Saravanan666/pypi-org huggingface dataset:
Package: imreadeval
imreadevalis a package to evaluate the performance of various pythonimreadfunctions from different packages.## Installpip install imreadevalPS. If you useanaconda, please install dependencies beforepip install. The default dependency install ispipbased.## Packages EvaluatedPillowOpenCVMatplotlibimageio (descendent ofscipy.io.imread, which is deprecated)SciKit-Image (depend on SciPy)## UsageJust evaluateimport imreadevalor just import a fastestimreadfunction byfrom imreadeval import imreador try to evaluate differentimreadfunctions using YOUR OWN IMAGESfrom imreadeval import imread_evaloptimum_package_name = imread_eval(filenames = [‘filename1.jpg’, ‘filename2.png’], times = 1000)## Developgit clonethe repogit@github.com:quxiaofeng/imreadeval.gitpip install -e .python -c “import imreadeval”
Package: imreco
ImrecoImage-Resizer-Converter.
A python cli program to perform basic operations on images using opencv. (WIP)This project was made possible by: @shana.
This project was done regardingtinkerhub co-coder.Installation:pipinstallimrecoUsage:# Syntax:imrecoresize-i<input_file>-o<output_file>--quality80imrecocompress-i<input_file>-o<output_file>--size300x400
imrecoconvert-i<input_file>-o<output_file>TODOS:Resizer: To change dimension of the provided image, and save it to a new file.Converter: To change file formats from png to jpg, etc.Compressor: To compress jpg files in file size.Config: Add user config options. (maybe argparse or typer)Checks multiple files or a directory.Chain functions and/or operationsHost as a flask api server?
Package: imreg
Imreg is a Python library that implements an FFT-based technique for
translation, rotation and scale-invariant image registration [1].Author:Christoph GohlkeLicense:BSD 3-ClauseVersion:2024.1.2QuickstartInstall the imreg package and all dependencies from thePython Package Index:python -m pip install -U imregSeeExamplesfor using the programming interface.Source code and support are available onGitHub.RequirementsThis revision was tested with the following requirements and dependencies
(other versions may work):CPython3.9.13, 3.10.11, 3.11.7, 3.12.1NumPy1.26.2Scipy1.11.4Matplotlib 3.8.2(optional for plotting)Revisions2024.1.2Add type hints.Drop support for Python 3.8 and numpy < 1.23 (NEP29).2022.9.27Fix scipy.ndimage DeprecationWarning.NotesImreg is no longer being actively developed.This implementation is mainly for educational purposes.An improved version is being developed athttps://github.com/matejak/imreg_dft.ReferencesAn FFT-based technique for translation, rotation and scale-invariant
image registration. BS Reddy, BN Chatterji.
IEEE Transactions on Image Processing, 5, 1266-1271, 1996An IDL/ENVI implementation of the FFT-based algorithm for automatic
image registration. H Xiea, N Hicksa, GR Kellera, H Huangb, V Kreinovich.
Computers & Geosciences, 29, 1045-1055, 2003.Image Registration Using Adaptive Polar Transform. R Matungka, YF Zheng,
RL Ewing. IEEE Transactions on Image Processing, 18(10), 2009.Examples>>> im0 = imread('t400')
>>> im1 = imread('Tr19s1.3')
>>> im2, scale, angle, (t0, t1) = similarity(im0, im1)
>>> imshow(im0, im1, im2)>>> im0 = imread('t350380ori')
>>> im1 = imread('t350380shf')
>>> t0, t1 = translation(im0, im1)
>>> t0, t1
(20, 50)
Package: imreg_dft
imreg_dftis your first-choice Python image registration utility.
In plain language, it implements means of guessing translation, rotation and scale variation between two images.
The name comes from the fact that it doesn’t work with those images directly, but it works with their spectrum (DFT using FFT), and its log-polar transformation[1], hence the_dftafterimreg.Basically, if you want to align two images that have different scale and are rotated and shifted against each other,imreg_dftis the tool you want to check out.Get startedin five minutes and see how it works for you!Authors:Matěj TýčPGP public keyChristoph GohlkeOrganization:Brno University of Technology, Brno, Czech RepublicLaboratory for Fluorescence Dynamics, University of California, IrvineCopyright:2014-2015, Matěj Týč2011-2014, Christoph GohlkeRequirementsimreg_dftisfree software, i.e. both free of charge (so it is free as free beer) and you as a user are grantedfour basic freedoms(so it is also free as free speech).Generally, you will neednumpyandscipyfor the algorithm functionality andmatplotlibfor plotting.
For the command-line tool, reading images is useful, so make sure you havepillow(orPIL, which is deprecated).QuickstartCheck the documentation onreadthedocs.ort(bleeding-edge) orpythonhosted.org(with images).
Or even better, generate the documentation yourself!Install the package by runningpython setup.py installin the project root.Install packages that are required for the documentation to compile (see therequirements_docs.txtfile.Go to thedocdirectory and runmake htmlthere.
The documentation should appear in the_buildsubfolder, so you may open_build/html/index.htmlwith your web browser to see it.Notesimreg_dftis based on thecodeby Christoph Gohlke.References[1]An FFT-based technique for translation, rotation and scale-invariant
image registration. BS Reddy, BN Chatterji.
IEEE Transactions on Image Processing, 5, 1266-1271, 1996
Package: imreg-dft-nw
imreg_dftis your first-choice Python image registration utility.
In plain language, it implements means of guessing translation, rotation and scale variation between two images.
The name comes from the fact that it doesn’t work with those images directly, but it works with their spectrum (DFT using FFT), and its log-polar transformation[1], hence the_dftafterimreg.Basically, if you want to align two images that have different scale and are rotated and shifted against each other,imreg_dftis the tool you want to check out.Get startedin five minutes and see how it works for you!Authors:Matěj TýčPGP public keyChristoph GohlkeOrganization:Brno University of Technology, Brno, Czech RepublicLaboratory for Fluorescence Dynamics, University of California, IrvineCopyright:2014-2015, Matěj Týč2011-2014, Christoph GohlkeRequirementsimreg_dftisfree software, i.e. both free of charge (so it is free as free beer) and you as a user are grantedfour basic freedoms(so it is also free as free speech).Generally, you will neednumpyandscipyfor the algorithm functionality andmatplotlibfor plotting.
For the command-line tool, reading images is useful, so make sure you havepillow(orPIL, which is deprecated).QuickstartCheck the documentation onreadthedocs.ort(bleeding-edge) orpythonhosted.org(with images).
Or even better, generate the documentation yourself!Install the package by runningpython setup.py installin the project root.Install packages that are required for the documentation to compile (see therequirements_docs.txtfile.Go to thedocdirectory and runmake htmlthere.
The documentation should appear in the_buildsubfolder, so you may open_build/html/index.htmlwith your web browser to see it.Notesimreg_dftis based on thecodeby Christoph Gohlke.References[1]An FFT-based technique for translation, rotation and scale-invariant
image registration. BS Reddy, BN Chatterji.
IEEE Transactions on Image Processing, 5, 1266-1271, 1996
Package: im-remind
im-remindInstallpip install im-remindUsageSee the help page.TL;DR:im-remind -hOr:python -m im-remindConfig fileConfig file can simplify the usage of im-remind.A template config file is provided in theexample_config.yaml.qq:token:<qq-token>api-path:<qq-api-path># session is used for simplify the command-linesession:qq:<your-qq>group:<your-qq-group>tg:token:<tg-token>api-path:<tg-api-path>Replace the appropriate fields with your own values.Then, move the config file to~/.im_remind.yamlor~/.config/im_remind.yamlExamplesim-remind qq "JOJO，我不想再做人了！！"
Package: imreplace
Module To Replace Values of Immutable Data Type
Installation:
CMD: pip install imreplace
In Code:
Import imreplace
How to Do:
Use trp to replace Tuple
Use srp to replcae String
- trp.replace(Your_Tuple,Index_Number_To_Replace,Value_to_Replace)
- srp.replace(Your_String,Index_Number_To_Replace,Value_to_Replace)Change Logversion : 0.0.1First Release
Package: imrepltool
这是做什么的？这是一个图像模板识别替换工具，根据提供的模板识别目标图像是否包含指定的logo或水印（或其他的图像模式），
可设置自动完成替换或渐变填充并保存或返回图像二进制流怎么安装？pip install imrepltool怎么使用导入并实例化：from imrepltool.imrepltool import ImreplTool

IT = ImreplTool(cover_up=False,
                cover_with_img=False,
                cover_color=None,
                log_level=None,
                optimize_t_sequence=True)在实例化时有数个选项可提供设置：cover_up                为bool值，设置是否覆盖图像被匹配的地方，设置后对于处理速度有一定影响
cover_with_img          为bool值，设置是否使用您自己的logo或水印来覆盖被匹配的区域；
cover_color             为颜色rgb值（列表: [0, 156, 255]）， 注意，当 cover_with_img 为True 时此项无效；
log_level               为日志级别，需要使用python 内置logging 包进行设置，例如： logging.ERROR， 默认是 logging.INFO 级别
optimize_t_sequence     为bool值， 设置是否在程序退出前对模板数据库排列进行优化，以提升下次运行速度若您只是想检查目标图像是否包含已知模板，则不需要设置以上选项，直接默认实例化即可:IT = ImreplTool()在第一次安装使用时，您需要导入模板数据:template_paths = [
    ['SJZG', 'templates/shijuezhongguo_template_1.jpg', 81],    # 这样的格式是必须的
    ['SJZG', 'templates/shijuezhongguo_template_4.jpg', 0],
]

IT.add_templates(templates=template_paths, update_if_existed=False) # update_if_existed 若已存在则更新template_paths 需要一个二维列表， 内部的每一条即为一个模板记录，每条记录需要三个数据：0： 模板名称（即上面的 SJZG 对应的位置）
1： 模板位置，可以是本地相对路径或绝对路径，也可以是图片网址（即上面的 .jpg 对应的位置）
2： 模板阈值，在匹配模板时阈值越高越精确，但也可能导致匹配不上，若不确定多少值，则设置为 0 即可（即上面的 81 对应的位置）若在初始化时设置了 cover_with_img=True，则还需要导入替换 logo 的信息：replacements = [
    ['SJZG', 'replacement/611_logo.png'],
]

IT.add_replace_img(repl_paths=replacements)与 template_paths 一样，需要一个二维列表， 但是每条记录只需要两个数据：0： logo对应的模板名称，即您希望此logo替换哪一个模板，注意此名称需要和 template_paths 的模板名称一致
1： logo位置，可以是本地相对路径或绝对路径，也可以是图片网址图像数据输入完毕，接下来进行初始化就可以使用了：IT.initialize_data(template_names='SJZG')初始化时可以指明使用哪一个模板进行识别，即 template_names，若有多个模板需要使用，则使用列表将模板名称包装起来：IT.initialize_data(template_names=['SJZG', 'other1', 'other2', ...])若您需要对图像有选择性的覆盖，则在设置了 cover_with_img=True 和导入替换logo信息后，还可以设置：IT.COVER_TEMPLATES = ['SJZG']即可指明程序覆盖哪一类模板，若不设置则自动覆盖所有匹配到的模板接下来可以正式使用匹配程序了：image = 'images/shijuezhongguo_1.jpg'
# image = 'http://chuantu.xyz/t6/702/1565924123x2890171859.jpg'  # 或者图片的网址
check_result = IT.check(image)需要注意的是，check 函数只接收单个图像地址， 若需要处理多张图片，请在初始化后使用循环条件调用 check返回的 check_result 如下：{'SJZG': True}
# {'SJZG': False}   # 若匹配不上则为 False此外若有需要，您还可以使用 score = IT.FINAL_SCORE 来查看对应的匹配分数，score的值为：{'SJZG': 0.9999}如果您想检查图像覆盖情况，则可以使用：IT.show()将显示处理后的图像，但是需要注意的是，若您有多个图像在循环处理，您需要手动关闭显示的图像才能继续运行，每次显示一张图片然后您可以保存处理后的图像：save_result = IT.save('replaced_image.jpg')如果您给定了保存地址，则保存成功后 save_result 的值为 True（保存失败则返回 False ），若没有给定保存地址，则 save_result 的值为处理后的图像的二进制流，您可以使用其他工具将其保存或者上传到网站到此程序流程完成其他选项其他的图像覆盖设置为可选项，若您有兴趣，或者发现覆盖效果不理想的时候，可以尝试调整这些选项查看效果IT.EP_METHOD = False                # encircling picking method 是否启用三点包围取点法， False 则启用直线取点法
IT.EP_DISTANCE = 3                  # 三点包围取点法的像素取点距离
IT.SP_PIX = 3                       # straight picking method 直线取点法的像素取点数
IT.BORDER_EXTEND = 8                # 匹配边界的覆盖扩展像素数
IT.KEEP_COVER_IMG_SCALE = True      # 填充logo是否保持原比例
IT.CONCENTRATION = 100              # 填充透明度（尚不完善）
Package: ims-api-connector
# ims-python-api-connectorConnector for the FTP Technologies IMS HTTP API## How to install`$ pip install ims-api-connector`## How to useThere are `.connect()` and `.close()` methods exposed as well as a context manager API (`__enter__()` and `__exit__()`) but you don't have to use either- simply calling `.get()`, `.post()` etc will handle the authentication for you.Additionally, it will retry authentication if it picks up an authentication error during a request (to handle expired tokens etc) but if the attempt to re-authenticate fails, it will stop retrying.```from ims_api_connector import IMSAPIConnectorconnector = IMSAPIConnector(url='https://some.ims.instance.com/api/',username='some_username',password='some_password',timeout=5,  # optional; seconds until a request times outretries=6,  # optional; number of re-auth attempts during a requestbackoff=5,  # optional; seconds between re-auth attemptsinsecure=False,  # optional; ignore certificate warningslogger=None,  # optional; Python logger object)connector.get('assets/?limit=50000')```Which returns an object like this:```Response(status_code=200,text='{"some": "data"}',json={'some': 'data'},error=None,  # any JSON parse exception objecttraceback=None,  # traceback string for JSON parse exception)```## How to developPrerequisites:* pip* virtualenvwrapper* entrInstructions:* Create a virtualenv* `mkvirtualenv ims-python-api-connector`* Install the requirements* `pip install -r requirements.txt`* Watch the tests* ./watch_tests.sh* Write some code
Package: imschrm
IMSC Hypothetical Render Model (HRM) Validator__  _  _  ____   ___  _  _  ____  _  _ 
(  )( \/ )/ ___) / __)/ )( \(  _ \( \/ )
 )( / \/ \\___ \( (__ ) __ ( )   // \/ \
(__)\_)(_/(____/ \___)\_)(_/(__\_)\_)(_/IntroductionimschrmvalidatesIMSCdocuments against theIMSC Hypothetical Render Model
(HRM), which constrains document complexity.imschrmconsists of a library and command line application written in pure Python, and usesttconv.Known issuesIssues are tracked athttps://github.com/sandflow/imscHRM/issues.Quick startpipinstallimschrmimschrm<inputIMSCdocument>Command linecli.py[-h][--itype{ttml,manifest}]inputinput: input file--itype: specifies whether the input file is a single IMSC document (ttml) (default) or a manifest (manifest) containing a
list of IMSC documents.The manifest is a JSON file that conforms to the schema atsrc/main/resources/json/manifest.json.schema.EXAMPLE:[{"begin":"12/24","end":1,"path":"doc001.ttml"},{"begin":1,"end":null,"path":"doc002.ttml"}]DependenciesGeneralThe project usespipenvto manage dependencies.Runtimepython >= 3.7ttconv == 1.0.1DevelopmentpylintDevelopment environmentrunpipenv install --devset thePYTHONPATHenvironment variable tosrc/main/python, e.g.export PYTHONPATH=src/main/pythonpipenv runcan then be usedFrom the root directory of the project:pipenvinstall--dev
mkdirbuildexportPYTHONPATH=src/main/python
pipenvrunpythonsrc/main/python/imschrm/cli.pysrc/test/resources/ttml/fail001.ttml
Package: imsearch
imSearch: A generic framework to build your own reverse image search engineimsearch helps to create your own custom, robust & scalable reverse image search engine. This project uses state of the art object detection algorithm (yolov3) at its core to extract the features from an image. It uses an efficient cross-platform similarity search libraryNMSLIBfor similarity search.Redisis used as a messaging queue between feature extractor and core engine.MongoDBis used to store the meta-data of all the indexed images. HD5 file system is used to store the feature vectors extracted from indexed images.InstallationFor the setup, a simpleinstall.shscript can be used or can be installed usingpip.
Follow these simple steps to install imsearch library.Feature extraction is GPU intensive process. So, to make the search real-time, running this engine on GPU enabled machine is recommended.Install CUDA & NVIDIA graphics drivers (here)InstallPyTorch(here)InstallMongoDB(here)InstallRedis(here)Run following commandspip install --no-binary :all: nmslib
pip install imsearchBuild from source usinginstall.shgit clone https://github.com/rikenmehta03/imsearch.git
chmod +x install.sh
./install.shExample usageimport glob
import imsearch

all_images = glob.glob('path/to/image/folder')

# Initialize the index
index = imsearch.init('test')

# Add single image to the index
index.addImage(all_images[0]) 

# Add multiple image to the index
index.addImageBatch(all_images[1:])

# Create index and make it ready for the search query
index.createIndex() 

# find k nearest similar images
# choose policy from 'object' or 'global'. Search results will change accordingly.
# object: Object level matching. The engine will look for similarity at object level for every object detected in the image.
# global: Overall similarity using single feature space on the whole image. 
similar = index.knnQuery('path/to/query/image', k=10, policy='object')For detailed usage seeexamples/index.pyCreditYOLOv3: An Incremental ImprovementJoseph Redmon, Ali FarhadiAbstractWe present some updates to YOLO! We made a bunch
of little design changes to make it better. We also trained
this new network that’s pretty swell. It’s a little bigger than
last time but more accurate. It’s still fast though, don’t
worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP,
as accurate as SSD but three times faster. When we look
at the old .5 IOU mAP detection metric YOLOv3 is quite
good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared
to 57.5 AP50 in 198 ms by RetinaNet, similar performance
but 3.8× faster. As always, all the code is online athttps://pjreddie.com/yolo/.[Paper][Project Webpage][Authors' Implementation]@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}PyTorch-YOLOv3Minimal PyTorch implementation of YOLOv3[GitHub]
Package: iM-Seeker
iM-SeekeriM-Seeker is commandline software designed to predict DNA i-Motif folding status and folding strength.
Details can be found athttps://github.com/YANGB1/iM-SeekerInstallation and UsageThe dependency packages can be installed by:pip3 install -r requirements.txtiM-Seeker can be installed by:pip3 install iM-SeekerAlternatively, the python script 'iM-Seeker.py' can be downloaded directly from Github. The stored directory can be added to the ‘PATH’ environmental variable or the scripts with full path can be run alternatively using command like:python3 iM-Seeker.py -hPlease pay attention !!!!!! The program needs two models 'pickle_model_classification.pkl' and 'pickle_model_regression.pkl' which are required as the input files of the software. Please find all the files athttps://figshare.com/s/e4e72e2e8ceaa0a4fbd6, where all these files can be downloaded directly.After intalled the package with 'pip',the help page can be checked by following command:iM-Seeker.py -hParameters can be configured according to the user's own needs. Here is an example:iM-Seeker.py --sequence input.fa --classification_model pickle_model_classification.pkl --regression_model pickle_model_regression.pkl --overlapped 2 --greedy 2 --stem_short 3 --stem_long 5 --loop1_short 1 --loop1_long 12 --loop2_short 1 --loop2_long 12 --loop3_short 1 --loop3_long 12 --representative_conformation 2 --output_folder output_pathInput and outputThe input sequences should be in fasta formation, for instance:>test1CCCTCCCCCTCCCCTCCCTCCCCCCCCTCCCCTCCCTCCCTCCCCCCCCTCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCCCCCCCCCTCCTCCCCTCCCCCTCCCCTCCCTCCCTCC>test2CCCCCTCCCCCTCCCCCTCCCCCTCCCCC>test3CCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC>test4CCCCGACCCCAACCCCTCCCCCAACCCCTCCCCThe output files are stored in the pre-set output folder.If --representative_conformation is set as 1, 'iM-seeker_result_average_conformation.txt' includes conformation A of pre-set iM structures.If --representative_conformation is set as 2, 'iM-seeker_result_side_shorter_conformation.txt' includes conformation B of pre-set iM structures.The prediction result is kept in 'iM-seeker_final_prediction.txt'."0" of folding status means unfolded while "1" means folded. Folding strength is a continuous number.
Package: imseqvis
Image & Sequence VisualizationThis package provides GUI widgets to show images and playback image sequences.InstallationIt is highly recommended to set up a separate virtual environment with an up-to-datepip:python3-mvenvvenvsourcevenv/bin/activate
python-mpipinstall-UpipThen, simply installimseqvisvia:python-mpipinstallimseqvisIf you want to try the latest alpha, i.e. the latestmainbranch packaged and
published toTestPyPI, you can instead install it via:python-mpipinstall--index-urlhttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simple"imseqvis[pyside2]"Qt Backendimseqvisrequires aQtbackend. In Python, you need to
either installPyQtorPySide.The default installationwill not installany of these backends, you have
to select one on your own.Optionally, you can installimseqviswith a specific backend. Currently,pyqt5,pyqt6,pyside2, andpyside6are supported:# PyQt5python-mpipinstall"imseqvis[pyqt5]"# OR PyQt6python-mpipinstall"imseqvis[pyqt6]"# OR PySide2python-mpipinstall"imseqvis[pyside2]"# OR PySide6python-mpipinstall"imseqvis[pyside6]"Usage as Standalone ApplicationTo quickly visualize all images within a folder or sequence (and nothing else),
you can use the provided wrappers to start a standalone GUI application:importimseqvis# Show all images in the given folder.imseqvis.show_folder('path/to/images')# Show all images in a random access container. For an exemplary data source# refer to `imseqvis.sequence_viewer.ImageSequence`.sequence=[...]imseqvis.show_sequence(sequence)Alternatively, you could simply use the providedshowfunctionality:importimseqvisimseqvis.show('path/to/images')sequence=[...]imseqvis.show(sequence)Usage as WidgetTo integrate the viewer into your own application, use theImageSequenceViewerwidget:# Prepare the image data source. This must allow random access to the images.# For an exemplary data source refer to# `imseqvis.sequence_viewer.ImageSequence`.sequence=[...]# Create & use the widget.viewer=SequenceViewer(image_sequence=sequence)layout.addWidget(viewer)...# Alternatively, the widget can also be created without a data source:viewer=SequenceViewer(image_sequence=None)...# Later on, the image sequence can be set via:viewer.setSequence(new_sequence)To show a different sequence within the same viewer, simply call:viewer.setSequence(new_sequence)More detailed usage examples are provided withinexamples/. These also
demonstrate how to use the available signals to be notified of the user's
interactions with the viewer:examples/demo_standalone.pydemonstrates the basic usage with a dummy
sequence.examples/demo_folder.pywill playback all images within a local folder.
Package: imserv
ImServSpin an image server, store images from Clipboard in single place, and prevent duplication.InstallationClone the project from GitHubNavigate to the project folder andpoetry installRun a Python script:fromimserv.dbimportcreate_all_tablescreate_all_tables()The folder~/Pictures/imservwill be created, and will be used for storing all images. (Tested only on Mac.)By default, it uses PostGRESQL of the database namedimserv, so you have to initialize the database first, before running the script.The image folder can also be changed inImServ(folder=IMG_FOLDER_PATH).UsageTo run an image server (before running Jupyter Notebook), run in a script:ImServ().runserver()This will open the server for the website:http://localhost:8000/, which leads to:where you can create an image directly from Clipboard. The name of the image will be randomized using UUID.After that, if you want to use it in Jupyter Notebook:>>>fromimservimportImServ>>>ims=ImServ()>>>ims.last()'The image accessed last will be shown.'More screenshotsRelated projectsjupyter-flashcard- Create a database of Jupyter Notebooks and convert them into flashcards.