Python OpenCV中常用图片处理函数小结

pip install opencv-python

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 显示图像
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 保存图像
cv2.imwrite('output.jpg)

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 调整图像大小为宽度为300像素，高度自动调整
resized_image = cv2.resize(image, (300, 0))

# 显示调整大小后的图像
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 将图像从BGR颜色空间转换为灰度颜色空间
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 显示灰度图像
cv2.imshow('Gray Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 高斯滤波
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

# 显示滤波后的图像
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 将图像转换为灰度图像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 使用Canny边缘检测算法检测边缘
edges = cv2.Canny(gray_image, 100, 200)

# 显示边缘检测结果
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# 读取图像
image = cv2.imread('example.jpg')

# 获取图像的高度和宽度
height, width = image.shape[:2]

# 旋转图像90度
rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

# 定义旋转矩阵（逆时针旋转45度）
rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), 45, 1)

# 进行仿射变换
affine_transformed_image = cv2.warpAffine(image, rotation_matrix, (width, height))

# 显示旋转后的图像和仿射变换后的图像
cv2.imshow('Rotated Image', rotated_image)
cv2.imshow('Affine Transformed Image', affine_transformed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image = cv2.imread('example.jpg')

# 将图像转换为灰度图像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 使用Canny边缘检测算法检测边缘
edges = cv2.Canny(gray_image, 100, 200)

# 寻找图像中的轮廓
contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
contour_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 2)

# 显示轮廓图像
cv2.imshow('Contours', contour_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像
image1 = cv2.imread('example1.jpg')
image2 = cv2.imread('example2.jpg')

# 将图像转换为灰度图像
gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)

# 创建SIFT检测器
sift = cv2.SIFT_create()

# 检测关键点和描述符
keypoints1, descriptors1 = sift.detectAndCompute(gray_image1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray_image2, None)

# 创建匹配器
bf = cv2.BFMatcher()

# 使用KNN匹配
matches = bf.knnMatch(descriptors1, descriptors2, k=2)

# 应用比率测试
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append([m])

# 绘制匹配结果
match_image = cv2.drawMatchesKnn(image1, keypoints1, image2, keypoints2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# 显示匹配结果
cv2.imshow('Matches', match_image)
cv2.waitKey(0)
cv2.destroyAllWindows()