Image Manipulation and Transformation with Python by Sandaruwan Herath Data Science and Machine Learning

NumPy is one of the core libraries in Python programming and provides support for arrays. An image is essentially a standard NumPy array containing pixels of data points. Therefore, by using basic NumPy operations, such as slicing, masking and fancy indexing, we can modify the pixel values of an image. You can then load the image using skimage and display it using Matplotlib. Therefore, by using basic NumPy operations, such as slicing, masking, and fancy indexing, you can modify the pixel values of an image.

Example of Comparison Operators in Python

If you specify outside the size of the image, it will be ignored. For example, in the case of JPG, you can pass the quality of the image as the argument quality. It ranges from 1 (the lowest) to 95 (the highest) and defaults to 75. With save(), parameters according to the format can be passed as arguments. Note that if the pixel value is represented by 0.0 to 1.0, it is necessary to multiply by 255 and convert to uint8 and save. Np.array() returns a rewritable ndarray, while np.asarray() returns a read-only ndarray.

Python Relational Operators

Its efficient implementations make it a popular choice for both research and industrial applications in fields like robotics, surveillance, and augmented reality. Python offers a rich ecosystem of libraries for image processing, to meet the diverse needs and expertise levels. OpenCV stands out as a powerful tool for real-time computer vision tasks, while Scikit-Image provides a comprehensive suite of algorithms for image processing. Pillow (PIL) simplifies basic image manipulation tasks with its user-friendly interface, while SciPy offers advanced functionalities for scientific computing, including image filtering and segmentation.

  1. Being aware of these nuances will help you use pd.concat more effectively and avoid common pitfalls that might lead to unexpected results or performance issues.
  2. If you prefer to remove this outline, you can crop the image using .crop().
  3. Allows you to run shell commands directly from the notebook cells.
  4. In this video course, you’ll learn how to manipulate images and perform basic image processing using the Python Pillow library.
  5. You can change the sensitivity of the thresholding process by varying the threshold value.

Rank Filters

In this comprehensive tutorial, you have learned how to use the pandas concat function to merge and combine data efficiently in Python. We started with a basic introduction to pd.concat, exploring its fundamental capabilities to concatenate pandas objects along a particular axis. This included simple examples of vertical and horizontal concatenations, which demonstrated how to combine DataFrame objects row-wise and column-wise. image manipulation By the end of this guide, you’ll be able to seamlessly integrate datasets from various sources, handle different types of data alignment issues, and optimize your data analysis workflow with pandas concat. This project involves implementing various image processing tasks using the OpenCV library in Python. You’ll start by loading an image into an OpenCV Mat variable and displaying it as a grayscale image.

In the earlier section discussing kernels, the box blur filter that you used was a 3×3 filter. This means that it had a radius of 1, because the filter extends by one pixel from the center. The Python Pillow library is a fork of an older library called PIL. PIL stands for Python Imaging Library, and it’s the original library that enabled Python to deal with images. To use its developers’ own description, Pillow is the friendly PIL fork that kept the library alive and includes support for Python 3. Get your FREE 17 page Computer Vision, OpenCV, and Deep Learning Resource Guide PDF.

SciPy is another of Python’s core scientific modules (like NumPy) and can be used for basic image manipulation and processing tasks. In particular, the submodule scipy.ndimage (in SciPy v1.1.0) provides functions operating on n-dimensional NumPy arrays. The package currently includes functions for linear and non-linear filtering, binary morphology, B-spline interpolation, and object measurements.

The required libraries include numpy for array manipulations, for image reading, skimage.color for color space conversions, and matplotlib.pylab for displaying images. In addition to Image, you also import the ImageFilter module from Pillow. You can use the .filter() method to apply filtering to the image. This method needs a convolution kernel as its argument, and you can use one of the several kernels available in the ImageFilter module in Pillow. The first set of filters that you’ll learn about deal with blurring, sharpening, and smoothing an image. The factor of 1/9 is there so that the overall weighting of the kernel is 1.

Python enables image processing through libraries like OpenCV, PIL, and scikit-image for diverse applications. As a result of the calculation, the data type dtype of numpy.ndarray is converted to the floating point number float. A grayscale image (2D array) can also be passed to Image.fromarray(). If you convert the image to grayscale with convert(‘L’) and then pass it to np.array(), it returns 2D ndarray whose shape is (row (height), column (width)). Even when using OpenCV, OpenCV for Python treats image data as ndarray, so it is useful to know how to use NumPy (ndarray). In addition to OpenCV, many libraries such as scikit-image treat images as ndarray.

For more advanced effects, you might want to explore other filters, color adjustments, and image processing techniques. Hello, welcome to my writing experience with the medium platform, at this time I will try to do a little bit of image manipulation in Python using the Pillow library. I am excited to leverage Pillow’s power to perform a variety of image processing tasks, create stunning visual effects, and bring imaginations to life through digital imagery.

The red square starts in a position displaced to the top-left of the center. In each successive frame, the red square moves closer to the center until it reaches the center in the final iteration of the loop. The blue square is initially shifted toward the bottom-right then moves towards the center with each iteration. You’ve segmented the image of the cat and extracted the cat from its background. Therefore, all the multiplications from step 2 will be zero, and their addition will also be zero. This function was used to generate all the displays that show more than one image in this tutorial.

In this example, one of the images is a JPEG image and the other is a PNG image. The extension that you use as a filname automatically determines the file format, or you can specify the format as an additional optional argument. In the code above, you also change the resolution of the cropped image using .resize(), which needs a tuple as a required argument.

It consists of a grid of pixels, where each pixel contains information about color and intensity. Images can be photographs, graphics, or scans, and they serve as a fundamental medium for visual communication and information representation. Matplotlib offers a wide range of visualization capabilities, but it is not specialized for image processing.

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