Douyin’s very popular picture multiple-choice special effects can be quickly implemented using the front end!

This article brings you relevant knowledge about front-end picture special effects. It mainly introduces to you how the front-end implements a picture multiple-choice special effect that has been very popular on Douyin recently. It is very comprehensive and detailed. Let’s take a look at it together. I hope Help those in need.

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//复制链接预览 https://code.juejin.cn/pen/7160886403805970445

Preface

Recently, there is aPicture Multiple Choice Questionin Douyin special effects that is particularly popular. Today, let’s talk about how to implement the front-end. Next, I will mainly talk abouthow to judge the left and right head swing.

Architecture and Concept

The abstract overall implementation idea is as follows

##MediaPipe Face Meshis a solution, 468 3D facial landmarks are estimated in real time even on mobile devices. It uses machine learning (ML) to infer 3D facial surfaces, requiring only a camera input and no dedicated depth sensor. The solution leverages a lightweight model architecture along with GPU acceleration throughout the pipeline to deliver critical real-time performance for real-time experiences.

Introduction

import '@mediapipe/face_mesh'; import '@tensorflow/tfjs-core'; import '@tensorflow/tfjs-backend-webgl'; import * as faceLandmarksDetection from '@tensorflow-models/face-landmarks-detection';

Create face model

Introduce tensorflow trained

face feature point detection model, prediction4863D facial feature points are used to infer the approximate facial geometry of the human face.

• maxFacesDefault is 1. The maximum number of faces the model will detect. The number of faces returned can be less than the maximum (for example, when there are no faces in the input). It is strongly recommended to set this value to the maximum expected number of faces, otherwise the model will continue to search for missing faces, which may slow down performance.
• refineLandmarksThe default is false. If set to true, refines landmark coordinates around the eyes and lips, and outputs additional landmarks around the iris. (I can setfalsehere because we are not using eye coordinates)
• solutionPathThe path to the location of the am binary and model files. (It is strongly recommended to put the model into domestic object storage. The first load can save a lot of time. The size is about10M)

async createDetector(){ const model = faceLandmarksDetection.SupportedModels.MediaPipeFaceMesh; const detectorConfig = { maxFaces:1, //检测到的最大面部数量 refineLandmarks:false, //可以完善眼睛和嘴唇周围的地标坐标，并在虹膜周围输出其他地标 runtime: 'mediapipe', solutionPath: 'https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh', //WASM二进制文件和模型文件所在的路径 }; this.detector = await faceLandmarksDetection.createDetector(model, detectorConfig); }

##人Face recognition

The faces list returned contains the detected faces for each face in the image. If the model cannot detect any faces, the list will be empty. For each face, it contains a bounding box of the detected face, and an array of keypoints. MediaPipeFaceMesh returns 468 keypoints. Each keypoint contains x and y, as well as a name.

Now you can use the detector to detect faces. The estimateFaces method accepts images and videos in a variety of formats, including:

HTMLVideoElement

,HTMLImageElement,HTMLCanvasElement, andTensor3D.

flipHorizontal
• Optional. Default is false. When image data comes from a camera, the result must be flipped horizontally.

  async renderPrediction() { var video = this.$refs['video']; var canvas = this.$refs['canvas']; var context = canvas.getContext('2d'); context.clearRect(0, 0, canvas.width, canvas.height); const Faces = await this.detector.estimateFaces(video, { flipHorizontal:false, //镜像 }); if (Faces.length > 0) { this.log(`检测到人脸`); } else { this.log(`没有检测到人脸`); } }

This box represents the bounding box of the face in the image pixel space, xMin, xMax represent x-bounds, yMin, yMax represent y-bounds, width, height Represents the dimensions of the bounding box. For keypoints, x and y represent the actual keypoint location in the image pixel space. z represents the depth at which the center of the head is the origin. The smaller the value, the closer the key point is to the camera. The size of Z uses roughly the same scale as x. This name provides a label for some key points, such as "lips", "left eye", etc. Note that not every keypoint has a label.

How to judge

Find the two points on the face

The first point

The center position of the forehead

The second pointChin center position

const place1 = (face.keypoints || []).find((e,i)=>i===10); //额头位置 const place2 = (face.keypoints || []).find((e,i)=>i===152); //下巴位置 /* x1,y1 | | | x2,y2 -------|------- x4,y4 x3,y3 */ const [x1,y1,x2,y2,x3,y3,x4,y4] = [ place1.x,place1.y, 0,place2.y, place2.x,place2.y, this.canvas.width, place2.y ];

Calculate

x1,y1,x2,y2,x3 through canvas.width

forehead center positionandchin center position,y3,x4,y4

getAngle({ x: x1, y: y1 }, { x: x2, y: y2 }){ const dot = x1 * x2 + y1 * y2 const det = x1 * y2 - y1 * x2 const angle = Math.atan2(det, dot) / Math.PI * 180 return Math.round(angle + 360) % 360 } const angle = this.getAngle({ x: x1 - x3, y: y1 - y3, }, { x: x2 - x3, y: y2 - y3, }); console.log('角度',angle)

通过获取角度，通过角度的大小来判断左右摆头。

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