The last modifications of this post were around 3 years ago, some information may be outdated!
This is a draft, the content is not complete and of poor quality!
This note will always be updated.
- Load and write an image from/to Google Drive.
- Change the image's size from
(height, weight, channels)to
(height x weight, channels)
- Reduce the image's quality using smaller number of clusters.
- Plot points with 2 lines which are corresponding to 2 eigenvectors.
- Plot & choose Principal Components.
- An example of choosing
- Visualization hand-written digits (the case of all digits and the case of only 2 digits -- 1 & 8).
- Using SVM to classifier data in the case of 1 & 8 and visualize the decision boundaries.
- When input is an image, the values of adjacent pixels are highly correlated.
- Import images from
scipyand Google Drive or Github (with
- Compress grayscale images and colored ones.
- Plot a grayscale version of a colorful images.
- Save output to file (Google Drive).
- Fix warning Lossy conversion from float64 to uint8. Range [...,...]. Convert image to uint8 prior to saving to suppress this warning.
- Fix warning Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
- Calculate a size (in
KB) of a image file.
- Using PCA to extract 150 fundamental components to feed into our SVG classifier.
- Grid search cross-validation to explore combinations of parameters (
- Classification report: precision, recall, f1-score, support.
- Confusion matrix.
- An example of using