Easily Manipulate pillow Image

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Image Processing With the Python Pillow Library

When you look at an image, you see the objects and people in it. However, when you read an image programmatically with Python or any other language, the computer sees an array of numbers. In this tutorial, you’ll learn how to manipulate images and perform basic image processing using the Python Pillow library.

Pillow and its predecessor, PIL, are the original Python libraries for dealing with images. Even though there are other Python libraries for image processing, Pillow remains an important tool for understanding and dealing with images.

To manipulate and process images, Pillow provides tools that are similar to ones found in image processing software such as Photoshop. Some of the more modern Python image processing libraries are built on top of Pillow and often provide more advanced functionality.

In this tutorial, you’ll learn how to:

Read images with Pillow
Perform basic image manipulation operations
Use Pillow for image processing
Use NumPy with Pillow for further processing
Create animations using Pillow

This tutorial provides an overview of what you can achieve with the Python Pillow library through some of its most common methods. Once you gain confidence using these methods, then you can use Pillow’s documentation to explore the rest of the methods in the library. If you’ve never worked with images in Python before, this is a great opportunity to jump right in!

In this tutorial, you’ll use several images, which you can download from the tutorial’s image repository:

With those images in hand, you’re now ready to get started with Pillow.

Basic Image Operations With the Python Pillow Library

The Python Pillow library is a fork of an older library called PIL. PIL stands for Python Imaging Library, and it’s the original Python library for image processing. Pillow was created as a modern alternative to PIL, with added features and improvements.

Pillow provides the Image module, which is responsible for reading images in various formats and representing them as Image objects. The Image class provides methods for manipulating and saving images.

To begin, you’ll need to install Pillow. Open a terminal or command prompt and run the following command:

pip install pillow

With Pillow installed, you can start working with images in Python. Open a new Python script or interactive shell, and import the Image module:

from PIL import Image

The Image Module and Image Class in Pillow

The Image module is the main entry point for working with images in Pillow. It contains the Image class, which represents an image and provides methods for manipulating it.

To open an image file with Pillow, you can use the Image.open() method. Provide the path to the image file as an argument:

image = Image.open('path/to/image.jpg')

This will create an Image object named image from the specified image file. You can now perform various operations on this image.

Basic Image Manipulation

Pillow provides several basic image manipulation methods that you can use to modify images. Here are a few examples:

Resize: You can resize an image to a specific width and height using the resize() method:
```
resized_image = image.resize((width, height))
```
Crop: You can crop an image to a specific rectangular region using the crop() method:
```
cropped_image = image.crop((left, upper, right, lower))
```
Rotate: You can rotate an image by a specified angle using the rotate() method:
```
rotated_image = image.rotate(angle)
```

These are just a few examples of the basic image manipulation methods available in Pillow. You can explore the documentation to learn more about the different methods and their parameters.

Bands and Modes of an Image in the Python Pillow Library

In Pillow, images are represented as a collection of bands, where each band represents a different channel of information. For example, a colored image may have separate bands for the red, green, and blue channels.

The number and types of bands in an image depend on its mode. The mode of an image defines how pixel values are represented. Here are some examples of common image modes:

L: Luminance (grayscale)
RGB: Red, green, blue
RGBA: Red, green, blue, alpha (transparency)

You can check the mode of an image using the mode attribute:

mode = image.mode

You can also split the bands of an image into separate Image objects using the split() method:

bands = image.split()

This will return a tuple containing separate Image objects for each band.

Understanding bands and modes is important when performing advanced image processing tasks, such as applying filters or manipulating individual channels.

Image Processing Using Pillow in Python

Now that you know the basics of working with images in Pillow, let’s dive into some more advanced image processing techniques.

Image Filters Using Convolution Kernels

Image filters are operations that manipulate pixels in an image to achieve various effects. Pillow provides several built-in filters that you can apply to an image.

To apply a filter, you’ll need to use a convolution kernel. A convolution kernel is a matrix of numbers that specifies how pixels are combined to compute a new value. Different convolution kernels produce different effects.

Here’s an example of applying a blur filter to an image using the GaussianBlur filter:

from PIL import ImageFilter

blurred_image = image.filter(ImageFilter.GaussianBlur(radius))

You can experiment with different filters and parameters to achieve different effects.

Image Blurring, Sharpening, and Smoothing

Blurring, sharpening, and smoothing are common image processing techniques used to enhance or alter an image.

Pillow provides methods for blurring, sharpening, and smoothing an image. Here are some examples:

Blur: You can blur an image using the filter() method with the BLUR or GAUSSIAN_BLUR filter:
```
blurred_image = image.filter(ImageFilter.BLUR)
```
Sharpen: You can sharpen an image using the filter() method with the SHARPEN filter:
```
sharpened_image = image.filter(ImageFilter.SHARPEN)
```
Smooth: You can smooth an image using the filter() method with the SMOOTH filter:
```
smoothed_image = image.filter(ImageFilter.SMOOTH)
```

Experiment with different values and combinations of these filters to achieve the desired effect.

Edge Detection, Edge Enhancement, and Embossing

Edge detection, edge enhancement, and embossing are techniques used to highlight or emphasize the edges of objects in an image.

Pillow provides methods for performing these operations. Here are a few examples:

Edge Detection: You can perform edge detection using the filter() method with the FIND_EDGES filter:
```
edges_image = image.filter(ImageFilter.FIND_EDGES)
```
Edge Enhancement: You can enhance the edges of an image using the filter() method with the EDGE_ENHANCE filter:
```
enhanced_image = image.filter(ImageFilter.EDGE_ENHANCE)
```
Embossing: You can create an embossed effect using the filter() method with the EMBOSS filter:
```
embossed_image = image.filter(ImageFilter.EMBOSS)
```

Play around with these techniques to see how they can bring out the details and edges of objects in an image.

Image Segmentation and Superimposition: An Example

Image segmentation is the process of dividing an image into multiple segments or regions. Superimposition is the process of combining multiple images into one.

In this section, you’ll learn how to perform image segmentation and superimposition using Pillow.

Image Thresholding

Image thresholding is a technique used to separate objects from the background in an image. It works by converting an image to black and white, where pixels with values above a certain threshold are set to white, and pixels below the threshold are set to black.

Here’s an example of performing image thresholding using Pillow:

threshold = 150
thresholded_image = image.convert('L').point(lambda x: 255 if x > threshold else 0, mode='1')

This will convert the image to grayscale ('L' mode), and then apply a custom thresholding function to convert the pixels to either black or white.

Erosion and Dilation

Erosion and dilation are morphological operations used to modify the shape and size of objects in an image.

Pillow provides methods for erosion and dilation. Here’s an example:

from PIL import ImageOps

eroded_image = ImageOps.erode(image, size)

This will erode the image by a specified size, shrinking the objects and removing small details.

Image Segmentation Using Thresholding

Now that you have learned about image thresholding, erosion, and dilation, you can combine these techniques to perform image segmentation.

thresholded_image = image.convert('L').point(lambda x: 255 if x > threshold else 0, mode='1')
segmented_image = ImageOps.erode(thresholded_image, size)

This will convert the image to grayscale, apply a threshold, and then perform erosion to segment the objects in the image.

Superimposition of Images Using Image.paste()

Superimposing images involves combining multiple images into one by placing one image onto another. Pillow provides the paste() method, which allows you to overlay one image onto another.

Here’s an example of superimposing two images using Pillow:

background = Image.open('path/to/background.jpg')
overlay = Image.open('path/to/overlay.png')

background.paste(overlay, (x, y), mask=overlay)

This will open two images, background and overlay, and superimpose overlay onto background at the specified position (x, y). The optional mask parameter is used to specify a transparency mask for the overlay.

Creation of A Watermark

Watermarking is the process of adding a visible mark or logo to an image to identify its source or protect its copyright.

Pillow provides methods for creating watermarks. Here’s an example:

watermark = Image.open('path/to/watermark.png')

watermarked_image = background.copy()
watermarked_image.paste(watermark, (x, y), mask=watermark)

This will open a watermark image and create a copy of the background image. It then pastes the watermark onto the copy at the specified position (x, y).

Image Manipulation With NumPy and Pillow

NumPy is a powerful Python library for numerical computing. When combined with Pillow, you can perform advanced image manipulation and processing tasks.

Using NumPy to Subtract Images From Each Other

NumPy provides functions for performing mathematical operations on arrays, including images. Here’s an example of using NumPy to subtract one image from another:

import numpy as np

image1 = np.array(image1)
image2 = np.array(image2)

subtracted_image = np.abs(image1 - image2)

This will convert the images to NumPy arrays, subtract one from the other, and then take the absolute value of the result to ensure positive values.

Using NumPy to Create Images

NumPy arrays can be used to create new images from scratch. Here’s an example:

import numpy as np

width, height = 500, 500
image = np.zeros((height, width, 3), dtype=np.uint8)

# Draw a rectangle
image[100:400, 200:300] = [255, 0, 0]  # Set color to blue

This will create a black image with the specified width and height, and then draw a blue rectangle on it.

Creating Animations

By combining NumPy and Pillow, you can create animations from a sequence of images. Here’s an example:

import numpy as np
from PIL import Image

images = []  # List of image files

# Load images and convert to NumPy arrays
for image_file in images:
    image = Image.open(image_file)
    array = np.array(image)
    images.append(array)

# Save the images as an animated GIF
Image.fromarray(np.stack(images)).save('animation.gif', format='GIF')

This will load a sequence of images, convert them to NumPy arrays, and then save them as an animated GIF.

Conclusion

In this tutorial, you’ve learned how to manipulate and process images using the Python Pillow library. You’ve seen how to perform basic image operations, apply filters, perform image segmentation and superimposition, and manipulate images using NumPy. With these techniques, you can explore and expand your knowledge of image processing in Python.

Remember that Pillow provides many more methods and functionality than what was covered in this tutorial. Make sure to refer to Pillow’s documentation for a comprehensive guide to all the available features and methods.

Now that you have a good understanding of how to work with images in Python, you can use Pillow to explore and experiment with different image processing tasks and techniques.