開源最前線（ID：OpenSourceTop） 猿妹編譯
鏈接：https://habr.com/ru/news/t/489616/
如果你想把一張照片的某個人物去除掉，通常用PS就可以輕鬆去除了，但是如果是一段視頻要你P掉一個人物，是不是就難倒你了呢？
最近，猿妹在GitHub上發現一個名為Real-Time-Person-Removal的神器，可以實時去除視頻中的人物，就像下圖這樣：
有沒有一種不明覺厲的趕腳，實現這樣一種效果，其實只需要使用JavaScript在網絡瀏覽器中使用一段200行的TensorFlow.js代碼就可以了。
該項目的創建者是一名叫Jason Mayes的谷歌工程師使用TensorFlow.js庫和JavaScript 開發的，Mayes表示，這只是一個試驗性的項目，其中的算法還是存在一些問題，比如它會受到人物背景的影響，人物整體背景越簡單，呈現出來的最終畫面就會越真實，如果你仔細看就會發現，其實處理過的畫面存在很多偽像痕跡。
不過，接下去Mayes會進一步改進他的算法，提高處理後的畫面水平，甚至達到可以一次性從視頻中刪除多個人物的效果。
（GitHub地址：https://github.com/jasonmayes/Real-Time-Person-Removal）
詳細的代碼也發上來供大家參考吧：
JS：
<code>/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/********************************************************************
* Real-Time-Person-Removal Created by Jason Mayes 2020.
*
* Get latest code on my Github:
* https://github.com/jasonmayes/Real-Time-Person-Removal
*
* Got questions? Reach out to me on social:
* Twitter: @jason_mayes
* LinkedIn: https://www.linkedin.com/in/creativetech
********************************************************************/
const video = document.getElementById('webcam');
const liveView = document.getElementById('liveView');
const demosSection = document.getElementById('demos');
const DEBUG = false;
// An object to configure parameters to set for the bodypix model.
// See github docs for explanations.
const bodyPixProperties = {
architecture: 'MobileNetV1',
outputStride: 16,
multiplier: 0.75,
quantBytes: 4
};
// An object to configure parameters for detection. I have raised
// the segmentation threshold to 90% confidence to reduce the
// number of false positives.
const segmentationProperties = {
flipHorizontal: false,
internalResolution: 'high',
segmentationThreshold: 0.9,
scoreThreshold: 0.2
};
// Render returned segmentation data to a given canvas context.
function processSegmentation(canvas, segmentation) {
var ctx = canvas.getContext('2d');
console.log(segmentation)
// Get data from our overlay canvas which is attempting to estimate background.
var imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
var data = imageData.data;
// Get data from the live webcam view which has all data.
var liveData = videoRenderCanvasCtx.getImageData(0, 0, canvas.width, canvas.height);
var dataL = liveData.data;
var minX = 100000;
var minY = 100000;
var maxX = 0;
var maxY = 0;
var foundBody = false;
// Go through pixels and figure out bounding box of body pixels.
for (let x = 0; x for (let y = 0; y let n = y * canvas.width + x;
// Human pixel found. Update bounds.
if (segmentation.data[n] !== 0) {
if(x minX = x;
}
if(y minY = y;
}
if(x > maxX) {
maxX = x;
}
if(y > maxY) {
maxY = y;
}
foundBody = true;
}
}
}
// Calculate dimensions of bounding box.
var width = maxX - minX;
var height = maxY - minY;
// Define scale factor to use to allow for false negatives around this region.
var scale = 1.3;
// Define scaled dimensions.
var newWidth = width * scale;
var newHeight = height * scale;
// Caculate the offset to place new bounding box so scaled from center of current bounding box.
var offsetX = (newWidth - width) / 2;
var offsetY = (newHeight - height) / 2;
var newXMin = minX - offsetX;
var newYMin = minY - offsetY;
// Now loop through update backgound understanding with new data
// if not inside a bounding box.
for (let x = 0; x for (let y = 0; y // If outside bounding box and we found a body, update background.
if (foundBody && (x newXMin + newWidth) || ( y newYMin + newHeight)) {
// Convert xy co-ords to array offset.
let n = y * canvas.width + x;
data[n * 4] = dataL[n * 4];
data[n * 4 + 1] = dataL[n * 4 + 1];
data[n * 4 + 2] = dataL[n * 4 + 2];
data[n * 4 + 3] = 255;
} else if (!foundBody) {
// No body found at all, update all pixels.
let n = y * canvas.width + x;
data[n * 4] = dataL[n * 4];
data[n * 4 + 1] = dataL[n * 4 + 1];
data[n * 4 + 2] = dataL[n * 4 + 2];
data[n * 4 + 3] = 255;
}
}
}
ctx.putImageData(imageData, 0, 0);
if (DEBUG) {
ctx.strokeStyle = "#00FF00"
ctx.beginPath();
ctx.rect(newXMin, newYMin, newWidth, newHeight);
ctx.stroke();
}
}
// Let's load the model with our parameters defined above.
// Before we can use bodypix class we must wait for it to finish
// loading. Machine Learning models can be large and take a moment to
// get everything needed to run.
var modelHasLoaded = false;
var model = undefined;
model = bodyPix.load(bodyPixProperties).then(function (loadedModel) {
model = loadedModel;
modelHasLoaded = true;
// Show demo section now model is ready to use.
demosSection.classList.remove('invisible');
});
/********************************************************************
// Continuously grab image from webcam stream and classify it.
********************************************************************/
var previousSegmentationComplete = true;
// Check if webcam access is supported.
function hasGetUserMedia() {
return !!(navigator.mediaDevices &&
navigator.mediaDevices.getUserMedia);
}
// This function will repeatidly call itself when the browser is ready to process
// the next frame from webcam.
function predictWebcam() {
if (previousSegmentationComplete) {
// Copy the video frame from webcam to a tempory canvas in memory only (not in the DOM).
videoRenderCanvasCtx.drawImage(video, 0, 0);
previousSegmentationComplete = false;
// Now classify the canvas image we have available.
model.segmentPerson(videoRenderCanvas, segmentationProperties).then(function(segmentation) {
processSegmentation(webcamCanvas, segmentation);
previousSegmentationComplete = true;
});
}
// Call this function again to keep predicting when the browser is ready.
window.requestAnimationFrame(predictWebcam);
}
// Enable the live webcam view and start classification.
function enableCam(event) {
if (!modelHasLoaded) {
return;
}
// Hide the button.
event.target.classList.add('removed');
// getUsermedia parameters.
const constraints = {
video: true
};
// Activate the webcam stream.
navigator.mediaDevices.getUserMedia(constraints).then(function(stream) {
video.addEventListener('loadedmetadata', function() {
// Update widths and heights once video is successfully played otherwise
// it will have width and height of zero initially causing classification
// to fail.
webcamCanvas.width = video.videoWidth;
webcamCanvas.height = video.videoHeight;
videoRenderCanvas.width = video.videoWidth;
videoRenderCanvas.height = video.videoHeight;
bodyPixCanvas.width = video.videoWidth;
bodyPixCanvas.height = video.videoHeight;
let webcamCanvasCtx = webcamCanvas.getContext('2d');
webcamCanvasCtx.drawImage(video, 0, 0);
});
video.srcObject = stream;
video.addEventListener('loadeddata', predictWebcam);
});
}
// We will create a tempory canvas to render to store frames from
// the web cam stream for classification.
var videoRenderCanvas = document.createElement('canvas');
var videoRenderCanvasCtx = videoRenderCanvas.getContext('2d');
// Lets create a canvas to render our findings to the DOM.
var webcamCanvas = document.createElement('canvas');
webcamCanvas.setAttribute('class', 'overlay');
liveView.appendChild(webcamCanvas);
// Create a canvas to render ML findings from to manipulate.
var bodyPixCanvas = document.createElement('canvas');
bodyPixCanvas.setAttribute('class', 'overlay');
var bodyPixCanvasCtx = bodyPixCanvas.getContext('2d');
bodyPixCanvasCtx.fillStyle = '#FF0000';
liveView.appendChild(bodyPixCanvas);
// If webcam supported, add event listener to button for when user
// wants to activate it.
if (hasGetUserMedia()) {
const enableWebcamButton = document.getElementById('webcamButton');
enableWebcamButton.addEventListener('click', enableCam);
} else {
console.warn('getUserMedia() is not supported by your browser');
}/<code>
CSS：
<code>/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/******************************************************
* Stylesheet by Jason Mayes 2020.
*
* Get latest code on my Github:
* https://github.com/jasonmayes/Real-Time-Person-Removal
* Got questions? Reach out to me on social:
* Twitter: @jason_mayes
* LinkedIn: https://www.linkedin.com/in/creativetech
*****************************************************/
body {
font-family: helvetica, arial, sans-serif;
margin: 2em;
color: #3D3D3D;
}
h1 {
font-style: italic;
color: #FF6F00;
}
h2 {
clear: both;
}
em {
font-weight: bold;
}
video {
clear: both;
display: block;
}
section {
opacity: 1;
transition: opacity 500ms ease-in-out;
}
header, footer {
clear: both;
}
button {
z-index: 1000;
position: relative;
}
.removed {
display: none;
}
.invisible {
opacity: 0.2;
}
.note {
font-style: italic;
font-size: 130%;
}
.webcam {
position: relative;
}
.webcam, .classifyOnClick {
position: relative;
float: left;
width: 48%;
margin: 2% 1%;
cursor: pointer;
}
.webcam p, .classifyOnClick p {
position: absolute;
padding: 5px;
background-color: rgba(255, 111, 0, 0.85);
color: #FFF;
border: 1px dashed rgba(255, 255, 255, 0.7);
z-index: 2;
font-size: 12px;
}
.highlighter {
background: rgba(0, 255, 0, 0.25);
border: 1px dashed #fff;
z-index: 1;
position: absolute;
}
.classifyOnClick {
z-index: 0;
position: relative;
}
.classifyOnClick canvas, .webcam canvas.overlay {
opacity: 1;
top: 0;
left: 0;
z-index: 2;
}
#liveView {
transform-origin: top left;
transform: scale(1);
}/<code>
HTML：
<code>html>
<title>Disappearing People Project/<title>
<link>
Disappearing People Project
<header>
Removing people from complex backgrounds in real time using TensorFlow.js
/<header>
How to use
Please wait for the model to load before trying the demos below at which point they will become visible when ready to use.
Here is a video of what you can expect to achieve using my custom algorithm. The top is the actual footage, the bottom video is with the real time removal of people working in JavaScript!
<iframe>
Demo: Webcam live removal
Try this out using your webcam. Stand a few feet away from your webcam and start walking around... Watch as you slowly disappear in the bottom preview.
<button>Enable Webcam/<button>
<video>
/
/<code>
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