Repository: nicknochnack/MediaPipePoseEstimation
Branch: main
Commit: 827ea243f823
Files: 2
Total size: 28.2 KB
Directory structure:
gitextract_1uy8wbaa/
├── .ipynb_checkpoints/
│ └── Media Pipe Pose Tutorial-checkpoint.ipynb
└── Media Pipe Pose Tutorial.ipynb
================================================
FILE CONTENTS
================================================
================================================
FILE: .ipynb_checkpoints/Media Pipe Pose Tutorial-checkpoint.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Install and Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install mediapipe opencv-python"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import mediapipe as mp\n",
"import numpy as np\n",
"mp_drawing = mp.solutions.drawing_utils\n",
"mp_pose = mp.solutions.pose"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# VIDEO FEED\n",
"cap = cv2.VideoCapture(0)\n",
"while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" cv2.imshow('Mediapipe Feed', frame)\n",
" \n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
" \n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Make Detections"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mp_drawing.DrawingSpec??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Determining Joints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://i.imgur.com/3j8BPdc.png\" style=\"height:300px\" >"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" print(landmarks)\n",
" except:\n",
" pass\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(landmarks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for lndmrk in mp_pose.PoseLandmark:\n",
" print(lndmrk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].visibility"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Calculate Angles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def calculate_angle(a,b,c):\n",
" a = np.array(a) # First\n",
" b = np.array(b) # Mid\n",
" c = np.array(c) # End\n",
" \n",
" radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])\n",
" angle = np.abs(radians*180.0/np.pi)\n",
" \n",
" if angle >180.0:\n",
" angle = 360-angle\n",
" \n",
" return angle "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
"elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
"wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shoulder, elbow, wrist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"calculate_angle(shoulder, elbow, wrist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tuple(np.multiply(elbow, [640, 480]).astype(int))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" # Get coordinates\n",
" shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
" elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
" wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
" \n",
" # Calculate angle\n",
" angle = calculate_angle(shoulder, elbow, wrist)\n",
" \n",
" # Visualize angle\n",
" cv2.putText(image, str(angle), \n",
" tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
" )\n",
" \n",
" except:\n",
" pass\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Curl Counter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"\n",
"# Curl counter variables\n",
"counter = 0 \n",
"stage = None\n",
"\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" # Get coordinates\n",
" shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
" elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
" wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
" \n",
" # Calculate angle\n",
" angle = calculate_angle(shoulder, elbow, wrist)\n",
" \n",
" # Visualize angle\n",
" cv2.putText(image, str(angle), \n",
" tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
" )\n",
" \n",
" # Curl counter logic\n",
" if angle > 160:\n",
" stage = \"down\"\n",
" if angle < 30 and stage =='down':\n",
" stage=\"up\"\n",
" counter +=1\n",
" print(counter)\n",
" \n",
" except:\n",
" pass\n",
" \n",
" # Render curl counter\n",
" # Setup status box\n",
" cv2.rectangle(image, (0,0), (225,73), (245,117,16), -1)\n",
" \n",
" # Rep data\n",
" cv2.putText(image, 'REPS', (15,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(counter), \n",
" (10,60), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
" \n",
" # Stage data\n",
" cv2.putText(image, 'STAGE', (65,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, stage, \n",
" (60,60), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mediapipe",
"language": "python",
"name": "mediapipe"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Media Pipe Pose Tutorial.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Install and Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install mediapipe opencv-python"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import mediapipe as mp\n",
"import numpy as np\n",
"mp_drawing = mp.solutions.drawing_utils\n",
"mp_pose = mp.solutions.pose"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# VIDEO FEED\n",
"cap = cv2.VideoCapture(0)\n",
"while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" cv2.imshow('Mediapipe Feed', frame)\n",
" \n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
" \n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Make Detections"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mp_drawing.DrawingSpec??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Determining Joints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://i.imgur.com/3j8BPdc.png\" style=\"height:300px\" >"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" print(landmarks)\n",
" except:\n",
" pass\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(landmarks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for lndmrk in mp_pose.PoseLandmark:\n",
" print(lndmrk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].visibility"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Calculate Angles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def calculate_angle(a,b,c):\n",
" a = np.array(a) # First\n",
" b = np.array(b) # Mid\n",
" c = np.array(c) # End\n",
" \n",
" radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])\n",
" angle = np.abs(radians*180.0/np.pi)\n",
" \n",
" if angle >180.0:\n",
" angle = 360-angle\n",
" \n",
" return angle "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
"elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
"wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shoulder, elbow, wrist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"calculate_angle(shoulder, elbow, wrist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tuple(np.multiply(elbow, [640, 480]).astype(int))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" # Get coordinates\n",
" shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
" elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
" wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
" \n",
" # Calculate angle\n",
" angle = calculate_angle(shoulder, elbow, wrist)\n",
" \n",
" # Visualize angle\n",
" cv2.putText(image, str(angle), \n",
" tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
" )\n",
" \n",
" except:\n",
" pass\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Curl Counter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap = cv2.VideoCapture(0)\n",
"\n",
"# Curl counter variables\n",
"counter = 0 \n",
"stage = None\n",
"\n",
"## Setup mediapipe instance\n",
"with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" \n",
" # Recolor image to RGB\n",
" image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" image.flags.writeable = False\n",
" \n",
" # Make detection\n",
" results = pose.process(image)\n",
" \n",
" # Recolor back to BGR\n",
" image.flags.writeable = True\n",
" image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
" \n",
" # Extract landmarks\n",
" try:\n",
" landmarks = results.pose_landmarks.landmark\n",
" \n",
" # Get coordinates\n",
" shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]\n",
" elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]\n",
" wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]\n",
" \n",
" # Calculate angle\n",
" angle = calculate_angle(shoulder, elbow, wrist)\n",
" \n",
" # Visualize angle\n",
" cv2.putText(image, str(angle), \n",
" tuple(np.multiply(elbow, [640, 480]).astype(int)), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
" )\n",
" \n",
" # Curl counter logic\n",
" if angle > 160:\n",
" stage = \"down\"\n",
" if angle < 30 and stage =='down':\n",
" stage=\"up\"\n",
" counter +=1\n",
" print(counter)\n",
" \n",
" except:\n",
" pass\n",
" \n",
" # Render curl counter\n",
" # Setup status box\n",
" cv2.rectangle(image, (0,0), (225,73), (245,117,16), -1)\n",
" \n",
" # Rep data\n",
" cv2.putText(image, 'REPS', (15,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, str(counter), \n",
" (10,60), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
" \n",
" # Stage data\n",
" cv2.putText(image, 'STAGE', (65,12), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
" cv2.putText(image, stage, \n",
" (60,60), \n",
" cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)\n",
" \n",
" \n",
" # Render detections\n",
" mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
" mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
" mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
" ) \n",
" \n",
" cv2.imshow('Mediapipe Feed', image)\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break\n",
"\n",
" cap.release()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mediapipe",
"language": "python",
"name": "mediapipe"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
gitextract_1uy8wbaa/ ├── .ipynb_checkpoints/ │ └── Media Pipe Pose Tutorial-checkpoint.ipynb └── Media Pipe Pose Tutorial.ipynb
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (32K chars).
[
{
"path": ".ipynb_checkpoints/Media Pipe Pose Tutorial-checkpoint.ipynb",
"chars": 14457,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 0. Install and Import Dependencie"
},
{
"path": "Media Pipe Pose Tutorial.ipynb",
"chars": 14457,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 0. Install and Import Dependencie"
}
]
About this extraction
This page contains the full source code of the nicknochnack/MediaPipePoseEstimation GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2 files (28.2 KB), approximately 8.7k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.