[{"_1":2,"_160":-5,"_161":-5},"loaderData",{"_3":4,"_17":18,"_25":26,"_139":140},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","routes/_layout",{"_19":-5,"_20":21,"_22":21,"_23":11,"_24":21},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":-5,"_89":90,"_128":-5,"_129":11,"_130":21,"_131":11,"_132":-5,"_133":11,"_134":-5,"_135":136,"_137":91,"_138":-7},"canonicalUrl","https://hub.docker.com/r/mfairul/video_analysis/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","mfairul","reponame","video_analysis","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2020-10-21T06:53:51.991913Z","description","","is_private","last_updated","2020-10-21T07:09:47.955036Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",840,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# Analyze live video streams using IBM Maximo Visual Inspection\n\nIn this Code Pattern we'll provide a web application that has the ability to capture frames from streaming cameras, and analyze each frame using IBM Maximo Visual Inspection. As analysis results are received, they can be grouped into positive or negative categories.\n\nThis code pattern is targeted towards users who have access to live streams or CCTV cameras, and would like to apply object detection and image classification to their live camera feeds.\n\n\n\n\n\n# Components\n\n* [IBM Maximo Visual Inspection](https://www.ibm.com/us-en/marketplace/ibm-powerai-vision). This is an image analysis platform that allows you to build and manage computer vision models, upload and annotate images, and deploy apis to analyze images and videos. Sign up for a trial account of IBM Maximo Visual Inspection [here](https://developer.ibm.com/linuxonpower/deep-learning-powerai/try-powerai/). This link includes options to provision a IBM Maximo Visual Inspection instance either locally on in the cloud.\n\n# Flow\n\n

\n\n\n1. User accesses web application and provides login credentials for both the Camera system and IBM Maximo Visual Inspection. User also fills out form to select model and classify images as positive or negative.\n2. Node.js backend connects to camera RTSP stream and forwards to frontend web application\n3. As frontend plays live video, user clicks \"Capture Frame\" or \"Start Interval\"\n4. Captured frames are forwarded to IBM Maximo Visual Inspection backend for analysis.\n5. Analysis results are rendered in web app, and grouped by user-defined positive/negative labels.\n\n# Prerequisites\n\n* An account on IBM Marketplace that has access to IBM Maximo Visual Inspection. This service can be provisioned [here](https://developer.ibm.com/linuxonpower/deep-learning-powerai/vision/access-registration-form/)\n\nYou will also need to train and deploy a custom model beforehand. This can be done following the steps in this [video](https://www.youtube.com/watch?v=-gzGuj3B__U)\n\n* Docker - Can be used to run the application in a virtual container. If running via docker, the remaining prerequisites can be bypassed, and you can skip ahead to the steps labeled **docker** in [step 2](#2-start-application)\n\n* ffmpeg - Package used here to connect to remote RTSP streams\n```\n# Linux\napt install ffmpeg\n\n# Macbook OS X\nbrew install ffmpeg\n```\n\n* Node.js\n\nSkip to [Steps](#maximo-live-analysis) if you already have node.js installed on your system.\n\n### Install Node.js packages\n\nIf expecting to run this application locally, please install [Node.js](https://nodejs.org/en/) and NPM. Windows users can use the installer at the link [here](https://nodejs.org/en/download/).\n\nIf you're using Mac OS X or Linux, and your system requires additional versions of node for other projects, we'd suggest using [nvm](https://github.com/creationix/nvm) to easily switch between node versions. Install nvm with the following commands.\n\n```bash\ncurl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash\n```\n\n\n```bash\n# Place next three lines in ~/.bash_profile\nexport NVM_DIR=\"$HOME/.nvm\"\n[ -s \"$NVM_DIR/nvm.sh\" ] && \\. \"$NVM_DIR/nvm.sh\" # This loads nvm\n[ -s \"$NVM_DIR/bash_completion\" ] && \\. \"$NVM_DIR/bash_completion\" # This loads nvm bash_completion\n```\n\n\n```bash\nnvm install v8.9.0\nnvm use 8.9.0\n```\n\n\n# Steps\n\nFollow these steps to setup and run this Code Pattern.\n\n1. [Clone repository](#1-clone-repository)\n2. [Start web application](#2-start-application)\n3. [Configure web application](#3-configure-web-application)\n4. [Observe results](#4-observe-results)\n\n\n## 1. Clone repository\n\n```\ngit clone https://github.com/IBM/maximo-streaming-video-analysis\n```\n\nNavigate to the project folder\n```\ncd maximo-streaming-video-analysis\n```\n\n## 2. Start application\n\n**Local Deployment**\nInstall frontend dependencies\n```\ncd maximo-streaming-video-analysis\ncd frontend\nnpm install\n```\n\nStart frontend\n```\nnpm run serve\n```\n\nIn a separate terminal tab, install backend dependencies\n```\ncd maximo-streaming-video-analysis\ncd backend\nnpm install\n```\n\nStart backend\n```\nPORT=3000 npm start\n```\n\n**Docker**\nStart Backend\n```\ndocker run -it -p 3000:3000 kkbankol/maximo-live /bin/bash -c \"cd /maximo-streaming-video-analysis/backend && PORT=3000 npm start\"\n```\n\nStart frontend\n```\n# frontend\ndocker run -it -p 8080:8080 kkbankol/maximo-live /bin/bash -c \"cd /maximo-streaming-video-analysis/frontend && npm run serve\"\n```\n\n## 3. Configure web application\nAccess the web application at [localhost:8080](localhost:8080).\n\nClick \"Login\" and provide the url, username, and password for the Maximo Visual Inspection instance. These credentials should have been provided in your welcome letter.\n\n

\n\nNext, click \"Configure Model\" in the Menu. Select your custom model. Then, select objects or classes associated with that model that you'd like to observe in the dashboard. These selected categories can be grouped as either negative or positive.\n\n

\n\nAfter selecting the relevant model categories, we can then being streaming a video to the application for analysis. Currently, this app supports the following video sources. To stop a video from playing, click \"Stop Stream\".\n\n1. Webcam - This is the default method, and requires no configuration\n2. [RTSP](https://en.wikipedia.org/wiki/Real_Time_Streaming_Protocol) - RTSP is a streaming protocol commonly used by CCTV cameras. To connect to an RTSP stream, click the \"Stream RTSP\" button, provide the URL and the login credentials (if applicable).\n3. Youtube - To playback a Youtube video for analysis, click the \"Stream RTSP\" button and enter the Youtube URL.\n4. Local Video File - Click the \"Upload Video\" button and drag and drop your video file.\n\n

\n\nAfter clicking \"Submit\" in the \"Stream RTSP\" or \"Upload File\" modal, the video should immediately begin playing in the browser.\n\n## 4. Observe results\n\nOnce the video begins playing, click the \"Capture Frame\" button to analyze a single frame with the configured model. Click the \"Capture Frame Interval\" button to analyze a frame every second for 10 seconds.\n\nAs results are received that match the model configuration, they will then be rendered in the \"Inference Results\" section on the right hand side like so.\n\n

\n\nClicking on one of the inference results will show a popup modal with detailed information for that particular inference, such as the identified class/object, heatmap/bounding boxes, and confidence score.\n\n

\n\n\n\n\n\n\n\n# License\n\nThis code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1 (DCO)](https://developercertificate.org/) and the [Apache Software License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt).\n\n[Apache Software License (ASL) FAQ](https://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN)\n","has_starred","hub_user","immutable_tags_settings",{"_75":11,"_76":77},"enabled","rules",[78],".*","is_automated","permissions",{"_82":21,"_83":11,"_84":11},"read","write","admin","user","storage_size",726972943,"orgData","recentTags",[91],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":100,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},"content_type","creator",10924695,"full_size",726965465,"id",122466139,"images",[101],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},"architecture","amd64","digest","sha256:c332494c3dba6ebaed419adf0f3d281a3c6d27f2fb46e2b0b07d754f152a7261","features","last_pulled","2026-10-07T12:24:01.194076904Z","last_pushed","2020-10-21T07:05:28Z","os","linux","os_features","os_version","size","variant","2020-10-21T07:09:46.182119Z","last_updater","last_updater_username","media_type","latest","repository",10068040,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"selectedTag","attestationsUrl","routes/_layout.r.$namespace.$reponame.tags",{"_38":141,"_148":149,"_135":158,"_159":11},{"_40":142,"_42":143,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":144,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":145,"_79":11,"_80":147,"_85":33,"_86":87},[],[44],[56],{"_75":11,"_76":146},[78],{"_82":21,"_83":11,"_84":11},"tags",{"_150":64,"_151":-5,"_152":-5,"_153":154},"count","next","previous","results",[155],{"_92":44,"_93":94,"_95":96,"_97":98,"_99":156,"_50":117,"_118":94,"_119":33,"_120":56,"_57":121,"_122":123,"_124":108,"_125":117,"_126":66,"_127":21},[157],{"_102":103,"_104":105,"_106":48,"_107":108,"_109":110,"_111":112,"_113":48,"_114":-5,"_115":96,"_63":66,"_116":-5},[],"canDeleteTags","actionData","errors"]

mfairul/video_analysis

Tags for mfairul/video_analysis