jasmcaus / Opencv Course
Licence: mit
Learn OpenCV in 4 Hours - Code used in my Python and OpenCV course on freeCodeCamp.
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OpenCV with Python in 4 Hours
Notes and code used in my Python and OpenCV course on freeCodeCamp.org.
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.
Course Outline (with timestamps)
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)
Credits
The images in the Photos and Videos folders were downloaded from Unsplash and Pixabay, unless otherwise mentioned.
The images in the Faces folder were procurred from a repo on Kaggle.
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