A labeled max array refers to an array that stores the maximum intensity value of each region in a . To find the maximum intensity value of a region, every pixel in that region is examined. Then, the intensity value of the brightest pixel is selected as the maximum intensity value. In simple terms, labeled max arrays are used to find the brightest regions of an image.

For example, let”s assume we have a region which consists of three pixels. The intensity value of the three pixels is 0.5, 0.2, and 0.8 respectively. Then the maximum intensity value of the region will be 0.8.

Labeled Max Array in Mahotas

In Mahotas, we can use the mahotas.labeled.labeled_max() function to create a labeled max array. The function iteratively searches for the brightest pixel in a region. Then it stores the intensity value of the brightest pixel in an array.

The resultant array is a labeled max array, having the maximum intensity value of each region of the image.

The mahotas.labeled.labeled_max() function

The mahotas.labeled.labeled_max() function takes an image and a labeled image as inputs. It returns an array that contains the maximum intensity value of each labeled region.

Syntax

Following is the basic syntax of the labeled_max() function in mahotas −

mahotas.labeled.labeled_max(array, labeled, minlength=None)

Where,

• array − It is the input image.

• labeled − It is the labeled image.

• minlength (optional) − It specifies the minimum number of regions to include in the output array (default is None).

Example

In the following example, we are finding the labeled max arrays in a labeled image using the labeled_max() function.

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread(''sea.bmp'')
# Converting it to grayscale
image = mh.colors.rgb2gray(image).astype(np.uint8)
# Applying thresholding
threshold = mh.thresholding.rc(image)
threshold_image = image > threshold
# Labeling the image
label, num_objects = mh.label(threshold_image)
# Getting the labeled max array
labeled_max = mh.labeled.labeled_max(image, label)
# Printing the labeled max array
print(''Labeled max array:'', labeled_max)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 2)
# Displaying the original image
axes[0].imshow(image, cmap=''gray'')
axes[0].set_title(''Original Image'')
axes[0].set_axis_off()
# Displaying the labeled image
axes[1].imshow(label, cmap=''gray'')
axes[1].set_title(''Labeled Image'')
axes[1].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

Output

Following is the output of the above code −

Labeled max array: [107 111 129 ... 141 119 109]

The image obtained is as follows −

Labeled Max Arrays of a Random Boolean Image

We can also find the labeled max arrays of a random Boolean image. A random boolean image refers to an image where each pixel has a value of either 0 or 1. The foreground pixels are represented by ”1”, and the background pixels are represented by ”0”.

In mahotas, to find the labeled max arrays of a random Boolean image, we first need to generate a random boolean image of a specific size using the np.zeros() function.

This image initially consists of only background pixels. We then assign integer values to a few portions of the image to create distinct regions.

Then, we find the labeled max arrays of the image using the labeled_max() function.

Example

In the example mentioned below, we are finding the labeled max arrays of a random boolean image.

import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Creating a random image
image = np.zeros((10, 10), bool)
# Assigning values to the regions
image[:2, :2] = 1
image[4:6, 4:6] = 1
image[8:, 8:] = 1
# Labeling the image
label, num_objects = mh.label(image)
# Random sampling
random_sample = np.random.random_sample(image.shape)
# Getting the labeled max array
labeled_max = mh.labeled.labeled_max(random_sample, label)
# Printing the labeled max array
print(''Labeled max array'')
for i, intensity in enumerate(labeled_max):
   print(''Region'', i, '':'', intensity)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 2)
# Displaying the original image
axes[0].imshow(image)
axes[0].set_title(''Original Image'')
axes[0].set_axis_off()
# Displaying the labeled image
axes[1].imshow(label)
axes[1].set_title(''Labeled Image'')
axes[1].set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()

Output

Output of the above code is as follows −

Labeled max array
Region 0 : 0.9950607583625318
Region 1 : 0.8626363785944107
Region 2 : 0.6343883551171169
Region 3 : 0.8162320509314726

We get the following image as output −