OpenCV Course - Full Tutorial with Python

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2022-09-22 18:20:15
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https://www.youtube.com/watch?v=oXlwWbU8l2o Opencv+VSCode Important Updates: caer.train_val_split() is a deprecated feature in caer. Use sklearn.model_selection.train_test_split() instead. See #9 for more details. 1. Installation Besides installing OpenCV, we cover the installation of the following package: Caer is a lightweight, high-performance Vision library for high-performance AI research. It simplifies your approach towards Computer Vision by abstracting away unnecessary boilerplate code giving you the flexibility to quickly prototype deep learning models and research ideas. $ pip install caer 2. Basic Concepts: Reading Images and Video (0:04:12) Resizing and Rescaling Images and Video Frames (0:12:57) Drawing Shapes and Placing text on images (0:20:21) 5 Essential Methods in OpenCV (0:31:55) Image Transformations (0:44:13) Contour Detection (0:57:06) 3. Advanced Concepts: Switching between Colour Spaces (RGB, BGR, Grayscale, HSV and Lab) (1:12:53) Splitting and Merging Colour Channels (1:23:10) Blurring (1:31:03) BITWISE operations (1:44:27) Masking (1:53:06) Histogram Computation (2:01:43) Thresholding/Binarizing Images (2:15:22) Advanced Edge Detection (2:26:27) 4. Face Detection and Recognition Face Detection using Haar Cascades (2:35:25) Face Recognition using OpenCV's LBPHFaceRecognizer algorithm (2:49:05) 5. Capstone: Deep Computer Vision Building a Deep Computer Vision model to classify between the characters in the popular TV series The Simpsons (3:11:57)
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