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1e216d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import cv2
import mediapipe as mp
import numpy as np
import gradio as gr
# Initialize MediaPipe Pose and Drawing utilities
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
mp_drawing = mp.solutions.drawing_utils
# Utility function to calculate angles
def calculate_angle(a, b, c):
a, b, c = np.array(a), np.array(b), np.array(c)
radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
angle = np.abs(radians * 180.0 / np.pi)
return angle
# Function to provide burpees feedback based on landmarks
def check_burpees_feedback(landmarks):
shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
squat_angle = calculate_angle(hip, knee, ankle)
pushup_angle = calculate_angle(shoulder, elbow, wrist)
jump_angle = calculate_angle(knee, hip, shoulder)
feedback = "Correct Burpee"
accuracy = 100
if squat_angle < 90:
feedback = "Squat too shallow - Go deeper!"
accuracy -= 30
elif squat_angle > 140:
feedback = "Squat too deep - Reduce depth!"
accuracy -= 30
if pushup_angle < 70:
feedback = "Push-up too low - Elbows too bent!"
accuracy -= 30
elif pushup_angle > 120:
feedback = "Push-up too high - Keep arms at 90 degrees!"
accuracy -= 30
if jump_angle < 130:
feedback = "Jump not high enough - Extend fully!"
accuracy -= 30
accuracy = max(0, min(accuracy, 100))
return feedback, accuracy
# Function to draw an accuracy bar on the frame
def draw_accuracy_bar(image, accuracy):
bar_x, bar_y = 50, 400
bar_width, bar_height = 200, 20
fill_width = int((accuracy / 100) * bar_width)
cv2.rectangle(image, (bar_x, bar_y), (bar_x + bar_width, bar_y + bar_height), (200, 200, 200), 2)
cv2.rectangle(image, (bar_x, bar_y), (bar_x + fill_width, bar_y + bar_height), (0, 255, 0), -1)
cv2.putText(image, f"Accuracy: {accuracy}%", (bar_x, bar_y - 10),
cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 2)
# Main function to analyze burpees form in a video
def analyze_burpees(video_path):
cap = cv2.VideoCapture(video_path)
frame_width, frame_height = int(cap.get(3)), int(cap.get(4))
fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
output_video = "output_burpees.mp4"
out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.pose_landmarks:
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
landmarks = results.pose_landmarks.landmark
feedback, accuracy = check_burpees_feedback(landmarks)
draw_accuracy_bar(image, accuracy)
color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)
cv2.putText(image, feedback, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, color, 3)
out.write(image)
cap.release()
out.release()
return output_video
# Gradio Interface for Burpees Analysis
gr.Interface(
fn=analyze_burpees,
inputs=gr.Video(),
outputs=gr.Video(),
title="Burpees Form Analyzer",
description="Upload a video of your burpees, and get feedback on your form!"
).launch()
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