In this article we will see how we can transform image using daubechies wavelet in mahotas. In general the Daubechies wavelets are chosen to have the highest number A of vanishing moments, (this does not imply the best smoothness) for given support width 2A. There are two naming schemes in use, DN using the length or number of taps, and dbA referring to the number of vanishing moments. So D4 and db2 are the same wavelet transform.
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.daubechies(img, ‘D8’)
Argument : It takes image object and string i.e one of ‘D2’, ‘D4’, … ‘D20’ 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 - Reconstructing image from transformed Daubechies wavelet image
- Mahotas - Making Image Wavelet Center
- Mahotas - Removing border effect from Wavelet Center Image
- Mahotas - Labelled Image from the Normal Image
- Mahotas - Getting Mean Value of Image
- Mahotas - Dilating Image
- Mahotas – Convolution of Image
- Mahotas - Resizing Image
- Mahotas – Creating RGB Image
- Mahotas - Cropping Image
- Mahotas - Image Stretch RGB
- Mahotas - Eccentricity of Image
- Mahotas - Roundness of Image
- Mahotas - Eroding Image
- Mahotas - Image Stretching
- Mahotas - Image Overlay
- Mahotas - Getting Image Moments
- Mahotas – Full Histogram of Image
- Mahotas - Loading image as grey
- Mahotas – Euler number of Image
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