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| ...
import * as posenet from '@tensorflow-models/posenet';
...
const defaultPoseNetArchitecture = 'MobileNetV1';
const defaultQuantBytes = 2;
const defaultMultiplier = 1.0;
const defaultStride = 16;
const defaultInputResolution = 257;
let minPartConfidence = 0.5;
let nmsRadius = 30.0;
let canvas_video = document.getElementById("canvas_video")
const ctx_video = canvas_video.getContext('2d');
console.log("model is downloading ... ");
let win_height = parseFloat($(document.body).height());
let videoHeight = win_height * 0.8;
let videoWidth = videoHeight * 0.7;
...
const load_menu = () => {
...
};
const show_image = (id) => {
...
}
const register = () => {
...
}
async function setupCamera() {
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
throw new Error(
'Browser API navigator.mediaDevices.getUserMedia not available');
}
const video = document.getElementById('video');
video.width = videoWidth;
video.height = videoHeight;
video.srcObject = await navigator.mediaDevices.getUserMedia({
'audio': false,
'video': {
facingMode: 'user',
width: videoWidth,
height: videoHeight,
},
});
return new Promise((resolve) => {
video.onloadedmetadata = () => {
resolve(video);
};
});
}
async function loadVideo() {
const video = await setupCamera();
video.play();
return video;
}
const load = () => {
...
}
function detectPoseInRealTime(net, video) {
let tmp_canvas = document.createElement("canvas");
let tmp_ctx = tmp_canvas.getContext("2d");
tmp_canvas.width = videoWidth;
tmp_canvas.height = videoHeight;
async function poseDetectionFrame() {
let pose = await net.estimateSinglePose(video, {
flipHorizontal: false,
decodingMethod: 'multi-person',
maxDetections: 1,
scoreThreshold: minPartConfidence,
nmsRadius: nmsRadius,
});
tmp_ctx.clearRect(0, 0, videoWidth, videoHeight);
tmp_ctx.save();
tmp_ctx.scale(-1, 1);
tmp_ctx.translate(-videoWidth, 0);
tmp_ctx.drawImage(video, 0, 0, videoWidth, videoHeight);
drawKeypoints(pose.keypoints, tmp_ctx, 1);
drawSkeleton(pose.keypoints, tmp_ctx, 1);
tmp_ctx.restore();
ctx_video.drawImage(tmp_canvas, 0, 0, videoWidth, videoHeight);
let video_pose_theta = getVideoPose(pose.keypoints);
let score = getRealScore(getScore(image_pose_theta, video_pose_theta));
p_score.innerText = parseInt(score);
requestAnimationFrame(poseDetectionFrame);
}
poseDetectionFrame();
}
async function bindPage() {
load();
$("#load").show();
$("#main").hide();
const net = await posenet.load({
architecture: defaultPoseNetArchitecture,
outputStride: defaultStride,
inputResolution: defaultInputResolution,
multiplier: defaultMultiplier,
quantBytes: defaultQuantBytes,
});
$("#load").hide();
$("#main").show();
let video;
try {
video = await loadVideo();
canvas_video.width = videoWidth;
canvas_video.height = videoHeight;
} catch (e) {
console.log(e);
throw e;
}
detectPoseInRealTime(net, video);
}
bindPage();
|
