Saravanan666/pypi-org huggingface dataset:
Package: visioncpp
GPU-accelerated vision processing usingVisionCpp.Requirementspython 2.7 or >= 3.3ComputeCppCommunity Edition Betaclang-format(optional)Installation$ pip install visioncppTo build from source:$virtualenvenv$sourceenv/bin/activate(env)$python./setup.pyinstallGet Started$python>>>importvisioncppasvp>>>vp.init("~/ComputeCpp-CE-0.1-Linux")# path to your ComputeCpp package>>>a=vp.Image("examples/lena.jpg")>>>b=vp.show(a)>>>vp.run(b)See thetutorialfor more information.
Package: visionegg
The Vision Egg is a programming library (with demo applications)
that uses standard, inexpensive computer graphics cards to produce
visual stimuli for vision research experiments.For more information, visit the website at www.visionegg.orgAny feedback, questions, or comments, should go to the mailing list atvisionegg@freelists.orgThe Vision Egg is Copyright (c) by its authors and is distributed
under the GNU Lesser General Public License (LGPL).  This software
comes with absolutely no warranties, either expressed or implied.
Package: vision-explanation-methods
pythonContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: visionfi
# outviz
This repository contain code and function for visualizing all types of different plots seperately
for both categorical and numerical data in one graph so we can explore different things in one click.
——————-
Univariate Plots
Multivariate PlotsThis repository also contain code and function for handling missing values and outliers and
have ability to drop or replace them with mean, mode, median or cap with min-max limit
——————-
Zscore_Outliers
IQR_Outliers#Replacce outliers with- mean, median, mode, capChange Log1.1.0 (28/02/2022)First Release
Package: visiongraph-ui
Visiongraph UIUser interface library for visiongraph projects. The idea behind this project is to create a structure for computer vision applications. The idea is to have aconfiguration,appandgraph. And if needed awindow.ExampleConfigurationclassDemoConfig:def__init__(self):container=ContainerHelper(self)# non-ui bound settingswithcontainer.section("Test"):self.enable_input=DataField(False)|ui.Boolean("Enable")|Argument(help="Enable input.")ApplicationclassDemoApp(VisiongraphApp[vg.VisionGraph,DemoConfig]):def__init__(self,config:DemoConfig):super().__init__(config)defcreate_graph(self)->vg.Visiongraph:graph=DemoGraph().create_graph(vg.VideoCaptureInput())graph.multi_threaded=TruereturngraphGraphclassDemoGraph:def__init__(self):self.on_frame_ready:Event[np.ndarray]=Event()self.graph:Optional[vg.VisionGraph]=Nonedef_on_frame_ready(self,frame:np.ndarray)->np.ndarray:self.on_frame_ready(frame)returnframedefcreate_graph(self,input_device:vg.BaseInput)->vg.VisionGraph:self.graph=(vg.create_graph(name="Demo Graph",input_node=input_device,multi_threaded=True).then(vg.custom(self._on_frame_ready)).build())returnself.graphWindowclassDemoWindow(VisiongraphUserInterface[DemoApp,DemoConfig]):def__init__(self,app:DemoApp):super().__init__(app)Usagedefmain():config=DemoConfig()app=DemoApp(config)withUIContext():window=DemoWindow(app)AboutThis is a first draft of the library and the api will change in future release.
Package: visionmadeeasy
Vision Made EasyThis project aims to remove a lot of the complexity of dealing with the Open CV for beginner level programmers to experiment with face detection and recognition.This project has been initially developed for use within my own classes that I teach but I hope it might find use for others too.PROJECT HOMEVisionMadeEasyINSTALLpip install visionmadeeasyTo successfully run the demo, you will also have to...Download a cascade file such ashaarcascade_frontalface_default.xmlfromhttps://github.com/opencv/opencv/tree/master/data/haarcascadesand save it into your project folderCreate a sub-folder called "datasets" in your project folder. This is where it will store your training photos.Make sure you have a web camera attached :-)DEMO CODEimportvisionmadeeasydefi_see_a_face(location,img):print(f"I see a face!!! It is at{location['x']},{location['y']}")returnTrue# must return True to keep the loop alivedefi_recognise_a_face(location,person_name,confidence,img):print(f"Hello{person_name}! I am{confidence}% sure it is you :-)")returnTrue# must return True to keep the loop aliveif__name__=="__main__":vme=visionmadeeasy.VisionMadeEasy(0,"dataset")quit=Falsewhilenotquit:print("Demonstration time! Menu of options...")print("1. Detect faces")print("2. Record faces")print("3. Train for faces recorded")print("4. Recognise faces (must do training first)")print("5. Exit")choice=int(input("Enter your option (1 to 5):"))ifchoice==1:print("[face_vision] Task: Searching for faces.\nLook at the camera! (press ESC to quit)")# Demo of detecting facesvme.detect_face(i_see_a_face)elifchoice==2:print("About to save 50 images of different angles etc of a person, saving to folder ./dataset")id=int(input("Enter unique person number: "))n=input("Enter person name: ")print("Smile! :-)")# Demo of recording facesvme.record_face_dataset(images_to_record=50,interval=1,person_identifier=id,person_name=n)elifchoice==3:print("[face_vision] Task: Training... please wait...")# Demo of training facesvme.train_from_faces()elifchoice==4:print("[face_vision] Task: Searching for faces I recognise.\nLook at the camera! (press ESC to quit)")# Demo of recognising facesvme.recognise_face(i_recognise_a_face)elifchoice==5:quit=Trueprint("Goodbye!")AUTHORPaul BaumgartenLICENSEMIT License (C) 2019 Paul Baumgarten
Package: visionmadeeasyplusmanna
refer to this sitehttps://pypi.org/project/visionmadeeasy/to setup facial recognition encryption system version 1 for droid \nfrom visionmadeeasyplusmanna import *save the oneliner as a scriptfollow instructionsexecute onelinepatience is virtuerome was not built in a daypip install pyAesCryptpip install visionmadeeasy
Package: vision-mamba
Vision MambaImplementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res imagesInstallationYou can install the package using pippipinstall-e.UsageCode Quality 🧹make styleto format the codemake check_code_qualityto check code quality (PEP8 basically)black .ruff . --fixTests 🧪pytestsis used to run our tests.LicenseMIT
Package: vision-mlp-oneflow
vision-mlp-oneflowVision MLP Models Based on OneFlowTODO LISTMlp-MixerResMLPgMLPS2-MLPVision-Permutator复现过程中遇到的问题tensor.chunk()方法没有和torch.tensor.chunk()对齐, 在输入的dim参数为负数的时候未作判断, 并且整体功能还变成了repeat操作缺少tensor.floor_()方法, 只能用tensor = tensor.floor()替代缺少tensor.erfinv()方法及其inplace版本, 无法复现trunc_norm_缺少tensor.new_empty()方法flow.shape返回的是oneflow._oneflow_internal.Size无法和tuple相加, 按以下方法使用会报错:# oneflow里报错x=flow.tensor(np.random.randn(1,16,512),dtype=flow.float32)x.shape+(4,)x.size()+(4,)>>>TypeError:unsupportedoperandtype(s)for+:'oneflow._oneflow_internal.Size'and'tuple'# torch里不报错x=torch.randn(1,16,512)x.shape+(4,)x.size()+(4,)>>>torch.Size([1,16,512,4])tensor.gather()未具体判定输入的dim为负数的情况
Package: vision-models-evaluation
vision_models_evaluationInstallTo install the library, just run:pipinstallvision_models_evaluationHow to useThis library provides a method that can help you in the process of model
evaluation. Using thescikit-learn validation
techniquesyou can validate your deep learning models.In order to validate your model, you will need to build and train
various versions of it (for example, using a KFold validation, it is
needed to build five different models).For doing so, you need to provide: theDataBlockhparams
(hyperparameters), theDataLoaderhparams, the technique used to split
the data, theLearnerconstruction hparams, the learning mode (whether
to use a pretrained model or not:fit_one_cycleorfinetune) and theLearnertraining hparams. So, the first step is to define them all:db_hparams={"blocks":(ImageBlock,MaskBlock(codes)),"get_items":partial(get_image_files,folders=['train']),"get_y":get_y_fn,"item_tfms":[Resize((480,640)),TargetMaskConvertTransform(),transformPipeline],"batch_tfms":Normalize.from_stats(*imagenet_stats)}dl_hparams={"source":path_images,"bs":4}technique=KFold(n_splits=5)learner_hparams={"arch":resnet18,"pretrained":True,"metrics":[DiceMulti()]}learning_hparams={"epochs":10,"base_lr":0.001,"freeze_epochs":1}learning_mode="finetune"Then, you need to call theevaluatemethod with those defined hparams.
After the execution, the method will return a dictionary of results (for
each metric used to test the model, the value obtained in each fold).r=evaluate(db_hparams,dl_hparams,technique,learner_hparams,learning_hparams,learning_mode)Finally, you can plot the metrics using a boxplot from pandas, for
example:importpandasaspddf=pd.DataFrame(r)df.boxplot("DiceMulti");print(df["DiceMulti"].mean(),df["DiceMulti"].std())You can use this method to evaluate your model, but you can also use it
to evaluate several models with distinct hparams: you can get the
results for each of them and then plot the average of their metrics.
Package: visionner
Installationstep 1gitclonehttps://github.com/charleslf2/visionnerstep 2cdvisionnerstep 3pysetup.pyinstallUsage>>>fromvisionner.coreimportDatasetImporter>>>your_dataset=DatasetImporter("path/to/your/dataset/",size=(28,28))>>>fromvisionner.coreimportSupervisedImporter>>>features,labels=SupervisedImporter("path/to/your/dataset",categories=["cat","dog"],size=(28,28))### normalize your dataset>>>fromvisionner.coreimportDatasetNormalizer>>>your_normalized_dataset=DatasetNormalizer(your_dataset)### create a trainset and a testset>>>fromvisionner.coreimportTrainTestSpliter>>>x_train,x_test=TrainTestSpliter(dataset,test_size=0.2)### visualize the first image of your dataset>>>importmatplotlib.pyplotasplt>>>plt.imshow(your_dataset[0])>>>plt.show()### save your dataset>>>fromvisionner.coreimportDatasetSaver>>>DatasetSaver("my_saved_dataset",your_dataset)### open your dataset>>>fromvisionner.coreimportDatasetOpener>>>my_saved_dataset=DatasetOpener("my_saved_dataset.npy")
Package: vision_oslo_extension
Project NameThis is VISION-OSLO extension package in order to support the simulation data post processing.Please refer to the document manual for instructions.CopyrightCopyright @ Engineering Services, Network RailThis package is developed by Jieming Ye. Senior Design Engineering, Engineering Services, Network Rail.
The source code is open to public.Network Rail Infrastructure Limited are registered in England and Wales under company number 02904587.InstallationThe repository of this package is saved on GitHub:https://github.com/NR-JYe/Vision-Oslo-ExtensionUsagePre-installtion of Python and VISION-OSLO required.FeatureVersion 0.6.0 (Beta version): Add bhtpbank input feature. Remove using deprecated pkg_resources. Change entry point by checking version update.Version 0.5.8 (Beta version): Bug Fix, improve packaging and improve text inputVersion 0.5.0 (Beta version): This version introduced model checking.Version 0.4.1 (Beta version): This version introduced full post-processing.Version 0.1.1 (Beta version): First version published for user testing.ContributingEngineering Services, part of Route Services, Network RailLicense
Package: visionPDF
visionPDFMódulo Python para Leitura, Tradução e Escrita em Arquivos PDF.Com uso dos módulos PyPDF2, reportlab e karkipytranslator , sintetizados em um único módulo, mas com todo o poder dos mesmos.Instalação:$ pip install visionPDFUso:>> import visionPDF

------- LER -------

>> reader = visionPDF.PDFReader(file_in)    # Arquivo PDF a ser lido.
>> reader.get_text(page)                    # Retorna o texto da Pagina.

------- TRADUZIR ------

>> transl = visionPDF.PDFTranslate(to_lang)  # to_lang -> Linguagem para 
                                                    qual deseja traduzir.
                                                         Ex: pt,kr,en,...
>> transl.translate(text)                    # text -> Texto a ser traduzido.

-------- ESCREVER ---------

>> writer = visionPDF.PDFWriter(file_out,orientation)   # file_out -> Arquivo de Saida.
                                                        # orientation -> Orientação do documento. Ex: portrait/landscape.
>> writer.addText(text,x,y,font,size,color)            # Escreve um texto no documento.
>> writer.save_page()                                   # Salva o conteudo gravado na pagina e fecha ela.
>> writer.close()                                       # Fecha o Documento PDF.Contato:@informeai
Package: visionpy
vision-python-sdk (visionpy)Vision Python Client Library.FeaturesGet Block, Transaction, TransactionInfo, Node;Build and transfer VS, VRC10, VRC20;Parse event logs of TransactionInfo;Decode contract function inputs;Asyncio support;Python 3.7.2 and late version required;Poetry for package management.How to useMore examples please checkexamplefolder.fromvisionpyimportVisionfromvisionpy.keysimportPrivateKey# Private key of VDGXn73Qgf6V1aGbm8eigoHyPJRJpALN9Fpriv_key=PrivateKey(bytes.fromhex("eed06aebdef88683ff5678b353d1281bb2b730113c9283f7ea96600a0d2c104f"))deftransfer():client=Vision(network='vpioneer')txn_ret=(client.vs.transfer("VTCYvEK2ZuWvZ5LXqrLpU2GoRkFeJ1NrD2","VSfD1o6FPChqdqLgwJaztjckyyo2GSM1KP",1_000).memo("test memo").build().inspect().sign(priv_key).broadcast())print(txn_ret)print(txn_ret.result())Async ClientimportasynciofromvisionpyimportAsyncVisionfromvisionpy.keysimportPrivateKey# private key of VTCYvEK2ZuWvZ5LXqrLpU2GoRkFeJ1NrD2priv_key=PrivateKey(bytes.fromhex("eed06aebdef88683ff5678b353d1281bb2b730113c9283f7ea96600a0d2c104f"))asyncdeftransfer():asyncwithAsyncVision(network='vpioneer')asclient:print(client)txb=(client.trx.transfer("VTCYvEK2ZuWvZ5LXqrLpU2GoRkFeJ1NrD2","VSfD1o6FPChqdqLgwJaztjckyyo2GSM1KP",1_000).memo("test memo").fee_limit(100_000_000))txn=awaittxb.build()print(txn)txn_ret=awaittxn.sign(priv_key).broadcast()print(txn_ret)print(awaittxn_ret.wait())if__name__=='__main__':asyncio.run(transfer())
Package: visionpy-sc
Functional Interpretation forscRNA-seq DataNOTE: THIS PACKAGE IS UNDER ACTIVE DEVELOPMENT AND IS NOT YET READY FOR USE.This is a Python port of the VISIONR package. VISION aids in the interpretation of single-cell RNA-seq (scRNA-seq) data by selecting for gene signatures which describe coordinated variation between cells. While the software only requires an expression matrix and a signature library (available in online databases), it is also designed to integrate into existing scRNA-seq analysis pipelines by taking advantage of precomputed dimensionality reductions, trajectory inferences or clustering results. The results of this analysis are made available through a dynamic web-app which can be shared with collaborators without requiring them to install any additional software.Nature Communications publicationFull R DocumentationR packageInstalling visionpyYou need to have Python 3.8 or newer installed on your system. If you don't have
Python installed, we recommend installingMiniconda.There are several alternative options to install visionpy:Install the latest release on PyPI:pipinstallvisionpy-scInstall the latest development version:pipinstallgit+https://github.com/yoseflab/visionpy.git@mainHow to run visionpyFrom the command linevisionpy --adata ./my_adata.h5ad --norm_data_key use_raw --compute_neighbors_on_key X_scvi --name Test VisionFrom Pythonfromvisionpy.apiimportstart_visionfromvisionpyimportsignatures_from_gmtadata.varm["signatures"]=signatures_from_gmt(["./signatures.gmt"],adata)start_vision(adata=adata,name="Test Session",norm_data_key="log1pcp10k",compute_neighbors_on_key="X_pca",signature_varm_key="signatures",)
Package: visionseed
python-sdk for VisionSeed - a camera module with AI abilityinstallpip3install--upgradevisionseedrun examplepython3example/example.pyrun example-cocos2dpip3installopencv-python
pip3installmatplotlib
pip3installcocos2d
python3example/example-cocos2d.pyrun example on WindowsYou need to modify the line in example.py, and replace "/dev/ttyACM0" to your VisionSeed's virtual port number, e.g. "COM3":vs=YtVisionSeed(serial.Serial("/dev/ttyACM0",115200,timeout=0.5))moreHomepage:https://visionseed.youtu.qq.com技术交流QQ群: 684338220(超多大佬)
Package: vision-stuff
vision_stuffTools for image conversion, which seems to be useful, at least for me.InfoCollection of functions for convertion images with python opencv. For now one useful function is shrink_image and its derivative shrink_and_store_images_dir. It let us to shrink image(s), to specified size. Useful, when we have a couple of different shape images and want all of them to be e.g. 640x640.Install.. code-block:: pythonpip install vision_stuffUsage from python.. code-block:: pythonfrom vision_stuff import shrink_image

# example
img = cv2.imread('image_to_be_shrinked.png', 1)
out = shrink_image(img, 400, 400)
cv2.imwrite('out.png', out).. code-block:: pythonfrom vision_stuff import shrink_and_store_images_dir

directory = 'different_shape_images'
shrink_and_store_images_dir(directory, width=640, height=640, resize=True)
# it convert images and store them into new directory named (directory + "_converted")Example of "shrink_image" function.. image:: examples/shrink_example.pngExample of "roll_layers" function.. image:: examples/roll_layers_example.jpgExample of "gradient_image" function.. image:: examples/gradient.pngExample of "margin" function.. image:: examples/margin.pngTodo status03.06.2020, todo:- add command line tools

- add more useful function/classes02.08.2020, info:- shrink_image fixed (it was bug in previous version :))

- roll_layers function added

- gradient_image function added

- margin function added
Package: visiontext
visiontextUtilities for deep learning on multimodal data.jupyter notebooks / jupyter lab / ipythonmatplotlibpandaswebdataset / tarpytorchInstallRequirespython>=3.8pytorchsqlitepipinstallvisiontextDev installClone repository and cd into, then:pipinstall-e.
pipinstallpytestpytest-covpylintpytest-lazy-fixture

python-mpytest--cov

pylintvisiontext
pylinttestsChangelog0.10.1: Test with python 3.120.8.1: Set minimum python version to 3.8 since PyTorch requires it
Package: vision-transformer-pytorch
Vision Transformer PytorchThis project is modified fromlukemelas/EfficientNet-PyTorchandasyml/vision-transformer-pytorchto provide out-of-box API for you to utilize VisionTransformer as easy as EfficientNet.QuickstartInstall withpip install vision_transformer_pytorchand load a pretrained VisionTransformer with:from vision_transformer_pytorch import VisionTransformer
model = VisionTransformer.from_pretrained('ViT-B_16')About Vision Transformer PyTorchVision Transformer Pytorch is a PyTorch re-implementation of Vision Transformer based on one of the best practice of commonly utilized deep learning libraries,EfficientNet-PyTorch, and an elegant implement of VisionTransformer,vision-transformer-pytorch. In this project, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible.If you have any feature requests or questions, feel free to leave them as GitHub issues!InstallationInstall via pip:pip install vision_transformer_pytorchOr install from source:git clone https://github.com/tczhangzhi/VisionTransformer-Pytorch
cd VisionTransformer-Pytorch
pip install -e .UsageLoading pretrained modelsLoad an EfficientNet:from vision_transformer_pytorch import VisionTransformer
model = VisionTransformer.from_name('ViT-B_16')Load a pretrained EfficientNet:from vision_transformer_pytorch import VisionTransformer
model = VisionTransformer.from_pretrained('ViT-B_16')
# inputs = torch.randn(1, 3, *model.image_size)
# model(inputs)
# model.extract_features(inputs)Default hyper parameters:Param\ModelViT-B_16ViT-B_32ViT-L_16ViT-L_32R50+ViT-B_16image_size384384384384384patch_size163216321emb_dim76876810241024768mlp_dim30723072409640963072num_heads1212161612num_layers1212242412num_classes10001000100010001000attn_dropout_rate0.00.00.00.00.0dropout_rate0.10.10.10.10.1If you need to modify these hyper parameters, please use:from vision_transformer_pytorch import VisionTransformer
model = VisionTransformer.from_name('ViT-B_16', image_size=256, patch_size=64, ...)ImageNetSeeexamples/imagenetfor details about evaluating on ImageNet.ContributingIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.I look forward to seeing what the community does with these models!
Package: visionx
Vision UI特性超越像素对比-视觉对比基于模板匹配-图像融合预训练模型-UI目标检测视觉语义-语义目标识别集成模型-文本识别效果展示UI目标检测App1App2App3语义目标识别image or text queryproposal backendresultui-infera toy dinosaurpatchesmariopatches更多效果展示LicenseThis project is licensed under theApache License
Package: visio-rust
Visio-RustThis project aims to provide an api to retreive and add metadata to images, jpgs, webp and png is supported.Usagefromvisio_rustimportget_metadata,set_metadatawithopen('xxxx.jpeg','rb')asf:input_data=f.read()icc_profile,exif_data=get_metadata(input_data)output_data=set_metadata(input_data,icc_profile,exif_data)print(input_data==output_data)DevelopmentClone the projectgitclonehttps://github.com/Gaspard-Bruno/visio-rust.gitgitclonegit@github.com:Gaspard-Bruno/visio-rust.gitInstallationcd visio-rust/ && make installTestingcd visio-rust/ && tox🚀 Author@AndersonCancado@MarcoJoao