The original implementation - slower.

Keeping this here for reference. This was the original implementation - which was slightly more granular in patch updates, but 10x slower.

script_original.js

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+
+/**
+ * @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
+};
+
+
+// Must be even. The size of square we wish to search for body parts.
+// This is the smallest area that will render/not render depending on
+// if a body part is found in that square.
+const SEARCH_RADIUS = 300;
+const SEARCH_OFFSET = SEARCH_RADIUS / 2;
+
+// RESOLUTION_MIN should be smaller than SEARCH RADIUS. About 10x smaller seems to 
+// work well. Effects overlap in search space to clean up body overspill for things
+// that were not classified as body but infact were.
+const RESOLUTION_MIN = 20;
+
+// Render returned segmentation data to a given canvas context.
+function processSegmentation(canvas, segmentation) {
+  var ctx = canvas.getContext('2d');
+
+  // 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;
+
+  // Now loop through and see if pixels contain human parts. If not, update 
+  // backgound understanding with new data.
+  for (let x = RESOLUTION_MIN; x < canvas.width; x += RESOLUTION_MIN) {
+    for (let y = RESOLUTION_MIN; y < canvas.height; y += RESOLUTION_MIN) {
+      // Convert xy co-ords to array offset.
+      let n = y * canvas.width + x;
+
+      let foundBodyPartNearby = false;
+
+      // Let's check around a given pixel if any other pixels were body like.
+      let yMin = y - SEARCH_OFFSET;
+      yMin = yMin < 0 ? 0: yMin;
+
+      let yMax = y + SEARCH_OFFSET;
+      yMax = yMax > canvas.height ? canvas.height : yMax;
+
+      let xMin = x - SEARCH_OFFSET;
+      xMin = xMin < 0 ? 0: xMin;
+
+      let xMax = x + SEARCH_OFFSET;
+      xMax = xMax > canvas.width ? canvas.width : xMax;
+
+      for (let i = xMin; i < xMax; i++) {
+        for (let j = yMin; j < yMax; j++) {
+
+          let offset = j * canvas.width + i;
+          // If any of the pixels in the square we are analysing has a body
+          // part, mark as contaminated.
+          if (segmentation.data[offset] !== 0) {
+            foundBodyPartNearby = true;
+            break;
+          } 
+        }
+      }
+
+      // Update patch if patch was clean.     
+      if (!foundBodyPartNearby) {
+        for (let i = xMin; i < xMax; i++) {
+          for (let j = yMin; j < yMax; j++) {
+            // Convert xy co-ords to array offset.
+            let offset = j * canvas.width + i;
+
+            data[offset * 4] = dataL[offset * 4];    
+            data[offset * 4 + 1] = dataL[offset * 4 + 1];
+            data[offset * 4 + 2] = dataL[offset * 4 + 2];
+            data[offset * 4 + 3] = 255;            
+          }
+        }
+      } else {
+        if (DEBUG) {
+          for (let i = xMin; i < xMax; i++) {
+            for (let j = yMin; j < yMax; j++) {
+              // Convert xy co-ords to array offset.
+              let offset = j * canvas.width + i;
+
+              data[offset * 4] = 255;    
+              data[offset * 4 + 1] = 0;
+              data[offset * 4 + 2] = 0;
+              data[offset * 4 + 3] = 255;            
+            }
+          } 
+        }
+      }
+
+    }
+  }
+  ctx.putImageData(imageData, 0, 0);
+}
+
+
+
+// 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;
+      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);
+
+// 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');
+}