Saravanan666/pypi-org huggingface dataset:
Package: seg
AboutThis is a collection of image segmentation projects adjusted for fundus blood vessel segmentation.1. unet_keras result:2. unet_torch result:4. mrsg_torch result (sota):RunProvide four flavors:ModuleHow to runModel weights locationNotebooksmodel.unet_kerasrun_training.py, run_testing.pytest/test_best_weights.h51. U-Net - Introduction.ipynb, 2. Fundus Blood Vessel Segmentation.ipynbmodel.unet_torchtrain.py, test.pyweights/checkpoint.pthREADME.mdmodel.multiple_torchall codes are inside .ipynb filesbest_binclass_model.h5, best_multiclass_model.h51. binary segmentation (camvid).ipynb and 2. multiclass segmentation (camvid).ipynbmodel.mrsg_torchpython train.py --cfg lib/All.yaml, python inference.py --lib/DRIVE.yamlresults/test/ALL/model/*.pthREADME.mdCreditsThe following github projects are used:Modulebased onurlmodel.unet_kerasRetina blood vessel segmentation with a convolution neural network (U-net)https://github.com/orobix/retina-unetmodel.unet_torchRetina-Blood-Vessel-Segmentation-in-PyTorchhttps://github.com/nikhilroxtomar/Retina-Blood-Vessel-Segmentation-in-PyTorchmodel.multiple_torchPython library with Neural Networks for Image Segmentation based on Keras and TensorFlow.https://github.com/qubvel/segmentation_modelsmodel.mrsg_torchRetinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)https://github.com/Limingxing00/Retinal-Vessel-Segmentation-ISBI2022/Todomake a thorough refactor; vessel region detection
Package: seg2-files
seg2_filesSimple file reading toolsseg2_files is a basic tool for reading binary SEG2 format seismic and ground-penetrating radar data files. It improves on other implementations by having few non-standard library dependencies (onlynumpy) and being able to read file headers containing any field supported by the SEG2 standard.Using this libraryFirst, import the function "seg2_load" from theseg2loadmodule in the library:fromseg2_files.seg2loadimportseg2_loadNext, load the trace data as a numpy array with traces as columns, and header as a dictionary from a SEG2 file:traces,header=seg2load('example_file.seg2')The header is a nested dictionary, with the following structure:headerKey:'tr' Value: dictionary of trace headersKey: trace header field e.g. 'SAMPLE_INTERVAL' for trace timestep (more are detailed in SEG2             specification), Value: List of values of the key field for each trace in the file in orderKey: 'rec' Value: Dictionary with field names as keys (found in SEG2 standard) containing information                     about the entire recording (file)As an example, to get the timestep (dt) between samples of the 3rd trace in a file, you can use:seis_data,seis_header=seg2_load('example_file.seg2')dt=seis_header['tr']['SAMPLE_INTERVAL'][2]And that's all there is to it! This library is simple but hopefully should cover most basic use cases. Don't hesitate to contact me if you have an additional feature idea that you think would be useful or if I missed part of the standard implementation.Note that this is library is supposed to be pretty bare-bones - it reads SEG2 files (maybe SEGY in the future) and turns them into Python data types. That's it. Processing, plotting, etc. are up to other libraries or you.
Package: segal
SegALSegAL is an active learning freamwork for semantice segmentation.InstallationSegAL is available on PyPI:pip install segalSegAL officially supports Python 3.8.Active Learning CycleTo understand what SegAL can do, we first introduce the pool-based active learning cycle.Step 0: Prepare seed data (a small number of labeled data used for training)Step 1: Train the model with seed dataStep 2: Predict unlabeled data with the trained modelStep 3: Query informative samples based on predictionsStep 4: Annotator (Oracle) annotate the selected samplesStep 5: Input the new labeled samples to labeled datasetStep 6: Retrain modelRepeat step2~step6 until the f1 score of the model beyond the threshold or annotation budget is no leftSegAL can simulate the whole active learning cycle.UsageThe user can execute the below command to run the active learning cycle.python examples/run_al_cycle.py --dataset CamVid  --data_path ./data/CamVid/ --model_name Unet --encoder resnet34 --encoder_weights imagenet --num_classes 12 --strategy LeastConfidence --seed_ratio 0.02 --query_ratio 0.02 --n_epoch 1dataset: which dataset to use,CamVid、VOC、CityScapesdata_path: the path where the data storenum_classes: number of classesmodel_name: name of segmentation model. More model names can be found inarchitecturesencoder: name of encoder used in model. More encoder names can be found inencodersencoder_weights: pretrained weights. Seeencoder tablewith available weights for each encoderstrategy: name of sampling strategy. Available strategies:RandomSampling,LeastConfidence,MarginSampling,EntropySampling,CealSampling,VoteSampling. You can find the papers for these strategy inhereseed_ratio: percentage of seed data. The  used for initial training.query_ratio: percentage of queried data in each roundn_epoch: number of epoch in each roundMore explanation can be found inusage
Package: seg-crf
Fully Connected CRFThis repo implements CRF as described in Deeplab paper it takes about 0.2 seconds per image. Following image is taken formDeepLabpaperRequirementsPython <= 3.6
pydensecrf
cv2
matplotlib
gray2colorIt takes following arguments.
For details visit projectpage.⚠ Zero pixels are consdered background
        img_path : path to an image, 
                        Format [H, W, 3]; values ranging from [0, 255]
        model_op_path : path model output of the same input image.
                        Format [H, W]; values ranging from [0, num_of_classes]
        num_of_classes : number of classes in a dataset e.g. in cityscape has 30 classes
        clr_op : color the output or not a bool
        pallet2use : see https://pypi.org/project/gray2color/ for details
        img_w : for resizing image and mask to same size default is 1024
        img_h : for resizing image and mask to same size default is 512
        apperance_kernel : The PairwiseBilateral term in CRF a list of values in order [sxy, srgb, compat]  
                            default values are [8, 164, 100]
        spatial_kernel : The PairwiseGaussian term in CRF a list of values in order [sxy, compat]  
                            default values are [3, 10]fromseg_crfimportSeg_CRFimg_path='D:/Anaconda/Image_analysis/cat.png'model_op_path='D:/Anaconda/Image_analysis/mask.png'crf=Seg_CRF(img_path,model_op_path,2,img_w=1024,img_h=512,clr_op=True,pallet2use='cityscape')gray,rgb=crf.start()plt.imshow(rgb)Appearance and Spatial Kernel# Default Values areapperance_kernel=[8,164,100]# PairwiseBilateral [sxy, srgb, compat]spatial_kernel=[3,10]# PairwiseGaussian  [sxy, compat]# or if you want to to specify seprately for each XY direction and RGB color channel thenapperance_kernel=[(1.5,1.5),(64,64,64),100]# PairwiseBilateral [sxy, srgb, compat]spatial_kernel=[(0.5,0.5),10]# PairwiseGaussian  [sxy, compat]# Use likecrf=Seg_CRF(img_path,model_op_path,2,img_w=1024,img_h=512,apperance_kernel=apperance_kernel,spatial_kernel=spatial_kernel,clr_op=True,pallet2use='cityscape')gray,rgb=crf.start()
Package: segdex
Reardon's Segregation Index for Continuous VariablesMany kinds of segregation index are used for various purposes of from policies to studies. While there are a wide range of categorical variables like race group to meausre an amount of segregation, continuous values like income are also important but do not fit very well with categorical segregation index.Reardon(2011),Reardon and Bischoff(2011)propsed a rank-order segregation index based on Theil index of the concept of Entropy. This index is widely accepted for practical and academic uses to calculate continuous value based segregation index like inChetty et al. 2014.The proposed method of them is a bit intricate however and there seems to be no good online library or code that implements it. Therefore, here is one. Python codes insidesrc/segregation_index.pyimplements Rank-Order Information Theory Index of Reardon(2011).Essentials of the IndexThe inequality indexHis an average of each value from a total of K sectors, which is total region's entropy(E) minus each sector's entropy(E_K). It is weighted by each sector's relative popultaion size(t_k/T). Here the entropy stands for how equally variables(ex. income) are distributed over sectors.Below is an equation to calculate entropy when there is two groups.pis a ratio of a group. As the variable here is continuous not categorical, one needs to integrate the below equation overpwith a range of 0≤p≤1. Thus transformation of raw values into rank ordered values is required.Combining above equations, we can calculate below one to get a Rank-Order Information Theory Index, which is the segregation index for continuous variables. 0 means perfect equality. 1 means perfect segregation.Usefrom src.segregation_index import estimate_Hp

# Say, each variables are income.
# Each inner list stands for a sector.
areas1 = [[80, 80, 70, 70], [50, 45, 40],[20, 20, 20, 10]]
areas2 = [[80, 70, 50], [80, 70, 45, 20, 20], [40, 20, 10]]

print(estimate_Hp(areas1))
print(estimate_Hp(areas2))

>> 0.7182    
>> 0.3191
# areas1 is more income-way segregated than areas2. In other words, areas2 is more mixed.
Package: segeval
Text segmentation is the task of splitting up any amount of text into segments by placing boundaries between some atomic unit (e.g., morphemes, words, lines, sentences, paragraphs, sections, etc.).  It’s a common pre-processing step in manyNatural Language Processing (NLP)tasks.This package is a collection of metrics and for comparing text segmentations and evaluating automatic text segmenters.  Both new (Boundary Similarity,Segmentation Similarity) and traditional (WindowDiff,Pk) are included, as well as inter-coder agreement coefficients and confusion matrices based upon a boundary edit distance.To see some examples of its usage,read the docs.Feature SupportIncluded is a variety of segmentation comparison metrics, including:Boundary Edit Distance (BED)Boundary Similarity (B)BED-based confusion matrices (and precision/recall/F1)Segmentation Similarity (S)WindowDiffPkAdditionally, B-based inter-coder agreement coefficients for segmentation that are suitable for 2 or more coders are provided, including:Fleiss’ Pi (i.e., Siegel and Castellan’s K)Fleiss’ KappaInstallationTo install SegEval, simply run:$pipinstallsegevalDocumentationDocumentation is available athttp://segeval.readthedocs.org/.Citing SegEvalIf you’re using this software for research, please cite theACL paper[PDF] and, if you need to go into details, thethesis[PDF] describing this work:Chris Fournier. 2013. Evaluating Text Segmentation using Boundary Edit Distance. Proceedings of 51st Annual Meeting of the Association for Computational Linguistics. (ACL 2013), to appear. Association for Computational Linguistics, Stroudsburg, PA, USA.Chris Fournier. 2013. Evaluating Text Segmentation. (Master’s thesis). University of Ottawa.BibTeX:@inproceedings{Fournier2013a,
        author          ={Fournier, Chris},
        year            ={2013},
        title           ={{Evaluating Text Segmentation using Boundary Edit Distance}},
        booktitle       ={Proceedings of 51st Annual Meeting of the Association for Computational Linguistics},
        publisher       ={Association for Computational Linguistics},
        location        ={Sophia, Bulgaria},
        pages           ={to appear},
        address         ={Stroudsburg, PA, USA}}@mastersthesis{Fournier2013b,
        author          ={Fournier, Chris},
        title           ={Evaluating Text Segmentation},
        school          ={University of Ottawa},
        year            ={2013}}
Package: segfast
SEGFASTInstallation•Benchmarks•Getting Startedsegfastis a library for interacting with SEG-Y seismic data. Main features are:Faster access to read data: both traces headers and valuesOptional bufferization, where user can provide a preallocated memory to load the data intoConvenient API that relies onnumpy.memmapfor most operations, while providingsegyioas a fallback engineInstallation# pip / pip3
pip3 install segfast

# developer version (add `--depth 1` if needed)
git clone https://github.com/analysiscenter/segfast.gitBenchmarksTimings for reading data along various projections:slide_islide_xslide_dcrop(256, 256, 500)batch(20, 256, 256, 500)segyio2.582547.166723041.3941.28516104.4segfast1.480563.3741850.135582.05742761.94segfastsegyio engine2.923795.69101225.13117.5713968.81seismiqb1.467633.4515450.3333151.8772738.86seismiqb+HDF51.042131.934141.8056781.35812616.83segfastquantized0.2524520.51848556.66727.711511212.74Getting startedAfter installation just importseismiQBinto your code. A quick demo of our primitives and methods:importsegfast# Open file and read some meta info. Engine can be `segyio` or `memmap`segfast_file=segfast.open('/path/to/cube.sgy',engine='memmap')# Load requested headers as dataframesegfast_file.load_headers(['INLINE_3D','CROSSLINE_3D',...])# Data access. All methods support optional buffer as target memorysegfast_file.load_traces([123,333,777],buffer=None)segfast_file.load_depth_slices([5,10,15],buffer=None)# Convert data format to IEEE float32: speeds up operations by a lotsegfast_file.convert(format=5)You can get more familiar with the library, its functional and timings by readingexamples.
Package: segjb
Segmentation wrapper ofJiebaChinese segmentation.https://github.com/kn45/SegJbpip install segjb(dependency: jieba)Lazy initialization.Initialization with user defined dict.Build-in stop-words dict, punctuations dict.Output control of stopwords, punctuations, minimum word length,
output delimiters etc..Support ngram.APIinit(stopwords_file, puncs_file, user_dict, silent, main_dict,
thread)– Initialize the segmentation utility instance.return: void.stopwords_file: stopword dictionary. Use “” to disable.
[SegJb.DEFAULT_STPW]puncs_file: punctuation dictionary. Use “” to disable.
[SegJb.DEFAULT_PUNC]user_dict: load user customized dictionary. Use “” to disable.
[SegJb.DEFAULT_DICT]silent: whether print initializing log. [True]thread: number of part to separate the corpus for parallel. [1]set_param(delim, min_word_len, ngram, keep_stopwords,
keep_puncs)– Set one or more parameters of the segmentation utility instance.
Refer to parameter description.return: voidcut2list(corp)– Cut a sentence to list due to configuration.return: listcorp: unicode or utf8 sentence.cut2str(corp)– Cut a sentence to a delimeter(can be set by set_param) joined
string.return: unicode string.corp: unicode or utf8 sentence.Parametersdelim [’ ‘]the delimeter used to constuct the segmentation result in string.min_word_len [1]word with length less than min_word_len will not in segmentation
result.ngram [1]result can be ngram.keep_stopwords [True]whether to keep stopwords in result.keep_puncs [True]whether to keep stopwords in result.Example:fromsegjbimportSegJbhdl_seg=SegJb()hdl_seg.init()hdl_seg.set_param(delim=' ',ngram=2,keep_stopwords=True,keep_puncs=False)printhdl_seg.cut2str('这是一场精彩的比赛')Reference:Bigdictfrom
iLife(562193561@qq.com)
Package: seglearn
seglearnSeglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator. Seglearn provides a flexible approach to multivariate time series and related contextual (meta) data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models. It is compatible withscikit-learn.DocumentationInstallation documentation, API documentation, and examples can be found on thedocumentation.Dependenciesseglearn is tested to work under Python 3.5, 3.6, and 3.8.
The dependency requirements are:scipy(>=0.17.0)numpy(>=1.11.0)scikit-learn(>=0.21.3)seglearn is now also compatible with sklearn 1.0+To run the examples, you need:matplotlib(>=2.0.0)keras (>=2.1.4) for the neural network examplespandasIn order to run the test cases, you need:pytestThe neural network examples were tested on keras using the tensorflow-gpu backend, which is recommended.Installationseglearn-learn is currently available on the PyPi’s repository and you can
install it viapip:pip install -U seglearnor if you use python3:pip3 install -U seglearnIf you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from GitHub and install all dependencies:git clone https://github.com/dmbee/seglearn.git
cd seglearn
pip install .Or install using pip and GitHub:pip install -U git+https://github.com/dmbee/seglearn.gitTestingAfter installation, you can usepytestto run the test suite from seglearn’s root directory:python -m pytestChange LogVersion history can be viewed in theChange Log.DevelopmentThe development of this scikit-learn-contrib is in line with the one
of the scikit-learn community. Therefore, you can refer to theirDevelopment Guide.Please submit new pull requests on the dev branch with unit tests and an example to
demonstrate any new functionality / api changes.Citing seglearnIf you use seglearn in a scientific publication, we would appreciate
citations to the following paper:@article{arXiv:1803.08118,
author  = {David Burns, Cari Whyne},
title   = {Seglearn: A Python Package for Learning Sequences and Time Series},
journal = {arXiv},
year    = {2018},
url     = {https://arxiv.org/abs/1803.08118}
}If you use the seglearn test data in a scientific publication, we would appreciate
citations to the following paper:@article{arXiv:1802.01489,
author  = {David Burns, Nathan Leung, Michael Hardisty, Cari Whyne, Patrick Henry, Stewart McLachlin},
title   = {Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch},
journal = {arXiv},
year    = {2018},
url     = {https://arxiv.org/abs/1802.01489}
}
Package: seglines
seglinesCompute segmented least squares for a dataset.  Probably assumes thatX = 1...Nor something.$ seglines 10 data.csv --plot
opt = 70.19
segment  1:   0 (1.330)      13 (13.880)    f(x) = 1.009·x + 1.693
segment  2:  14 (1.340)      27 (14.190)    f(x) = 0.997·x + -11.887
segment  3:  28 (3.350)      41 (15.760)    f(x) = 0.987·x + -25.083
segment  4:  42 (3.420)      55 (17.630)    f(x) = 0.991·x + -37.058
segment  5:  56 (5.160)      69 (18.600)    f(x) = 0.949·x + -46.523
segment  6:  70 (3.870)      83 (17.660)    f(x) = 0.973·x + -63.211
segment  7:  84 (5.930)      97 (17.490)    f(x) = 0.977·x + -76.830
segment  8:  98 (5.210)     111 (17.590)    f(x) = 0.897·x + -82.366
segment  9: 112 (3.810)     125 (16.410)    f(x) = 0.963·x + -102.544
segment 10: 126 (16.780)    139 (3.190)     f(x) = -1.036·x + 147.696Installpip install seglinesDepends only onnumpy.  When using--plot, we also needmatplotlib.UsageThere is a--helpoption:seglines --helpUseseglines L data.csvwhereLis the number of segments you want to segmentize into.In case you want to generate anL-segmented linear dataset, useseglines --generate 5 10 > data.csvand thenseglines 5 data.csvThis will output the segments, e.g.opt = 16.49
segment  1:  0 (0.410)      9 (10.330)    f(x) = 0.961·x + 1.355
segment  2: 10 (3.750)     19 (13.260)    f(x) = 0.987·x + -5.741
segment  3: 20 (13.530)    29 (4.210)     f(x) = -1.031·x + 33.960
segment  4: 30 (13.880)    37 (7.420)     f(x) = -0.913·x + 41.254
segment  5: 38 (5.190)     49 (14.720)    f(x) = 0.904·x + -29.629To create a plot of the dataset, add--plot:seglines 5 data.csv --plot
Package: segm8
SegM8PiRaspberry Pi library for interaction with a chain of a particular SegM8 7-segment indicator modules.Enable SPI InterfaceIf you haven’t enabled SPI support in your Raspbian Linux yet, open the terminal and run the
following commands:Runsudo raspi-config.Use the down arrow to select5 Interfacing Options.Arrow down toP4 SPI.Select<Yes>when it asks you to enable SPI.Press<Ok>when it tells you that SPI is enabled.Use the right arrow to select the<Finish>button.Reboot your Raspberry Pi to make the SPI interface appear.After reboot, log in and enter the following command:$ls/dev/spi*The Pi should respond with:/dev/spidev0.0/dev/spidev0.1These represent SPI devices on chip enable pins 0 and 1, respectively. These pins are hardwired
within the Pi.InstallationUsepipto install the library:pip3installsegm8Quickstart exampleimporttimeimportsegm8# Create an object for working with the Segm8 module.segm8_module=segm8.SegM8(0,1)# Display numbers from 0 to 9.fornumberinrange(10):segm8_module.display_int(number,0,1)time.sleep(1)# Clear all segments of the module.segm8_module.clear()time.sleep(1)message="End"# Display each letter in the message.forletterinmessage:segm8_module.display_string(letter,0,1)time.sleep(1)See fullAPI reference in API.md
Package: segmate
This repository is a toolkit for using theSegment Anything Model (SAM)developed by Meta AI. It is capable of accurately “cutting out” any object from an image with just a single click.SAM is a promptable segmentation system that exhibits zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. By providing prompts that specify what needs to be segmented in an image, SAM can perform a wide range of segmentation tasks without requiring additional training.FeaturesEasy inferencewith SAM, supporting bounding boxes, points, masks, and text promptsAutomatic mask generationFinetuneSAM on custom datasetsGroundingDINOintegrationfor text prompt segmentationTraining acustom decoderto auto segment a specific type of objectTraining aprompt embeddingto auto segment a specific type of objectInstallationFirst, installgroundingdinofrom its repository, this is a dependency forsegmate:NOTE: There is an issue with the setup script in the GroundingDINO repository causing it not able to installtorchproperly, pleasemanually install PyTorchfor now. For other issues, refer to theinstallation guide:pip install -U git+https://github.com/IDEA-Research/GroundingDINO.gitThen, installsegmatefromPyPI:pip install segmateExample UsageTo use the provided code snippets, follow the steps below:Import the required modules and initialize the necessary objects:importtorchfromsegmate.segmenterimportSAMfromsegmate.object_detectorimportGroundingDINOimportsegmate.utilsasutils# Model checkpoint path for GroundingDINO is optional. If no path provided, it will download from HuggingFaceod=GroundingDINO(device='cuda',ckpt_path='PATH_TO_CHECKPOINT')sm=SAM(model_type='MODEL_TYPE',checkpoint='PATH_to_CHECKPOINT',device='cuda')Perform segmentation with bounding box prompts:masks=sm.segment(image=input_image,boxes_prompt=bbox)utils.show_masks(image,masks)DocumentationDetailed package documentation:SegMate DocsIf you have any questions or need assistance, please don’t hesitate to reach out to our support team or join our community forum. We hope you find this toolkit valuable and look forward to seeing the incredible applications you create with SAM!LicenseThe code in this repository is published under 3-Clause BSD license (seeLICENSEfile).