安装 OpenCV：

Python：直接 pip install opencv-python（核心库）和 opencv-contrib-python（扩展功能）。

pip install opencv-python
pip install opencv-contrib-python

验证安装：

import cv2
print(cv2.__version__)  # 输出版本号

以下代码来源于：链接

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：

    # # 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：

    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()

图像旋转、平移、翻转：

    # 图像旋转image = cv2.imread('3.png')(h, w) = image.shape[:2]center = (w // 2, h // 2)M = cv2.getRotationMatrix2D(center, 45, 1.0)  # 旋转 45 度rotated_img = cv2.warpAffine(image, M, (w, h))cv2.imshow('rotated_img', rotated_img)# 图像平移M = np.float32([[1, 0, 100], [0, 1, 50]])  # 向右平移 100 像素，向下平移 50 像素translated_img = cv2.warpAffine(image, M, (w, h))cv2.imshow('translated_img', translated_img)# 图像翻转flipped_img = cv2.flip(image, 1)  # 水平翻转cv2.imshow('flipped_img', flipped_img)cv2.waitKey(0)cv2.destroyAllWindows()