Basic Image Processing with OpenCV: Reading, Displaying, and Manipulating Images in Python

Goal: Learn basic image processing techniques.

Learning content: OpenCV basics, practice using OpenCV for image reading, display, and basic processing.

Code examples include the following steps:

1. Import required libraries

import cv2

import numpy as np

import matplotlib.pyplot as plt

2. Image reading

# 读取图像

image_path = 'path_to_your_image.jpg'  # 替换为你的图像路径

image = cv2.imread(image_path)

# 检查图像是否成功读取

if image is None:

print("图像读取失败，请检查路径是否正确。")

else:

print("图像读取成功！")

3. Image display

# 使用 OpenCV 显示图像

cv2.imshow('原图', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示图像

plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

plt.title('原图')

plt.axis('off')

plt.show()

4. Image basic information

# 获取图像的基本信息

height, width, channels = image.shape

print(f"图像高度: {height} 像素")

print(f"图像宽度: {width} 像素")

print(f"图像通道数: {channels}")

5. Image grayscale conversion

# 将图像转换为灰度图像

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 显示灰度图像

cv2.imshow('灰度图', gray_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示灰度图像

plt.imshow(gray_image, cmap='gray')

plt.title('灰度图')

plt.axis('off')

plt.show()

6. Image cropping

# 裁剪图像

cropped_image = image[100:400, 100:400]

# 显示裁剪后的图像

cv2.imshow('裁剪图', cropped_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示裁剪后的图像

plt.imshow(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB))

plt.title('裁剪图')

plt.axis('off')

plt.show()

7. Image resizing

# 缩放图像

resized_image = cv2.resize(image, (width // 2, height // 2))

# 显示缩放后的图像

cv2.imshow('缩放图', resized_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示缩放后的图像

plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))

plt.title('缩放图')

plt.axis('off')

plt.show()

8. Image rotation

# 旋转图像

center = (width // 2, height // 2)

angle = 45

scale = 1.0

rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale)

rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))

# 显示旋转后的图像

cv2.imshow('旋转图', rotated_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示旋转后的图像

plt.imshow(cv2.cvtColor(rotated_image, cv2.COLOR_BGR2RGB))

plt.title('旋转图')

plt.axis('off')

plt.show()

9. Image flipping

# 翻转图像

flipped_image = cv2.flip(image, 1)  # 1 表示水平翻转，0 表示垂直翻转，-1 表示水平和垂直翻转

# 显示翻转后的图像

cv2.imshow('翻转图', flipped_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 使用 Matplotlib 显示翻转后的图像

plt.imshow(cv2.cvtColor(flipped_image, cv2.COLOR_BGR2RGB))

plt.title('翻转图')

plt.axis('off')

plt.show()

10. Image saving

# 保存处理后的图像

output_path = 'processed_image.jpg'

cv2.imwrite(output_path, flipped_image)

print(f"处理后的图像已保存到 {output_path}")

Summary: After completing these exercises, you should be able to perform basic image processing tasks with OpenCV, including reading, displaying, converting to grayscale, cropping, resizing, rotating, flipping, and saving images.