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from flask import Flask, request, jsonify # Request and jsonify are used to handle incoming requests and send JSON responses
from flask_cors import CORS # CORS is used to handle cross-origin requests
import cv2
import mediapipe as mp
import numpy as np
import pandas as pd
import os
import base64
# import time -> MAY BE USED LATER
from joblib import load
app = Flask(__name__)
CORS(app) # Allow cross-origin requests
# Initialize MediaPipe Pose
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
script_dir = os.path.dirname(os.path.abspath(__file__))
MODEL_FILE = os.path.join(script_dir, "pushup_model.joblib")
ENCODER_FILE = os.path.join(script_dir, "label_encoder.joblib")
# Path to model files
# DATA_DIR = "push_ups" -> MAY BE USED LATER
# Load model and label encoder
try:
model = load(MODEL_FILE)
label_encoder = load(ENCODER_FILE)
print("Model loaded successfully!")
print(f"Classes: {label_encoder.classes_}")
except Exception as e:
print(f"Error loading model: {e}")
model = None
label_encoder = None
# Initialize pose detector
pose = mp_pose.Pose(
min_detection_confidence=0.7,
min_tracking_confidence=0.7,
static_image_mode=False # For video processing
)
def calculate_angle(a, b, c):
a = np.array(a) # First point
b = np.array(b) # Mid point
c = np.array(c) # End point
# Calculate vectors
ba = a - b
bc = c - b
# Calculate angle using the dot product
cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
cosine_angle = np.clip(cosine_angle, -1.0, 1.0) # Ensure value is within domain of arccos
angle = np.arccos(cosine_angle)
# Convert to degrees
angle = np.degrees(angle)
return angle
@app.route('/api/health', methods=['GET'])
def health_check():
"""Simple health check endpoint"""
return jsonify({
'status': 'ok',
'model_loaded': model is not None,
'classes': label_encoder.classes_.tolist() if label_encoder else None
})
@app.route('/api/process-frame', methods=['POST'])
def process_frame():
"""Process a single video frame for pose detection"""
if not request.json or 'image' not in request.json:
return jsonify({'error': 'No image data provided'}), 400
if model is None or label_encoder is None:
return jsonify({'error': 'Model not loaded'}), 500
try:
# Decode base64 image
image_data = request.json['image'].split(',')[1] if ',' in request.json['image'] else request.json['image']
image_bytes = base64.b64decode(image_data)
nparr = np.frombuffer(image_bytes, np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# Process image
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_rgb.flags.writeable = False
results = pose.process(image_rgb)
if not results.pose_landmarks:
return jsonify({
'position': 'unknown',
'confidence': 0,
'landmarks': None,
'angles': None
# 'form_feedback': 'No pose detected' # FUTURE IMPLEMENTATION
})
# Extract landmarks
landmarks = results.pose_landmarks.landmark
# Prepare features
features = []
for i in range(33): # MediaPipe has 33 landmarks
features.extend([landmarks[i].x, landmarks[i].y])
# Create a DataFrame with the same column names used during training
feature_cols = []
for i in range(33):
feature_cols.extend([f'x_{i}', f'y_{i}'])
X = pd.DataFrame([features], columns=feature_cols)
# Model Predicts if the user is in the 'UP' or 'DOWN' position
prediction_prob = model.predict_proba(X)[0]
predicted_class_idx = np.argmax(prediction_prob)
predicted_class = label_encoder.inverse_transform([predicted_class_idx])[0]
confidence = float(prediction_prob[predicted_class_idx])
# Calculate arm angles for form analysis
# Right arm angle (shoulder - elbow - wrist)
right_shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]
right_elbow = [landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y]
right_wrist = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y]
right_elbow_angle = calculate_angle(right_shoulder, right_elbow, right_wrist)
# Left arm angle
left_shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
left_elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
left_wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
left_elbow_angle = calculate_angle(left_shoulder, left_elbow, left_wrist)
# FUTURE IMPLEMENTATION: ******************************************
# Form analysis based on arm angles
# form_feedback = ""
# form_score = 0
# if predicted_class == 'down':
# # Check for proper elbow angles in down position (should be around 90 degrees)
# avg_elbow_angle = (right_elbow_angle + left_elbow_angle) / 2
# if avg_elbow_angle < 70:
# form_feedback = "Go deeper - lower your chest"
# form_score = 0.6
# elif avg_elbow_angle > 110:
# form_feedback = "Bend your elbows more"
# form_score = 0.7
# else:
# form_feedback = "Good form!"
# form_score = 0.9
# elif predicted_class == 'up':
# # Check for proper arm extension in up position
# avg_elbow_angle = (right_elbow_angle + left_elbow_angle) / 2
# if avg_elbow_angle < 150:
# form_feedback = "Extend arms fully at the top"
# form_score = 0.7
# else:
# form_feedback = "Good form!"
# form_score = 0.9
# FUTURE IMPLEMENTATION: ******************************************
# Prepare landmark data for frontend
landmark_list = []
for i, landmark in enumerate(landmarks):
landmark_list.append({
'x': landmark.x,
'y': landmark.y,
'z': landmark.z,
'visibility': landmark.visibility
})
# Return results
return jsonify({
'position': predicted_class,
'confidence': confidence,
'landmarks': landmark_list,
'angles': {
'right_elbow': float(right_elbow_angle),
'left_elbow': float(left_elbow_angle)
}
# 'form_feedback': form_feedback, -> FUTURE IMPLEMENTATION
# 'form_score': float(form_score) -> FUTURE IMPLEMENTATION
})
except Exception as e:
print(f"Error processing frame: {str(e)}")
import traceback
traceback.print_exc()
return jsonify({'error': str(e)}), 500
@app.route('/api/record-exercise', methods=['POST'])
def record_exercise():
"""Record completed exercise to user profile"""
if not request.json:
return jsonify({'error': 'No data provided'}), 400
required_fields = ['userId', 'exercise', 'reps', 'formScore']
if not all(field in request.json for field in required_fields):
return jsonify({'error': 'Missing required fields'}), 400
user_id = request.json['userId']
exercise_type = request.json['exercise']
reps = request.json['reps']
form_score = request.json['formScore']
# Calculate XP based on reps and form quality
base_xp_per_rep = 20
# form_multiplier = (0.5 + form_score / 2) # FUTURE IMPLEMENTATION -> (Ex: ok (.5), good(1), great(1.5), etc...)
total_xp = int(reps * base_xp_per_rep) # # FUTURE IMPLEMENTATION -> int(reps * base_xp_per_rep * form_multiplier)
# Return the results
return jsonify({
'success': True,
'xpEarned': total_xp,
'newReps': reps,
'message': f"Recorded {reps} {exercise_type}s earning {total_xp} XP!"
})
if __name__ == "__main__": # This block ensures that the Flask app runs only when this script is executed directly
app.run(host='0.0.0.0', port=7860, debug=True) |