pip install opencv-python

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 顯示圖像
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 保存圖像
cv2.imwrite('output.jpg)

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 調(diào)整圖像大小為寬度為300像素，高度自動(dòng)調(diào)整
resized_image = cv2.resize(image, (300, 0))

# 顯示調(diào)整大小后的圖像
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 將圖像從BGR顏色空間轉(zhuǎn)換為灰度顏色空間
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 顯示灰度圖像
cv2.imshow('Gray Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 高斯濾波
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

# 顯示濾波后的圖像
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 將圖像轉(zhuǎn)換為灰度圖像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 使用Canny邊緣檢測(cè)算法檢測(cè)邊緣
edges = cv2.Canny(gray_image, 100, 200)

# 顯示邊緣檢測(cè)結(jié)果
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# 讀取圖像
image = cv2.imread('example.jpg')

# 獲取圖像的高度和寬度
height, width = image.shape[:2]

# 旋轉(zhuǎn)圖像90度
rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

# 定義旋轉(zhuǎn)矩陣（逆時(shí)針旋轉(zhuǎn)45度）
rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), 45, 1)

# 進(jìn)行仿射變換
affine_transformed_image = cv2.warpAffine(image, rotation_matrix, (width, height))

# 顯示旋轉(zhuǎn)后的圖像和仿射變換后的圖像
cv2.imshow('Rotated Image', rotated_image)
cv2.imshow('Affine Transformed Image', affine_transformed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像
image = cv2.imread('example.jpg')

# 將圖像轉(zhuǎn)換為灰度圖像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 使用Canny邊緣檢測(cè)算法檢測(cè)邊緣
edges = cv2.Canny(gray_image, 100, 200)

# 尋找圖像中的輪廓
contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 繪制輪廓
contour_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 2)

# 顯示輪廓圖像
cv2.imshow('Contours', contour_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 讀取圖像
image1 = cv2.imread('example1.jpg')
image2 = cv2.imread('example2.jpg')

# 將圖像轉(zhuǎn)換為灰度圖像
gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)

# 創(chuàng)建SIFT檢測(cè)器
sift = cv2.SIFT_create()

# 檢測(cè)關(guān)鍵點(diǎn)和描述符
keypoints1, descriptors1 = sift.detectAndCompute(gray_image1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray_image2, None)

# 創(chuàng)建匹配器
bf = cv2.BFMatcher()

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

# 應(yīng)用比率測(cè)試
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append([m])

# 繪制匹配結(jié)果
match_image = cv2.drawMatchesKnn(image1, keypoints1, image2, keypoints2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

# 顯示匹配結(jié)果
cv2.imshow('Matches', match_image)
cv2.waitKey(0)
cv2.destroyAllWindows()