Saravanan666/pypi-org huggingface dataset:
Package: video-summary
Video Summary SDK for PythonEnhance your video processing workflow with theVideo Summary SDKfor Python. This SDK offers a powerful and easy-to-use solution for transcribing, summarizing, and extracting chapter information from videos. It supports a wide range of sources, including local files and hosted URLs.Key FeaturesTranscription: Accurately convert speech in videos into text.Summarization: Efficiently summarize video content.Chapter Extraction: Easily identify and extract distinct chapters from videos.Versatile Source Support: Compatible with local files, URLs, and various cloud storage solutions.InstallationInstall the package using pip:pipinstallvideo-summaryGetting StartedFirst, obtain your free API key fromVideoSummary.io. Then, install the SDK and integrate it into your Python projects as follows:fromvideo_summaryimportVideoSummarySDK# Initialize the SDK with your API keyvideo_summary=VideoSummarySDK('your_api_key')# Example usagetry:# Local file path or hosted video URLvideo_path='./path/to/your/video.mp4'result=video_summary.summarize(video_path)print("Video Summary Result:",result)exceptExceptionase:print("Error using Video Summary SDK:",e)API MethodsThe Python SDK offers several methods to interact with your videos. Theidandcallback_urlparameters are optional. Theidcan be used for your reference if you have an asset ID. Thecallback_urlis for receiving a webhook when the video processing is completed.If you don't provide a callback url, the call will be synchronous and wait for the processing to complete.summarize(url, [id], [callback_url])Summarizes the video content. Provide the URL of the video, and optionally, include an ID and a callback URL for asynchronous processing.transcribe(url, [id], [callback_url])Transcribes the audio content of the video into text. Input the video URL, and if desired, an ID and a callback URL.chapter(url, [id], [callback_url])Extracts chapters from the video for easier navigation and understanding. This method requires the video URL, with optional ID and callback URL.summarizeAndChapter(url, [id], [callback_url])Performs both summarization and chapter extraction on the video. Provide the video URL, and optionally, an ID and a callback URL.Each method returns a response object containing relevant data about the video processing, including transcripts, summaries, chapters, and file IDs.output{"transcript":{"speakers":[{"speaker":"SPEAKER_00","text":" video","timestamp":[10,10.26],"start":10,"end":10.26},{"speaker":"SPEAKER_00","text":" products.","timestamp":[10.26,10.9],"start":10.26,"end":10.9}],"chunks":[{"text":" video","timestamp":[Array]},{"text":" products","timestamp":[Array]},],"text":"..."},"chapters":[{"start":0,"end":10.9,"title":"Introduction to VideoSummary.io","text":"Introducing videosummary.io. a simple api to transcribe, chapter and summarize audio and video files."}],"summary":"Developers, check this out. You need VideoSummary.io in your life. It lets you build video products much easier with features like video summarization and video chaptering. Grab it now and start building game-changing video products.","fileId":"xxx-xxx-4ffc-a2a5-13d3cee085dd"}
Package: videotoframes
VideoToFramesA simple Python script to break videos into frames. This package doesNOTrequire ffmpeg.InstallationVideoToFrames requires Python 3.4+pipinstallvideotoframesExample Usage:VideoToFrames can be run from any command prompt or imported into a Python
project.Assuming there is a video at./videos/example.mp4and you want to create
frames in./frames:videotoframes-i./vidoes/example.mp4-o./framesIf you want to create a limited number of frames that are evenly distributed:videotoframes-i./videos/example.mp4-o./frames--max-frames=20--evenYou can also use VideoToFrames with many videos at once:videotoframes-i./videos-o./framesUsing VideoToFrames in a Python project:fromvideotoframesimportconvertvideo_base_64='...'frames=convert(video_base_64=video_base_64,max_frames=10,frame_rate=1)GitHub Projecthttps://github.com/brandonschabell/videotoframesPyPi Projecthttps://pypi.org/project/videotoframes/ContactPlease feel free to email me atbrandonschabell@gmail.comwith any questions or feedback.
Package: videoToImages
Videos To images command lineThis repository can be used to convertmp4files to images, more extensions will be added soon.Installationpip install videoToImagesUsageConvert video folder to imagesvideoToimages--videoFolder[pathToVideoFolder]example:videoToimages --videoFolder "c:/videos"Running the above command will generate images from the video and store in the directoryimagesConvertedin the videos folder.
Alternatively, you can also pass output folder name:--outFolderConvert video folder to images at specific rate (fps)videoToimages--videoFolder[pathToVideoFolder]--fps10example:example: videoToimages --videoFolder "c:/videos" --fps 10This command will capture 10 frames from each second of video data. You can use anyinteger value < original fps.Useful Arguments--videoFolder- (string) Full path of path of folder containing videos. example:~/videos--outFolder- (string) Full path of path of folder to store output images (.png). example:~/videos/images--fps- (int) sampling rate per second from the videos. example:15--img_size- (tuple) or (int) resize output images to specific size. example:(512, 512)LicenseApache-2.0 License
Package: video-to-mp3
Develop a package to convert video to mp3Table of ContentsAbout The ProjectBuilt WithInstallationUsageTestsLicenseContactAbout The ProjectWelcome to the documentation of video_to_mp3, a Python package that simplifies the conversion of videos to audio files. This versatile tool is designed to be used both as a library in your Python scripts and via the command line for quick and easy usage.(back to top)Built WithPython 3.10+moviepy==1.0.3click(back to top)InstallationInstall video_to_mp3pipinstallvideo_to_mp3(back to top)UsageUsage as a Python libraryfromvideo_to_mp3importvideo_to_mp3# Specify the input and output paths.video_path="path/to/your/video_file.mp4"audio_path="path/to/your/output/audio_audio"# Optional# Call function to conversionvideo_to_mp3.convert(video_path,audio_path)Command Line UsageAfter installation, you can use video_to_mp3 directly in your terminalvideo_to_mp3--input=path/to/your_video.mp4--output=path/to/output/your_audioAvailable command line options:--input: Path to the input video.--output: Path to the output audio file."(back to top)GitHub RepositoryFind the source code, contribute, and stay updated onGitHub."(back to top)LicenseDistributed under the MIT License. SeeLICENSEfor more information.(back to top)ContributionsContributions are welcome! Feel free to open an issue to report problems or submit a pull request with improvements.AcknowledgmentsThank you for using video_to_mp3! If you have any questions or suggestions, please feel free to share them on GitHub.contactJoel Edmond Nguemeta👇🏻Email :joeledmond95@yahoo.comLinkedIn :https://www.linkedin.com/in/joelproxi/(back to top)
Package: video-toolkits
:movie_camera: video_toolkitsThis repo provides functions to read/write a video and draw keypoints.** Defult BGR **Installationgitclonehttps://github.com/Celeste-cj/video_toolkits.gitcdvideo_toolkits
pythonsetup.pyinstallHow To UseVideoReaderfromvideo_toolkitsimportVideoReader,VideoReaderCV2video_path='path to video'reader=VideoReader(video_path)# VideoReader - pyav, only support video file/stream# reader = VideoReader(0)                   # webcam 0   VideoReaderCV2 supportforimginreader:passVideoWriterfromvideo_toolkitsimportVideoWriterimg_seqs=[]out_path='{VIDEO_NAME}.mp4'# currently support .mp4/.avisucceed=VideoWriter.imgseq2video(img_seqs,out_path,fps=30)# generate video on the flyframe_size=(720,1280)video_writer=VideoWriter.video_writer_generator(out_path,frame_size)video_writer.send(None)forimginimg_seqs:video_writer.send(img)video_writer.close()Convert flv to mp4fromvideo_toolkitsimportflv2mp4input_vid="{INPUT_FOLDER}/{VID_NAME}.flv"output_folder="{OUTPUT_FOLDER}"# if output_folder == '', will use {INPUT_FOLDER}succeed=flv2mp4(input_vid,out_folder=output_folder)Visualizationfromvideo_toolkitsimportdisplay,draw_sklts,put_text,draw_bboxfromvideo_toolkitsimportdraw_points,draw_linesimg=Nonekpts=[]# default (22, 4)text=""bbox=[]display(img)# plot image with matplotlib.pyplotimg=draw_sklts(img,kpts,color=None,sklts=None)# draw skeletonsimg=put_text(img,text)# put textimg=draw_bbox(img,bbox)img=draw_points(img,[[0,0],[1,1]])img=draw_lines(img,[[0,0]],[[1,1]],arrowed=False)load/dump jsonfromvideo_toolkitsimportload_json,dump_jsonjson_obj=[]file_path=""annts=load_json(file_path)dump_json(json_obj,file_path)LicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, use, compile this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.
Package: videotosmi
VideoToSMICreate a smi file based on the video.Copyright (c) 2019InfoLab(Donggun LEE)How to installpipinstallVideoToSMIother version# 0.0.1pipinstallVideoToSMI==0.0.1Functionsvideo=Video()# Goto https://github.com/Sotaneum/deepgeodefadd_model(model_name,lib_name,config_data):pass# TO SMIdefdetect(file_url,model_name,frame_set,rotation,filter)returnFILEHow to useImport VideoToSMIfromvideotosmiimportVideoVideo To SMIvideo=Video()video.add_model('mscoco','maskrcnn','d:/config.json')filter_=['car','bus','truck']video.detect('d:/test.mov','mscoco',ftr=filter_)# rotation 180video.detect('d:/test.mov','mscoco',rotation=180,ftr=filter_)#Frame 60video.detect('d:/test.mov','mscoco',frame_set=60,rotation=180,ftr=filter_)
Package: video-to-sqlite
video-to-sqliteLoad data about a video file into SQLiteInstallationInstall this tool usingpip:pip install video-to-sqliteUsageTo create a SQLite database with metadata about a video, run:video-to-sqlite videos.db my_video.mp4The tool will create two tables:videosandframes. Ifvideos.dbalready exists, the video will be added to thevideostable.To create the tables with a prefix, use--prefix prefix. For example:video-to-sqlite videos.db cool_video.mp4 --prefix cool_This will create tables calledcool_videosandcool_frames.Extra processingUsing the library programatically allows extra columns to be added to the database via a callback. Specify thecallbackargument tocli.main().The function will be called once per frame in the video. It should accept a numpy ndarray as the single argument, and
return a dict. The dict's keys will be added as columns to the DB, and values will be the corresponding values for that
row.An example:import video_to_sqlite

def my_callback(frame):
    has_a_cat_in_it = ai_cat_image_detector(frame)
    return {'cat': has_a_cat_in_it}

video_to_sqlite.main('my_database.db', 'cool_video.mp4', 'cool_', my_callback)Seecallback.pyfor a working example.video-to-sqlite --helpUsage: video-to-sqlite DB_PATH VIDEO_FILE PREFIX

  Load data about Python packages from PyPI into SQLite

  Usage example:

      video-to-sqlite videos.db my_video.mp4 my_

  Created tables will be videos and frames

  To create tables called cool_videos, cool_frames use
  --prefix cool_:

      video-to-sqlite videos.db my_video.mp4 --prefix cool_

Options:
  --version            Show the version and exit.
  --prefix TEXT        Prefix to use for the created database tables
  --help               Show this message and exit.
Package: videoutils
Video Utilities for OpenCVvideoutilslets you get rid of writing boilerplate code for reading video and adds some convenience on top of that.Installpip install videoutilsHow to usefromvideoutils.ioimportread_video,as_tensor,bgr2rgb,resizefname='files/interstellar-waves-edit.mp4'x=read_video(fname)len(x)x[0].shape1578






(480, 720, 3)By default,read_videoreturns a list ofnp.arrays of shape(height, width, channels).However, you can define precisely which frames you'd like to grab in a number of ways. This is done by using either the {start_idx,end_idx,frame_stride} ortarget_framesarguments.Grab the firstnframesn=50x=read_video(fname,end_idx=n)x2=read_video(fname,target_frames=(0,n))len(x)len(x)==len(x2)50






TrueGrab everynth framen=5x=read_video(fname,frame_stride=n,end_idx=50)len(x)10x=read_video(fname,frame_stride=50)# total frames = 1578len(x)32Grab frames at specific indicesx=read_video(fname,target_frames=[10,50,76,420])len(x)4x=read_video(fname,start_idx=10,end_idx=15)x2=read_video(fname,target_frames=(10,15))len(x)len(x)==len(x2)5






TrueReturn astorch.TensorYou can pass any function that transforms anp.arrayof shape(height, width, channels)to theapplyargument.videoutilsprovidesas_tensorfor convenience -- if you use this function,read_videowill automatically calltorch.stackand return the collection of frames as a 4D tensor, else it will return alistof 3D arrays/tensors.importtorchfromfunctoolsimportpartialx=read_video(fname,end_idx=10,apply=as_tensor)x2=read_video(fname,end_idx=10,apply=partial(as_tensor,normalise=True))x2=torch.stack(x2)# since we aren't using `as_tensor`, but a partial (thus different) functionx.shapex.shape==x2.shapex.mean(),x2.mean()torch.Size([10, 480, 720, 3])






True






(tensor(36.8276), tensor(0.1443))Resize Videoread_videohas an optional argumentresize_funcwhich is meant to be a function that resizes anp.arrayof shape(height, width, channels).You can use the predefinedresizefunction or pass in a custom function here.help(resize)Help on function resize in module videoutils.utils:

resize(image, height=None, width=None, keep_aspect_ratio=True, scale_factor=1.0)
    Resize by `scale_factor` if preserving aspect ratio else
    resize by custom `height` and `width`x=read_video(fname,target_frames=[0,1,2],apply=as_tensor,resize_func=partial(resize,scale_factor=2.))x.shapetorch.Size([3, 960, 1440, 3])x=read_video(fname,target_frames=[0,1,2],apply=as_tensor,resize_func=partial(resize,width=200,height=100,keep_aspect_ratio=False))x.shapetorch.Size([3, 100, 200, 3])
Package: video-utils
Video UtilsThis library provides utilities for dealing with TV show and Movie files and the metadata around them.Example Usagefromvideo_utilsimportFileMapdefrun():f=FileMap("/path/to/videos")f.load()# By default, this will load the cached metadata, and then update files that have changed in sizefordirectoryinf.contents:forvideoinf.contents[directory]:codec=video.codecprint(codec.pretty_name)# x265print(video.quality)# 1080pprint(video.full_path)print(video.size)# in bytesprint(video)video.refresh()# force a refresh of the video metadata, will only occur if filesize has changed.run()
Package: video-vit
Video VitOpen source implementation of a vision transformer that can understand Videos using max vit as a foundation. This uses max vit as the backbone vit and then packs the video tensor into a 4d tensor which is the input to the maxvit model. Implementing this because the new McVit came out and I need more practice. This is fully ready to train and I believe would perform amazingly.Installation$ pip install video-vitUsageimporttorchfromvideo_vit.mainimportVideoViT# Instantiate the VideoViT model with the specified parametersmodel=VideoViT(num_classes=10,# Number of output classesdim=64,# Dimension of the token embeddingsdepth=(2,2,2),# Depth of each stage in the modeldim_head=32,# Dimension of the attention headwindow_size=7,# Size of the attention windowmbconv_expansion_rate=4,# Expansion rate of the Mobile Inverted Bottleneck blockmbconv_shrinkage_rate=0.25,# Shrinkage rate of the Mobile Inverted Bottleneck blockdropout=0.1,# Dropout ratechannels=3,# Number of input channels)# Create a random tensor with shape (batch_size, channels, frames, height, width)x=torch.randn(1,3,10,224,224)# Perform a forward pass through the modeloutput=model(x)# Print the shape of the output tensorprint(output.shape)LicenseMIT
Package: video-vocal-rt
video-vocal-RTA minimal package to record vocal response to video stimuli. Useful for vocal reaction time research with video stimuli instead of pictures. Uses open CV for video playing, among other things.UsageI will provide more information soon 😉Installation with pippip install video-vocal-rtInstallation with poetryFirst, clone the repositorygit clone https://github.com/LoonanChauvette/video-vocal-RT.gitThen, inside the package folder, install the dependencies with poetrycd video-vocal-RT
poetry installPoetry creates a virtual environment, you should be able to activate it using :poetry shellThen you can run the script using :poetry run python video_vocal_rt/main.pyHow-toPlace your video files in the VIDEO_FILES directory (only supports .avi files for now). For the moment, you need to go inside video_vocal_rt/main.py before running the experiement. There you should make sure to set the audio recording duration in seconds (default is 6 seconds, but should be the typical length of your videos). You also need to set the PARTICIPANT_ID to the desired value.You can also set fixation parameters with FIXATION_DUR for the duration (default is 1000 ms). You can provide change the file "fixation.png" to customize the fixation. By default, there is a whiteout period after the video, its duration can be changed with WHITEN_DUR (default is 1000 ms).CitationChauvette, L. (2023). Video-Vocal-RT: a minimal package for vocal response to video.
https://github.com/LoonanChauvette/video-vocal-RTBibtex:@manual{Video-Vocal-RT,
  title={{Video-Vocal-RT: a minimal package for vocal response to video}},
  author={{Chauvette, Loonan}},
  year={2023},
  url={https://github.com/LoonanChauvette/video-vocal-RT},
}
Package: videoxt
VideoXTis a simple library and CLI tool for extracting audio, individual frames, short clips and GIFs from videos.See thedocumentationfor more details.InstallationUsageContributingUsed ByInstallationAvailable onpypi.pipinstallvideoxtUsageFrom the command-line:# extract audio from a video file (default: 'mp3')$videoxtaudioMyVideo.mp4{"video":{"filepath":"C:/Users/gurrutia/MyVideo.mp4",...},"start_time":0,...}# extracting audio...{"success":true,...}$ls
MyVideo.mp4MyVideo.mp3As a library:$python>>># extract all frames from a video file (default: 'jpg')>>>importvideoxt>>>filepath='C:/Users/gurrutia/MyVideo.mp4'# or <class 'pathlib.Path'>>>>result=videoxt.extract_frames(filepath)# or videoxt.extract('frames', filepath)>>>result.destpathpathlib.Path('C:/Users/gurrutia/MyVideo.mp4_frames')>>>result.elapsed_time3.14159265358979323>>>len(list(result.destpath.glob('*.jpg')))# default: 'jpg'100# number of frames extracted>>>result.json(){'success':True,...}ContributingFor guidance on setting up a development environment and to make a contribution, see thecontributing guidelines.Used ByBest Buy Teen Tech CenteratGrand St. Settlement, allowing filmmaking instructors to gather film stills that aid in constructing lesson plans for their youth workshops.Download a workshop example here.
Package: vidgenerator
This lib will help you to generate id for youID GeneratorA package used to generate random ids of variable lengths and types.How to useAfter installing the package use following import:from idgenerator import generate_password,generate_guid,generate_credit_card_number,
generate_pin_number,
generate_object_idThen use following commands:password = generate_password(length= 'your_desired_length')guid = generate_guid()credit_card_number = generate_credit_card_number(length='your_desired_length')pin = generate_pin_number(length='your_desired_length')objid = generate_object_id()
Package: vidhash
VidhashVidhashis a perceptual video hashing and checking library, to detect similar videos, or videos containing similar scenes.How it worksBasically, this video hashing works by scaling the video down a bit, and taking 5 frames a second as images, and performing image hashes on those frames.
Then it can do checks by checking how many image hashes from one video match up with image hashes from another.How to useThis documentation is a little sparse at the moment, but the basic summary is that to hash a video, usevideo_hash = hash_video(video_path).This returns aVideoHashobject.You can also provide aHashSettingsobject. HashSettings need to match for two video hashes to be compared.
Currently HashSettings allow specifying theWhen checking video hashes against each-other, usevideo_hash.check_match(other_hash).
You can optionally provide aMatchOptionsobject as a second argument, or use a MatchOptions object and call theMatchOptions.check_match(hash1, hash2)method on it.There are 3 supported types of MatchSettings:FrameCountMatchChecks whether a specified number of frames match between the two videosAllows specifying the hamming distance between two frames which should be considered a "match"Allows ignoring blank framesPercentageMatchChecks whether a specified percentage of the shorter video's frames match the longer videoAllows specifying the hamming distance between two frames which should be considered a "match"Allows ignoring blank framesDurationMatchChecks whether a specified "duration" of frames match up in order between the two videose.g. 3 seconds duration, at 5 fps, would check whether 15 frames match, in a row, between the two videosAllows specifying the hamming distance between two frames which should be considered a "match"TodoCodeWrapper for imagehash.ImageHashDatastore(For looking up matching videos from a collection)More documentationMore tests
Package: vidhub-control
vidhub-controlOverviewInterface with Videohub SDI Matrix Switchers and SmartView Monitors byBlackmagic Design.The primary purpose is for use as a library in other applications, but a GUI
application is included (requires installation of theKivy framework)Since neither the devices nor the software for them support presets or macros,
a need arose for instantaneous multiple routing changes.  This, as well as
setting the names for inputs and outputs within a single application can be
accomplished using this project.LinksReleases:https://pypi.org/project/vidhub-control/Source code:https://github.com/nocarryr/vidhub-controlDocumentation:https://nocarryr.github.io/vidhub-control/DependenciesThis project relies heavily onasyncioand other features available inPython v3.5or later.Corepython-dispatchjson-object-factoryzeroconfpython-oscpidUser interface (optional)KivyInstallationDownloadFor basic installation, clone or download the source code:gitclonehttps://github.com/nocarryr/vidhub-controlcdvidhub-controlCreate virtual environment (optional, but recommended)Linux/MacOSvirtualenv--python=python3venvsourcevenv/bin/activateWindowsvirtualenv--python=python3venvvenv/Scripts/activateInstall vidhub-controlpythonsetup.pyinstallInstall KivyoptionalEnsure all dependencies are met for your platform. Instructions can be found
on thekivy download pageLinux (Ubuntu)Follow the instructions for“Installation in a Virtual Environment”.Windowspipinstalldocutilspygmentspypiwin32kivy.deps.sdl2kivy.deps.glewpipinstallkivy.deps.sdl2pipinstallkivyMacOSFollow the instructions forhomebreworMacPorts.UsageTo launch the user interface (Kivy required):vidhubcontrol-uiNote for WindowsThevidhubcontrol-uiscript may not work. If this is the case, it can be
launched by:pythonvidhubcontrol/kivyui/main.py
Package: vidi3d
vidi3dVisualizes 3D/4D NumPy arrays using Matplotlib and PyQt5.Installation and RequirementsThe following libraries are required:Python 3Numpy: General purpose array-processing package.Matplotlib:  Python plotting package .PyQt5: Python bindings for Qt application framework.To install, use: pip3 install vidi3d.The software can also be found athttps://github.com/AlanKuurstra/vidi3d.Usageimport vidi3d as v
# compare a list of 2d images (an additional temporal dimension is optional)
v.compare2d(listOf2dImages) 
# compare a list of 3d images (an additional temporal dimension is optional)
v.compare3d(listOf3dImages)Some simple examples can be found in theExamples/folder.