenvrunpythonmain.py💡 ContributingAnyone can help to improve the project, submit a Feature Request, a bug report or even correct a simple spelling mistake.The steps to contribute can be found in theCONTRIBUTING.mdfile.📄 LicenseMIT
Package: coconut-develop
Coconut (coconut-lang.org) is a variant ofPythonthatadds on top of Python syntaxnew features for simple, elegant, Pythonicfunctional programming.Coconut is developed onGitHuband hosted onPyPI. Installing Coconut is as easy as opening a command prompt and entering:pip install coconutTo help you get started, check out these links for more information about Coconut:Tutorial: If you’re new to Coconut, a good place to start is Coconut’stutorial.Documentation: If you’re looking for info about a specific feature, check out Coconut’sdocumentation.Online Interpreter: If you want to try Coconut in your browser, check out Coconut’sonline interpreter.FAQ: If you have general questions about Coconut—like who Coconut is built for and whether or not you should use it—Coconut’s frequently asked questions are often the best place to start.Create a New Issue: If you’re having a problem with Coconut, creating a new issue detailing the problem will allow it to be addressed as soon as possible.Gitter: For any questions, concerns, or comments about anything Coconut-related, ask around at Coconut’s Gitter, a GitHub-integrated chat room for Coconut developers.Releases: Want to know what’s been added in recent Coconut versions? Check out the release log for all the new features and fixes.CreditsContributorsThis project exists thanks to all the people who contribute!Become a contributor.BackersThank you to all our backers!Become a backer.SponsorsSupport Coconut by becoming a sponsor. Your logo will show up here with a link to your website.Become a sponsor.
Package: coconut-lib
CoconutCoconutis a novel selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. Coconut integrates with blockchains to ensure confidentiality, authenticity and availability even when a subset of credential issuing authorities are malicious or offline. Coconut uses short and computationally efficient credentials, and ourevaluationshows that most Coconut cryptographic primitives take just a few milliseconds on average, with verification taking the longest time (10 milliseconds). We implement and evaluate a generic Coconut smart contract library forChainspaceandEthereum.A link to the full paper is available at the following address:https://arxiv.org/abs/1802.07344Pre-requisitesCoconutis built on top ofpetlibandbplib, make sure to followthese instructionsto install all the pre-requisites.InstallIf you havepipinstalled, you can installCoconutwith the following command:$ pip install coconut-libotherwise, you can build it manually as below:$ git clone https://github.com/asonnino/coconut
$ cd coconut
$ pip install -e .TestTests can be run as follows:$ pytest -v --cov=coconut tests/or simply using tox:$ toxLicenseThe Apache 2.0 license
Package: coconutpy
Client Library for encoding Videos with CoconutCoconut is a Video Encoding Web Service built for developers.For more information:Coconut:https://coconut.coAPI Documentation:https://docs.coconut.coTwitter: @openCoconutChangelogs3.0.0
New pytonh library compatible with Coconut APIv22.4.3
Fix Authorization Header:
“TypeError: a bytes-like object is required, not ‘str’” was returned before2.4.2
Don’t wait for a 401 challenge to pass the auth token to Coconut APIs.2.4.0
Implements new Job methods to get info about a job and get metadata (new metadata API).2.2.0
Added a new method #config to generate a full configuration based on the given parameters. It’s especially useful to handle dynamic settings like source or variables that can be set directly in code.2.0.0
New version of the client library which uses the HeyWatch API v2. This library is not compatible with 1.x1.0.0
First version
Package: coco-orm
coco_ormcoco_orm is an ORM built to simplify working with datasets in COCO format.ExamplesCreate a new annotations file and fill it with data in COCO format:from coco_orm import CocoDataset
from coco_orm.models import Image, Category, Annotation

# create an annotations.json file containing COCO dataset annotations.
coco_dataset = CocoDataset(".../dataset/annotations.json")

# create a new image
image = Image(id=1, file_name="01.jpg", width=320, height=320)
coco_dataset.images.append(image)

# create a new category
category = Category(id=1, name="cat")
coco_dataset.categories.append(category)

# create a new annotation
annotation = Annotation(image_id=1, category_id=1, bbox=[260, 177, 231, 199])
coco_dataset.annotations.append(annotation)

# save to the .json file
coco_dataset.save()Apply filters to the COCO dataset collectionsfrom coco_orm import CocoDataset
from coco_orm.filters import ImageFilters

# read annotations file
coco_dataset = CocoDataset(".../dataset/annotations.json")

# filter an image collection by ids
image_filters = (ImageFilters().ids([1, 3]))
coco_dataset.images.filter(image_filters, inplace=True)

# save filtered dataset to the separate file
coco_dataset.save(".../dataset/filtered_annotations.json")Further infoCreated by a team of Computer Vision enjoyers of Igor Sikorsky Kyiv Polytechnic Institute.Support Ukraine - Stop the WarSince 20 February 2014 Ukraine has been facing Russian military aggression that has left over 14 000 people killed and over 30 000 injured. Your support is crucial as it helps Ukrainian people to stand against Russian aggression.We are fighting for the future without tyranny.