How to use Python to distort images

How to use Python to distort pictures

Introduction:
The picture distortion effect is a common image processing technique that can change the shape and shape of the image. Deformation. Python has a powerful image processing library that makes it very simple to achieve distortion effects. This article will introduce how to use Python to realize the distortion effect of images, and attach a code example.

1. Install the required libraries
Before we start, we need to install some Python libraries. Here we use the Pillow library to process images and the NumPy library to process image data. First we need to install these two libraries:

pip install pillow
pip install numpy

After the installation is complete, we can start writing code.

2. Import the required libraries
First, we need to import the required libraries:

from PIL import Image
import numpy as np

3. Load and convert images
Before starting image processing, we need to load and convert the image to a NumPy array. The following code shows how to load an image and convert it to a NumPy array:

# 加载图像
image = Image.open('input.jpg')

# 转换为NumPy数组
image_array = np.array(image)

4. Create a distortion effect
There are many ways to achieve an image distortion effect, the most common of which is to use the fisheye effect . The following code shows how to achieve the fisheye effect:

# 创建鱼眼效果
def create_fisheye(image_array, strength):
    height, width, channels = image_array.shape
    cy, cx = height // 2, width // 2
    radius = min(cy, cx)

    result = np.zeros_like(image_array)

    for y in range(height):
        for x in range(width):
            dy = y - cy
            dx = x - cx
            distance = np.sqrt(dx * dx + dy * dy)
            if distance < radius:
                theta = np.arctan2(dy, dx)
                r = distance / radius
                r = r ** strength * radius
                nx = cx + r * np.cos(theta)
                ny = cy + r * np.sin(theta)
                result[y, x] = image_array[int(ny), int(nx)]

    return result

In the above code, we first calculate the center coordinates (cx, cy) and radius of the image, and then traverse each pixel of the image to calculate the current The distance from the pixel point to the center point. For points whose distance is less than the radius, we calculate the position (nx, ny) of the new pixel based on the distance and strength value, and then assign the pixel at the corresponding position of the original image to the new pixel. Finally return the new image array.

5. Save the results and display them
The last step is to save the results and display them. The following code shows how to save the results and display the image using the Matplotlib library:

# 创建扭曲效果
distorted_image_array = create_fisheye(image_array, strength=0.5)

# 转换为PIL图像
distorted_image = Image.fromarray(distorted_image_array)

# 保存结果
distorted_image.save('distorted.jpg')

# 展示图像
import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2)

axes[0].imshow(image)
axes[0].set_title('Original Image')
axes[0].axis('off')

axes[1].imshow(distorted_image)
axes[1].set_title('Distorted Image')
axes[1].axis('off')

plt.show()

In the above code, we first call the create_fisheye function to create the distortion effect. We then convert the warped image array into a PIL image and save it locally. Finally, we use the Matplotlib library to display the original image and the distorted image.

6. Summary
This article introduces how to use Python to distort images, and attaches corresponding code examples. By studying this article, readers can master how to use Python for image processing and further expand their skills in image processing.

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