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)