安装 OpenCV：
Python ：直接 pip install opencv-python（核心库）和 opencv-contrib-python（扩展功能）。
bash
pip install opencv-python
pip install opencv-contrib-python验证安装：
python
import cv2
print(cv2.__version__) # 输出版本号以下代码来源于：链接
python
if __name__ == '__main__':
# 读取图像
image = cv2.imread('1.png')
# 显示图像
# cv2.imshow('Image', image)
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print('gray_image = ', gray_image)
# cv2.imshow('Image', gray_image)
edges = cv2.Canny(gray_image, threshold1=50, threshold2=150)
print('edges = ', edges)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
dilated = cv2.dilate(edges, kernel, iterations=1)
cv2.imshow('Image', dilated)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 特征检测
# image = cv2.imread('3.png')
# orb = cv2.ORB_create()
# keypoints, descriptors = orb.detectAndCompute(image, None)
# image_with_keypoints = cv2.drawKeypoints(image, keypoints, None, color=(0, 255, 0))
# cv2.imshow('Keypoints', image_with_keypoints)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# 人脸检测
# image = cv2.imread('3.png')
# face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
# for (x, y, w, h) in faces:
# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# cv2.imshow('Faces', image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# 交通标志识别
# image = cv2.imread('5.png')
#
# gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# edges = cv2.Canny(gray_image, threshold1=50, threshold2=150)
#
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
# dilated = cv2.dilate(edges, kernel, iterations=1)
# contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for contour in contours:
# if cv2.contourArea(contour) > 100:
# x, y, w, h = cv2.boundingRect(contour)
# cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
#
# cv2.imshow('Traffic Sign Detection', image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# 显示图像
# image = cv2.imread('3.png')
# gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# # 下面这行代码效果等价于上面两行代码
# gray_scare_image = cv2.imread('3.png', cv2.IMREAD_GRAYSCALE)
# ret, binary = cv2.threshold(gray_scare_image, 127, 255, cv2.THRESH_BINARY)
# ret_inv, binary_inv = cv2.threshold(gray_scare_image, 127, 255, cv2.THRESH_BINARY_INV)
# cv2.imshow('gray_image', gray_image)
# cv2.imshow('gray_scare_image', gray_scare_image)
# cv2.imshow('binary', binary)
# cv2.imshow('binary_inv', binary_inv)
# cv2.waitKey(0)
# cv2.destroyAllWindows()这个链接也看了：链接
拆分通道：
python
# # 显示图像
# image = cv2.imread('3.png')
# cv2.imshow('Image', image)
# gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# cv2.imshow('gray_image', gray_image)
# hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# cv2.imshow('hsv_image', hsv_image)
# yuv_image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)
# cv2.imshow('yuv_image', yuv_image)
#
# # BGR通道拆分显示
# b, g, r = cv2.split(image)
# cv2.imshow("BGR - B", b)
# cv2.imshow("BGR - G", g)
# cv2.imshow("BGR - R", r)
#
# # HSV通道拆分显示
# h, s, v = cv2.split(hsv_image)
# cv2.imshow("HSV - H", h) # 色相（颜色种类）
# cv2.imshow("HSV - S", s) # 饱和度（颜色鲜艳程度）
# cv2.imshow("HSV - V", v) # 明度（亮度）
#
# # YUV通道拆分显示
# y, u, v = cv2.split(yuv_image)
# cv2.imshow("YUV - Y", y) # 亮度（类似灰度图）
# cv2.imshow("YUV - U", u) # 蓝色色度
# cv2.imshow("YUV - V", v) # 红色色度腐蚀与膨胀：
python
image = cv2.imread('3.png')
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
# 腐蚀（Erosion）：将图像中的白色区域收缩。
eroded_img = cv2.erode(image, kernel, iterations=1)
# 膨胀（Dilation）：将图像中的白色区域扩展。
dilated_img = cv2.dilate(image, kernel, iterations=1)
# 开运算（先腐蚀再膨胀）：用于去除小物体。
opening_img = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
# 闭运算（先膨胀再腐蚀）：用于填补图像中的小孔洞。
closing_img = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)
cv2.imshow('Image', image)
cv2.imshow('eroded_img', eroded_img)
cv2.imshow('dilated_img', dilated_img)
cv2.imshow('opening_img', opening_img)
cv2.imshow('closing_img', closing_img)
cv2.waitKey(0)
cv2.destroyAllWindows()