{"action":"created","comment":{"url":"https://api.github.com/repos/andriworld/dancehack/pulls/comments/238121976","pull_request_review_id":180586162,"id":238121976,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzODEyMTk3Ng==","diff_hunk":"@@ -79,253 +80,274 @@ function isMobile() {\n async function setupCamera() {\n   if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {\n     throw new Error(\n-        'Browser API navigator.mediaDevices.getUserMedia not available');\n+      'Browser API navigator.mediaDevices.getUserMedia not available'\n+    )\n   }\n \n-  const video = document.getElementById('video');\n-  video.width = videoWidth;\n-  video.height = videoHeight;\n+  const video = document.getElementById('video')\n+  video.width = videoWidth\n+  video.height = videoHeight\n \n-  const mobile = isMobile();\n+  const mobile = isMobile()\n   const stream = await navigator.mediaDevices.getUserMedia({\n-    'audio': false,\n-    'video': {\n+    audio: false,\n+    video: {\n       facingMode: 'user',\n       width: mobile ? undefined : videoWidth,\n-      height: mobile ? undefined : videoHeight,\n-    },\n-  });\n-  video.srcObject = stream;\n+      height: mobile ? undefined : videoHeight\n+    }\n+  })\n+  video.srcObject = stream\n \n-  return new Promise((resolve) => {\n+  return new Promise(resolve => {\n     video.onloadedmetadata = () => {\n-      resolve(video);\n-    };\n-  });\n+      resolve(video)\n+    }\n+  })\n }\n \n async function loadVideo() {\n-  const video = await setupCamera();\n-  video.play();\n+  const video = await setupCamera()\n+  video.play()\n \n-  return video;\n+  return video\n }\n \n const guiState = {\n   algorithm: 'multi-pose',\n   input: {\n     mobileNetArchitecture: isMobile() ? '0.50' : '0.75',\n     outputStride: 16,\n-    imageScaleFactor: 0.5,\n+    imageScaleFactor: 0.5\n   },\n   singlePoseDetection: {\n     minPoseConfidence: 0.1,\n-    minPartConfidence: 0.5,\n+    minPartConfidence: 0.5\n   },\n   multiPoseDetection: {\n     maxPoseDetections: 5,\n     minPoseConfidence: 0.15,\n     minPartConfidence: 0.1,\n-    nmsRadius: 30.0,\n+    nmsRadius: 30.0\n   },\n   output: {\n     showVideo: true,\n     showSkeleton: true,\n     showPoints: true,\n-    showBoundingBox: false,\n+    showBoundingBox: false\n   },\n-  net: null,\n-};\n+  net: null\n+}\n \n /**\n  * Sets up dat.gui controller on the top-right of the window\n  */\n function setupGui(cameras, net) {\n-  guiState.net = net;\n+  guiState.net = net\n \n   if (cameras.length > 0) {\n-    guiState.camera = cameras[0].deviceId;\n+    guiState.camera = cameras[0].deviceId\n   }\n \n-  const gui = new dat.GUI({width: 300});\n+  const gui = new dat.GUI({ width: 300 })\n+  dat.GUI.toggleHide()\n \n   // The single-pose algorithm is faster and simpler but requires only one\n   // person to be in the frame or results will be innaccurate. Multi-pose works\n   // for more than 1 person\n-  const algorithmController =\n-      gui.add(guiState, 'algorithm', ['single-pose', 'multi-pose']);\n+  const algorithmController = gui.add(guiState, 'algorithm', [\n+    'single-pose',\n+    'multi-pose'\n+  ])\n \n   // The input parameters have the most effect on accuracy and speed of the\n   // network\n-  let input = gui.addFolder('Input');\n+  let input = gui.addFolder('Input')\n   // Architecture: there are a few PoseNet models varying in size and\n   // accuracy. 1.01 is the largest, but will be the slowest. 0.50 is the\n   // fastest, but least accurate.\n   const architectureController = input.add(\n-      guiState.input, 'mobileNetArchitecture',\n-      ['1.01', '1.00', '0.75', '0.50']);\n+    guiState.input,\n+    'mobileNetArchitecture',\n+    ['1.01', '1.00', '0.75', '0.50']\n+  )\n   // Output stride:  Internally, this parameter affects the height and width of\n   // the layers in the neural network. The lower the value of the output stride\n   // the higher the accuracy but slower the speed, the higher the value the\n   // faster the speed but lower the accuracy.\n-  input.add(guiState.input, 'outputStride', [8, 16, 32]);\n+  input.add(guiState.input, 'outputStride', [8, 16, 32])\n   // Image scale factor: What to scale the image by before feeding it through\n   // the network.\n-  input.add(guiState.input, 'imageScaleFactor').min(0.2).max(1.0);\n-  input.open();\n+  input\n+    .add(guiState.input, 'imageScaleFactor')\n+    .min(0.2)\n+    .max(1.0)\n+  input.open()\n \n   // Pose confidence: the overall confidence in the estimation of a person's\n   // pose (i.e. a person detected in a frame)\n   // Min part confidence: the confidence that a particular estimated keypoint\n   // position is accurate (i.e. the elbow's position)\n-  let single = gui.addFolder('Single Pose Detection');\n-  single.add(guiState.singlePoseDetection, 'minPoseConfidence', 0.0, 1.0);\n-  single.add(guiState.singlePoseDetection, 'minPartConfidence', 0.0, 1.0);\n-\n-  let multi = gui.addFolder('Multi Pose Detection');\n-  multi.add(guiState.multiPoseDetection, 'maxPoseDetections')\n-      .min(1)\n-      .max(20)\n-      .step(1);\n-  multi.add(guiState.multiPoseDetection, 'minPoseConfidence', 0.0, 1.0);\n-  multi.add(guiState.multiPoseDetection, 'minPartConfidence', 0.0, 1.0);\n+  let single = gui.addFolder('Single Pose Detection')\n+  single.add(guiState.singlePoseDetection, 'minPoseConfidence', 0.0, 1.0)\n+  single.add(guiState.singlePoseDetection, 'minPartConfidence', 0.0, 1.0)\n+\n+  let multi = gui.addFolder('Multi Pose Detection')\n+  multi\n+    .add(guiState.multiPoseDetection, 'maxPoseDetections')\n+    .min(1)\n+    .max(20)\n+    .step(1)\n+  multi.add(guiState.multiPoseDetection, 'minPoseConfidence', 0.0, 1.0)\n+  multi.add(guiState.multiPoseDetection, 'minPartConfidence', 0.0, 1.0)\n   // nms Radius: controls the minimum distance between poses that are returned\n   // defaults to 20, which is probably fine for most use cases\n-  multi.add(guiState.multiPoseDetection, 'nmsRadius').min(0.0).max(40.0);\n-  multi.open();\n-\n-  let output = gui.addFolder('Output');\n-  output.add(guiState.output, 'showVideo');\n-  output.add(guiState.output, 'showSkeleton');\n-  output.add(guiState.output, 'showPoints');\n-  output.add(guiState.output, 'showBoundingBox');\n-  output.open();\n-\n+  multi\n+    .add(guiState.multiPoseDetection, 'nmsRadius')\n+    .min(0.0)\n+    .max(40.0)\n+  multi.open()\n+\n+  let output = gui.addFolder('Output')\n+  output.add(guiState.output, 'showVideo')\n+  output.add(guiState.output, 'showSkeleton')\n+  output.add(guiState.output, 'showPoints')\n+  output.add(guiState.output, 'showBoundingBox')\n+  output.open()\n \n   architectureController.onChange(function(architecture) {\n-    guiState.changeToArchitecture = architecture;\n-  });\n+    guiState.changeToArchitecture = architecture\n+  })\n \n   algorithmController.onChange(function(value) {\n     switch (guiState.algorithm) {\n       case 'single-pose':\n-        multi.close();\n-        single.open();\n-        break;\n+        multi.close()\n+        single.open()\n+        break\n       case 'multi-pose':\n-        single.close();\n-        multi.open();\n-        break;\n+        single.close()\n+        multi.open()\n+        break\n     }\n-  });\n+  })\n }\n \n /**\n  * Sets up a frames per second panel on the top-left of the window\n  */\n function setupFPS() {\n-  stats.showPanel(0);  // 0: fps, 1: ms, 2: mb, 3+: custom\n-  document.body.appendChild(stats.dom);\n+  stats.showPanel(0) // 0: fps, 1: ms, 2: mb, 3+: custom\n+  // hide stats for performance\n+  // document.body.appendChild(stats.dom)","path":"2018/pose-particles/00-posenet/camera.js","position":319,"original_position":319,"commit_id":"5eb256171f61e9598402262b9933f5411984f3ba","original_commit_id":"5eb256171f61e9598402262b9933f5411984f3ba","user":{"login":"micahstubbs","id":2119400,"node_id":"MDQ6VXNlcjIxMTk0MDA=","avatar_url":"https://avatars3.githubusercontent.com/u/2119400?v=4","gravatar_id":"","url":"https://api.github.com/users/micahstubbs","html_url":"https://github.com/micahstubbs","followers_url":"https://api.github.com/users/micahstubbs/followers","following_url":"https://api.github.com/users/micahstubbs/following{/other_user}","gists_url":"https://api.github.com/users/micahstubbs/gists{/gist_id}","starred_url":"https://api.github.com/users/micahstubbs/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/micahstubbs/subscriptions","organizations_url":"https://api.github.com/users/micahstubbs/orgs","repos_url":"https://api.github.com/users/micahstubbs/repos","events_url":"https://api.github.com/users/micahstubbs/events{/privacy}","received_events_url":"https://api.github.com/users/micahstubbs/received_events","type":"User","site_admin":false},"body":"here we comment out the fps meter that was drawn on the top left","created_at":"2018-12-03T00:08:29Z","updated_at":"2018-12-03T00:08:29Z","html_url":"https://github.com/andriworld/dancehack/pull/1#discussion_r238121976","pull_request_url":"https://api.github.com/repos/andriworld/dancehack/pulls/1","author_association":"NONE","_links":{"self":{"href":"https://api.github.com/repos/andriworld/dancehack/pulls/comments/238121976"},"html":{"href":"https://github.com/andriworld/dancehack/pull/1#discussion_r238121976"},"pull_request":{"href":"https://api.github.com/repos/andriworld/dancehack/pulls/1"}}},"pull_request":{"url":"https://api.github.com/repos/andriworld/dancehack/pulls/1","id":235296703,"node_id":"MDExOlB1bGxSZXF1ZXN0MjM1Mjk2NzAz","html_url":"https://github.com/andriworld/dancehack/pull/1","diff_url":"https://github.com/andriworld/dancehack/pull/1.diff","patch_url":"https://github.com/andriworld/dancehack/pull/1.patch","issue_url":"https://api.github.com/repos/andriworld/dancehack/issues/1","number":1,"state":"open","locked":false,"title":"add 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