In this article we will see how we can apply majority filter to the image in mahotas. In Majority filters for each group of pixels considered in the input map, a majority filter assign the predominant (=mostly frequently occurring) value or class name of these to the center pixel in the output map.
In this tutorial we will use “lena” image, below is the command to load it.
Below is the lena image
In order to do this we will use
Syntax : mahotas.majority_filter(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]
Below is the implementation
- Mahotas - 2D Laplacian filter
- Mahotas - Mean filter
- Mahotas - Median filter
- Mahotas - Rank Filter
- Spatial Filters - Averaging filter and Median filter in Image Processing
- Mahotas - Re-Labeling
- Mahotas - Labelled Image from the Normal Image
- Mahotas - Getting Mean Value of Image
- Mahotas - Element Structure for Eroding Image
- Mahotas - RGB to Gray Conversion
- Mahotas - Dilating Image
- Mahotas - Eroding Image
- Mahotas - Opening Process on Image
- Mahotas - Element Structure for Dilating Image
- Labeled Image Function in Python Mahotas
- Where's Wally Problem using Mahotas
- Python Mahotas - Introduction
- Loading Image using Mahotas - Python
- Mahotas - Sizes of Labeled Region
- Mahotas - Weight of Labeled Region
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