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How to read and display image using cv2

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16.01.2020

Harish Gadade

OpenCV Python Tutorial For Beginners 3 - How to Read, Write, Show Images in OpenCV

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In this video on OpenCV Python Tutorial For Beginners, I am going to show How to Read, Write, Show Images in OpenCV. we will se how to use cv2.imread('lena.jpg', -1) to read and image, cv2.imshow('image', img) to show an image in a window, cv2.waitKey(0) to wait for a key event, cv2.destroyAllWindows() to destroy all windows, cv2.imwrite('lena_copy.png', img) to write images to file. OpenCV github page 🤍 Gist of code I used in this video - 🤍 Install PyCharm ( Python IDE ) on Windows 10 + First Project - 🤍 Read an image cv2.imread() Second argument is a flag which specifies the way image should be read. flag integer value description cv2.IMREAD_COLOR 1 Loads a color image. cv2.IMREAD_GRAYSCALE 0 Loads image in grayscale mode cv2.IMREAD_UNCHANGED -1 Loads image as such including alpha channel pip is a package management system used to install and manage software packages written in Python. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C, and Python. it is Open Source and free. opencv is easy to use and install. Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV. At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science. Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. #ProgrammingKnowledge #ComputerVision #OpenCV ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ 🤍 ⚫️ 🤍 Java Programming Course ➡️ 🤍 ⚫️ 🤍 Bash Shell Scripting Course ➡️ 🤍 ⚫️ 🤍 Linux Command Line Tutorials ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 PHP Programming Course ➡️ 🤍 ⚫️ 🤍 Android Development Course ➡️ 🤍 ⚫️ 🤍 C# Programming Course ➡️ 🤍 ⚫️ 🤍 JavaFx Programming Course ➡️ 🤍 ⚫️ 🤍 NodeJs Programming Course ➡️ 🤍 ⚫️ 🤍 Jenkins Course For Developers and DevOps ➡️ 🤍 ⚫️ 🤍 Scala Programming Tutorial Course ➡️ 🤍 ⚫️ 🤍 Bootstrap Responsive Web Design Tutorial ➡️ 🤍 ⚫️ 🤍 MongoDB Tutorial Course ➡️ 🤍 ⚫️ 🤍 QT C GUI Tutorial For Beginners ➡️ 🤍 ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - 🤍 Data Science - 🤍 | 🤍 Machine Learning - 🤍 | 🤍 Artificial Intelligence - 🤍 | 🤍 MERN Stack E-Degree Program - 🤍 | 🤍 DevOps E-degree - 🤍 | 🤍 Data Analytics with R - 🤍 | 🤍 AWS Certification Training - 🤍 | 🤍 Projects in Java - 🤍 | 🤍 Machine Learning With TensorFlow - 🤍 | 🤍 Angular 8 - Complete Essential Guide - 🤍 Kotlin Android Development Masterclass - 🤍 Learn iOS Programming Building Advance Projects - 🤍 ★★★ Follow ★★★ My Website - 🤍 DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

Tutorial -2 Image representation, cv2.imread() and cv2.imwrite()

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29.03.2020

In this video you will learn how images are represented in image processing applications and some basic opencv functions like cv2.imread(), cv2.imshow() You could find the code in : 🤍

Read Image from file using Python and OpenCV

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06.11.2014

Read Image from file using Python and OpenCV

Read and Resize Image in Python using cv2

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00:14:59
03.01.2023

Read and Resize Images in Python using cv2 Reading Image - 🤍 Resizing Image - 🤍 Machine Learning Projects: Link - 🤍 ✅ HealthCure - an all in one medical solution - medical project - 7 disease detections ✅ Object Detection using SSD ✅ Social Distancing using YOLOv3 - Object Detection ✅ Face Recognition Based Attendance System with source code - Flask App - With GUI ✅ Self Organizing Maps ✅ Pedestrian Detection using HOGs in Python ✅ IPL Score Prediction with Flask app ✅ Weight Category prediction using Random Forest ✅ How to perform Face Recognition using KNN ✅ Flight Price Prediction with Flask app - data visualizations ✅ House Price Prediction - USA Housing Data ✅ Stock Sentiment Analysis using headlines ✅ Wine Quality Prediction ✅ Bank Note Authentication using Random Forest ✅ Flipkart Reviews extraction and sentiment analysis with Flask app ✅ How to find the most dominant colors in an image using KMeans clustering ✅ Movie Recommendation System in 2 ways ✅ Credit Card Fraud Detection ✅ Face and Eye detection in cv2 using Haarcascades ✅ Spam Detection using Count Vectorizer ✅ House Tax Prediction using Random Forest - Boston Housing Data Deep Learning Projects: Link - 🤍 ✅ Realtime Number Plate Detection using Yolov7 ✅ Easiest way to Train yolov7 on the custom dataset ✅ Google Stock Price Prediction using LSTM ✅ Image Captioning using Deep Learning ✅ Generating cifar-10 fake images using Deep Convolutional Generative Adversarial Networks (DCGAN) ✅ Helmet and Number Plate Detection and Recognition using YOLOv3 ✅ Invisible Man using Mask-RCNN ✅ Neural Style Transfer ✅ Sudoku Solver ✅ Human Segmentation using U-Net ✅ Milk Production prediction for next year using LSTM ✅ Emotion Detector using Keras - easy implementation ✅ MNIST Handwritten Number Recognition - using Deep Neural Networks ✅ Monkey Breed Classification using Transfer Learning - easiest code explanation - easy implementation ✅ MNIST Handwritten number recognition using Keras - with live predictor ✅ AI learns to play Flappy Bird Game ✅ Age Detection using CNN with Keras - easy implementation ✅ Fire and Smoke Detection using CNN with Keras ✅ Cats and Dogs Classifier ✅ Dimensionality Reduction using Autoencoders Computer Vision Projects: Link - 🤍 ✅ Drowsiness Detection using cv2 in Python ✅ Realtime Number Plate Detection using Yolov7 ✅ Easiest way to Train yolov7 on the custom dataset ✅ Blur Faces in Live Feed using OpenCV and Python ✅ Generating cifar-10 fake images using Deep Convolutional Generative Adversarial Networks (DCGAN) ✅ Helmet and Number Plate Detection and Recognition using YOLOv3 ✅ Invisible Man using Mask-RCNN ✅ Sudoku Solver ✅ How to detect shapes using cv2- with source code ✅ Document Scanner using OpenCV ✅ Face Landmarks Detection using dlib - easy implementation ✅ Make your Sketch using OpenCV in Python ✅ How to perform Face Recognition using KNN ✅ Immortal Snake game in Python using OpenCV- with source code ✅ Top 5 Types of thresholding techniques in Python using OpenCV ✅ Face and Eye detection in cv2 using Haarcascades ✅ Harry’s Invisibility Cloak - less than 50 lines of code ✅ How to split and merge channels in cv2 ✅ How to perform edge detection using Sobel X and Sobel Y in cv2 ✅ Rotating and Scaling Images using cv2 - a fun Python application ✅ How to repair damaged images using inpainting methods in Python using OpenCV Flask Projects: Link - 🤍 ✅ Create an API in Flask and deploy it online ✅ Portfolio Website in Python using Flask ✅ Live Weather Forecast Flask App ✅ Youtube Subtitles Downloader Flask App ✅ How to Deploy a Flask app online using Pythonanywhere ✅ Sketchy - Sketch making Flask App ✅ IPL Score Prediction with Flask app ✅ Flight Price Prediction with Flask app - data visualizations NLP Projects: Link - 🤍 ✅ Fake news Classifier using LSTM ✅ Singular Value Decomposition ✅ Topic Modeling using Latent Dirichlet Allocation ✅ Words to Vectors using Spacy AWS Tutorials: Link - 🤍 ✅ Easiest way to schedule a Python Script in AWS Glue as a Job ✅ Easiest Way to use an Amazon S3 trigger to invoke a Lambda function ✅ How to Deploy a Lambda function as a Component on an Edge Machine using AWS Greengrass v2 ✅ How to build, test, and deploy your first Lambda on AWS ✅ How to create an API in AWS API Gateway using AWS Lambda ✅ How to create an AWS Lambda using AWS ECR Docker container Image in Python #ai #ml #machinelearning #deeplearning #machinelearningprojects #deeplearningprojects #python

Reading, Writing and Displaying images with Opencv| OpenCV Tutorial

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Here is a detailed explanation about how to read, write and display images using Opencv Github url: 🤍 Follow me on facebook: 🤍 Below are the other playlist Machine learning: 🤍 Deep Learning : 🤍 Statistics in ML :🤍 Feature Engineering:🤍 Data Preprocessing Techniques: 🤍

How to Read, View and Export Images using OpenCV for Python // OpenCV for Beginners

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20.10.2021

Wassup! Welcome to the OpenCV Basics series. In this series, we'll be going through all the basics of OpenCV from the ground up. In this first video you'll learn how to get up and running with OpenCV. In this video you'll learn how to: 1. Read in images into Python using OpenCV 2. Visualise images inside a Jupyter Notebook with Matplotlib 3. Write out transformed images Get the code: 🤍 Links Documentation: 🤍 Python Tutorials: 🤍 Download Samples from here: 🤍 Chapters: 0:00 - Start 0:30 - Explainer 1:15 - Tutorial Kickoff 3:17 - Read in an Image 7:34 - Render images using Matplotlib 10:16 - Reordering Channels 11:46 - Exporting images 15:56 - Wrap up Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!

Opencv with Google Colab | Read, Display, and Write Images using OpenCV in google Colab

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20.03.2021

How to display image in colab ? How to read image in google colab? how to write image in google colab? Read, display and write images using Opencv in google colab

25 - Reading Images, Splitting Channels, Resizing using openCV in Python

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20.05.2019

openCV is a library of programming functions mainly aimed at computer vision but also very helpful for processing microscope images. This tutorial explains a few basic operations in openCV to read & resize images and to split & merge channels. The code from this video is available at: 🤍

[第1期] Install and use OpenCV to read image/安裝與使用OpenCV讀取圖片

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13.02.2021

This video shows how to install OpenCV in Anaconda and write program to read image in the Spyder IDE. 這個影片說明如何在Anaconda中安裝OpenCV以及在Spyder開發環境中寫一個如圖片的程式。 Steps/步驟: 1. Install OpenCV by using Anaconda Prompt/使用Anaconda Prompt安裝OpenCV (pip install opencv-python) 2. cv2.imread: Read Image/讀取圖片 3. cv2.imshow: Show image on the window/將圖片呈現在視窗上

OpenCV Part 2 | Image processing in Python |Open CV2 | imshow() not working in kaggle

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27.06.2021

how to work with OpenCV in python using command prompt working with images in Kaggle Reading and showing images using OpenCV in Kaggle

OpenCV Python Tutorial For Beginners 4 - How to Read, Write, Show Videos from Camera in OpenCV

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17.02.2019

In this video on OpenCV Python Tutorial For Beginners, I am going to show How to Read, Write, Show Videos from Camera in OpenCV. We will see How to read video, display video and save video, How to capture from Camera and display it, we will see these functions : cv.VideoCapture(), cv.VideoWriter(). So we will Capture Video from Camera using opencv. So let us see How to process images of a video, frame by frame in video streaming using Opencv python. The list of available codes can be found in fourcc.org - 🤍 Gist of code I used in this video (Display the webcam in Python using OpenCV (cv2)) - 🤍 VideoCaptureProperties 🤍 Python: cv.CAP_PROP_FRAME_WIDTH: Width of the frames in the video stream. Python: cv.CAP_PROP_FRAME_HEIGHT: Height of the frames in the video stream. Color image loaded by OpenCV is in BGR mode. But Matplotlib displays in RGB mode. pip is a package management system used to install and manage software packages written in Python. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C, and Python. it is Open Source and free. opencv is easy to use and install. Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV. At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science. Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. #ProgrammingKnowledge #ComputerVision #OpenCV ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ 🤍 ⚫️ 🤍 Java Programming Course ➡️ 🤍 ⚫️ 🤍 Bash Shell Scripting Course ➡️ 🤍 ⚫️ 🤍 Linux Command Line Tutorials ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 PHP Programming Course ➡️ 🤍 ⚫️ 🤍 Android Development Course ➡️ 🤍 ⚫️ 🤍 C# Programming Course ➡️ 🤍 ⚫️ 🤍 JavaFx Programming Course ➡️ 🤍 ⚫️ 🤍 NodeJs Programming Course ➡️ 🤍 ⚫️ 🤍 Jenkins Course For Developers and DevOps ➡️ 🤍 ⚫️ 🤍 Scala Programming Tutorial Course ➡️ 🤍 ⚫️ 🤍 Bootstrap Responsive Web Design Tutorial ➡️ 🤍 ⚫️ 🤍 MongoDB Tutorial Course ➡️ 🤍 ⚫️ 🤍 QT C GUI Tutorial For Beginners ➡️ 🤍 ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - 🤍 Data Science - 🤍 | 🤍 Machine Learning - 🤍 | 🤍 Artificial Intelligence - 🤍 | 🤍 MERN Stack E-Degree Program - 🤍 | 🤍 DevOps E-degree - 🤍 | 🤍 Data Analytics with R - 🤍 | 🤍 AWS Certification Training - 🤍 | 🤍 Projects in Java - 🤍 | 🤍 Machine Learning With TensorFlow - 🤍 | 🤍 Angular 8 - Complete Essential Guide - 🤍 Kotlin Android Development Masterclass - 🤍 Learn iOS Programming Building Advance Projects - 🤍 ★★★ Follow ★★★ My Website - 🤍 DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

Read, Write Image & Destroy windows | OpenCV Python Tutorials

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17.04.2022

* Content summary: - Read Image from computer directory - Write image and saved image with new name - Closed a window - Closed all windows - Wait for typing key from keyboard

How To Read Image-Video-Webcam [1] | OpenCV Python Tutorials for Beginners

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07.10.2019

In this tutorial, we will learn how to read an image and video placed in a folder using OpenCV. We will also look at how to run a webcam and how to set its parameters. Code & Text Based Version: 🤍 Links: OpenCV Python Tutorial Playlist: 🤍 Premium Courses: ✔️ Computer Vision Game Development Course: 🤍 ✔️ Computer Vision with Arduino Course: 🤍 ✔️ Advanced Drone Programming Course: 🤍 ✔️ Learn to Build Computer Vision Mobile Apps: 🤍 ✔️ Jetson Nano Premium Course: 🤍 Follow Me: TikTok: 🤍 Facebook Group: 🤍 Discord: 🤍 Facebook Page: 🤍 Instagram : 🤍 Website: 🤍 Github: 🤍 Product Links: Recommend Webcam for Computer Vision: 🤍 Budget Webcam: 🤍 Computer Vision Robot Arm : 🤍 Cheap Drone for OpenCV: 🤍 DC Motors + Wheels + Chassis: 🤍 DC Motors + Wheels: 🤍 Arduino UNO: 🤍 Motor Driver: 🤍 Battery: 🤍 Raspberry Pi 4 Best Starter Kit: 🤍 Raspberry Pi Recommended Battery: 🤍 My Setup: Mouse: 🤍 Mechanical Keyboard: 🤍 Normal Keyboard: 🤍 GPU: 🤍 CPU: 🤍 SSD: 🤍 MIC: 🤍 Camera: 🤍 3D Printer: 🤍 Sim Race: 🤍 #ComputerVision #OpenCV #CVZone

Computer Vision with Python and OpenCV - Reading and Displaying images

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17.09.2017

In this video, we will learn how to read and display images with OpenCV. The link to the github repository is as follows, 🤍 Credits: Music: 🤍 Music: 🤍 I teach Programming, Electronics, Science, and Mathematics on Udemy. 🤍udemy.com/user/AshwinPajankar #Python #OpenCV #ImageProcessing

OpenCV Python Tutorial #1 - Introduction & Images

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09.02.2021

Welcome to a brand new series on OpenCV and Python. I'll start this episode with a quick introduction to OpenCV, in case some of your aren't familiar with it. I'll also talk about how to install OpenCV, loading an image into OpenCV, as well as manipulating an image within OpenCV. 💻 AlgoExpert is the coding interview prep platform that I used to ace my Microsoft and Shopify interviews. Check it out and get a discount on the platform using the code "techwithtim" 🤍 📄 Relevant Documentation: 🤍 📝 Code For This Series: 🤍 📺 Fix Pip on Windows: 🤍 📺 Fix Pip on Mac: 🤍 🔍 Playlist: 🤍 ⭐️ Timestamps ⭐️ 00:00 | Introduction & Series Overview 01:46 | Installation & Setup 05:45 | Loading an Image 07:56 | Displaying an Image 10:35 | Resizing an Image 12:45 | Rotating an Image ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 💰 Courses & Merch 💰 💻 The Fundamentals of Programming w/ Python: 🤍 👕 Merchandise: 🤍 🔗 Social Medias 🔗 📸 Instagram: 🤍 📱 Twitter: 🤍 ⭐ Discord: 🤍 📝 LinkedIn: 🤍 🌎 Website: 🤍 📂 GitHub: 🤍 🔊 Podcast: 🤍 🎬 My YouTube Gear 🎬 🎥 Main Camera (EOS Canon 90D): 🤍 🎥 Secondary Camera (Panasonic Lumix G7): 🤍 📹 Main Lens (EFS 24mm f/2.8): 🤍 🕹 Tripod: 🤍 🎤 Main Microphone (Rode NT1): 🤍 🎤 Secondary Microphone (Synco Wireless Lapel System): 🤍 🎤 Third Microphone (Rode NTG4+): 🤍 ☀️ Lights: 🤍 ⌨ Keyboard (Daskeyboard 4Q): 🤍 🖱 Mouse (Logitech MX Master): 🤍 📸 Webcam (Logitech 1080p Pro): 🤍 📢 Speaker (Beats Pill): 🤍 🎧 Headphones (Bose Quiet Comfort 35): 🤍 🌞 Lamp (BenQ E-reading Lamp): 🤍 🌞 Secondary Lamp (BenQ Screenbar Plus): 🤍 💻 Monitor (BenQ EX2780Q): 🤍 💻 Monitor (LG Ultrawide 34WN750): 🤍 🎙 Mic Boom Arm (Rode PSA 1): 🤍 🎚 Audio Interface (Focusrite Scarlet 4i4): 🤍 💸 Donations 💸 💵 One-Time Donations: 🤍 💰 Patreon: 🤍 ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ ⭐️ Tags ⭐️ - Images within OpenCV - How to install OpenCV - Python - Computer vision - Pip on Windows Mac ⭐️ Hashtags ⭐️ #OpenCV #Python

How to Preprocess Images for Text OCR in Python (OCR in Python Tutorials 02.02)

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07.04.2021

If you enjoy this video, please subscribe. ✅Be my Patron: 🤍 ✅PayPal: 🤍 How to Open an Image with OpenCV: 4:10 01. Invert an Image: 9:47 03: Binarization: 13:33 04: Noise Reduction: 20:40 05: Dilation and Erosion: 28:33 06: Rotation and Deskewing: 35:07 07: Removing Borders: 42:18 08: Missing Borders: 49:09 GitHub Notebook: 🤍 If there's a specific video you would like to see or a tutorial series, let me know in the comments and I will try and make it. If you liked this video, check out 🤍PythonHumanities.com, where I have Coding Exercises, Lessons, on-site Python shells where you can experiment with code, and a text version of the material discussed here. You can follow me at: 🤍

How to Read and Show an Image using OpenCV? | Python Project 🐍

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12.11.2022

In this video, learn How to Read and Show an Image using OpenCV? | Python Project 🐍. Find all the videos of the Top Python Project for Advanced Leval in this playlist: 🤍 💎 Get Access to Premium Videos and Live Streams: 🤍 WsCube Tech is a leading Web, Mobile App & Digital Marketing company, and institute in India. We help businesses of all sizes to build their online presence, grow their business, and reach new heights. 👉For Digital Marketing services (Brand Building, SEO, SMO, PPC, SEM, Content Writing), Web Development and App Development solutions, visit our website: 🤍 👉Want to learn new skills and improve existing ones with in-depth and practical sessions? Enroll in our advanced online courses now and make yourself job-ready: 🤍 All the courses are job-oriented, up-to-date with the latest algorithms and modules, fully practical, and provide you hands-on projects. 👉 Want to learn and acquire skills in English? Visit WsCube Tech English channel: 🤍 📞 For more info about the courses, call us: +91-7878985501, +91-9269698122 ✅ CONNECT WITH THE FOUNDER (Mr. Kushagra Bhatia) - 👉 Instagram - 🤍 👉 LinkedIn - 🤍 Connect with WsCube Tech on social media for the latest offers, promos, job vacancies, and much more: ► Subscribe: 🤍 ► Facebook: 🤍 ► Twitter: 🤍 ► Instagram: 🤍 ► LinkedIn : 🤍 ► Youtube: 🤍 ► Website: 🤍 | Thanks |- #opencv #python #pythonprojects

Extracting Frames from Video as Images in Python Using CV2.

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30.04.2021

Using Python to read video files and save each frame as an image in a separate folder. Github Code- 🤍 Instagram- 🤍

YR2 - How to process multiple images in python?

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17.11.2020

This video is produced upon viewer request. It explains 4 ways of automating image processing to apply a function or series of functions to each file in a folder or to each image in a multidimensional stack. Code associated with these tutorials can be downloaded from here: 🤍

PNG Overlay and Image Rotation using OpenCV | CVZone

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02.08.2021

In this tutorial, we are going to learn how to overlay PNG Images using OpenCV. We will also learn how to overlay logos on images and webcams. Along with that, we will learn how to rotate images with a simple function and create awesome looking gear mechanism. Code and Files: 🤍 Premium Courses: ✔️ Computer Vision Game Development Course: 🤍 ✔️ Computer Vision with Arduino Course: 🤍 ✔️ Advanced Drone Programming Course: 🤍 ✔️ Learn to Build Computer Vision Mobile Apps: 🤍 ✔️ Jetson Nano Premium Course: 🤍 Follow Me: TikTok: 🤍 Facebook Group: 🤍 Discord: 🤍 Facebook Page: 🤍 Instagram : 🤍 Website: 🤍 Github: 🤍 Product Links: Recommend Webcam for Computer Vision: 🤍 Budget Webcam: 🤍 Computer Vision Robot Arm : 🤍 Cheap Drone for OpenCV: 🤍 DC Motors + Wheels + Chassis: 🤍 DC Motors + Wheels: 🤍 Arduino UNO: 🤍 Motor Driver: 🤍 Battery: 🤍 Raspberry Pi 4 Best Starter Kit: 🤍 Raspberry Pi Recommended Battery: 🤍 My Setup: Mouse: 🤍 Mechanical Keyboard: 🤍 Normal Keyboard: 🤍 GPU: 🤍 CPU: 🤍 SSD: 🤍 MIC: 🤍 Camera: 🤍 3D Printer: 🤍 Sim Race: 🤍 #ComputerVision #OpenCV #CVZone 00:00 Introduction 00:44 Image Overlay 10:30 Logo Overlay 15:10 Webcam Overlay 21:00 Gear Rotation #ComputerVision #OpenCV #CVZone

OpenCV - Read & write an Image in color or grayscale -Do it yourself- part -2

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13.11.2018

OpenCV with Python - Read & write an Image in color or grayscale -Do it yourself- Google Drive Link - 🤍 OpenCV Basics, OpenCV for beginners, What is OpenCV, An intro to OpenCV Bharati DW Consultancy cell: +1-562-646-6746 email: bharati.dwconsultancy🤍gmail.com website: 🤍 🤍 Twitter: 🤍BharatDWCons Youtube: BharatiDWConsultancy Whatsapp: +1-562-646-6746 (+1-56-COGNOS-46)

Text Detection with OpenCV in Python | OCR using Tesseract (2020)

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23.04.2020

In this video, we are going to learn how to detect text in images. We will learn how to detect individual characters and words and how to place bounding boxes around them. We will also look at how to detect only digits. Code & Text-Based Version (Code can be found in the projects section): Premium Courses: ✔️ Computer Vision Game Development Course: 🤍 ✔️ Computer Vision with Arduino Course: 🤍 ✔️ Advanced Drone Programming Course: 🤍 ✔️ Learn to Build Computer Vision Mobile Apps: 🤍 ✔️ Jetson Nano Premium Course: 🤍 Follow Me: TikTok: 🤍 Facebook Group: 🤍 Discord: 🤍 Facebook Page: 🤍 Instagram : 🤍 Website: 🤍 Github: 🤍 Product Links: Recommend Webcam for Computer Vision: 🤍 Budget Webcam: 🤍 Computer Vision Robot Arm : 🤍 Cheap Drone for OpenCV: 🤍 DC Motors + Wheels + Chassis: 🤍 DC Motors + Wheels: 🤍 Arduino UNO: 🤍 Motor Driver: 🤍 Battery: 🤍 Raspberry Pi 4 Best Starter Kit: 🤍 Raspberry Pi Recommended Battery: 🤍 My Setup: Mouse: 🤍 Mechanical Keyboard: 🤍 Normal Keyboard: 🤍 GPU: 🤍 CPU: 🤍 SSD: 🤍 MIC: 🤍 Camera: 🤍 3D Printer: 🤍 Sim Race: 🤍 #ComputerVision #OpenCV #CVZone

Feature Detection and Matching + Image Classifier Project | OPENCV PYTHON

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00:45:32
16.06.2020

In this video, we will learn how to create an Image Classifier using Feature Detection. We will first look at the basic code of feature detection and description and then move on to the classifier project. Code & Files: coming soon Premium Courses: ✔️ Computer Vision Game Development Course: 🤍 ✔️ Computer Vision with Arduino Course: 🤍 ✔️ Advanced Drone Programming Course: 🤍 ✔️ Learn to Build Computer Vision Mobile Apps: 🤍 ✔️ Jetson Nano Premium Course: 🤍 Follow Me: TikTok: 🤍 Facebook Group: 🤍 Discord: 🤍 Facebook Page: 🤍 Instagram : 🤍 Website: 🤍 Github: 🤍 Product Links: Recommend Webcam for Computer Vision: 🤍 Budget Webcam: 🤍 Computer Vision Robot Arm : 🤍 Cheap Drone for OpenCV: 🤍 DC Motors + Wheels + Chassis: 🤍 DC Motors + Wheels: 🤍 Arduino UNO: 🤍 Motor Driver: 🤍 Battery: 🤍 Raspberry Pi 4 Best Starter Kit: 🤍 Raspberry Pi Recommended Battery: 🤍 My Setup: Mouse: 🤍 Mechanical Keyboard: 🤍 Normal Keyboard: 🤍 GPU: 🤍 CPU: 🤍 SSD: 🤍 MIC: 🤍 Camera: 🤍 3D Printer: 🤍 Sim Race: 🤍 #ComputerVision #OpenCV #CVZone

Fast Window Capture - OpenCV Object Detection in Games #4

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28.05.2020

Learn how to capture window data in real-time as a video stream for processing with OpenCV. We try several different methods searching for the fastest way possible. In this tutorial I also discuss the importance of good Google search skills as a programmer, and we revisit some basic object-oriented programming concepts. Full tutorial playlist: 🤍 Grab the code on GitHub: 🤍 Research discussed: OpenCV getting started with videos: 🤍 Fastest way to take a screenshot with Python on Windows: 🤍 Convert PyAutoGUI image to OpenCV: 🤍 Convert SaveBitmapFile to an OpenCV image instead: 🤍 Best Numpy reference: 🤍 List all your windows: 🤍 0:47 Main capture loop 3:16 Using PyAutoGUI screenshot() 6:24 Measure FPS 8:27 Using Pillow ImageGrab 9:31 Using Pywin32 for screenshots 13:48 Converting win32ui.CreateBitmap() for OpenCV 17:23 Confining screenshots to a specific window 18:42 Creating a WindowCapture class 25:05 Trimming the window capture 28:15 Image to screen position conversion 29:35 Wrap up Read the full written tutorial with code samples here: 🤍 Up to this point, we've been using OpenCV to detect objects in static images. Now we're ready to apply those same techniques to video games in real time. Remember that video is just a series of still images shown in rapid succession. In this tutorial our goal is to capture screenshots as fast as possible and display them in an OpenCV window, so that we get a real time video stream of the game we're interested in. This will set us up to begin processing image data with OpenCV in real-time. OpenCV has a tutorial on "Getting Started with Videos" that will serve as the basis for our code. Our starting point differs from the official tutorial only in that we are preparing to work with screenshot data instead of frames from a camera. When defining get_screenshot() you could simply use pyautogui.screenshot() from the PyAutoGUI library, or ImageGrab.grab() from the Python Image Library. And this would work, but there are several benefits to calling the Windows API directly instead. Firstly, we approach the theoretical limit for how fast we can take these screenshots by dealing right with the operating system itself. Secondly, the Windows API has methods that will allow us to grab the screen data for only the window we're interested in, even when it's minimized or off screen. To do this, we must first pip install pywin32 to get access to the Win32 API in Python. Let's start with some code to capture a single screenshot of our entire desktop and save that to a file. This will confirm for us that the Windows API calls are working. By calling this function, you should end up with a debug.bmp screenshot file. The next step is to modify this function so that instead of saving an image file, it instead returns the image data, formatted to work with OpenCV. Now we can call this function from our original infinite loop and get a real-time stream of our desktop. To improve upon this, we can use win32gui.FindWindow(None, window_name) to capture just the window we're interested in. Replace the window_name with a string that contains the name found in the title bar of the window you want to capture. Doing so will allow you to capture the frames from that window even when it's hidden behind other windows. If you're having trouble figuring out the name of the window you want, you can use this code to list the names of all your existing windows: We can improve our code further by trimming off the excess around the window we're interested in. When you run the above code, you will notice black space to the right and below the window image, as well as the window borders and title bar. Removing these will not only clean things up, it will also improve our frame rate. We can also get improvements by not calling win32gui.FindWindow() on every call to get_screenshot(), so let's turn this into a class. Finally, we'll need a way to convert positions we detect in our screenshots back to pixel positions on our actual monitor. In the WindowCapture class constructor, I've already included code to calculate the window offset using the window position data from win32gui.GetWindowRect(). Let's add a method to our class that uses this offset to return that converted screen position. Continue with the written tutorial here: 🤍

Lecture 3 - Image Read, Write and Show | Image Processing | Image Operations | OpenCV functions |

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07.11.2020

Image Procession and Computer Vision with OpenCV python full tutorial in Hindi. In this video on OpenCV Python Tutorial For Beginners, I am going to show How to Read, Write, Show Images in OpenCV. we will se how to use cv2.imread('lena.jpg', -1) to read and image, cv2.imshow('image', img) to show an image in a window, cv2.waitKey(0) to wait for a key event, cv2.destroyAllWindows() to destroy all windows, cv2.imwrite('imagename.png', img) to write images to file. Topic Cover- What is imread function in OpenCV? What is imshow function in OpenCV? What is Gray scale image and BGR image? Save image using imwrite fucntion Code and Theory - 🤍 Subscribe To AskitLoud - 🤍 With the help of this channel, we are sharing our knowledge related to programming and technologies with you. Our motive is to provide the best quality content and share deep knowledge about the topics within minimum time. We support all keen learners. As per the industry requirement, People are switching their jobs into the programming domain but they suffer a lot while learning to program. So we are focused on these types of learners and we are preparing our content which can be easily understood by everyone even if you are from a non-programming background. We help everyone to upgrade their knowledge and achieve goals. Other Resources For Learning Python: - Python Book - 🤍 Best Laptops for Programming - 🤍 Follow us on- Facebook - 🤍 Twitter - 🤍 LinkedIn - 🤍 Ask It Loud - 🤍 Email id - askitloud.edu🤍gmail.com - Image Processing and Computer Vision in hindi Full Course -🤍 Python Basics Full Course - 🤍 Python Intermediate Full Course - 🤍 Python ClassRoom Lectures - 🤍 Python Projects in 10 mins - 🤍 Python Game Development - 🤍 Python Full Course Tutorials in one Video - 🤍 Python Exercise and Examples - 🤍 Python Live Sessions and Workshops - 🤍 #askitloud#opencv#imageoperations

Display Image using Opencv cv2.imshow() function in Python | Opencv Turorial in Hindi

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16.01.2021

In Python Opencv Tutorial, I have explained how to read, show image in Hindi using the OpenCV python library. Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite and it help a lot you can also try it! 🤍 Read Image using OpenCV in Python Hindi: 🤍 Introduction of OpenCV Tutorial in Python in Hindi: 🤍 Anaconda Navigator, Jupyter Notebook, Spyder Installation: 🤍 Installation of OpenCV, TensorFlow, Keras: 🤍 Source code & study material: 🤍 Installation of OpenCV, TensorFlow, Keras: 🤍 ……………………………………………………………………………………………………………………………………………………………. Course Playlists- Machine Learning & Data Science - Beginner to Professional Hands-on Python Course in Hindi: 🤍 Python NumPy Tutorial in Hindi: 🤍 Python Pandas Tutorial in Hindi 🤍 Python Matplotlib Tutorial in Hindi: 🤍 Python Seaborn Tutorial in Hindi: 🤍 Deep Learning Project End to End in Hindi : 🤍 Machine Learning Project in Hindi: 🤍 …………………………………………………………………………………………………………………… For more information: Contact Us: = -Website: 🤍 -Facebook: 🤍 -Instagram: 🤍 -Twitter: 🤍 -LinkedIn: 🤍 …………………………………………………………………………………………………………………… #OpencvTutorial #opencv #ComputerVision #MachineLearningProject #AIProject #MachineLearningTutorial #IndianAIProduction Disclaimer- Some contents are used for educational purpose under fair use. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. All credit for copyright materiel used in video goes to respected owner.

cv2 images function in google colab

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#google_colab #tensorflow #opencv_python #cv2_images #python #machine_learning

Loading Video Source - OpenCV with Python for Image and Video Analysis 2

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In this OpenCV with Python tutorial, we're going to cover some basic operations with video and webcams. Aside from the beginning lines, handling frames from a video is identical to handling for images. Text-based tutorial and sample code: 🤍 🤍 🤍

OpenCV Python Tutorial #4 - Drawing (Lines, Images, Circles & Text)

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17.02.2021

Welcome to another tutorial in the OpenCV Python series! In this video, I'll be talking about drawing within OpenCV, including drawing lines, images, circles, and text. Drawing various shapes within OpenCV is simple, but will come in handy later when it's time to detect things inside OpenCV. 💻 AlgoExpert is the coding interview prep platform that I used to ace my Microsoft and Shopify interviews. Check it out and get a discount on the platform using the code "techwithtim" 🤍 📄 Relevant Documentation: 🤍 📝 Code For This Series: 🤍 🔍 Playlist: 🤍 ⭐️ Timestamps ⭐️ 00:00 | Introduction 01:01 | Drawing Lines 04:56 | Drawing Rectangles 08:00 | Drawing Text 13:35 | Outro ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 💰 Courses & Merch 💰 💻 The Fundamentals of Programming w/ Python: 🤍 👕 Merchandise: 🤍 🔗 Social Medias 🔗 📸 Instagram: 🤍 📱 Twitter: 🤍 ⭐ Discord: 🤍 📝 LinkedIn: 🤍 🌎 Website: 🤍 📂 GitHub: 🤍 🔊 Podcast: 🤍 🎬 My YouTube Gear 🎬 🎥 Main Camera (EOS Canon 90D): 🤍 🎥 Secondary Camera (Panasonic Lumix G7): 🤍 📹 Main Lens (EFS 24mm f/2.8): 🤍 🕹 Tripod: 🤍 🎤 Main Microphone (Rode NT1): 🤍 🎤 Secondary Microphone (Synco Wireless Lapel System): 🤍 🎤 Third Microphone (Rode NTG4+): 🤍 ☀️ Lights: 🤍 ⌨ Keyboard (Daskeyboard 4Q): 🤍 🖱 Mouse (Logitech MX Master): 🤍 📸 Webcam (Logitech 1080p Pro): 🤍 📢 Speaker (Beats Pill): 🤍 🎧 Headphones (Bose Quiet Comfort 35): 🤍 🌞 Lamp (BenQ E-reading Lamp): 🤍 🌞 Secondary Lamp (BenQ Screenbar Plus): 🤍 💻 Monitor (BenQ EX2780Q): 🤍 💻 Monitor (LG Ultrawide 34WN750): 🤍 🎙 Mic Boom Arm (Rode PSA 1): 🤍 🎚 Audio Interface (Focusrite Scarlet 4i4): 🤍 💸 Donations 💸 💵 One-Time Donations: 🤍 💰 Patreon: 🤍 ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ ⭐️ Tags ⭐️ - Drawing in OpenCV - Shapes in OpenCV - Image Fundamentals OpenCV - Python - Computer vision - Cameras and video capture ⭐️ Hashtags ⭐️ #OpenCV #Python #CameraCapture

Dominating an Online Game with Object Detection Using OpenCV - Template Matching.

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19.04.2021

Detect objects with No GPU, No Neural Network, and No training. Template matching has some unique advantages including being really easy to set up. You only need a few lines of code to get started. You can also do it without any high-end computing resources. In this video, I walk you through a simple example of how to use template matching and then show you how I used it to dominate an online game. 🤍 Want to chat with me and other programmers join our discord! 🤍 Want to help support the channel? 🤍

Color Filtering - OpenCV with Python for Image and Video Analysis 7

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27.12.2015

In this OpenCV with Python tutorial, we're going to cover how to create a sort of filter, revisiting the bitwise operations, where we will filter for specifically a certain color, attempting to just show it. Alternatively, you could also specifically filter out a specific color, and then replace it with a scene, like we did with replacing a ROI (region of image) with something else, much like how a green screen works. In order to filter like this you have a few options. Generally, you are probably going to convert your colors to HSV, which is "Hue Saturation Value." This can help you actually pinpoint a more specific color, based on hue and saturation ranges, with a variance of value, for example. If you wanted, you could actually produce filters based on BGR values, but this would be a bit more difficult. If you're having a hard time visualizing HSV, don't feel silly, check out the Wikipedia page on HSV, there is a very useful graphic there for you to visualize it. Hue for color, saturation for the strength of the color, and value for light is how I would best describe it personally. Now let's hop in. 🤍 🤍

Extract Text From Images in Python (OCR)

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07.10.2021

In this video we learn how to use OCR to extract text from images using Python and Tesseract. ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾ 📚 Programming Books & Merch 📚 🐍 The Python Bible Book: 🤍 💻 The Algorithm Bible Book: 🤍 👕 Programming Merch: 🤍 🌐 Social Media & Contact 🌐 📱 Website: 🤍 📷 Instagram: 🤍 🐦 Twitter: 🤍 🤵 LinkedIn: 🤍 📁 GitHub: 🤍 🎙 Discord: 🤍 🎵 Outro Music From: 🤍

cv2 tutorial in hindi | reading image in cv2 using python in hindi 2

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hello guys, In this tutorial on OpenCV Python Tutorial For Beginners, I am going to show How to read and show the image with Python / Python3. if you are facing problem with the installing cv package then go through my last video , link is below: 🤍 I hope you all like this video please subscribe to my channel for geting more updates.

OpenCV Python Tutorial For Beginners 26 - Understanding image Histograms using OpenCV Python

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11.07.2019

In this video on OpenCV Python Tutorial For Beginners, I am going to show How to use image Histograms using OpenCV Python. Histograms Organize data into groups by counting how much data is in each group. Gist of code I used in this video (Drawing a histogram in opencv Python Tutorial ) - 🤍 So What is a Histogram? A Histogram is a variation of a bar chart in which data values are grouped together and put into different classes. This grouping allows you see how frequently data in each class occur in the data set. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C, and Python. it is Open Source and free. opencv is easy to use and install. Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV. At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science. Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. #ProgrammingKnowledge #ComputerVision #OpenCV ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ 🤍 ⚫️ 🤍 Java Programming Course ➡️ 🤍 ⚫️ 🤍 Bash Shell Scripting Course ➡️ 🤍 ⚫️ 🤍 Linux Command Line Tutorials ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 C Programming Course ➡️ 🤍 ⚫️ 🤍 PHP Programming Course ➡️ 🤍 ⚫️ 🤍 Android Development Course ➡️ 🤍 ⚫️ 🤍 C# Programming Course ➡️ 🤍 ⚫️ 🤍 JavaFx Programming Course ➡️ 🤍 ⚫️ 🤍 NodeJs Programming Course ➡️ 🤍 ⚫️ 🤍 Jenkins Course For Developers and DevOps ➡️ 🤍 ⚫️ 🤍 Scala Programming Tutorial Course ➡️ 🤍 ⚫️ 🤍 Bootstrap Responsive Web Design Tutorial ➡️ 🤍 ⚫️ 🤍 MongoDB Tutorial Course ➡️ 🤍 ⚫️ 🤍 QT C GUI Tutorial For Beginners ➡️ 🤍 ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - 🤍 Data Science - 🤍 | 🤍 Machine Learning - 🤍 | 🤍 Artificial Intelligence - 🤍 | 🤍 MERN Stack E-Degree Program - 🤍 | 🤍 DevOps E-degree - 🤍 | 🤍 Data Analytics with R - 🤍 | 🤍 AWS Certification Training - 🤍 | 🤍 Projects in Java - 🤍 | 🤍 Machine Learning With TensorFlow - 🤍 | 🤍 Angular 8 - Complete Essential Guide - 🤍 Kotlin Android Development Masterclass - 🤍 Learn iOS Programming Building Advance Projects - 🤍 ★★★ Follow ★★★ My Website - 🤍 DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

Computer Vision CV2 | how IMAGE is Formed | opencv python in Hindi

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04.06.2020

#RanjanSharma How Image is formed ? How Pixels are arranged Image ? Gray Scale Image and Colored Image having RGB Channels. PPT is uploaded in to the Google Drive Link and Github link Python Playlist: 🤍 AI PlayList: 🤍 Join Whatsapp Group for AI 🤍 Telegram Group : 🤍 Subscribe my Channel 🤍 Google Drive: 🤍 Github : 🤍 Connect with me on below Channels LinkedIn: 🤍 Medium : 🤍 Instagram : 🤍 Email : iamranjan.sh🤍gmail.com Keep Practicing :-) Happy Learning !!

OpenCV Course - Full Tutorial with Python

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Learn everything you need to know about OpenCV in this full course for beginners. You will learn the very basics (reading images and videos, image transformations) to more advanced concepts (color spaces, edge detection). Towards the end, you'll have hands-on experience building a Deep Computer Vision model to classify between the characters in the popular TV series "The Simpsons". ⭐️ Code ⭐️ 🔗Github link: 🤍 🔗The Caer Vision library: 🤍 🎥 Course from Jason Dsouza: - Check out his Youtube channel: 🤍 - Follow him on Twitter: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:07) Installing OpenCV and Caer Section #1 - Basics ⌨️ (0:04:12) Reading Images & Video ⌨️ (0:12:57) Resizing and Rescaling Frames ⌨️ (0:20:21) Drawing Shapes & Putting Text ⌨️ (0:31:55) 5 Essential Functions in OpenCV ⌨️ (0:44:13) Image Transformations ⌨️ (0:57:06) Contour Detection Section #2 - Advanced ⌨️ (1:12:53) Color Spaces ⌨️ (1:23:10) Color Channels ⌨️ (1:31:03) Blurring ⌨️ (1:44:27) BITWISE operations ⌨️ (1:53:06) Masking ⌨️ (2:01:43) Histogram Computation ⌨️ (2:15:22) Thresholding/Binarizing Images ⌨️ (2:26:27) Edge Detection Section #3 - Faces: ⌨️ (2:35:25) Face Detection with Haar Cascades ⌨️ (2:49:05) Face Recognition with OpenCV's built-in recognizer Section #4 - Capstone ⌨️ (3:11:57) Deep Computer Vision: The Simpsons ⭐️ More ways to connect with Jason Dsouza ⭐️ - Medium: 🤍 - Twitter: 🤍 - LinkedIn: 🤍 ✏️ Check out Jason's Deep Learning Crash Course for Beginners: 🤍 ⭐️ Special thanks to our Champion supporters! ⭐️ 🏆 Loc Do 🏆 Joseph C 🏆 DeezMaster Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍

Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2

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18.08.2018

Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges! First, we need a dataset. Let's grab the Dogs vs Cats dataset from Microsoft: 🤍 Text tutorials and sample code: 🤍 Discord: 🤍 Support the content: 🤍 Twitter: 🤍 Facebook: 🤍 Twitch: 🤍 G+: 🤍

OpenCV Object Detection in Games Python Tutorial #1

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Learn how to use OpenCV for object detection in video games. This intro tutorial will show you how to install OpenCV for Python and get started with simple image template matching. This will serve as our foundation as we explore many different computer vision techniques in future videos. Full tutorial playlist: 🤍 GitHub Repo: 🤍 OpenCV documentation: 🤍 Official template matching tutorial: 🤍 0:24 How to install OpenCV 1:45 How OpenCV relates to PyAutoGUI 2:52 How to navigate the OpenCV documentation 4:53 Comparison methods visualized 5:39 Writing code for cv.matchTemplate() OpenCV is an open source computer vision library with hundreds of functions for processing and understanding images. In this tutorial, I'm going to show you how to get started with OpenCV in Python by using it to find an image inside another image. This simple form of object detection will be a good starting point before we move on to more advanced image recognition techniques. The quickest way to get started with OpenCV is: pip install opencv-python Once installed, you can use the library by importing cv2. Numpy is used extensively when working with OpenCV data, so the top of your Python files will look like this: import cv2 as cv import numpy as np That's all there is for setup. Now let's grab an image we want to process. I'm going to be using this screenshot from Albion Online, but any screenshot will do. What we're going to do is crop out a small section from our screenshot, save that as a separate image file, and then we're going to use OpenCV to find the position of the smaller image inside our entire screenshot. From my screenshot, I'll crop out one of the cabbages. The OpenCV function we'll be focusing on is called matchTemplate(). In the documentation, we can see we're going to give this function an image to search over, an image to search for, and a method type for doing the comparison. And we'll end up with a result array. You'll want to experiment with the different comparison methods to see what works best for your use-case. Alright, let's write some code. The first thing we want to do is load our image files. haystack_img = cv.imread('albion_farm.jpg', cv.IMREAD_UNCHANGED) needle_img = cv.imread('albion_cabbage.jpg', cv.IMREAD_UNCHANGED) The "haystack" image is our screenshot, and we'll be search that for the "needle" image we cropped out. In imread() the first parameter is the image file path, and the second parameter is a flag that allows us to do some pre-processing when loading the images. In this case, we're loading them in unchanged. Now that we have our images loaded, we can go ahead and call matchTemplate(). I've had good luck using the TM_CCOEFF_NORMED comparison algorithm. result = cv.matchTemplate(haystack_img, needle_img, cv.TM_CCOEFF_NORMED) We can quickly see the results from matchTemplate() by displaying that data with imshow(). cv.imshow('Result', result) cv.waitKey() In imshow(), the first parameter is the window name and the second is the image we want to show. I've also called waitKey() to pause our script while we review the image. Without this, our script would quickly close before we could see the image. Pressing any key on the keyboard will trigger waitKey() to stop waiting, thus ending our script. In this result image, the bright white pixels represent the positions that best match the cropped image. The black pixels are the worst matches. Note that these best match positions correspond with the upper left corner of where you'd place the needle image. Now that we've visualized the results of matchTemplate(), let's get those best match coordinates. We can do that using minMaxLoc(). The minMaxLoc() function returns four values. First are the confidence values for the worst and best matches, on a scale from 0 to 1. These are how black or how white the darkest/brightest pixels are in our result image, where 0 would be perfect black and 1 would be perfect white. The last two values minMaxLoc() returns are the positions of those worst/best match pixels in the form of an (X,Y) tuple. For every needle image that we give matchTemplate(), we will always get back some values from minMaxLoc(), even if that cropped image appears nowhere in the haystack. We can tell when we didn't find a good match because the max confidence value will be low. How low is too low depends on the images you're working with and what you're trying to achieve. Now that we've found a good match, let's outline where we found it in the haystack image. We can do that using OpenCV's rectangle() function. Continue with the written tutorial here: 🤍 Join me on Discord: 🤍

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