Category: scikit-image

Scikit Image – Reading Images Reading images is a fundamental step when performing operations like cropping, resizing, rotating, or applying filters using image processing tools. The process of reading images involves capturing the pixel values and metadata from an image file and representing them in a suitable data structure, such as a NumPy array or matrix. In the Scikit-image library, the io module provides a function imread() for loading image files into memory as NumPy arrays. Once the image is loaded into memory as a NumPy array, we can get access to a wide range of image processing functions available in Scikit-image to perform tasks like filtering, segmentation, feature extraction, and much more. The imread() method The io.imread() method in Scikit-image is capable of reading images in various formats, including JPEG, PNG, TIFF, BMP, and more. The function internally utilizes different libraries based on the specific image format, such as imageio, PIL, or tifffile. Syntax Following is the syntax and parameters of this method − skimage.io.imread(fname, as_gray=False, plugin=None, **plugin_args) Fname − A string representing the file name or path or a URL of the image. as_gray (optional) − If set to True, it converts color images to grayscale represented as 64-bit floats. Images that are already in grayscale format are not converted. plugin (optional) − A string specifying the plugin to use for reading the image. If the plugin parameter is not provided, the function will automatically try different plugins, starting with imageio, until a suitable one is found. However, if the file name (fname) has a “.tiff” extension, the tifffile plugin will be used by default. **plugin_args (optional) − Additional keyword arguments that are passed to the specified plugin. Return Value The method returns a NumPy array that represents the image. The array contains the pixel values, and its shape corresponds to the dimensions of the image. The color bands or channels of the image are stored in the third dimension of the array. For instance, a grayscale image would have dimensions MxN, an RGB image would have dimensions MxNx3, and an RGBA image would have dimensions MxNx4. Example 1 The following example demonstrates how to load an image using the file name. import skimage from skimage import io # Read an image image = io.imread(”Images/logo.png”) # Display image properties from the image array print(”The following are the properties of the loaded image:”) print(“Image shape:”, image.shape) print(“Image data type:”, image.dtype) print(“Number of color channels:”, image.shape[2]) Input Image Output The following are the properties of the loaded image: Image shape: (225, 225, 4) Image data type: uint8 Number of color channels: 4 Example 2 The following example demonstrates how to load an image using the image URL. import skimage from skimage import io # Read an image image = io.imread(”https://www.tutorialspoint.com/images/logo.png”) # Display image properties from the image array print(”The following are the properties of the loaded image:”) print(“Image shape:”, image.shape) print(“Image data type:”, image.dtype) print(“Number of color channels:”, image.shape[2]) Input Image Output The following are the properties of the loaded image: Image shape: (140, 568, 3) Image data type: uint8 Number of color channels: 3 Example 3 The following example demonstrates how to convert color images to grayscale by using the as_gray parameter of the imread() method. import skimage from skimage import io # Read an image image = io.imread(”https://www.tutorialspoint.com/images/logo.png”, as_gray=True) # Display image properties from the image array print(”The following are the properties of the loaded image:”) print(“Image shape:”, image.shape) print(“Image data type:”, image.dtype) Output The following are the properties of the loaded image: Image shape: (140, 568) Image data type: float64

Scikit Image – Using Plotly Plotly in Python is commonly referred to as “plotly.py”. It is a free and open-source plotting library built on top of “plotly.js”. Plotly.py provides a rich set of features and supports more than 40 unique chart types. It is widely used for financial analysis, geographical mapping, scientific visualization, 3D plotting, and data analysis applications. It offers an interactive interface that allows users to explore and interact with data visualizations. It provides functionalities like zooming, panning, tooltips, and hover effects, making it easy to analyse and understand complex datasets. Scikit Image Using Plotly Plotly.py can be used along with the scikit-image library to achieve various data visualization tasks related to image processing. To set up plotly, you need to ensure that the library is installed and properly configured. Installing plotly using pip Execute the below commands in the command prompt to install the plotly module. It is an easy way to install the latest package of Plotly from PyPi. pip install plotly Installing plotly using conda If you”re using the Anaconda distribution already in your system, then you can directly use the conda package manager to install plotly. conda install -c plotly plotly Once Plotly is installed, you can import it into your Python scripts or interactive sessions using the following statement − import plotly This imports the necessary modules from Plotly to create interactive and customizable visualizations. Below are a few basic Python programs that demonstrate how to use the Plotly along with scikit-image to perform data visualization in image processing tasks effectively. Example 1 The following example displays an RBG image using the Plotly.express.imshow() method. import plotly.express as px from skimage import io # Read an image image = io.imread(”Images/Tajmahal.jpg”) # Display the image using Plotly fig = px.imshow(image) fig.show() Output On executing the above program, you will get the following output − Example 2 The following example demonstrates how to apply a circular mask to an image using scikit-image and display the original image and the masked image side by side using Plotly. import matplotlib.pyplot as plt from skimage import io import numpy as np # Load the image image_path = ”Images_/Zoo.jpg” image = io.imread(image_path) image_copy = np.copy(image) # Create circular mask rows, cols, _ = image.shape row, col = np.ogrid[:rows, :cols] center_row, center_col = rows / 2, cols / 2 radius = min(rows, cols) / 2 outer_disk_mask = ((row – center_row)**2 + (col – center_col)**2 > radius**2) # Apply mask to image image[outer_disk_mask] = 0 # Display the original and masked images using Matplotlib fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) axes[0].imshow(image_copy) axes[0].set_title(”Original Image”) axes[0].axis(”off”) axes[1].imshow(image) axes[1].set_title(”Masked Image”) axes[1].axis(”off”) plt.tight_layout() plt.show() Output

Scikit Image – Numpy Images NumPy (also known as “Numerical Python”) is one of the most crucial fundamental packages in Python for numerical computing. The core data structure of NumPy is the ndarray (N-dimensional array), which is a homogeneous collection of elements of the same data type. These arrays can be of any dimension, such as 1D, 2D, or even higher-dimensional arrays. NumPy provides a vast collection of mathematical functions to operate on these N-dimensional arrays efficiently. Images in scikit-image are represented as NumPy ndarrays (multidimensional arrays). The scikit-image library is built on top of NumPy and it uses the NumPy arrays to represent images. Hence, the scikit-image library can perform various image-processing tasks effectively. Representing images as NumPy arrays Representing images as NumPy arrays, provide a convenient and efficient way to store and manipulate image data. Here, the dimensions of the NumPy array correspond to the image dimensions, such as height, width, and color channels. For grayscale images, the array is typically two-dimensional (height x width). For color images, the array is three-dimensional (height x width x 3), where the last dimension represents the Red, Green, and Blue color channels. Example 1 The following example demonstrates how a color image can be represented as a NumPy array in scikit-image. from skimage import io # Read an image as a grayscale image img_array = io.imread(”Images/Dog.jpg”) # Display image properties from the image array print(”The following are the properties of the loaded image:”) print(“Data type of the image object:”, type(img_array)) print(“Image shape:”, img_array.shape) print(“Image data type:”, img_array.dtype) Input Image Output The following are the properties of the loaded image: Data type of the image object: <class ”numpy.ndarray”> Image shape: (479, 500, 3) Image data type: uint8 Example 2 Let”s see the NumPy array representation of a grayscale image. from skimage import io # Read an image as a grayscale image img_array = io.imread(”Images/dog.jpg”, as_gray=True) # Display image properties from the image array print(”The following are the properties of the loaded image:”) print(“Data type of the image object:”, type(img_array)) print(“Image shape:”, img_array.shape) print(“Image data type:”, img_array.dtype) Output The following are the properties of the loaded image: Data type of the image object: <class ”numpy.ndarray”> Image shape: (479, 500) Image data type: float64 Indexing and Slicing NumPy”s indexing and slicing feature can be used to access and manipulate image data. Croping images, selecting specific color channels, or applying operations to specific regions within the image can be possible by using NumPy”s flexible indexing and slicing syntax. Example The following example demonstrates how the indexing and slicing syntax of NumPy can be used to modify an image in Scikit-image. from skimage import io # Read an image as a grayscale image img_array = io.imread(”Images/Tajmahal.jpg”) # Get the value of the pixel at the 10th row and 20th column pixel_value = img_array[10, 20] print(”The pixel at the 10th row and 20th column of the image array”, pixel_value) # Set value 0 to the pixel at the 3rd row and 10th column img_array[3, 10] = 0 # Select a region in the image roi = img_array[100:200, 200:300] # Set the pixel values in the selected region to red (255, 0, 0) roi[:] = (255, 0, 0) # Display the modified image io.imshow(img_array) io.show() Input Image Output Running the above code gives us the following result − The pixel at the 10th row and 20th column of the image array [ 81 97 110] In addition it generates the following image −

Scikit Image – Using Mayavi Mayavi is an application and library for interactive scientific data visualization and 3D plotting in Python. It provides a simple and clean scripting interface in Python for 3D visualization. It offers ready-to-use 3D visualization functionality similar to MATLAB or Matplotlib, especially when using the mlab module. This module provides a high-level interface that allows you to easily create various types of 3D plots and visualizations. Mayavi also offers an object-oriented programming interface, allowing you to have more control and flexibility over your 3D visualizations. And it can work natively and transparently with numpy arrays, which makes it convenient to visualize scientific data stored in NumPy arrays without the need for data conversion or preprocessing. Scikit Image with Mayavi To use Mayavi as a plotting engine in your Python scripts, you can use the mlab scripting API, which provides a simple and convenient way to work with Mayavi and generate TVTK datasets using NumPy arrays or other sequences. Installing Mayavi To set up Mayavi and run the visualizations generated by the code, you need to install PyQt along with the Mayavi library. PyQt is a dependency that provides the necessary graphical user interface (GUI) functionality for displaying the visualizations created with Mayavi. pip install mayavi pip install PyQt5 It is recommended to use pip, the Python Package Installer, for installing Python packages from the PyPI. This installs the latest version of Mayavi available on PyPI. Once the required packages are installed successfully, you can import Mayavi into your Python scripts or interactive sessions using − from mayavi import mlab This imports the necessary modules from Mayavi for 3D visualization and scientific data plotting in your Python scripts. Below are a few basic Python programs that demonstrate how to use the scikit-image along with Mayavi to perform data visualization in image processing tasks effectively. Example 1 The following example demonstrates how to display an image using Mayavi”s mlab.imshow() function. from mayavi import mlab from skimage import io import numpy as np # Read an image image = np.random.random((10, 10)) # Display the masked image using Mayavi mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) mlab.imshow(image) mlab.show() Output Example 2 Here is another example that demonstrates how to use Mayavi and scikit-image (skimage) together to display a grayscale image using Mayavi”s visualization capabilities. from mayavi import mlab from skimage import io # Read an image image = io.imread(”Images/logo-w.png”, as_gray=True) # Display the masked image using Mayavi mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) mlab.imshow(image) mlab.show() Output

Scikit Image – Introduction Scikit-image (also known as skimage) is one of the open-source image-processing libraries for the Python programming language. It provides a powerful toolbox of algorithms and functions for various image processing and computer vision tasks. And it is built on top of popular scientific libraries like NumPy and SciPy.ndimage. Features of scikit-image Following are the main features of Scikit Image − Scikit-image is an open-source package in Python. This means that it is available free of charge and free of restriction. Easy to read and write images of various formats. The library offers multiple plugins and methods to read and write images of various formats, such as JPEG, PNG, TIFF, and more. Images in scikit-image are represented by NumPy ndarrays (multidimentional containers). Hence, many common operations can be achieved using standard NumPy methods for manipulating arrays. It provides a vast collection of image Processing Algorithms such as filtering, segmentation, feature extraction, morphology, and more. And it offers a user-friendly API that simplifies the process of performing image processing tasks. History of scikit-image Scikit-image was initially developed by an active, international team of researchers and contributors. It originated from the combination of several existing image processing projects, including scipy.ndimage, matplotlib, and others. Advantages of scikit-image scikit-image offers several advantages that make it a valuable tool for image processing tasks − Easy Integration with Python”s Scientific Tools − It is built on top of NumPy, SciPy, and other scientific libraries. This enables users to combine image processing with other scientific computing tasks, such as data analysis, machine learning, and visualization. Comprehensive Image Processing Tools − scikit-image provides a wide range of tools and algorithms for image processing tasks. It includes comprehensive image filters, morphological operations, image transformations, feature extraction, and more. These tools allow users to perform complex image processing operations with ease and flexibility. User-Friendly Visualization − scikit-image includes a simple graphical user interface (GUI) for visualizing results and exploring parameters. Scikit Image – Environmental setup To set up the environment for scikit-image, it is recommended to use a package manager such as pip or conda to install scikit-image and its dependencies. pip is the default package manager for Python, while Conda is a popular choice for managing packages in Anaconda environments. Installing scikit-image using pip To install scikit-image using pip, just run the below command in your command prompt − pip install scikit-image This will download the scikit-image package, wait for download completion. If you see any pip up-gradation error, then just upgrade the pip by the following command − python -m pip install –upgrade pip And run “pip install scikit-image” command again, this time it will work. Installing scikit-image using Conda If you”re using the Anaconda distribution already in your system then you can directly use the conda package manager to install scikit-image. Following is the command − conda install scikit-image If the scikit-image package is already installed on your computer, running the conda install scikit-image command will display the below message − Collecting package metadata (current_repodata.json): …working… done Solving environment: …working… done # All requested packages already installed. Retrieving notices: …working… done Note: you may need to restart the kernel to use updated packages. Verification To check whether scikit-image is already installed or to verify if an installation has been successful, you can execute the following code in a Python shell or Jupyter Notebook − import skimage # Check the version of scikit-image print(“scikit-image version:”, skimage.__version__) If the above code executes without any errors, it means that scikit-image is installed successfully and ready to be used.