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pythonioncoderpythonioncoder
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Added path functionality so users can use their own paths
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‎Prototype_2_Electric_Boogalooo.ipynb

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@@ -5,7 +5,7 @@
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"colab": {
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"provenance": [],
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"mount_file_id": "1YJfEK8zha4PfUk2yP9y-XbO4vtmn83gE",
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"authorship_tag": "ABX9TyOxdY25CPapUx8uMRKR6IDa",
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"authorship_tag": "ABX9TyOBWPsdBZG7A+YZa8hU7Fjw",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -40,49 +40,39 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "gmwahO7oITMz",
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"outputId": "5a88f36a-ef77-4550-ee4f-2dee36b2526d",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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"id": "gmwahO7oITMz"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m33.9/33.9 MB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h"
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]
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}
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],
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"outputs": [],
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"source": [
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"!pip install -q mediapipe==0.10.0"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Make sure a copy of the **Fit Form AI Resources** folder is in your personal Drive or at the relevant directory before the next step\n",
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"\n",
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"Folder can be found here: https://drive.google.com/drive/folders/1RFTh9ed50zVZaIyutPCo6-VTEQanqE4_?usp=sharing"
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],
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"metadata": {
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"id": "e1sSWM10JfE7"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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"drive.mount('/content/drive')\n",
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"root_path = '/content/drive/MyDrive'\n",
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"\n",
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"if input(\"Are you using your local drive without any subfolders? (Y / N)\\n\") != 'Y': # Don't include the Fit Form AI Resources folder itself\n",
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" root_path = input(\"Enter path to the Fit Form AI Resources folder:\\n\")"
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],
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"metadata": {
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"id": "8xGt3oqXze3o",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "34c50804-976f-484f-fbe1-32b9669dbbd6"
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"id": "8xGt3oqXze3o"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mounted at /content/drive\n"
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]
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
@@ -173,7 +163,7 @@
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" # Calculates the angles the exercise requires (determines necessity if weight value signals its needed)\n",
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" # Compares the calculated angle with the min-max of the exercise\n",
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" def feedback(self, exercise):\n",
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" with open(f'/content/drive/MyDrive/Fit Form AI Resources/CSV Files/{exercise}.csv') as anglefile:\n",
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" with open(root_path + f'/Fit Form AI Resources/CSV Files/{exercise}.csv') as anglefile:\n",
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" anglereader = csv.reader(anglefile)\n",
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" for row in anglereader:\n",
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" # First check to see if you should calculate the angle\n",
@@ -291,19 +281,6 @@
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Make sure to import **Pose Landmarker Lite** into Drive folder before next step\n",
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"\n",
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"Can be found in Drive folder: https://drive.google.com/drive/folders/1RFTh9ed50zVZaIyutPCo6-VTEQanqE4_?usp=sharing\n",
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"\n",
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"If needed, download latest from MediaPipe website: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/index#models"
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],
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"metadata": {
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"id": "e1sSWM10JfE7"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
@@ -316,7 +293,7 @@
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{
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"cell_type": "code",
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"source": [
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"model_path = f'/content/drive/MyDrive/Fit Form AI Resources/pose_landmarker_{input(\"What model to use? \")}.task'"
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"model_path = root_path + f'/Fit Form AI Resources/pose_landmarker_{input(\"What model to use? \")}.task'"
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],
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"metadata": {
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"id": "Zng2DvUJcJjc",
@@ -339,9 +316,7 @@
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{
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"cell_type": "markdown",
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"source": [
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"If you would like to save a video of the live camera feed, type **'Y'** below. If not, **'N'** or leave blank.\n",
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"\n",
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"***Note: Videos are saved in a PUBLIC GitHub repo with little option for deletion. Once a recording is made, expect it to stay there.***"
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"If you would like to save a video of the live camera feed, type **'Y'** below. If not, **'N'** or leave blank."
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],
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"metadata": {
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"id": "-VP0c854aaGl"
@@ -351,8 +326,10 @@
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"cell_type": "code",
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"source": [
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"record = False\n",
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"vid_path = ''\n",
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"if input(\"Would you like to record and save the video you create below? (Y / N)\\n\") == 'Y':\n",
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" record = True"
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" record = True\n",
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" vid_path = root_path + '/Fit Form AI Resources/Videos/'"
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],
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"metadata": {
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"id": "brFyKQV5aEuK"
@@ -370,7 +347,7 @@
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"PoseLandmarkerResult = vision.PoseLandmarkerResult # Stores result from model\n",
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"out_video = None\n",
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"if record:\n",
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" out_video = cv.VideoWriter('/content/drive/MyDrive/Fit Form AI Resources/Videos/' + time.asctime(time.localtime()) + '.mp4',cv.VideoWriter_fourcc(*'DIVX'), 24, (640, 480))\n",
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" out_video = cv.VideoWriter(vid_path + time.asctime(time.localtime()) + '.mp4',cv.VideoWriter_fourcc(*'DIVX'), 24, (640, 480))\n",
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"landmarks_list = []\n",
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"feedback_list = [\"\"]\n",
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"selected_exercise = input(\"What exercise are you tracking?\\n\")\n",
@@ -552,192 +529,10 @@
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" out_video.release()"
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],
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"metadata": {
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"id": "GHzmhoO6Jc50",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 856
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},
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"outputId": "4d009091-47a2-49c6-e8e8-70a776fb6d19"
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"id": "GHzmhoO6Jc50"
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},
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"execution_count": null,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"What exercise are you tracking?\n",
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"Push Up\n"
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]
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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],
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"application/javascript": [
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"\n",
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" var video;\n",
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" var div = null;\n",
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" var stream;\n",
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" var captureCanvas;\n",
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" var imgElement;\n",
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" var labelElement;\n",
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"\n",
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" var pendingResolve = null;\n",
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" var shutdown = false;\n",
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"\n",
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" function removeDom() {\n",
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" stream.getVideoTracks()[0].stop();\n",
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" video.remove();\n",
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" div.remove();\n",
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" video = null;\n",
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" div = null;\n",
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" stream = null;\n",
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" imgElement = null;\n",
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" captureCanvas = null;\n",
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" labelElement = null;\n",
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" }\n",
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"\n",
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" function onAnimationFrame() {\n",
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" if (!shutdown) {\n",
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" window.requestAnimationFrame(onAnimationFrame);\n",
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" }\n",
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" if (pendingResolve) {\n",
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" var result = \"\";\n",
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" if (!shutdown) {\n",
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" captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
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" result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
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" }\n",
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" var lp = pendingResolve;\n",
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" pendingResolve = null;\n",
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" lp(result);\n",
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" }\n",
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" }\n",
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"\n",
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" async function createDom() {\n",
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" if (div !== null) {\n",
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" return stream;\n",
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" }\n",
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" div = document.createElement('div');\n",
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" div.style.border = '2px solid black';\n",
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" div.style.padding = '3px';\n",
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" div.style.width = '100%';\n",
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" div.style.maxWidth = '600px';\n",
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" document.body.appendChild(div);\n",
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"\n",
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"\n",
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" video = document.createElement('video');\n",
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" video.style.display = 'block';\n",
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" video.width = div.clientWidth - 6;\n",
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" video.setAttribute('playsinline', '');\n",
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" video.onclick = () => { shutdown = true; };\n",
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" stream = await navigator.mediaDevices.getUserMedia(\n",
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" {video: { facingMode: \"environment\"}});\n",
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" div.appendChild(video);\n",
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" imgElement = document.createElement('img');\n",
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" imgElement.style.position = 'absolute';\n",
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" imgElement.style.zIndex = 1;\n",
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" imgElement.onclick = () => { shutdown = true; };\n",
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" div.appendChild(imgElement);\n",
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"\n",
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" const instruction = document.createElement('div');\n",
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" instruction.innerHTML =\n",
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" '<span style=\"blue: red; font-weight: bold;\">' +\n",
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" 'click here to stop the video</span>';\n",
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" div.appendChild(instruction);\n",
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" instruction.onclick = () => { shutdown = true; };\n",
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"\n",
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" video.srcObject = stream;\n",
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" await video.play();\n",
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" captureCanvas = document.createElement('canvas');\n",
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" captureCanvas.width = 640;\n",
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" captureCanvas.height = 480;\n",
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" window.requestAnimationFrame(onAnimationFrame);\n",
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"\n",
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" return stream;\n",
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" }\n",
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" async function stream_frame() {\n",
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" if (shutdown) {\n",
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" removeDom();\n",
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" shutdown = false;\n",
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" return '';\n",
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" }\n",
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" var preCreate = Date.now();\n",
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" stream = await createDom();\n",
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"\n",
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" var preShow = Date.now();\n",
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"\n",
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"\n",
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"\n",
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" var preCapture = Date.now();\n",
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" var result = await new Promise(function(resolve, reject) {\n",
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" pendingResolve = resolve;\n",
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" });\n",
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" shutdown = false;\n",
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"\n",
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" return {'create': preShow - preCreate,\n",
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" 'show': preCapture - preShow,\n",
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" 'capture': Date.now() - preCapture,\n",
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" 'img': result};\n",
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" }\n",
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" "
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]
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},
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Try Straightening Your Arm Somewhat\n",
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"Right Arm Isn't Visible\n",
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"Try Straightening Your Arm Somewhat\n",
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"Left Arm Isn't Visible\n",
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"Right Arm Isn't Visible\n",
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"Try Straightening Your Hip Somewhat\n",
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"Try Straightening Your Arm Somewhat\n",
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"Try Straightening Your Hip Somewhat\n",
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"Try Straightening Your Arm Somewhat\n",
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"Right Leg Isn't Visible\n",
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"Try Straightening Your Arm Somewhat\n",
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"Try Straightening Your Hip Somewhat\n",
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"Try Spreading Your Arms Out More\n",
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"Good Form\n",
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"Try Straightening Your Legs More\n",
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"Try Spreading Your Arms Out More\n",
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"Good Form\n",
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"Try Spreading Your Arms Out More\n",
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"Good Form\n",
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"Try Spreading Your Arms Out More\n",
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"Good Form\n",
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"Try Spreading Your Arms Out More\n",
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"Try Straightening Your Arm Somewhat\n",
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"Try Spreading Your Arms Out More\n",
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"Good Form\n",
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"Right Leg Isn't Visible\n",
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"Try Straightening Your Hip Somewhat\n"
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]
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}
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]
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"outputs": []
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}
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]
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}

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