Kotlin Bytedeco OpenCV 图像图像51.2 光流背景消除
- 1 添加依赖
- 2 测试代码
- 3 测试结果
| 方法 | 适用场景 | 优点 | 计算量 |
|---|---|---|---|
| MOG2 | 复杂背景 | 可检测阴影 | 中等 |
| KNN | 动态背景 | 适用于波动场景 | 较高 |
| Running Average | 静态背景 | 计算简单 | 低 |
| Frame Differencing | 低光照变化场景 | 计算快 | 低 |
| Optical Flow | 复杂运动场景 | 精度高 | 高 |
如果你的场景是 一般的视频监控，推荐使用 MOG2 或 KNN。
如果你的场景是 背景变化缓慢，推荐 Running Average。
1 添加依赖
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xmlns="http://maven.apache.org/POM/4.0.0"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.xu</groupId><artifactId>KotlinOpenCV</artifactId><version>1.0</version><properties><kotlin.version>2.0.0</kotlin.version><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><kotlin.code.style>official</kotlin.code.style><kotlin.compiler.jvmTarget>1.8</kotlin.compiler.jvmTarget></properties><repositories><repository><id>mavenCentral</id><url>https://repo1.maven.org/maven2/</url></repository></repositories><dependencies><dependency><groupId>cn.hutool</groupId><artifactId>hutool-all</artifactId><version>5.8.29</version></dependency><dependency><groupId>org.apache.commons</groupId><artifactId>commons-compress</artifactId><version>1.27.0</version></dependency><dependency><groupId>org.tukaani</groupId><artifactId>xz</artifactId><version>1.10</version></dependency><dependency><groupId>org.jetbrains.kotlinx</groupId><artifactId>kotlinx-coroutines-core</artifactId><version>1.9.0-RC</version></dependency><!-- <dependency>--><!-- <groupId>org.opencv</groupId>--><!-- <artifactId>opencv</artifactId>--><!-- <version>4100</version>--><!-- <scope>system</scope>--><!-- <systemPath>${project.basedir}/lib/opencv/opencv-4100.jar</systemPath>--><!-- </dependency>--><dependency><groupId>org.bytedeco</groupId><artifactId>opencv-platform</artifactId><version>4.10.0-1.5.11</version></dependency><!-- <dependency>--><!-- <groupId>org.bytedeco</groupId>--><!-- <artifactId>ffmpeg-platform</artifactId>--><!-- <version>6.1.1-1.5.10</version>--><!-- </dependency>--><dependency><groupId>org.jetbrains.kotlin</groupId><artifactId>kotlin-test-junit5</artifactId><version>2.0.0</version><scope>test</scope></dependency><dependency><groupId>org.junit.jupiter</groupId><artifactId>junit-jupiter</artifactId><version>5.10.0</version><scope>test</scope></dependency><dependency><groupId>org.jetbrains.kotlin</groupId><artifactId>kotlin-stdlib</artifactId><version>2.0.0</version></dependency></dependencies><build><sourceDirectory>src/main/kotlin</sourceDirectory><testSourceDirectory>src/test/kotlin</testSourceDirectory><plugins><plugin><groupId>org.jetbrains.kotlin</groupId><artifactId>kotlin-maven-plugin</artifactId><version>2.0.0</version><executions><execution><id>compile</id><phase>compile</phase><goals><goal>compile</goal></goals></execution><execution><id>test-compile</id><phase>test-compile</phase><goals><goal>test-compile</goal></goals></execution></executions></plugin><plugin><artifactId>maven-surefire-plugin</artifactId><version>2.22.2</version></plugin><plugin><artifactId>maven-failsafe-plugin</artifactId><version>2.22.2</version></plugin><plugin><groupId>org.codehaus.mojo</groupId><artifactId>exec-maven-plugin</artifactId><version>1.6.0</version><configuration><mainClass>MainKt</mainClass></configuration></plugin></plugins></build></project>
2 测试代码
package com.xu.com.xu.videoimport org.bytedeco.javacpp.Loader
import org.bytedeco.opencv.global.opencv_core
import org.bytedeco.opencv.global.opencv_highgui
import org.bytedeco.opencv.global.opencv_imgproc
import org.bytedeco.opencv.global.opencv_video
import org.bytedeco.opencv.opencv_core.Mat
import org.bytedeco.opencv.opencv_core.MatVector
import org.bytedeco.opencv.opencv_core.Size
import org.bytedeco.opencv.opencv_videoio.VideoCaptureobject BSM {init {Loader.load(opencv_core::class.java)}@JvmStaticfun main(args: Array<String>) {flow()}fun flow() {// 打开视频流val capture = VideoCapture("lib/video/video_003.avi")if (!capture.isOpened) {return}val prev = Mat() // 存储上一帧灰度图像val gray = Mat() // 当前帧灰度图像val flow = Mat() // 存储光流结果val org = Mat() // 存储当前原始帧val mask = Mat() // 用于存储前景掩码while (true) {// 读取当前帧capture.read(org)if (org.empty()) break// 转换为灰度图opencv_imgproc.cvtColor(org, gray, opencv_imgproc.COLOR_BGR2GRAY)if (!prev.empty()) {// 计算光流opencv_video.calcOpticalFlowFarneback(prev, gray, flow,0.5, // `pyrScale` 适当降低（0.4-0.6），减少背景干扰3, // `levels` 适当增加（2-4），提高检测范围1, // `windowSize` 增大（3→5），增强检测20, // `iterations` 增加迭代次数，提高准确度5, // `polyN`（5-7），适当提高减少噪声1.2, // `polySigma` 增大（1.2 → 1.5），平滑光流0 // `flags` 默认为 0)// 计算光流的幅度val channels = MatVector()opencv_core.split(flow, channels) // 拆分 X / Y 分量val mag = Mat()opencv_core.magnitude(channels[0], channels[1], mag)// 归一化幅度（norm 的类型为 CV_32F）val norm = Mat()opencv_core.normalize(mag, norm, 0.0, 255.0, opencv_core.NORM_MINMAX, -1, Mat())// 将 norm 转换为 8 位图像（CV_8U）val calc = Mat()norm.convertTo(calc, opencv_core.CV_8U)// 进行二值化处理，提取运动区域（mask 的类型为 CV_8UC1）//opencv_imgproc.threshold(calc, mask, 10.0, 255.0, opencv_imgproc.THRESH_BINARY)opencv_imgproc.adaptiveThreshold(calc, mask, 255.0,opencv_imgproc.ADAPTIVE_THRESH_GAUSSIAN_C,opencv_imgproc.THRESH_BINARY,10, 2.0)// 形态学处理去噪声val kernel = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, Size(1, 1))opencv_imgproc.morphologyEx(mask, mask, opencv_imgproc.MORPH_OPEN, kernel)opencv_imgproc.morphologyEx(mask, mask, opencv_imgproc.MORPH_CLOSE, kernel)// 确保类型一致val comm = Mat()opencv_imgproc.cvtColor(mask, comm, opencv_imgproc.COLOR_GRAY2BGR)// 确保大小一致if (comm.size() != org.size()) {opencv_imgproc.resize(comm, comm, org.size())}// 进行拼接显示val show = Mat()opencv_core.hconcat(org, comm, show)opencv_highgui.imshow("Optical Flow BSM", show)}// 复制当前帧用于下一次计算gray.copyTo(prev)// 检查用户按键if (opencv_highgui.waitKey(30) == 'q'.code) break}// 释放资源capture.release()opencv_highgui.destroyAllWindows()}fun gmm() {// 打开视频流（可使用摄像头或者视频文件路径）val capture = VideoCapture(0)capture.open("lib/video/video_003.avi")// 创建背景减除器val bg = opencv_video.createBackgroundSubtractorMOG2().apply {history = 500 // 历史帧数varThreshold = 16.0 // 方差阈值detectShadows = true // 检测阴影}// 创建Mat对象用于存储视频帧和结果val org = Mat()val dst = Mat()while (true) {// 读取当前帧capture.read(org)if (org.empty()) break// 应用背景减除器bg.apply(org, dst)// 可选：对前景蒙版进行一些后处理（如形态学操作）val kernel = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, Size(1, 1))opencv_imgproc.morphologyEx(dst, dst, opencv_imgproc.MORPH_CLOSE, kernel)// 确保类型一致val mat = Mat()opencv_imgproc.cvtColor(dst, mat, opencv_imgproc.COLOR_GRAY2BGR)// 确保大小一致if (dst.size() != org.size()) {opencv_imgproc.resize(mat, mat, org.size())}// 进行拼接val show = Mat()opencv_core.hconcat(org, mat, show)opencv_highgui.imshow("MOG2 BSM", show)// 检查用户按键if (opencv_highgui.waitKey(30) == 'q'.code) break}// 释放资源capture.release()opencv_highgui.destroyAllWindows()}fun knn() {// 打开视频流（可使用摄像头或者视频文件路径）val capture = VideoCapture("lib/video/video_003.avi")if (!capture.isOpened) {return}// 创建 KNN 背景减除器val bg = opencv_video.createBackgroundSubtractorKNN().apply {history = 500 // 历史帧数dist2Threshold = 400.0 // 距离阈值，控制前景检测的敏感度detectShadows = true // 是否检测阴影}// 创建 Mat 对象用于存储视频帧和结果val org = Mat()val dst = Mat()while (true) {// 读取当前帧capture.read(org)if (org.empty()) break// 应用背景减除器bg.apply(org, dst)// 可选：对前景蒙版进行一些后处理（如形态学操作）val kernel = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, Size(3, 3))opencv_imgproc.morphologyEx(dst, dst, opencv_imgproc.MORPH_CLOSE, kernel)// 确保类型一致val mat = Mat()opencv_imgproc.cvtColor(dst, mat, opencv_imgproc.COLOR_GRAY2BGR)// 确保大小一致if (dst.size() != org.size()) {opencv_imgproc.resize(mat, mat, org.size())}// 进行拼接val show = Mat()opencv_core.hconcat(org, mat, show)// 显示结果opencv_highgui.imshow("KNN BSM", show)// 检查用户按键if (opencv_highgui.waitKey(30) == 'q'.code) break}// 释放资源capture.release()opencv_highgui.destroyAllWindows()}}
3 测试结果
