当前位置： 首页 > news >正文

网站点击快速排名,互联网网站解决方案,麻豆秋白色蕾丝半身裙,重庆哪个区最繁华Python OpenCV 4.10 库详解文档 核心模块覆盖#xff1a; Core模块#xff1a;基本数据结构、矩阵操作、数学运算 ImgProc模块#xff1a;图像处理的核心功能#xff0c;包括颜色转换、几何变换、滤波、边缘检测 VideoIO模块#xff1a;视频和摄像头操作 HighGUI模块 Core模块基本数据结构、矩阵操作、数学运算 ImgProc模块图像处理的核心功能包括颜色转换、几何变换、滤波、边缘检测 VideoIO模块视频和摄像头操作 HighGUI模块用户界面功能窗口管理、事件处理 Features2D模块特征检测和匹配SIFT、ORB等 ObjDetect模块目标检测算法 DNN模块深度学习模型集成 Video模块视频分析光流、背景减除 Calib3D模块相机标定和3D重建 Photo模块计算摄影学HDR、去噪、修复 ML模块机器学习算法 简介 OpenCV (Open Source Computer Vision Library) 是一个开源的计算机视觉和机器学习库。OpenCV 4.10 是该库的最新版本提供了丰富的图像处理、计算机视觉、机器学习和深度学习功能。本文档将详细介绍 OpenCV 4.10 的主要子模块和 API。 安装 pip install opencv-python4.10.* pip install opencv-contrib-python4.10.* # 包含额外模块核心架构概述 OpenCV 4.10 采用模块化设计主要模块包括 core: 核心功能数据结构和基本操作imgproc: 图像处理imgcodecs: 图像编解码videoio: 视频I/Ohighgui: GUI功能video: 视频分析calib3d: 相机标定和3D重建features2d: 2D特征检测objdetect: 目标检测dnn: 深度神经网络ml: 机器学习photo: 计算摄影学 Core 模块 - 核心功能 基本数据结构 import cv2 import numpy as np# 创建不同类型的矩阵 mat_zeros np.zeros((480, 640, 3), dtypenp.uint8) # 3通道图像 mat_ones np.ones((100, 100), dtypenp.float32) # 单通道浮点矩阵 mat_random np.random.randint(0, 255, (200, 200, 3), dtypenp.uint8)# 矩阵属性 print(f形状: {mat_zeros.shape}) print(f数据类型: {mat_zeros.dtype}) print(f通道数: {mat_zeros.shape[2] if len(mat_zeros.shape) 3 else 1}) print(f元素总数: {mat_zeros.size})基本矩阵操作

矩阵创建

mat cv2.Mat(np.zeros((100, 100, 3), dtypenp.uint8))# 克隆和复制 mat_clone mat.copy() mat_ref mat # 引用不是复制# 矩阵类型转换 float_mat mat.astype(np.float32) / 255.0 uint8_mat (float_mat * 255).astype(np.uint8)# ROI (Region of Interest) 操作 roi mat[50:150, 100:200] # 提取感兴趣区域 mat[0:100, 0:100] [255, 0, 0] # 设置区域颜色# 通道分离和合并 b, g, r cv2.split(mat) # 分离BGR通道 merged cv2.merge([b, g, r]) # 合并通道数学运算

基本算术运算

img1 np.ones((300, 300, 3), dtypenp.uint8) * 100 img2 np.ones((300, 300, 3), dtypenp.uint8) * 50# 加法运算 add_result cv2.add(img1, img2) add_weighted cv2.addWeighted(img1, 0.7, img2, 0.3, 0)# 减法运算 sub_result cv2.subtract(img1, img2) absdiff_result cv2.absdiff(img1, img2)# 位运算 bitwise_and cv2.bitwise_and(img1, img2) bitwise_or cv2.bitwise_or(img1, img2) bitwise_xor cv2.bitwise_xor(img1, img2) bitwise_not cv2.bitwise_not(img1)# 逻辑运算和比较 comparison cv2.compare(img1, img2, cv2.CMP_GT) min_result cv2.min(img1, img2) max_result cv2.max(img1, img2)ImgProc 模块 - 图像处理 颜色空间转换

读取图像

img cv2.imread(example.jpg)# 常见颜色空间转换 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hsv cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lab cv2.cvtColor(img, cv2.COLOR_BGR2LAB) rgb cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# YUV颜色空间 yuv cv2.cvtColor(img, cv2.COLOR_BGR2YUV) xyz cv2.cvtColor(img, cv2.COLOR_BGR2XYZ)# 单通道转多通道 gray_bgr cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)几何变换

获取图像尺寸

height, width img.shape[:2]# 缩放 scaled_up cv2.resize(img, None, fx2, fy2, interpolationcv2.INTER_CUBIC) scaled_down cv2.resize(img, (width//2, height//2), interpolationcv2.INTER_AREA)# 旋转 center (width//2, height//2) rotation_matrix cv2.getRotationMatrix2D(center, 45, 1.0) rotated cv2.warpAffine(img, rotation_matrix, (width, height))# 平移 translation_matrix np.float32([[1, 0, 100], [0, 1, 50]]) translated cv2.warpAffine(img, translation_matrix, (width, height))# 仿射变换 pts1 np.float32([[50, 50], [200, 50], [50, 200]]) pts2 np.float32([[10, 100], [200, 50], [100, 250]]) affine_matrix cv2.getAffineTransform(pts1, pts2) affine_result cv2.warpAffine(img, affine_matrix, (width, height))# 透视变换 pts1 np.float32([[56, 65], [368, 52], [28, 387], [389, 390]]) pts2 np.float32([[0, 0], [300, 0], [0, 300], [300, 300]]) perspective_matrix cv2.getPerspectiveTransform(pts1, pts2) perspective_result cv2.warpPerspective(img, perspective_matrix, (300, 300))滤波操作

平滑滤波

blur cv2.blur(img, (15, 15)) # 均值滤波 gaussian cv2.GaussianBlur(img, (15, 15), 0) # 高斯滤波 median cv2.medianBlur(img, 15) # 中值滤波 bilateral cv2.bilateralFilter(img, 9, 75, 75) # 双边滤波# 形态学操作 kernel np.ones((5, 5), np.uint8) erosion cv2.erode(img, kernel, iterations1) # 腐蚀 dilation cv2.dilate(img, kernel, iterations1) # 膨胀 opening cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) # 开运算 closing cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) # 闭运算 gradient cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel) # 梯度 tophat cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel) # 顶帽 blackhat cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel) # 黑帽# 自定义卷积核 kernel_sharpen np.array([[-1, -1, -1],[-1, 9, -1],[-1, -1, -1]]) sharpened cv2.filter2D(img, -1, kernel_sharpen)边缘检测

Canny边缘检测

edges cv2.Canny(gray, 100, 200)# Sobel算子 sobelx cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize3) sobely cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize3) sobel_combined cv2.magnitude(sobelx, sobely)# Laplacian算子 laplacian cv2.Laplacian(gray, cv2.CV_64F)# Scharr算子 scharrx cv2.Scharr(gray, cv2.CV_64F, 1, 0) scharry cv2.Scharr(gray, cv2.CV_64F, 0, 1)轮廓检测和分析

查找轮廓

contours, hierarchy cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 绘制轮廓 contour_img img.copy() cv2.drawContours(contour_img, contours, -1, (0, 255, 0), 2)# 轮廓分析 for contour in contours:# 轮廓面积area cv2.contourArea(contour)# 轮廓周长perimeter cv2.arcLength(contour, True)# 边界框x, y, w, h cv2.boundingRect(contour)cv2.rectangle(img, (x, y), (xw, yh), (255, 0, 0), 2)# 最小外接圆(x, y), radius cv2.minEnclosingCircle(contour)cv2.circle(img, (int(x), int(y)), int(radius), (0, 255, 0), 2)# 椭圆拟合if len(contour) 5:ellipse cv2.fitEllipse(contour)cv2.ellipse(img, ellipse, (0, 0, 255), 2)# 轮廓近似epsilon 0.02 * cv2.arcLength(contour, True)approx cv2.approxPolyDP(contour, epsilon, True)# 凸包hull cv2.convexHull(contour)ImgCodecs 模块 - 图像编解码 图像读取和保存

读取图像

img_color cv2.imread(image.jpg, cv2.IMREAD_COLOR) # 彩色 img_gray cv2.imread(image.jpg, cv2.IMREAD_GRAYSCALE) # 灰度 img_unchanged cv2.imread(image.png, cv2.IMREAD_UNCHANGED) # 包含alpha通道# 保存图像 cv2.imwrite(output.jpg, img, [cv2.IMWRITE_JPEG_QUALITY, 90]) cv2.imwrite(output.png, img, [cv2.IMWRITE_PNG_COMPRESSION, 9])# 图像编解码

将图像编码为内存中的字节数组

ret, buffer cv2.imencode(.jpg, img, [cv2.IMWRITE_JPEG_QUALITY, 80]) if ret:# 从字节数组解码图像img_decoded cv2.imdecode(buffer, cv2.IMREAD_COLOR)# 支持的格式 formats [.jpg, .jpeg, .png, .bmp, .tiff, .webp, .pbm, .pgm, .ppm]VideoIO 模块 - 视频输入输出 视频读取和写入

打开视频文件

cap cv2.VideoCapture(video.mp4)# 获取视频属性 fps cap.get(cv2.CAP_PROP_FPS) width int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) total_frames int(cap.get(cv2.CAP_PROP_FRAME_COUNT))print(fFPS: {fps}, 尺寸: {width}x{height}, 总帧数: {total_frames})# 创建视频写入对象 fourcc cv2.VideoWriter_fourcc(*XVID) out cv2.VideoWriter(output.avi, fourcc, fps, (width, height))# 逐帧处理视频 while True:ret, frame cap.read()if not ret:break# 图像处理processed_frame cv2.GaussianBlur(frame, (15, 15), 0)# 写入处理后的帧out.write(processed_frame)# 显示帧cv2.imshow(Frame, processed_frame)if cv2.waitKey(1) 0xFF ord(q):break# 释放资源 cap.release() out.release() cv2.destroyAllWindows()摄像头操作

打开默认摄像头

cap cv2.VideoCapture(0)# 设置摄像头属性 cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) cap.set(cv2.CAP_PROP_FPS, 30)# 实时处理 while True:ret, frame cap.read()if not ret:break# 实时图像处理gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)edges cv2.Canny(gray, 50, 150)# 显示结果cv2.imshow(Original, frame)cv2.imshow(Edges, edges)if cv2.waitKey(1) 0xFF ord(q):breakcap.release() cv2.destroyAllWindows()HighGUI 模块 - 图形用户界面 窗口操作

创建窗口

cv2.namedWindow(Image, cv2.WINDOW_AUTOSIZE) cv2.namedWindow(Resizable, cv2.WINDOW_NORMAL)# 显示图像 cv2.imshow(Image, img)# 移动和调整窗口 cv2.moveWindow(Image, 100, 100) cv2.resizeWindow(Resizable, 800, 600)# 等待按键 key cv2.waitKey(0) if key 27: # ESC键cv2.destroyAllWindows()# 保存窗口截图 cv2.imwrite(screenshot.png, img)鼠标事件处理

鼠标回调函数

def mouse_callback(event, x, y, flags, param):if event cv2.EVENT_LBUTTONDOWN:print(f左键点击: ({x}, {y}))cv2.circle(img, (x, y), 5, (255, 0, 0), -1)elif event cv2.EVENT_RBUTTONDOWN:print(f右键点击: ({x}, {y}))cv2.circle(img, (x, y), 5, (0, 255, 0), -1)elif event cv2.EVENT_MOUSEMOVE:if flags cv2.EVENT_FLAG_LBUTTON:cv2.circle(img, (x, y), 3, (0, 0, 255), -1)# 设置鼠标回调 img np.zeros((512, 512, 3), np.uint8) cv2.namedWindow(Mouse Events) cv2.setMouseCallback(Mouse Events, mouse_callback)while True:cv2.imshow(Mouse Events, img)if cv2.waitKey(1) 0xFF ord(q):breakcv2.destroyAllWindows()滑动条控件

滑动条回调函数

def trackbar_callback(val):global img_result# 根据滑动条值调整图像alpha val / 100.0img_result cv2.addWeighted(img1, alpha, img2, 1-alpha, 0)cv2.imshow(Blended, img_result)# 创建滑动条 img1 cv2.imread(image1.jpg) img2 cv2.imread(image2.jpg) img_result img1.copy()cv2.namedWindow(Blended) cv2.createTrackbar(Alpha, Blended, 50, 100, trackbar_callback)# 初始显示 trackbar_callback(50) cv2.waitKey(0) cv2.destroyAllWindows()Features2D 模块 - 特征检测 角点检测

Harris角点检测

gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) harris_corners cv2.cornerHarris(gray, 2, 3, 0.04) img[harris_corners 0.01 * harris_corners.max()] [0, 0, 255]# Shi-Tomasi角点检测 corners cv2.goodFeaturesToTrack(gray, maxCorners100, qualityLevel0.01, minDistance10, blockSize3) if corners is not None:corners np.int0(corners)for corner in corners:x, y corner.ravel()cv2.circle(img, (x, y), 3, (0, 255, 0), -1)SIFT特征检测

创建SIFT检测器

sift cv2.SIFT_create()# 检测关键点和描述符 keypoints, descriptors sift.detectAndCompute(gray, None)# 绘制关键点 img_keypoints cv2.drawKeypoints(img, keypoints, None, flagscv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)print(f检测到 {len(keypoints)} 个关键点)ORB特征检测

创建ORB检测器

orb cv2.ORB_create(nfeatures500)# 检测关键点和描述符 keypoints, descriptors orb.detectAndCompute(gray, None)# 绘制关键点 img_orb cv2.drawKeypoints(img, keypoints, None, color(0, 255, 0))特征匹配

读取两幅图像

img1 cv2.imread(image1.jpg, 0) img2 cv2.imread(image2.jpg, 0)# 检测特征点 orb cv2.ORB_create() kp1, des1 orb.detectAndCompute(img1, None) kp2, des2 orb.detectAndCompute(img2, None)# 创建匹配器 bf cv2.BFMatcher(cv2.NORM_HAMMING, crossCheckTrue)# 匹配特征点 matches bf.match(des1, des2) matches sorted(matches, keylambda x: x.distance)# 绘制匹配结果 img_matches cv2.drawMatches(img1, kp1, img2, kp2, matches[:20], None, flagscv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)# 使用FLANN匹配器 FLANN_INDEX_KDTREE 1 index_params dict(algorithmFLANN_INDEX_KDTREE, trees5) search_params dict(checks50) flann cv2.FlannBasedMatcher(index_params, search_params)# 对于SIFT/SURF描述符 sift cv2.SIFT_create() kp1, des1 sift.detectAndCompute(img1, None) kp2, des2 sift.detectAndCompute(img2, None)matches flann.knnMatch(des1, des2, k2)# 应用比值测试 good_matches [] for m, n in matches:if m.distance 0.7 * n.distance:good_matches.append(m)ObjDetect 模块 - 目标检测 Haar级联分类器

加载人脸检测器

face_cascade cv2.CascadeClassifier(cv2.data.haarcascades haarcascade_frontalface_default.xml) eye_cascade cv2.CascadeClassifier(cv2.data.haarcascades haarcascade_eye.xml)# 检测人脸 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces face_cascade.detectMultiScale(gray, scaleFactor1.1, minNeighbors5, minSize(30, 30))# 绘制检测结果 for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (xw, yh), (255, 0, 0), 2)# 在人脸区域检测眼睛roi_gray gray[y:yh, x:xw]roi_color img[y:yh, x:xw]eyes eye_cascade.detectMultiScale(roi_gray)for (ex, ey, ew, eh) in eyes:cv2.rectangle(roi_color, (ex, ey), (exew, eyeh), (0, 255, 0), 2)HOG行人检测

创建HOG描述符

hog cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())# 检测行人 pedestrians, weights hog.detectMultiScale(img, winStride(8, 8), padding(32, 32), scale1.05)# 绘制检测框 for (x, y, w, h) in pedestrians:cv2.rectangle(img, (x, y), (xw, yh), (0, 255, 0), 2)DNN 模块 - 深度神经网络 加载和使用预训练模型

加载YOLO模型

net cv2.dnn.readNet(yolov3.weights, yolov3.cfg)# 获取输出层名称 layer_names net.getLayerNames() output_layers [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]# 加载类别名称 with open(coco.names, r) as f:classes [line.strip() for line in f.readlines()]# 图像预处理 height, width, channels img.shape blob cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, cropFalse)# 设置输入并进行前向传播 net.setInput(blob) outs net.forward(output_layers)# 解析检测结果 class_ids [] confidences [] boxes []for out in outs:for detection in out:scores detection[5:]class_id np.argmax(scores)confidence scores[class_id]if confidence 0.5:# 目标框坐标center_x int(detection[0] * width)center_y int(detection[1] * height)w int(detection[2] * width)h int(detection[3] * height)# 矩形框x int(center_x - w / 2)y int(center_y - h / 2)boxes.append([x, y, w, h])confidences.append(float(confidence))class_ids.append(class_id)# 非极大值抑制 indexes cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)if len(indexes) 0:for i in indexes.flatten():x, y, w, h boxes[i]label str(classes[class_ids[i]])confidence confidences[i]cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 2)cv2.putText(img, f{label}: {confidence:.2f}, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)OpenCV DNN支持的框架

加载不同框架的模型# TensorFlow模型

net_tf cv2.dnn.readNetFromTensorflow(model.pb, config.pbtxt)# Caffe模型 net_caffe cv2.dnn.readNetFromCaffe(deploy.prototxt, model.caffemodel)# ONNX模型 net_onnx cv2.dnn.readNetFromONNX(model.onnx)# Darknet模型 net_darknet cv2.dnn.readNetFromDarknet(config.cfg, weights.weights)# 设置计算后端 net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)# 使用GPU加速如果可用

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)

net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)Video 模块 - 视频分析

光流估计

Lucas-Kanade光流

cap cv2.VideoCapture(video.mp4)# 获取第一帧 ret, old_frame cap.read() old_gray cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)# 检测角点 p0 cv2.goodFeaturesToTrack(old_gray, maskNone, maxCorners100, qualityLevel0.3, minDistance7, blockSize7)# 创建颜色数组 colors np.random.randint(0, 255, (100, 3)) mask np.zeros_like(old_frame)while True:ret, frame cap.read()if not ret:breakframe_gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 计算光流p1, st, err cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, winSize(15, 15), maxLevel2)# 选择好的点good_new p1[st 1]good_old p0[st 1]# 绘制轨迹for i, (tr, to) in enumerate(zip(good_new, good_old)):a, b tr.ravel()c, d to.ravel()mask cv2.line(mask, (int(a), int(b)), (int©, int(d)), colors[i].tolist(), 2)frame cv2.circle(frame, (int(a), int(b)), 5, colors[i].tolist(), -1)img cv2.add(frame, mask)cv2.imshow(Frame, img)if cv2.waitKey(30) 0xFF ord(q):break# 更新前一帧old_gray frame_gray.copy()p0 good_new.reshape(-1, 1, 2)cap.release() cv2.destroyAllWindows()背景减除

创建背景减除器

backSub cv2.createBackgroundSubtractorMOG2(detectShadowsTrue)cap cv2.VideoCapture(video.mp4)while True:ret, frame cap.read()if not ret:break# 应用背景减除fgMask backSub.apply(frame)# 形态学操作去噪kernel cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))fgMask cv2.morphologyEx(fgMask, cv2.MORPH_OPEN, kernel)# 查找轮廓contours, _ cv2.findContours(fgMask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 绘制边界框for contour in contours:if cv2.contourArea(contour) 500: # 过滤小区域x, y, w, h cv2.boundingRect(contour)cv2.rectangle(frame, (x, y), (xw, yh), (0, 255, 0), 2)cv2.imshow(Frame, frame)cv2.imshow(FG Mask, fgMask)if cv2.waitKey(30) 0xFF ord(q):breakcap.release() cv2.destroyAllWindows()Calib3D 模块 - 相机标定和3D重建 相机标定

设置棋盘格参数

criteria (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) pattern_size (9, 6) # 内角点数量# 准备对象点 objp np.zeros((pattern_size[0] * pattern_size[1], 3), np.float32) objp[:, :2] np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2)# 存储点 objpoints [] # 3D点 imgpoints [] # 2D点# 读取标定图像 import glob images glob.glob(calibration/*.jpg)for fname in images:img cv2.imread(fname)gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 查找棋盘格角点ret, corners cv2.findChessboardCorners(gray, pattern_size, None)if ret:objpoints.append(objp)corners2 cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)imgpoints.append(corners2)# 绘制角点cv2.drawChessboardCorners(img, pattern_size, corners2, ret)cv2.imshow(img, img)cv2.waitKey(500)cv2.destroyAllWindows()# 相机标定 ret, mtx, dist, rvecs, tvecs cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)print(相机矩阵:) print(mtx) print(\n畸变系数:) print(dist)立体视觉

立体标定

criteria (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)# 立体标定 retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F \cv2.stereoCalibrate(objpoints, imgpoints_left, imgpoints_right, mtx1, dist1, mtx2, dist2, gray.shape[::-1], criteria)# 立体校正 R1, R2, P1, P2, Q, validPixROI1, validPixROI2 \cv2.stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, gray.shape[::-1], R, T)# 计算校正映射 map1x, map1y cv2.initUndistortRectifyMap(cameraMatrix1, distCoeffs1, R1, P1, gray.shape[::-1], cv2.CV_32FC1) map2x, map2y cv2.initUndistortRectifyMap(cameraMatrix2, distCoeffs2, R2, P2, gray.shape[::-1], cv2.CV_32FC1)# 立体匹配 stereo cv2.StereoBM_create(numDisparities16*5, blockSize21)# 对于每对立体图像 left_img cv2.imread(left.jpg, 0) right_img cv2.imread(right.jpg, 0)# 校正图像 left_rectified cv2.remap(left_img, map1x, map1y, cv2.INTER_LINEAR) right_rectified cv2.remap(right_img, map2x, map2y, cv2.INTER_LINEAR)# 计算视差图 disparity stereo.compute(left_rectified, right_rectified)# 可视化视差图 disparity_normalized cv2.normalize(disparity, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)Photo 模块 - 计算摄影学 图像修复

图像修复

damaged_img cv2.imread(damaged.jpg) mask cv2.imread(mask.jpg, 0)# 使用Navier-Stokes方法 restored_ns cv2.inpaint(damaged_img, mask, 3, cv2.INPAINT_NS)# 使用快速行进方法 restored_telea cv2.inpaint(damaged_img, mask, 3, cv2.INPAINT_TELEA)图像去噪

非局部平均去噪

denoised cv2.fastNlMeansDenoising(gray, None, 10, 7, 21)# 彩色图像去噪 denoisedcolor cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)# 多帧去噪视频 imgToDenoiseIndex 2 temporalWindowSize 7 imgs [cv2.imread(fframe{i}.jpg) for i in range(5)] denoised_video cv2.fastNlMeansDenoisingColoredMulti(imgs, imgToDenoiseIndex, temporalWindowSize, None, 4, 4, 7, 21)HDR成像

读取不同曝光的图像

img_list [] exposuretimes np.array([¹⁄₃₀.0, 0.25, 2.5, 15.0], dtypenp.float32)for i in range(4):img cv2.imread(fexposure{i}.jpg)img_list.append(img)# 校准相机响应函数 calibrateDebevec cv2.createCalibrateDebevec() responseDebevec calibrateDebevec.process(img_list, exposure_times)# 合并HDR图像 mergeDebevec cv2.createMergeDebevec() hdrDebevec mergeDebevec.process(img_list, exposure_times, responseDebevec)# 色调映射 tonemapDrago cv2.createTonemapDrago(1.0, 0.7) ldrDrago tonemapDrago.process(hdrDebevec)# 保存结果 cv2.imwrite(hdr_image.hdr, hdrDebevec) cv2.imwrite(ldr_drago.jpg, ldrDrago * 255)ML 模块 - 机器学习 K-Means聚类

图像颜色量化

data img.reshape((-1, 3)) data np.float32(data)# 定义K-Means参数 criteria (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 20, 1.0) k 8# 执行K-Means _, labels, centers cv2.kmeans(data, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)# 重构图像 centers np.uint8(centers) segmented_data centers[labels.flatten()] segmented_image segmented_data.reshape(img.shape)SVM分类器

创建SVM分类器

svm cv2.ml.SVM_create() svm.setType(cv2.ml.SVM_C_SVC) svm.setKernel(cv2.ml.SVM_LINEAR)# 准备训练数据 train_data np.random.randint(0, 100, (100, 2)).astype(np.float32) train_labels np.random.randint(0, 2, (100, 1)).astype(np.int32)# 训练模型 svm.train(train_data, cv2.ml.ROW_SAMPLE, train_labels)# 预测 test_data np.random.randint(0, 100, (10, 2)).astype(np.float32) result svm.predict(test_data)实际应用示例 实时人脸识别系统 import cv2 import numpy as npclass FaceRecognitionSystem:def init(self):self.face_cascade cv2.CascadeClassifier(cv2.data.haarcascades haarcascade_frontalface_default.xml)self.recognizer cv2.face.LBPHFaceRecognizer_create()self.cap cv2.VideoCapture(0)def collect_faces(self, person_id, num_samples100):收集人脸样本faces []labels []count 0while count num_samples:ret, frame self.cap.read()if not ret:continuegray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)detected_faces self.face_cascade.detectMultiScale(gray, 1.3, 5)for (x, y, w, h) in detected_faces:face_roi gray[y:yh, x:xw]face_roi cv2.resize(face_roi, (200, 200))faces.append(face_roi)labels.append(person_id)count 1cv2.rectangle(frame, (x, y), (xw, yh), (255, 0, 0), 2)cv2.putText(frame, fCollecting: {count}/{num_samples}, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)cv2.imshow(Collecting Faces, frame)if cv2.waitKey(1) 0xFF ord(q):breakreturn faces, labelsdef train_model(self, faces, labels):训练识别模型self.recognizer.train(faces, np.array(labels))def recognize_faces(self):实时人脸识别while True:ret, frame self.cap.read()if not ret:breakgray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)faces self.face_cascade.detectMultiScale(gray, 1.3, 5)for (x, y, w, h) in faces:face_roi gray[y:yh, x:xw]face_roi cv2.resize(face_roi, (200, 200))# 识别人脸person_id, confidence self.recognizer.predict(face_roi)if confidence 100: # 置信度阈值name fPerson {person_id}color (0, 255, 0)else:name Unknowncolor (0, 0, 255)cv2.rectangle(frame, (x, y), (xw, yh), color, 2)cv2.putText(frame, f{name} ({confidence:.1f}), (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)cv2.imshow(Face Recognition, frame)if cv2.waitKey(1) 0xFF ord(q):breakdef release(self):self.cap.release()cv2.destroyAllWindows()图像拼接全景图 class PanoramaStitcher:def init(self):self.detector cv2.SIFT_create()self.matcher cv2.BFMatcher()def stitch_images(self, images):拼接多张图像生成全景图if len(images) 2:return images[0] if images else None# 从左到右依次拼接result images[0]for i in range(1, len(images)):result self.stitch_pair(result, images[i])if result is None:print(f无法拼接第{i1}张图像)breakreturn resultdef stitch_pair(self, img1, img2):拼接两张图像# 检测特征点kp1, des1 self.detector.detectAndCompute(img1, None)kp2, des2 self.detector.detectAndCompute(img2, None)if des1 is None or des2 is None:return None# 特征匹配matches self.matcher.knnMatch(des1, des2, k2)# 筛选好的匹配点good_matches []for m, n in matches:if m.distance 0.7 * n.distance:good_matches.append(m)if len(good_matches) 10:return None# 计算单应性矩阵src_pts np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)dst_pts np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)H, mask cv2.findHomography(dst_pts, src_pts, cv2.RANSAC, 5.0)if H is None:return None# 图像变换和拼接h1, w1 img1.shape[:2]h2, w2 img2.shape[:2]# 计算拼接后的画布大小corners np.float32([[0, 0], [w2, 0], [w2, h2], [0, h2]]).reshape(-1, 1, 2)transformed_corners cv2.perspectiveTransform(corners, H)all_corners np.concatenate([[[0, 0]], [[w1, 0]], [[w1, h1]], [[0, h1]], transformed_corners], axis0)[x_min, y_min] np.int32(all_corners.min(axis0).ravel())[x_max, y_max] np.int32(all_corners.max(axis0).ravel())# 平移矩阵translation np.array([[1, 0, -x_min], [0, 1, -y_min], [0, 0, 1]])# 创建拼接画布canvas_width x_max - x_mincanvas_height y_max - y_min# 变换第二张图像H_translated translation.dot(H)warped_img2 cv2.warpPerspective(img2, H_translated, (canvas_width, canvas_height))# 将第一张图像放到画布上result np.zeros((canvas_height, canvas_width, 3), dtypenp.uint8)result[-y_min:-y_minh1, -x_min:-x_minw1] img1# 混合图像mask (warped_img2 0).astype(np.uint8)result cv2.bitwise_and(result, 255 - mask * 255) warpedimg2return result# 使用示例 stitcher PanoramaStitcher() images [cv2.imread(fpanorama{i}.jpg) for i in range(3)] panorama stitcher.stitch_images(images) if panorama is not None:cv2.imwrite(panorama_result.jpg, panorama)性能优化和最佳实践 内存管理 OpenCV 4.10 中的图像数据通常以 NumPy 数组的形式存在需要注意内存使用。对于大图像处理建议及时释放不需要的变量使用适当的数据类型如 uint8 而不是 float64以及在可能的情况下进行就地操作来减少内存拷贝。 多线程处理 OpenCV 内部许多函数已经进行了多线程优化。可以通过 cv2.setNumThreads() 设置线程数或者在应用层面使用 Python 的 concurrent.futures 模块进行并行处理。 GPU加速 对于支持CUDA的系统可以使用OpenCV的GPU模块来加速计算密集型操作。许多函数都有对应的GPU版本通常以 gpu 或 cuda 前缀命名。 错误处理 在实际应用中应该对OpenCV操作进行适当的错误处理检查返回值和异常情况。特别是在处理用户输入的图像文件时需要验证文件格式和内容的有效性。 OpenCV 4.10 提供了强大而全面的计算机视觉功能通过合理使用这些API可以构建出高效的图像处理和计算机视觉应用程序。