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In this article, we’ll talk about how to use the Pillow library to analyze and process images based on their content. The ability to analyze and process images can be very useful in various applications, such as object detection, motion tracking, pattern recognition, and image quality enhancement.
With Pillow, we can perform various operations to analyze and process images. Below are some examples, oh! Put the code inside a code block:
Resizing an image
In this code, something very useful is done: an image is resized to a specific size. Why is it important to resize an image? Well, sometimes you need an image to have a specific size to fit a design or to display correctly on a web page. Or maybe you need to reduce the size of an image to take up less space on the hard drive. Whatever the case may be, resizing an image is a very common task in image editing.
This code uses the PIL (Python Imaging Library) to open an image in Python. The original image is specified as ‘image.jpg’, but you can change this name to match the name of the image you want to resize. After opening the original image, a variable called ‘size’ is created that contains the desired size of the resized image. In this case, the size is 224 x 224 pixels. You can change this size to any other size you need for your project.
Once the desired size has been defined, the ‘resize()’ method from the PIL library is used to resize the original image to that size. The result is stored in the variable ‘image’. It is important to note that this method changes the size of the original image and creates a new image with the desired size.
Finally, the resized image is saved using the ‘save()’ method from the PIL library. The image is saved with the name ‘imagen_resized.jpg’, but you can change this name to any other name you wish.
This code is very simple, but it is very useful for image editing in Python. If you need to resize images for a project, you can use this code as a starting point and adjust it to your needs.
from PIL import Image # Open the original image image = Image.open('image.jpg') # Resize the image to a specific size size = (224, 224) image = image.resize(size) # Save the resized image image.save('imagen_resized.jpg')
Once we have resized the image, we can use Keras to detect objects. Keras has pre-trained models for object detection, such as the YOLO model. To use the YOLO model in Keras, we can load the pre-trained model and then use the predict() method to detect objects in an image.
Contours and shapes
Contours are the lines that delimit the shape of an object in an image. To detect contours in an image with Pillow, we can use the find_contours() method from the scikit-image library.
The first thing we have to do is to install the dependencies, in this case scikit-image and Pillow:
pip install scikit-image pip install Pillow
And here’s the code:
from PIL import Image, ImageDraw from skimage import measure import numpy as np # Open the image image = Image.open('image.jpg') # Convert the image to grayscale gray_image = image.convert('L') # Convert the image to a numpy array image_array = np.array(gray_image) # Detect the contours contours = measure.find_contours(image_array, 0.8) # Draw the contours on the original image draw = ImageDraw.Draw(image) for contour in contours: for i in range(len(contour) - 1): draw.line((contour[i], contour[i], contour[i+1], contour[i+1]), fill='red', width=2) # Show the image with the contours image.show()
Improving image quality
The quality of an image can be improved in various ways, such as increasing contrast, saturation, and sharpness. To increase the contrast of an image with Pillow, we can use the enhance() method.
from PIL import Image, ImageEnhance # Open the image image = Image.open('image.jpg') # Increase the contrast enhancer = ImageEnhance.Contrast(image) image = enhancer.enhance(1.5) # Show the image with increased contrast image.show()
These are just some examples of how to use Pillow to analyze and process images based on their content. Pillow is a very powerful and versatile library that allows us to perform a wide variety of image processing operations.