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Master Computer Vision With Deep Learning, Opencv4 & Python - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials) +--- Thread: Master Computer Vision With Deep Learning, Opencv4 & Python (/Thread-Master-Computer-Vision-With-Deep-Learning-Opencv4-Python--515996) |
Master Computer Vision With Deep Learning, Opencv4 & Python - OneDDL - 08-21-2024 ![]() Free Download Master Computer Vision With Deep Learning, Opencv4 & Python Last updated 5/2023 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 12.83 GB | Duration: 12h 3m Become a Computer Vision Guru, Implement object detection, tracking and recognition using OpenCV4, Dlib & Deep Learning What you'll learn Become a Master Computer Vision Programmer by learning from the field experts. Start from the ground up by Coding Image Processing basics i.e. (Image access and manipulations) Make your CV Projects interactive by learning to utilize Trackbars and Mouse Events Deep-dive into CV_101 topics i.e. (Transformations, Filtering, Segmentation, Contours and Features ) Conquer advanced techniques like Object detection, Object Tracking, Object Recognition Understand, Train and deploy Haar Cascade and YOLOv3 for Object detection Perform Single Object Tracking using CSRT, KCF etc. and Multi-Object Tracking using DeepSort. Implement and compare Simple Face-Recognizers like LBP, FisherFaces with highly accurate dlib Implementation. Secure Access: Create a python executable that only allows access on your pc to authorized personnel's through a complete end-to-end development. Requirements Python basic Programming Description This course is your ultimate guide for entering into the realm of Computer Vision. We will start from the very basics i.e Image Formation and Characteristics, Perform basic image processing (Read/Write Image & Video + Image Manipulation), make CV applications interactive using Trackbars and Mouse events, build your skillset with Computer Vision techniques (Segmentation, Filtering & Features) before finally Mastering Advanced Computer Vision Topics i.e Object Detection, Tracking, and recognition. Right at the end, we will develop a complete end-to-end Visual Authorization System (Secure Access). The course is structured with below main headings.Computer Vision FundamentalsImage Processing Basics (Coding)CV-101 (Theory + Coding)Advanced [Detecion] (Theory + Coding)Advanced [Tracking] (Theory + Coding)Project: PeopleTrackr ( Crowd Monitoring System )Advanced [Recognition] (Theory + Coding)Project: EasyAttend ( Live Attendance System )Project: Secure Access (End-to-end project development & deployment)GoodbyeFrom Basics to Advanced, each topic will accompany a coding session along with theory. Programming assignments are also available for testing your knowledge. Python Object Oriented programming practices will be utilized for better development.Learning Outcomes - Computer VisionRead/Write Image & Video + Image ManipulationInteractive CV applications with Trackbars & MouseEventsLearn CV Techniques i.e (Transformation, Filtering, Segmentation, and Features)Understand, train, and deploy advanced topics i.e (Object Detection, Tracking, and Recognition) Test your knowledge by completing assignments with each topic. [Project-1] PeopleTrackr: Crowd Monitoring System [Project-2] EasyAttend: Live attendance System for Classrooms and offices.[Final-Project] Secure Access: End-to-end Visual Authorization System for your Computer.- AlgorithmsFacial recognition algorithms like LBP and Dlib-ImplementationLBP (Fast-Less accurate)Dlib-Implementation (Slow-Accurate)Single Object TrackersCSRT, KCFMultiple Object TrackersDeepSort (Slow-Accurate)Object DetectionHaar Cascades (Fast-Less accurate)YoloV3 (Slow-Accurate)Computer Vision TechniquesSift | Orb Feature MatchingCanny Edge detectionBinary, Otsu, and Adaptive ThresholdingKmeans SegmentationConvex hull ApproximationPre-Course RequirmentsSoftware BasedOpenCV4PythonSkill BasedBasic Python ProgrammingMotivated mind ![]() ![]() Overview Section 1: Prerequisites Lecture 1 GitHub Resources And Dependencies Lecture 2 Environment Setup Section 2: Introduction Lecture 3 Computer Vision Fundamentals Section 3: Image Processing Basics Lecture 4 Reading/Writing image and Videos in OpenCV (Coding) Lecture 5 Image Manipulation (Coding) Lecture 6 Important Functions (Coding) Lecture 7 Make CV Applications Interactive #1 : Trackbars Lecture 8 Make CV Applications Interactive #2 : MouseEvents Section 4: CV-101 Lecture 9 Image Transformations (Theory) Lecture 10 Image Transformations (Coding) Lecture 11 Image Filtering (Theory) Lecture 12 Image Filtering (Coding) Lecture 13 Image Segmentation (Theory) Lecture 14 Image Segmentation (Coding) Lecture 15 Image Contours (Theory) Lecture 16 Image Contours (Coding) Lecture 17 Image Features - Keypoints (Theory) Lecture 18 Image Features - Keypoints (Coding) Lecture 19 Image Features - Descriptors (Theory) Lecture 20 Image Features - Descriptors (Coding) Lecture 21 Feature Matching Section 5: Advanced ( Detection ) Lecture 22 Object detection (Theory) Lecture 23 Haar Cascades (Theory) - Object Detection Lecture 24 Training a Custom Haar Cascade detector (Coding) - Object detection Lecture 25 Detection Using Haar Cascade (Coding) - Object Detection Lecture 26 YOLO (Theory) - Object detection Lecture 27 Detection Using YOLO in OpenCV (Coding) - Object detection Lecture 28 Training YOLO on a Custom Dataset (Coding) - Object detection Section 6: Advanced (Tracking) Lecture 29 What is Object Tracking? Lecture 30 Single Object Tracking (Theory) - Comparison Lecture 31 Single Object Tracking (Coding) Lecture 32 Multiple Object Tracking (T + C) Lecture 33 MOT: DeepSort (Theory) Lecture 34 MOT: DeepSort (Setup) - Environment Setup Lecture 35 MOT: DeepSort (Theory) - Algorithm Overview Lecture 36 MOT: DeepSort (Coding) Section 7: Project 1: PeopleTrackr Lecture 37 Feature 1: Live-People-Counter Lecture 38 Feature 2: Displaying trajectories Lecture 39 Feature 3: Focus On Suspicious Individual Section 8: Advanced (Recognition) Lecture 40 What is Image Recognition? Lecture 41 Face Recognition OpenCV (Theory) - Comparison Lecture 42 Face Recognition OpenCV (Coding) Section 9: Project 2: Live Attendance System Lecture 43 Part A: Workflow and Dataset Acquisition Lecture 44 Part B: Implementation Section 10: Final Project : Secure Access Lecture 45 Part A: Face Detection Lecture 46 Part B: Tracking Lecture 47 Part C: Alignment & Representation Lecture 48 Part D: Matching Lecture 49 Part E: MultiProcessing Section 11: Goodbye Lecture 50 Message to Students Beginner Python developers curious about Computer Vision,Undergrads wanting to investigate/opt-in to Computer Vision,University Graduates looking to add Computer Vision in their skillset,Computer Vision Programmers wanting to brush up on some basics Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live No Password - Links are Interchangeable |