How to plot images using Matplotlib, including color maps and image annotations

by Dejaswarooba Updated: Jul 7, 2023

Solution Kit

Matplotlib is a used plotting library in Python. It offers many functionalities for visualizing data. Among its capabilities, Matplotlib provides robust support for plotting images. Using the imshow() function, you can plot images using pre-loaded image data. You can apply various color maps to enhance the visualization of the images. You can even add color annotations or color bars to provide extra information. Matplotlib offers extensive customization options. It adjusts figure size, scale, color limits, interpolation methods, and face and edge colors. Once you have created your desired plot, save it as an image file using the savefig() function.

Basics of Plotting Images with Matplotlib:

Introduction to Matplotlib plots:

Matplotlib is a comprehensive plotting library. It allows us to create appealing and informative plots.

Setting up Matplotlib for inline plotting:

Using the magic command %matplotlib inline in Jupyter Notebooks. It enables us to display plots within the notebook.

Importing necessary libraries:

To get started, we must import Matplotlib and its dependencies, including NumPy.

Understanding image data:

Images are represented as multidimensional arrays and NumPy. It provides powerful tools for working with such data.

Using the imshow() function:

The imshow() function is a fundamental tool for plotting images in Matplotlib. We can pass the image data as an argument to visualize it.

Exploring Color Maps and Image Annotations:

Introduction to color maps:

Color maps, or colormaps, play a vital role in enhancing the visualization of images. Matplotlib provides a wide range of predefined color maps.

Applying color maps:

We can specify the desired colormap using the cmap parameter in the imshow() function.

Adding color annotations:

Color annotations provide extra information about the color mapping scheme. We can achieve this by adding a color bar using the colorbar() function.

Working with many Images and Grayscale Images

Plotting images:

Matplotlib allows us to plot many images in a single figure. It makes it easier to compare and analyze different datasets.

Visualizing grayscale images:

Grayscale images represent pixel intensities using a single channel. We will explore how to plot and manipulate grayscale images using Matplotlib.

Advanced Techniques and Customization Options

Fine-tuning figure size and appearance:

We can customize the size and appearance of the figure to suit our specific requirements.

Managing axis labels and y-axis scale:

Adding informative axis labels and adjusting the y-axis scale contribute to better understanding.

Controlling color scale and limits:

We have control over the color scale and limits. It allows us to highlight specific features within the image.

Interpolation methods:

Matplotlib provides various interpolation methods, such as Bicubic interpolation. It enhances the visual quality of the plotted image.

Saving Figures and Exporting Image Files

Saving figures:

We can save the plotted images as image files, such as PNG, using the savefig() function.

Understanding image formats:

Matplotlib supports various image formats, including PNG, which saves figures.

Exploring the broader SciPy stack:

Matplotlib integrates with other libraries in the SciPy ecosystem. It enhances its capabilities for image plotting.

Additional functionalities:

Matplotlib offers a wide range of plotting options beyond images. It includes bar charts, errorbar plots, pie charts, violin plots, and contour plots.

Preview of the output obtained when the below code is run

Code

Plot multiple images with identical color assignments using matplotlib's imshow

PythonLines of Code : 55License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np

C_p = 11 # Classes

colour_names = [ # Your predefined colours
    "blue",
    "red",
    "yellow",
    "orange",
    "black",
    "purple",
    "green",
    "turquoise",
    "grey",
    "maroon",
    "silver",
    "white"
]

colour_dict = { # Color mapping (class -> colour)
    i: mpl.colors.to_rgb(colour_names[i])
    for i in range(C_p + 1)
}

# Create a colormap (optional)
colours_rgb = [colour_dict[i] for i in range(C_p)]
colours = mpl.colors.ListedColormap(colours_rgb)

norm = mpl.colors.BoundaryNorm(np.arange(C_p + 1) - 0.5, C_p)

plt.figure() # If you only want to plot one
plt.imshow(t2, cmap=colours, norm=norm)
cb = plt.colorbar(ticks=np.arange(C_p))
plt.axis("off")

fig, axs = plt.subplots(1,3)
axs[0].imshow(t1, cmap = colours, norm=norm)
axs[0].set_title("t1")
axs[0].axis('off')
axs[1].imshow(t2, cmap = colours, norm=norm)
axs[1].set_title("t2")
axs[1].axis('off')
im = axs[2].imshow(t3, cmap = colours, norm=norm)
axs[2].set_title("t3")
axs[2].axis('off')
p0 = axs[0].get_position().get_points().flatten()
p1 = axs[1].get_position().get_points().flatten()
p2 = axs[2].get_position().get_points().flatten()
ax_cbar = fig.add_axes([p0[0], 0.08, p2[0], 0.05])
plt.colorbar(im, cax=ax_cbar, ticks=np.arange(C_p), orientation='horizontal')
fig.tight_layout()

Follow the steps carefully to get the output easily.

Install Visual Studio Code in your computer.
Install the required library by using the following command -

pip install matplotlib

pip install numpy

If your system is not reflecting the installation, try running the above command by opening windows powershell as administrator.
Open the folder in the code editor, copy and paste the above kandi code snippet in the python file.
Add these lines after the import statements.

t1 = plt.imread('path')
t2 = plt.imread('path')
t2 = plt.imread('path')

Replace the path in the above lines to your corresponding path of the images.
Run the code using the run command.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to plot images using Matplotlib, including color maps and image annotations" in kandi. You can try any such use case!

Dependent Libraries

numpyby numpy

Python

23755

Version:v1.25.0rc1

License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

Support

Quality

Security

License

Reuse

numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

Support

Quality

Security

License

Reuse

matplotlibby matplotlib

Python

17559

Version:v3.7.1

License: No License (null)

matplotlib: plotting with Python

Support

Quality

Security

License

Reuse

matplotlibby matplotlib

Python 17559 Version:v3.7.1 License: No License

matplotlib: plotting with Python

Support

Quality

Security

License

Reuse

If you do not have matplotlib that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the page in kandi.

You can search for any dependent library on kandi like matplotlib.

Environment tested

This code had been tested using python version 3.8.0
matplotlib version 3.7.1 has been used.
numpy version 1.24.2 has been used.

Support

For any support on kandi solution kits, please use the chat
For further learning resources, visit the Open Weaver Community learning page.

FAQ

1. What is Matplotlib, and how can it be used to plot images?

Matplotlib is a Python library. It is used for creating visualizations and plots. It can plot images by loading the image data and using the imshow() function to display it.

2. How do I create a scatter plot using Matplotlib?

To create a scatter plot using Matplotlib, follow these steps. First, import the necessary libraries by including `import matplotlib.pyplot as plt`. Next, prepare the data by having two arrays:

one for the x-values
another for the corresponding y-values.

Then, use the `plt.scatter()` function, passing in the x-values and y-values as arguments. Customize the plot by adding labels, titles, and legends or modifying marker appearance. Lastly, display the scatter plot by calling `plt.show()`.

3. How do you create a luminance image using Matplotlib?

To create a luminance image using Matplotlib:

Load the image data as a 2D array or grayscale image.
Use the `imshow()` function with the colormap set to `'gray'` to display the image as a luminance image.

4. Are there any specific types of image plots that Matplotlib supports?

Yes, Matplotlib supports various types of image plots. Some of the used image plots in Matplotlib include:

Grayscale Image: Displaying grayscale images using the 'gray' colormap.
Color Image: Displaying color images with RGB or RGBA color channels.
Heatmap: Visualizing data as a heatmap using color gradients to represent values.

5. Can RGB and RGBA images be created with Matplotlib or only black and white ones?

Matplotlib supports the creation and display of both RGB and RGBA images. You can create and visualize images by arraying data representing the color channels.

For RGB images, you would use a 3D array with dimensions. It is where each pixel contains the intensity values for the red, green, and blue channels.

For RGBA images, you would use a 4D array with dimensions. It is where each pixel has the intensity values and alpha channel for transparency.

6. How can I save my plots as PNG images using Matplotlib?

After creating your plot, call the savefig() function to save the plot as a PNG image.

plt.savefig('plot.png')

See similar Kits and Libraries

Open Weaver – Develop Applications Faster with Open Source

Terms
Privacy policy

How to plot images using Matplotlib, including color maps and image annotations

Basics of Plotting Images with Matplotlib:

Exploring Color Maps and Image Annotations:

Working with many Images and Grayscale Images

Advanced Techniques and Customization Options

Saving Figures and Exporting Image Files

Code

Dependent Libraries

Environment tested

Support

FAQ

Open Weaver – Develop Applications Faster with Open Source

kandi

Community and Support

Company

Follow