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dc5fca9 | 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 | import cv2
import mediapipe as mp
import numpy as np
import gradio as gr
# Initialize MediaPipe Pose and drawing utils.
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
# Calculate angle between three points.
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
# Process the video and overlay lunge feedback.
def analyze_lunges(video_path):
cap = cv2.VideoCapture(video_path)
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = cap.get(cv2.CAP_PROP_FPS) if cap.get(cv2.CAP_PROP_FPS) > 0 else 30
output_video = "output_lunges.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_video, fourcc, fps, (frame_width, frame_height))
# Check lunge form based on leg angles.
def check_lunge_feedback(landmarks):
left_hip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
left_knee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
left_ankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
right_hip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]
right_knee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]
right_ankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]
left_leg_angle = calculate_angle(left_hip, left_knee, left_ankle)
right_leg_angle = calculate_angle(right_hip, right_knee, right_ankle)
left_accuracy = max(0, min(100, (1 - abs(left_leg_angle - 90) / 30) * 100))
right_accuracy = max(0, min(100, (1 - abs(right_leg_angle - 90) / 30) * 100))
feedback = "Correct Lunge"
if left_leg_angle < 70 or right_leg_angle < 70:
feedback = "Incorrect Lunge - Deep enough"
elif left_leg_angle > 110 or right_leg_angle > 110:
feedback = "Incorrect Lunge - Lower hips"
return feedback, left_accuracy, right_accuracy
# Draw separate accuracy bars for the left and right legs.
def draw_accuracy_bar(image, left_accuracy, right_accuracy):
bar_x, bar_y = 50, 400
bar_width, bar_height = 200, 20
left_fill_width = int((left_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 + left_fill_width, bar_y + bar_height), (0, 255, 0), -1)
cv2.putText(image, f"Left Leg Accuracy: {left_accuracy}%", (bar_x, bar_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
right_bar_y = bar_y + 50
right_fill_width = int((right_accuracy / 100) * bar_width)
cv2.rectangle(image, (bar_x, right_bar_y), (bar_x + bar_width, right_bar_y + bar_height), (200, 200, 200), 2)
cv2.rectangle(image, (bar_x, right_bar_y), (bar_x + right_fill_width, right_bar_y + bar_height), (0, 255, 0), -1)
cv2.putText(image, f"Right Leg Accuracy: {right_accuracy}%", (bar_x, right_bar_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Process frame with MediaPipe.
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)
feedback, left_accuracy, right_accuracy = check_lunge_feedback(results.pose_landmarks.landmark)
draw_accuracy_bar(image, left_accuracy, right_accuracy)
color = (0, 255, 0) if "Correct" in feedback else (0, 0, 255)
cv2.putText(image, feedback, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3)
out.write(image)
cap.release()
out.release()
return output_video
# Gradio Interface for lunge analysis.
gr.Interface(
fn=analyze_lunges,
inputs=gr.Video(),
outputs=gr.Video(),
title="Lunge Form Analyzer",
description="Upload a video of your lunges, and get feedback on your form!",
).launch()
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