In this article we will see how we can do image haar transform in mahotas. The haar wavelet is a sequence of rescaled “square-shaped” functions which together form a wavelet family or basis. Wavelet analysis is similar to Fourier analysis in that it allows a target function over an interval to be represented in terms of an orthonormal basis. The Haar sequence is now recognised as the first known wavelet basis and extensively used as a teaching example.
In this tutorial we will use “luispedro” image, below is the command to load it.
Below is the luispedro image
In order to do this we will use
Syntax : mahotas.haar(img)
Argument : It takes image object as argument
Return : It returns image object
Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
- Mahotas - Reversing Haar Transform
- Mahotas - Hit & Miss transform
- Python | Haar Cascades for Object Detection
- Mahotas - XYZ to RGB Conversion
- Mahotas - Getting Mean Value of Image
- Mahotas - RGB to XYZ Conversion
- Mahotas - Re-Labeling
- Mahotas - XYZ to LAB Conversion
- Mahotas - RGB to LAB Conversion
- Mahotas - Mean filter
- Mahotas - Filtering Region
- Python Mahotas - Introduction
- Mahotas - Filtering Labels
- Mahotas - Setting Threshold
- Mahotas - Image Stretching
- Mahotas - Image Stretch RGB
- Mahotas - Gaussian filtering
- Mahotas - Zernike Moments
- Mahotas - Eroding Image
- Mahotas - Zernike Features
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