How to segment images using Python

How to use Python to segment images

Image segmentation is a commonly used technology in the field of computer vision. It divides an image into multiple independent image areas so that the pixels in each area have similar characteristics. Image segmentation has wide application value in recognition, target detection, image processing and other applications. This article will introduce how to use Python to segment images, and attach code examples.

First, we need to install Python’s image processing library Pillow. Pillow can help us load, process, and save images. You can install Pillow through the following command:

pip install pillow

After installing Pillow, we can start the practice of image segmentation. First, we need to import the necessary libraries:

from PIL import Image
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt

Next, we define a function to load the image and convert it into an array:

def load_image(image_path):
    image = Image.open(image_path)
    return np.array(image)

Then, we define a function to perform image segmentation ：

def image_segmentation(image, num_segments):
    height, width, _ = image.shape
    image_flat = image.reshape((-1, 3))
    kmeans = KMeans(n_clusters=num_segments, random_state=0).fit(image_flat)
    labels = kmeans.labels_
    image_segmented = np.zeros_like(image_flat)
    for segment in range(num_segments):
        image_segmented[labels == segment] = kmeans.cluster_centers_[segment]
    image_segmented = image_segmented.reshape((height, width, 3))
    return image_segmented

In the above code, we use the KMeans algorithm to cluster the image pixels and determine the area for image segmentation. Then, we attribute each pixel to the corresponding cluster center and generate image segmentation results.

Finally, we define a function to display the results of image segmentation:

def show_image(image):
    plt.imshow(image.astype(np.uint8))
    plt.axis('off')
    plt.show()

Now, we can combine the functions defined above to conduct image segmentation experiments. The following is the complete sample code:

from PIL import Image
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt

def load_image(image_path):
    image = Image.open(image_path)
    return np.array(image)

def image_segmentation(image, num_segments):
    height, width, _ = image.shape
    image_flat = image.reshape((-1, 3))
    kmeans = KMeans(n_clusters=num_segments, random_state=0).fit(image_flat)
    labels = kmeans.labels_
    image_segmented = np.zeros_like(image_flat)
    for segment in range(num_segments):
        image_segmented[labels == segment] = kmeans.cluster_centers_[segment]
    image_segmented = image_segmented.reshape((height, width, 3))
    return image_segmented

def show_image(image):
    plt.imshow(image.astype(np.uint8))
    plt.axis('off')
    plt.show()

image_path = "image.jpg"
num_segments = 4

image = load_image(image_path)
image_segmented = image_segmentation(image, num_segments)
show_image(image_segmented)

In the above example, we loaded an image named "image.jpg" and divided it into 4 areas. Finally, we show the results of image segmentation.

To summarize, this article introduces how to use Python to segment images. We used the Pillow library to load and save images, the KMeans algorithm for image segmentation, and finally displayed the segmentation results. I hope this article will help you understand the principles and practices of image segmentation.

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