适用场景
人体骨骼关键点检测，主要检测人体的一些关键点，通过关键点描述人体骨骼信息。具体应用主要集中在智能视频监控，病人监护系统，人机交互，虚拟现实，人体动画，智能家居，智能安防，运动员辅助训练等等。
支持17个关键点的识别，具体为鼻子，左右眼，左右耳，左右肩，左右肘、左右手腕、左右髋、左右膝、左右脚踝。
效果如下图所示：
开发步骤
-
在使用骨骼点检测时，将实现骨骼点检测相关的类添加至工程。
import { image } from '@kit.ImageKit'; import { hilog } from '@kit.PerformanceAnalysisKit'; import { BusinessError } from '@kit.BasicServicesKit'; import { fileIo } from '@kit.CoreFileKit'; import { skeletonDetection, visionBase } from '@kit.CoreVisionKit'; import { photoAccessHelper } from '@kit.MediaLibraryKit'; -
通过photoAccessHelper.PhotoViewPicker拉起图库选择图片，使用fileIo与image模块将URI转换为PixelMap，为后续检测接口准备输入数据。
Button('选择图片') .type(ButtonType.Capsule) .fontColor(Color.White) .alignSelf(ItemAlign.Center) .width('80%') .margin(10) .onClick(() => { // 拉起图库，获取图片资源 void this.selectImage(); })选择图片与解码图片的方法实现如下：
private async selectImage() { let uri = await this.openPhoto(); if (!uri) { hilog.error(0x0000, 'skeletonDetectSample', 'Failed to define uri.'); return; } this.loadImage(uri); } private async openPhoto(): Promise<string> { return new Promise<string>((resolve, reject) => { let photoPicker: photoAccessHelper.PhotoViewPicker = new photoAccessHelper.PhotoViewPicker(); photoPicker.select({ MIMEType: photoAccessHelper.PhotoViewMIMETypes.IMAGE_TYPE, maxSelectNumber: 1 }).then(res => { resolve(res.photoUris[0]); }).catch((err: BusinessError) => { hilog.error(0x0000, 'skeletonDetectSample', `Failed to get photo image uri. code: ${err.code}, message: ${err.message}`); reject(err); }); }); } private loadImage(name: string) { setTimeout(async () => { let fileSource = await fileIo.open(name, fileIo.OpenMode.READ_ONLY); this.imageSource = image.createImageSource(fileSource.fd); this.chooseImage = await this.imageSource.createPixelMap(); await fileIo.close(fileSource); }, 100); } -
实例化visionBase.Request对象，将PixelMap封装为输入参数；调用SkeletonDetector.create()创建检测器实例，再调用其process方法，获取图片中人体的17个关键点信息，并将结果展示在界面上。
Button('开始骨骼点识别') .type(ButtonType.Capsule) .fontColor(Color.White) .alignSelf(ItemAlign.Center) .width('80%') .margin(10) .onClick(() => { // 调用封装的异步处理函数 void this.handleSkeletonDetection(); })骨骼点识别的方法实现如下：
private async handleSkeletonDetection() { try { if (!this.chooseImage) { hilog.error(0x0000, 'skeletonDetectSample', 'Failed to choose image.'); return; } // 调用骨骼点识别接口 let request: visionBase.Request = { inputData: { pixelMap: this.chooseImage } }; let detector = await skeletonDetection.SkeletonDetector.create(); let data: skeletonDetection.SkeletonDetectionResponse = await detector.process(request); await detector.destroy(); let poseJson = JSON.stringify(data); hilog.info(0x0000, 'skeletonDetectSample', `Succeeded in skeleton detection: ${poseJson}`); this.dataValues = poseJson; } catch (err) { const error = err as BusinessError; hilog.error(0x0000, 'skeletonDetectSample', `Skeleton detection error. Code: ${error.code}, message: ${error.message}`); } }
[h2]Index.ets
import { image } from '@kit.ImageKit';
import { hilog } from '@kit.PerformanceAnalysisKit';
import { BusinessError } from '@kit.BasicServicesKit';
import { fileIo } from '@kit.CoreFileKit';
import { skeletonDetection, visionBase } from '@kit.CoreVisionKit';
import { photoAccessHelper } from '@kit.MediaLibraryKit';
@Entry
@Component
struct Index {
private imageSource: image.ImageSource | undefined = undefined;
@State chooseImage: PixelMap | undefined = undefined;
@State dataValues: string = '';
build() {
Column() {
Image(this.chooseImage)
.objectFit(ImageFit.Fill)
.height('60%')
Text(this.dataValues)
.copyOption(CopyOptions.LocalDevice)
.height('15%')
.margin(10)
.width('60%')
Button('选择图片')
.type(ButtonType.Capsule)
.fontColor(Color.White)
.alignSelf(ItemAlign.Center)
.width('80%')
.margin(10)
.onClick(() => {
// 拉起图库
void this.selectImage();
})
Button('开始骨骼点识别')
.type(ButtonType.Capsule)
.fontColor(Color.White)
.alignSelf(ItemAlign.Center)
.width('80%')
.margin(10)
.onClick(() => {
// 调用封装的异步处理函数
void this.handleSkeletonDetection();
})
}
.width('100%')
.height('100%')
.justifyContent(FlexAlign.Center)
}
// 封装骨骼点识别的异步逻辑
private async handleSkeletonDetection() {
try {
if (!this.chooseImage) {
hilog.error(0x0000, 'skeletonDetectSample', 'Failed to choose image.');
return;
}
// 调用骨骼点识别接口
let request: visionBase.Request = {
inputData: { pixelMap: this.chooseImage }
};
let detector = await skeletonDetection.SkeletonDetector.create();
let data: skeletonDetection.SkeletonDetectionResponse = await detector.process(request);
await detector.destroy();
if (!data) {
hilog.error(0x0000, 'skeletonDetectSample', 'Invalid skeleton detection result');
return;
}
let poseJson = JSON.stringify(data);
hilog.info(0x0000, 'skeletonDetectSample', `Succeeded in skeleton detection: ${poseJson}`);
this.dataValues = poseJson;
} catch (err) {
const error = err as BusinessError;
hilog.error(0x0000, 'skeletonDetectSample', `Skeleton detection error. Code: ${error.code}, message: ${error.message}`);
}
}
private async selectImage() {
let uri = await this.openPhoto();
if (!uri) {
hilog.error(0x0000, 'skeletonDetectSample', 'Failed to define uri.');
return;
}
this.loadImage(uri);
}
private async openPhoto(): Promise<string> {
return new Promise<string>((resolve, reject) => {
let photoPicker: photoAccessHelper.PhotoViewPicker = new photoAccessHelper.PhotoViewPicker();
photoPicker.select({
MIMEType: photoAccessHelper.PhotoViewMIMETypes.IMAGE_TYPE,
maxSelectNumber: 1
}).then(res => {
resolve(res.photoUris[0]);
}).catch((err: BusinessError) => {
hilog.error(0x0000, 'skeletonDetectSample', `Failed to get photo image uri. code: ${err.code}, message: ${err.message}`);
reject(err);
});
});
}
private loadImage(name: string) {
setTimeout(async () => {
let fileSource = await fileIo.open(name, fileIo.OpenMode.READ_ONLY);
this.imageSource = image.createImageSource(fileSource.fd);
this.chooseImage = await this.imageSource.createPixelMap();
await fileIo.close(fileSource);
}, 100);
}
}