In this article we will see how we can implement otsu’s method in mahotas. In computer vision and image processing, Otsu’s method, named after Nobuyuki Otsu, is used to perform automatic image thresholding. In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background.
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.otsu(image)
Argument : It takes image object as argument
Return : It returns integer
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 - Riddler-Calvard Method
- 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
- Mahotas - Filtering Region
- Mahotas - Removing Bordered Labeled
- Mahotas - Filtering Labels
- Mahotas - Setting Threshold
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