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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": "ABX9TyOEtdKbdYSy497U7kO4OScE",
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"authorship_tag": "ABX9TyOxdY25CPapUx8uMRKR6IDa",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -173,7 +173,7 @@
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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/Exercise App Resources/CSV Files/{exercise}.csv') as anglefile:\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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" 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",
@@ -316,7 +316,7 @@
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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/Exercise App Resources/pose_landmarker_{input(\"What model to use? \")}.task'"
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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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],
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"metadata": {
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"id": "Zng2DvUJcJjc",
@@ -370,7 +370,7 @@
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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/Exercise App Resources/Videos/' + time.asctime(time.localtime()) + '.mp4',cv.VideoWriter_fourcc(*'DIVX'), 24, (640, 480))\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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"landmarks_list = []\n",
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"feedback_list = [\"\"]\n",
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"selected_exercise = input(\"What exercise are you tracking?\\n\")\n",

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