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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: 🤍
In this video, we are going to learn everything required to get started with OpenCV in Python. We will be using Python since it is one of the most popular programming languages. And it has opened numerous job opportunities in various sectors. We will start from the installation process right up to creating exciting projects such as detecting colors, shapes humans, and even vehicle number plates. So If you are a beginner don't worry this course is for you. We will skip all the boring theory stuff and focus on the practical implementation. So you can get the computer vision skill set you have always wanted in your CV. By the end of the course, you will become familiar with the core principle of OpenCV and apply different techniques to solve real-world problems using computer vision. Code & Text Based Version: 🤍 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 Time Stamps: 00:00 Intro 2:17 Introduction to Images 4:37 Installations 9:09 Chapter 1 17:01 Chapter 2 27:31 Chapter 3 34:12 Chapter 4 44:59 Chapter 5 50:04 Chapter 6 56:14 Chapter 7 1:15:37 Chapter8 1:40:31 Chapter 9 1:46:03 Project 1 2:15:45 Project 2 2:56:34 Project 3 Download Links: PyCharm Community edition: 🤍 Python: 3.7.6: 🤍
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
What is OpenCV?OpenCV (Open Source Computer Vision) is an open-source library of computer vision, image analysis, and machine learning. To do this, it has an infinity of algorithms that allow, just by writing a few lines of code, identifying faces, recognizing objects, classifying them, detecting hand movements ... OpenCV is a multiplatform library available for Windows, Mac, Linux, and Android distributed under a BSD license. It can be programmed with C, C , Python, Java, and Matlab. JOIN OUR OpenCV Course 97% off [ 🔥FIRST 30 COUPON 🔥] 🔥🔥OpenCV Practical with Python - 3 Complete Projects + CODE FULL course With 96% Off ➡️ 🤍 🔥🔥 Do you want to Make a Practical Application Using Python and OpenCV? If yes then this course is designed for you. In this course, we are going to make 3 Interactive Projects using Python+ OpenCV 🌟🌟What you'll learn🌟🌟 3 Different Projects Using OpenCV PROJECT 1: Motion Detector App [ Which Detects Any Motion in Webcam or Video] PROJECT 2: Building a Hand Detector App [Which Detects Motion of Your Hand] PROJECT 3: Face Recognition App 🌟🌟Who this course is for:🌟🌟 Data Science / Machine Learning Enthusiastic Who Wants to Acquire Practical OpenCV Knowledge and Skills Who Wants To Level Up there OpenCV knowledge WATCH 12+ HOURS DATA SCIENCE MASTERCLASS FOR FREE- 🤍 JOIN OUR 10+ HOURS OF FREE AWS Solution Architect certification MASTERCLASS 🤍 FREE 5+ HOURS OF POWER BI MASTERCLASS 🤍 JOIN OUR OpenCV Course 97% off [ 🔥FIRST 30 COUPON 🔥] ➡️🤍
Learn how to use OpenCV for Computer Vision and AI in this full course for beginners. You will learn and get exposed to a wide range of exciting topics like Image & Video Manipulation, Image Enhancement, Filtering, Edge Detection, Object Detection and Tracking, Face Detection and the OpenCV Deep Learning Module. At the end of the course you will hear from Dr. Satya Mallick (CEO, OpenCV.org) where he shares his views on the limitless opportunities in the Computer Vision and AI job market and how to confidently prepare yourself in a structured manner for a fulfilling career in AI. 🔗 Course Website: 🤍 🔗 Official OpenCV Courses: 🤍 🔗 Kickstarter: 🤍 💻 Code: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:03:20) Module 1: Getting Started with Images ⌨️ (0:22:22) Module 2: Basic Image Manipulation ⌨️ (0:30:56) Module 3: Image Annotation ⌨️ (0:35:39) Module 4: Image Enhancement ⌨️ (0:52:35) Module 5: Accessing the Camera ⌨️ (0:55:28) Module 6: Read and Write Videos ⌨️ (0:59:08) Module 7: Image Filtering and Edge Detection ⌨️ (1:11:24) Module 8: Image Features and Image Alignment ⌨️ (1:24:16) Module 9: Image Stitching and Creating Panoramas ⌨️ (1:27:13) Module 10: High Dynamic Range Imaging (HDR) ⌨️ (1:38:28) Module 11: Object Tracking ⌨️ (1:49:28) Module 12: Face Detection ⌨️ (1:59:41) Module 13: Object Detection ⌨️ (2:08:33) Module 14: Pose Estimation using OpenPose ⌨️ (2:22:21) Interview with OpenCV CEO, Dr. Satya Mallick Learn More: 🔗 🤍 🤍 🔗 🤍 🤍 This course was made possible through a grand from OpenCV. 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 Treehouse Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Welcome to this 3 hours course on OpenCV with Python. This course is ideal for beginners in OpenCV and computer vision! Enjoy! Code: 🤍 00:00 Intro 00:57 Lesson 1: What are images? 09:29 Lesson 2: Input / Output 28:12 Lesson 3: Basic operations 38:57 Lesson 4: Colorspaces 51:39 Lesson 5: Blurring 1:06:56 Lesson 6: Threshold 1:22:13 Lesson 7: Edge detection 1:31:31 Lesson 8: Drawing 1:45:17 Lesson 9: Contours 2:01:24 Bonus lesson 2:09:30 Project 1: Color detection 2:28:48 Project 2: Face anonymizer 3:10:32 Outro #computervision #opencv #computervisioncourse #opencvcourse #machinelearning #python
In this Introduction to Image Processing with Python, kaggle grandmaster Rob Mulla shows how to work with image data in python! Python image processing is very important for anyone interested in computer vision and data science. Using the popular python packages matplotlib and opencv you will learn how to open image data, how the data is formatted, some ways to manipulate the data and save it off in a different format. If you enjoy you can also check out my live twitch streams (below). Image data is extremely powerful especially with machine learning and computer vision techniuqes becoming more common. Learn about this important part of your data science toolbelt! Timeline 00:00 Intro 00:57 Imports 02:06 Reading in Images 04:20 Image Array 06:22 Displaying Images 07:14 RGB Representation 09:40 OpenCV vs Matplotlib imread 11:50 Image Manipulation 13:26 Resizing and Scaling 16:25 Sharpening and Blurring 19:03 Saving the Image 20:17 Outro The notebook used in this video: 🤍 Follow me on twitch for live coding streams: 🤍 Intro to Pandas video: 🤍 Exploritory Data Analysis Video: 🤍 Working with Audio data in Python: 🤍 * Youtube: 🤍 * Discord: 🤍 * Twitch: 🤍 * Twitter: 🤍 * Kaggle: 🤍 #python #matplotlib #opencv #computervision #datascience
Welcome to this courese on OpenCV Python Tutorial For Beginners. 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 #python 1 - Introduction to OpenCV 2 - How to Install OpenCV for Python on Windows 10 3 - How to Read, Write, Show Images in OpenCV 4 - How to Read, Write, Show Videos from Camera in OpenCV 5 - Draw geometric shapes on images using Python OpenCV 6 - Setting Camera Parameters in OpenCV Python 7 - Show Date and Time on Videos using OpenCV Python 8 - Handle Mouse Events in OpenCV 9 - More Mouse Event Examples in OpenCV Python 10 - cv.split, cv.merge, cv.resize, cv.add, cv.addWeighted, ROI 11- Bitwise Operations (bitwise AND, OR, NOT and XOR) 12 - How to Bind Trackbar To OpenCV Windows 13 - Object Detection and Object Tracking Using HSV Color Space 14 - Simple Image Thresholding 15 - Adaptive Thresholding 16 - matplotlib with OpenCV 17 - Morphological Transformations 18 - Smoothing Images | Blurring Images OpenCV 19 - Image Gradients and Edge Detection 20 - Canny Edge Detection in OpenCV 21 - Image Pyramids with Python and OpenCV 22 - Image Blending using Pyramids in OpenCV 22 - Image Blending using Pyramids in OpenCV 23 - Find and Draw Contours with OpenCV in Python 24 - Motion Detection and Tracking Using Opencv Contours 25 - Detect Simple Geometric Shapes using OpenCV in Python 26 - Understanding image Histograms using OpenCV Python 27 - Template matching using OpenCV in Python 28 - Hough Line Transform Theory 29 - Hough Line Transform using HoughLines method in OpenCV 30 - Probabilistic Hough Transform using HoughLinesP in OpenCV 31 - Road Lane Line Detection with OpenCV (Part 1) 32 - Road Lane Line Detection with OpenCV (Part 2) 33 - Road Lane Line Detection with OpenCV (Part 3) 34 - Circle Detection using OpenCV Hough Circle Transform 35 - Face Detection using Haar Cascade Classifiers 36 - Eye Detection Haar Feature based Cascade Classifiers 37 - Detect Corners with Harris Corner Detector in OpenCV 38 - Detect Corners with Shi Tomasi Corner Detector in OpenCV 39 - How to Use Background Subtraction Methods in OpenCV 40 - Mean Shift Object Tracking 41 - Object Tracking Camshift Method #ProgrammingKnowledge #ComputerVision #OpenCV Welcome to the Python OpenCV for Beginners Full Course - Learn Computer Vision! In this comprehensive course, you'll learn everything you need to know to get started with computer vision using the powerful Python OpenCV library. Whether you're a beginner or an experienced programmer, this course will take you through the fundamental concepts and techniques of computer vision, including image processing, feature detection, object recognition, and more. You'll get hands-on experience working with real-world examples and projects, as well as plenty of coding exercises to practice your skills. Throughout the course, you'll learn how to use Python and OpenCV to build powerful computer vision applications, such as face detection, object tracking, and image segmentation. You'll also discover how to optimize your code for performance, and how to integrate your computer vision applications with other popular tools and libraries. So if you're ready to take your Python programming skills to the next level and start working with computer vision, this course is for you! Join us today and let's get started.
Programmers don't need money. We just need Python. 💥 30 lines of Python code is enough to build a security camera with advanced burglar detection techniques. This is gonna be super-fun this New Year. Save that money you got to buy the security camera and code your own for free in just 30 lines of Python. Nobody can even make out. How smart you are! 😁 It's super-simple, super-fun, super-crazy. Let's get started. 🤙🏻 Whole code: 🤍 If you face any coding related issues or anything, just join our Discord server and we're waiting for you: 🤍 Credit for this video goes to a ProgrammingKnowledge video (🤍 Minhaj, Mujahid, Sufi, Shajedul and the whole Programming Hero family. Special thanks to Grandma's GrandKids. #python #hack #opencv #securitycamera #surveillance #camera # #computervision #automate #motiondetection #movementtrack #imageprocessing #pythonhack #free #programming #fun #project WHAT IS THE VIDEO ABOUT? Save your security maintenance money by simply building your own in just 30 lines. 💥 Your siblings will be afraid to steal anything from your room. 😎 Learn OpenCV, computer vision, motion detection, tracking movement, image processing and so much more. 👊🏻 By the way, what are you going to do with all of that money? 🤔😎 Now, if you're new to the programming world and don't know what to do, go check out our app and build your own game immediately while learning. Android App: 🤍 iPhone Version: 🤍 VIDEO TIMESTAMPS Have to go call your special one in some time? We understand your pain. Timestamp might help. 00:28 - Grandma rocks 00:51 - Project starts 01:50 - Main code starts - First Step 01:50 to 07:00 - Getting started with computer vision in 7 lines 01:58 - OpenCV package install - Second Step 07:10 to 21:10 - Detecting different objects (motion detection) - Final And Fun Step 21:11 to 28:00 - Making some noise (working with burglar alarm) - 26:26 - Final Result (Boom!) CHECK OUT Wanna become a world class programmer? Here are 10 project ideas for you to quickly become that: 🤍 ENJOYED THE VIDEO? Save yourself from Grandma — she'll spy whenever you chat with your crush and turn off the WiFi if you don't click on the Like button and also turn the Subscribe button from red to white. If you like and subscribe to this cool video, she'll bring a new video in just a few days. OUR SOCIAL MEDIA Watch us on Facebook: 🤍 Peep us on Instagram: 🤍 Fly with us on Twitter: 🤍 Board with us on Pinterest: 🤍 Don't share this with your friends. They'll know your secret. 😁 We're always with you. If you face any coding related issues or anything, just join our server and we're waiting for you: 🤍
This is an OpenCV C course that will teach you everything you need to know to get started. This course is based on my previous OpenCV Python course that now has more than a million views and 98.8% positive feedback. Like before we are going to learn the basics that include Processing images videos webcams and finding shapes colors, humans, and vehicle number plates. We will also have 3 example projects that will cover all the basics we have learned. C is an excellent language for implementation and creating real-world products. so having this advanced computer vision skill on your CV will really make it stand out from the competition. And Don't worry if you are a beginner we will go step by step right from the installation up to creating exciting projects, And our main focus will be practical implementation so we will skip all the boring theory stuff. Course Link - Code + 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 Time Stamps: 00:00 Intro 1:24 Introduction to Images 3:48 Installation - Windows 11:51 Installation - Mac 22:05 Chapter 1 - Read Images Videos and Webcams 35:23 Chapter 2 - Basic Functions 50:21 Chapter 3 - Resize and Crop 58:31 Chapter 4 - Drawing Shapes and Text 01:11:07 Chapter 5 - Warp Perspective 01:22:40 Chapter 6 - Color Detection 01:37:17 Chapter 7 - Shapes/Contour Detection 02:14:52 Chapter8 - Face Detection 02:22:21 Project 1 - Virtual Painter 03:02:52 Project 2 - Document Scanner 03:46:14 Project 3 - License Plate Detector
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? 🤍
🔥 Python Developer Masters Program (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): 🤍 This Edureka Live video on "𝐈𝐦𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 𝐔𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧" will provide you with a comprehensive and detailed knowledge of Image processing and how it can be implemented using OpenCV library. In this video, you will be working on Image processing with Python and also create a model using a convolutional neural network. Finally, we will build an end-to-end model to process and identify the handwritten images. These are the following topics that are covered in this video on Image Processing Tutorial Using Python : 00:00:00 Introduction 00:00:52 What Is Image Processing? 00:02:40 Python For Image Processing 00:03:20 Image Processing Concepts 00:08:53 Digit Recognition Board 🔹Edureka Python Tutorial Playlist: 🤍 🔹Edureka Python Tutorial Blog Series: 🤍 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: 🤍 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: 🤍 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: 🤍 📌𝐌𝐞𝐞𝐭𝐮𝐩: 🤍 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: 🤍 #Edureka #PythonEdureka #PythonImageProcessing #ComputerVision #PythonProgramming #PythonTraining #PythonOpenCV #EdurekaTraining -𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧- 🔵 DevOps Online Training: 🤍 🌕 Python Online Training: 🤍 🔵 AWS Online Training: 🤍 🌕 RPA Online Training: 🤍 🔵 Data Science Online Training: 🤍 🌕 Big Data Online Training: 🤍 🔵 Java Online Training: 🤍 🌕 Selenium Online Training: 🤍 🔵 PMP Online Training: 🤍 🌕 Tableau Online Training: 🤍 🔵 Microsoft Azure Online Training: 🤍 🌕 Power BI Online Training: 🤍 -𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬- 🔵 DevOps Engineer Masters Program: 🤍 🌕 Cloud Architect Masters Program: 🤍 🔵 Data Scientist Masters Program: 🤍 🌕 Big Data Architect Masters Program: 🤍 🔵 Machine Learning Engineer Masters Program: 🤍 🌕 Business Intelligence Masters Program: 🤍 🔵 Python Developer Masters Program: 🤍 🌕 RPA Developer Masters Program: 🤍 -𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐆𝐏 𝐂𝐨𝐮𝐫𝐬𝐞𝐬- 🔵Artificial and Machine Learning PGP: 🤍 🟣CyberSecurity PGP: 🤍 🔵Digital Marketing PGP: 🤍 🟣Big Data Engineering PGP: 🤍 🔵Data Science PGP: 🤍 🟣Cloud Computing PGP: 🤍 - About the Python Certification Training by Edureka This Python course is live, instructor-led & helps you master various Python libraries such as Pandas, Numpy and Matplotlib to name a few, with industry use cases. Enroll now to learn Python online & be a certified Python professional with Edureka. - - - - - - - - - - - - - - - - - - - What will you learn in Edureka’s Python Training Edureka’s Python Certification Training will help you to learn more about how to write code in python with examples. Along with that you will also learn more about python syntax, Python basics, python ide, and many more. Not just this, you’ll also learn about various other Python Fundamentals in this Python Online Training like: Introduction to Python Python Sequences and File Operations Python Functions Python OOP Python Modules Exception Handling in Python Python Libraries like NumPy, Pandas, Matplotlib Python GUI Programming Computer Vision using Python OpenCV - Who should go for Python Training? Edureka’s Python certification course is a good fit for the professionals like Programmers, Developers, Technical Leads, and Architects. Even the developers who are aspiring to be a ‘Machine Learning Engineer' or the Analytics Managers who are leading a team of analysts can learn Python Programming. - For more information, Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free)
Learn advanced computer vision using Python in this full course. You will learn state of the art computer vision techniques by building five projects with libraries such as OpenCV and Mediapipe. If you are a beginner, don't be afraid of the term advance. Even though the concepts are advanced, they are not difficult to follow. ✏️ This course was developed by Murtaza Hassan. Check out his Murtaza's Workshop YouTube Channel: 🤍 💻 Get the code here: 🤍 🔗 Learn to build computer vision mobile apps from Murtaza: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:01:18) Chapter 1 - Hand Tracking - Basics ⌨️ (0:26:57) Chapter 1 - Hand Tracking - Module ⌨️ (0:49:20) Chapter 2 - Pose Estimation - Basics ⌨️ (1:08:25) Chapter 2 - Pose Estimation - Module ⌨️ (1:28:25) Chapter 3 - Face Detection - Basics ⌨️ (1:52:38) Chapter 3 - Face Detection - Module ⌨️ (2:12:55) Chapter 4 - Face Mesh - Basics ⌨️ (2:33:09) Chapter 4 - Face Mesh - Module ⌨️ (2:52:10) Project 1 - Gesture Volume Control ⌨️ (3:27:45) Project 2 - Finger Counter ⌨️ (4:05:43) Project 3 - AI Personal Trainer ⌨️ (4:52:55) Project 4 - AI Virtual Painter ⌨️ (6:01:26) Project 5 - AI Virtual Mouse Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
Welcome to this Python OpenCV tutorial. In this video, I'll be talking about cameras and video capture within OpenCV. Specifically, I'll be showing you how we can load our webcam, how we can view that in live time, and how we can manipulate the image from the camera feed. 💻 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 00:50 | Displaying video capture device 07:11 | Mirroring video multiple times ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 💰 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 - Image Manipulation OpenCV - Image Fundamentals OpenCV - Python - Computer vision - Cameras and video capture ⭐️ Hashtags ⭐️ #OpenCV #Python #CameraCapture
Subscribe For More! Article with All Steps - 🤍 Actively search and classify all kinds of household objects and common animals with a palm sized single board computer. Then use specific object detection to control GPIO pins. Make sure to use the Previous Raspberry Pi 'Buster' OS with this Guide. Related Information Backyard BirdCam Project (Amazing Project that utilises this exact technology) - 🤍 Flashing 'Buster' OS onto a Raspberry Pi - 🤍 Facial Recognition with the Raspberry Pi - 🤍 Face and Movement Tracking System For Raspberry Pi - 🤍 Controlling a Servo Motor with a Raspberry Pi - 🤍 Speed Camera with Raspberry Pi - 🤍 Hand Tracking & Gesture Control With Raspberry Pi - 🤍 Control Your Raspberry Pi Remotely Using Your Phone (RaspController Guide) - 🤍 Coco Dataset Library - 🤍 Have you ever wanted to get your Raspberry Pi 4 Model B to actively search and identify common household objects and commonplace animals? Then you have found the right place. I'll show you exactly how to do this so you can set up a similar system in your own Maker-verse. Furthermore, I will demonstrate how you can refine the identification so it searches only for particular desired targets. Then we’ll take this to the next step and demonstrate how you can alter the code to make the Raspberry Pi control physical hardware when it identifies that particular target. This guide is going to blend machine learning and open-source software together with the Raspberry Pi ecosystem. One of the open-source software used here is Open-CV which is a huge resource that helps solve real-time computer vision and image processing problems. This will be a second foray into Open-CV landscape with Raspberry Pi and Facial Recognition being the first. We will also utilise an already trained library of objects and animals from the Coco Library. The Coco (Common Object in Context) Library is large-scale object detection, segmentation, and captioning dataset. This trained library is how the Raspberry Pi will know what certain objects and animals generally look like. You can also find pre-trained libraries for all manner of objects, creatures, sounds, and animals so if this particular library here does not suit your needs you can find many others freely accessible online. The library used here will enable our Raspberry Pi will be able to identify 91 unique objects/animals and provide a constantly updating confidence rating. Machine learning has never been more accessible and this video will demonstrate this. If you have any questions about this content or want to share a project you're working on head over to our maker forum, we are full time makers and here to help - 🤍 Core Electronics is located in the heart of Newcastle, Australia. We're powered by makers, for makers. Drop by if you are looking for: Raspberry Pi 4 Model B (4GB) Ultimate Kit Bundle (AVALIABLE!) - 🤍 Raspberry Pi 4 Model B 4GB: 🤍 Raspberry Pi High Quality Camera (Used Here): 🤍 Raspberry Pi 6mm Wide Angle Camera Lens (Used Here): 🤍 Raspberry Pi Official Camera Module V2 : 🤍 Makeblock 9g Micro Servo Pack (used here): 🤍 Raspberry Pi 4 Power Supply: 🤍 0:00 Intro 0:17 Video Overview 0:56 What You Will Need 1:30 Set Up 3:10 Grab Some Objects 3:35 Its Working! 4:02 Some Values Worth Tinkering 4:55 GPIO Control with Identified Objects 5:36 Acknowledgments 5:47 Outro
In this tutorial, you'll find out how to draw basic shapes with OpenCV - which is a very popular computer vision library, not only for Python! 🔴🟢🔵 We will create a drawing of some trees on a sunny background, we will add some text and we will save it as a "png" file to our computer. ⭐ Image Into Matrix - Convert Like a Pro!! ⭐ 🤍 * Want to learn more about ⭐Computer Vision?⭐ 🎥 Stream Videos with OpenCV 🎥 🤍 😴 Image Processing with Pillow 😴 🤍 Also, if you don't feel like coding along, you can still access my script: 🤍 * ⏰ timestamps ⏰ * 00:00 - Intro 00:26 - import OpenCV / import cv2 00:41 - image data type overview 01:06 - create blank image with Numpy / np.zeros() 02:30 - show image with OpenCV 02:50 - cv.waitKey(0) 03:16 - cv.destroyAllWindows 04:01 - draw rectangle with OpenCV 05:24 - fill rectangle with OpenCV 06:19 - draw circle with OpenCV 07:11 - outline line thickness with OpenCV 07:46 - draw line with OpenCV 08:50 - draw triangle with OpenCV and Numpy 10:36 - write text with OpenCV 12:32 - repetition with classes (coming soon) 13:17 - save image with OpenCV / imwrite 13:56 - thanks for watching! * See you in the next tutorial, where we will create an entire forest of tree objects! Thanks for watching ♥
In this video tutorial you will learn how to work with video data in python and openCV. Video processing and data analysis has many applications in machine learning, including object detection, pose estimation, and object tracking. Before you can run machine learning on videos you first need a good understanding of how to read and write video files using python and openCV. This tutorial walks through the basics, step by step, with some examples. Notebook used in this video: 🤍 Timeline: 00:00 Video Data & Python 01:08 What is Video Data? 04:08 Getting Setup 06:19 Converting Videos 08:23 Displaying Video 09:13 Video Metadata 11:32 Pulling Images 17:31 Add Annotations 25:48 Saving processed video 31:33 Summary My other videos: Speed Up Your Pandas Code: 🤍 Speed up Pandas Code: 🤍 Intro to Pandas video: 🤍 Exploratory Data Analysis Video: 🤍 Working with Audio data in Python: 🤍 Efficient Pandas Dataframes: 🤍 * Youtube: 🤍 * Discord: 🤍 * Twitch: 🤍 * Twitter: 🤍 * Kaggle: 🤍 #python #computervision #datascience
This is the result of measuring vehicle speed using optical flow. GitHub: 🤍 (If you found this video and code helpful, please give me a star on GitHub.) E-mail: sue3630🤍khu.ac.kr If you have a question, feel free to send an e-mail.
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. Install OpenCV in Jupyter and Colab 2. Import OpenCV into a Notebook 3. Run Hist and Optical Flow Samples Get the code: 🤍 Links Documentation: 🤍 Python Tutorials: 🤍 Download Samples from here: 🤍 Chapters: 0:00 - Start 0:24 - Gameplan 1:53 - Install OpenCV 2:40 - Importing OpenCV 3:51 - Testing Samples 6:33 - Run Image Histogram Sample 8:54 - Run Optical Flow Sample 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!
Welcome to the world's first Computer Vision with Arduino Course. Here we are going to learn the basics of how to create real-world Computer Vision applications using the extremely popular Arduino Microcontroller. We will first start with basic exercises and move on to creating exciting projects such as Lamp Gesture Control, PID FAce Tracking, Angle Finder, Hand Gesture Control, Color Sorter with Conyeor belt, and a lot more. The aim of this course is not to create random projects. But to understand the fundamentals and to give you enough knowledge and experience so you can build your own million-dollar ideas. This course has a clear pathway from basic to advanced with lots of practice examples. This video is part of the course. The complete course can be found on our CVZone Platform. Computer Vision with Arduino - Full Course: 🤍 Download Code, Files and CVZone Arduino Library: 🤍 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 Trailer 01:06 Introduction - Arduino Basics 10:47 Introduction - Arduino Sensor 22:46 Introduction - PWM 27:42 Installation - Python 29:11 Installation - Pycharm IDE 30:45 Installation - Arduino IDE 34:16 Insatllation - CVZone Library 35:42 Led Wiring 36:50 Led Arduino Code 52:32 Led Python Code 01:00:09 Led Graphics 01:07:31 Potentiometer Wiring 01:15:37 Potentiometer Arduino Code 01:24:15 Potentiometer Python Code 01:26:01 Potentiometer Graphics 01:40:01 Face Detection LED - Detecting Faces 01:47:01 Face Detection LED - Arduino Code 01:48:59 Face Detection LED - Python 01:52:42 Face Detection RGB - Wiring 01:53:23 Face Detection RGB - Basic 01:58:43 Face Detection RGB - RGB Serial 02:02:02 Face Detection RGB - Python Code
Welcome to another OpenCV tutorial! We'll be talking about template matching (object detection). In this video, we'll use an image of a soccer practice and use OpenCV to detect the soccer ball and find it's exact location. For example, the image of the ball will be our template, and we'll use OpenCV to match it. 💻 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 | Setup & Images 02:35 | Loading Template & Base Images 04:55 | Template Matching Methods 07:00 | Theory Behind Template Matching 14:10 | Displaying Matches ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 💰 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 ⭐️ - Object detection in OpenCV - Template matching OpenCV - Python - Computer vision - Cameras and video capture ⭐️ Hashtags ⭐️ #OpenCV #Python #TemplateMatching
Thanks to my amazing friends: Jayant, Raghav, Dev and Farhan for their help. the code is on GitHub but its difficult to understand and I am too lazy to improve it so sorry in advance 🙂 🤍 here's the video you all have been wailing for: 🤍
Source code: 🤍 You will learn in this video how to Track objects using Opencv with Python. In this specific lesson we will focus on two main steps: on the first one we will do Object detection and on the second one Object tracking. ➤ Full Videocourses: Object Detection: 🤍 ➤ Follow me on: Instagram: 🤍 LinkedIn: 🤍 ➤ For business inquiries: 🤍
I made Camerax App with Chaquopy in Android Studio - ImageCapture use case capture the image(jpg) - Convert it to Bitmap - Convert Bitmap to bytearray - Pass bytearray to Python - Python OpenCV set image resolution and return it to Android (like a string) - Show it on Textview Repository is on Github link, free to download: 🤍 Now you have captured image from camera, in OpenCV_Python, on your Android phone. Computer Vision is in your hand... Use your imagination... Sky is limit... :) Special thanks to: MALCOM SMITH -Creator of Chaquopy M.Van Luke - CameraX code 🤍 ProgrammingFever - Chaquopy 🤍 Murtaza's Workshop - Opencv Python 🤍 Slano/Dubrovnik/Croatia
Blog : 🤍 In this special video, I'm going to help you solve the doubts you have about object tracking and you'll learn how to build an Object Tracking algorithm from scratch. ➤ Courses: Full Computer Vision course: 🤍 Training Mask R-CNN PRO (Notebook + Mini-Course): 🤍 ➤ Follow me on: LinkedIn: 🤍 ➤ For business inquiries: 🤍 #ObjectTracking #AI #DeepLearning
Welcome to the second video of the series on OpenCV and Python. I'll start this episode with Image Manipulation, how images are represented in the computer. I'll also give you information on how images work as they are going to be very important for this series. 💻 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 | Intro 01:45 | Image Representation 04:02 | Values that Represent our Pixels 07:20 | Accessing Pixel Values 08:45 | Changing Pixel Colors 11:37 | Copying & Pasting Parts of Image 15:07 | 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 ⭐️ - Images within OpenCV - Image Manipulation OpenCV - Image Fundamentals OpenCV - Python - Computer vision - Pip on Windows Mac ⭐️ Hashtags ⭐️ #OpenCV #Python
Welcome to this OpenCV Python tutorial! In this video, I'll be showing you how to do a live face and eye detection and tracking in Python using OpenCV. The code for the face/eye detection is very straightforward, and we'll be using Haar Cascade. 💻 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: 🤍 📄 StackOverflow Post: 🤍 📝 Code For This Series: 🤍 🔍 Playlist: 🤍 ⭐️ Timestamps ⭐️ 00:00 | Introduction & Overview 00:53 | Haar Cascade explanation 02:43 | Loading Haar Cascade Classifiers 03:52 | Face Detection 11:43 | Eye Detection 15:12 | Finished Code/Demo ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 💰 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 ⭐️ - Face detection in OpenCV - Haar Cascade OpenCV - Python - Computer vision - Cameras and video capture - Eye detection ⭐️ Hashtags ⭐️ #OpenCV #Python #FaceDetection
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: 🤍
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 video you'll learn how to: 1. Access your webcam using OpenCV and cv2.VideoCapture 2. Take Photos with your Webcam or USB device using Python 3. Produce a real time webcam feed using Python and OpenCV Get the code: 🤍 Links Documentation: 🤍 Python Tutorials: 🤍 Download Samples from here: 🤍 Chapters: 0:00 - Start 0:36 - Explainer 1:29 - Tutorial Kickoff 2:02 - Whiteboard 5:43 - Import Dependencies 6:58 - Connect to a Webcam 9:14 - The Camera I use for Computer Vision 9:36 - Finding Your Video Capture Device 12:17 - Take a Photo with OpenCV 15:06 - Accesss Video from Your Webcam 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!
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In this video, we are going to learn how to perform object measurement using OpenCV and Python. We will use an A4 paper as our guide and find the width and height of objects placed in this region. Code & Text Based Version: 🤍 ################################################ Full OpenCV 3 Hour Course: 🤍 ################################################ 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
This video will be about How To Install OpenCV in Visual Studio Code on Windows 11. This allows you to get started with OpenCV in your Python codes in VSCode. The OpenCV Library in an open-source library commonly used for things like image processing and machine learning. It is available for several different programming languages, however today we are focusing on Python. How To Install Visual Studio Code On Windows 11: 🤍 This timeline is meant to help you better understand How To Install OpenCV in Visual Studio Code on Windows 11: 0:00 Introduction. 0:22 How to add OpenCV to Visual Studio Code. 2:30 Sample OpenCV code to ensure the OpenCV library installed correctly in VSCode. 2:50 Outro Follow & Support StudySession: Channel Memberships: 🤍 Email Us: StudySessionBusiness🤍gmail.com Twitter: 🤍 Instagram: 🤍 What is Python and why you should learn Python? Python programming, in particular Python 3, is a growing programming language that is loved by many programmers due to its simple syntax and ease of use. Python allows for relatively easy debugging of your codes and there are many beautiful Python IDE’s available for free to make coding more enjoyable. Python is also very popular among Data Scientists and many machine learning applications. #studysession #opencvpython #vscode
CLIQUE AQUI PARA SABER MAIS SOBRE O CURSO COMPLETO PYTHON IMPRESSIONADOR: 🤍 PARA BAIXAR O MINICURSO GRATUITO DE PYTHON: 🤍 - ► Arquivos Utilizados no Vídeo: 🤍 ► Vídeo de Instalação do Jupyter: 🤍 ► Vídeo de Como Controlar WebCam com Python: 🤍 - Caso prefira o vídeo em formato de texto: 🤍 - Na aula de hoje eu quero te mostrar como fazer um reconhecimento de rosto com Python (reconhecimento facial com Python) utilizando a sua webcam. Aqui no canal já temos um vídeo de como controlar a webcam com Python e isso vai ser útil para a aula de hoje, pois vamos utilizar a webcam para esse reconhecimento facial no Python. Além disso nós vamos utilizar a biblioteca OpenCV, que é uma biblioteca para tratamento de imagens muito utilizada. E vamos utilizar também a biblioteca mediapipe que possui uma ferramenta para reconhecimento de imagens criada pela Google, então você vai ter a possibilidade de fazer reconhecimento de imagens e não só de rosto. E aí, vamos pra aula aprender esse conteúdo incrível já para você aplicar no seu próximo projeto em Python? - Hashtag Programação ► Inscreva-se em nosso canal: 🤍 ► Ative as notificações (clica no sininho)! ► Curta o nosso vídeo! - Redes Sociais ► Blog: 🤍 ► YouTube: 🤍 ► Instagram: 🤍 ► Facebook: 🤍 Aqui nos vídeos do canal da Hashtag Programação ensinamos diversas dicas de Python para que você consiga se desenvolver nessa linguagem de programação! - #python #hashtagprogramacao
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: 🤍 This Edureka Python Tutorial video on OpenCV explains all the basics of OpenCV. It also explains how to create a face recognition system and motion detector. Subscribe to our Edureka YouTube channel to get video updates: 🤍 Check out our complete Python playlist: 🤍 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬- 🔵Python Programming Certification: 🤍 🔵Python Certification Training for Data Science: 🤍 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🔵Data Scientist Masters Program: 🤍 🔵Machine Learning Engineer Masters Program: 🤍 -𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🌕Post Graduate Diploma in Artificial Intelligence Course offered by E&ICT Academy NIT Warangal: 🤍 #PythonOpenCV #FaceRecognition #Edureka - Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in Python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in the future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, Please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).
Welcome to another OpenCV tutorial! In this OpenCV video, we'll be talking about corner detection. The point is to not just show you the corner detection, but to also introduce some of the interesting algorithms that OpenCV has built in. In this video, we'll only need 3-4 lines of code to find all the corners in 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: 🤍 🔍 Playlist: 🤍 ⭐️ Timestamps ⭐️ 00:00 | Introduction 01:37 | Corner detection 09:42 | Drawing corners 15:04 | Drawing lines between corners 18:35 | 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 ⭐️ - Corner detection in OpenCV - Corners OpenCV - Python - Computer vision - Cameras and video capture ⭐️ Hashtags ⭐️ #OpenCV #Python #CameraCapture
In this video 📝 I'm going to show you How To Install OpenCV C and Set It Up in Visual Studio Code with CMake. We will go over the installation of OpenCV and CMake. After that, we will open up Visual Studio Code and install the extensions we are going to use. We will then use CMake to configure our project and build it with OpenCV. * REMEMBER TO DOWNLOAD: visual studio 2019 with c devolvement tool * The Main file and the CMakeLists file are available on my GitHub: 🤍 - You can just copy everything from those files and set it up as in the video and you are ready to create your OpenCV programs in Visual Studio Code with c OpenCV Download: 🤍 CMake Download: 🤍 _ ⚒️Freelance Work: 🤍 _ 🧑🏻💻 My AI and Computer Vision Courses⭐: 📕Enroll in CustomGPT Implementation Course: 🤍 📚 Research Paper Implementation Course: 🤍 📕 Object Detection Course:🤍… 📗 OpenCV GPU Course: 🤍... 📘 SegFormer Course: 🤍... 📙 Object Tracking Course: 🤍 🦾 Online Courses with Job Guarantee on Springboard (Save $1000 with: "NICOLAINIELSEN") 🤍 _ 📞 Connect with Me: 🌍 My Website: 🤍 🤖 GitHub: 🤍 👉 LinkedIn: 🤍 🐦 Twitter: 🤍 _ ⌛TimeStamps⏳ 0:00 Intallation 2:15 Setup 8:12 CMAKE Configuration 11:48 Test Tags: #OpenCV #VisualStudioCode #OpenCVInstall #OpenCVSetup #OpenCVcpp #ComputerVision
Simulate Self-Driving Cars with Computer Vision & Deep Learning - Full Course on sale for $10! (normally $200): 🤍 Rayan Slim's channel: 🤍 Road Image Link: 🤍 (for Computer Vision tutorial 1) Road Video Link: 🤍 (for last Computer Vision tutorial) This video was done in collaboration with Rayan Slim and ProgrammingKnowledge. Computer Vision helps the computer see the world as we do. Learn & Master Computer Vision techniques in this fun and exciting video with top instructor Rayan Slim. You'll go from beginner to Computer Vision competent and your instructor will complete each task with you step by step on screen. By the end of the tutorial, you will be able to build a lane-detection algorithm fuelled entirely by Computer Vision. 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!
Learn how to use OpenCV in the cloud with Python. OpenCV is a library of programming functions mainly aimed at real-time computer vision. You will learn how to create computer vision applications in the cloud on Google Colab. You will use AI and machine learning. 💻 Code: 🤍 ✏️ This course was developed by Misbah Mohammed. Check out his channel: 🤍 ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:18) Lesson 1: Changing color profiles in an image ⌨️ (0:05:01) Image Properties ⌨️ (0:15:17) Lesson 2: Edge Detection ⌨️ (0:21:08) Erosion and Dilation ⌨️ (0:30:40) Lesson 3: Image Manipulation-Noise Removal ⌨️ (0:41:56) Lesson 4: Drawing Shapes and Writing Text on Images ⌨️ (1:00:10) Intermediate Exercise 1: Color Detection ⌨️ (1:21:05) Intermediate Exercise 2: Face Detection ⌨️ (1:37:52) Intermediate Exercise 3: Shape Detection ⌨️ (2:01:55) Project 1: Ball Tracking ⌨️ (2:29:43) Project 2: Face Recognition ⭐️ 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: 🤍
Tired of searching for your Uber? Trying to get a better idea of who’s stealing your car park? Just want an awesome Computer Vision project to try out using Python? Well, ANPR might just be the perfect thing for your to try out! In this video we’ll go through a full blown walkthrough of performing Automatic Number Plate Recognition (ANPR) using OpenCV and EasyOCR. We just edge detection and filtering techniques combined with deep learning powered optical character recognition to be able to extract number plate text from images in just 25 minutes. In this video you’ll learn how to: 0:00 - Start 5:59 - Reading and visualising images using OpenCV with Python 7:37 - Applying color shifts and changes to images (e.g. grayscaling and BGR2RGB) 9:48 - Detecting contours using OpenCV findCountours 14:32 - Masking number plates to improve text extraction for OCR 18:40 - Extracting number plate text using EasyOCR GET THE CODE! 🤍 Links Mentioned: Circuit Digest Article: 🤍 OpenCV Documentation: 🤍 EasyOCR: 🤍 PyTorch: 🤍 If you have any questions, please drop a comment below! Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!
Tennis Ball trajectory detection and tracking during a tennis match using openCV and computer vision
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![[Optical Flow] Vehicle Speed Estimation using OpenCV, Python](https://i.ytimg.com/vi/AEd7tev39Ns/mqdefault.jpg "[Optical Flow] Vehicle Speed Estimation using OpenCV, Python")
