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import cv2
import mediapipe as mp
import numpy as np
from flask import Flask, render_template, request
import tensorflow as tf
# Initialize the Flask app
app = Flask(__name__)
# Initialize Mediapipe for face detection
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
# Load AI models for skin care, health, makeup, and fashion recommendations
# You should have these models pre-trained and available
# For simplicity, placeholders are used
skin_care_model = tf.keras.models.load_model('skin_care_model.h5') # Example placeholder
makeup_model = tf.keras.models.load_model('makeup_model.h5') # Example placeholder
health_model = tf.keras.models.load_model('health_model.h5') # Example placeholder
fashion_model = tf.keras.models.load_model('fashion_model.h5') # Example placeholder
# Function to detect faces using Mediapipe
def detect_faces(image):
with mp_face_detection.FaceDetection(min_detection_confidence=0.2) as face_detection:
# Convert the image to RGB for Mediapipe
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = face_detection.process(rgb_image)
if results.detections:
for detection in results.detections:
bboxC = detection.location_data.relative_bounding_box
ih, iw, _ = image.shape
x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), int(bboxC.width * iw), int(bboxC.height * ih)
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
return image
# Placeholder recommendation functions (you should replace these with actual AI models)
def get_skin_care_recommendation(face_image):
# Analyze the skin condition (dummy function for example)
return "Recommended product: Vitamin C Serum"
def get_makeup_recommendation(face_image):
# Suggest makeup based on facial features (dummy function for example)
return "Suggested makeup: Natural look foundation"
def get_health_recommendation(face_image):
# Analyze health metrics (dummy function for example)
return "Health alert: Normal blood pressure"
def get_fashion_recommendation(face_image):
# Suggest outfits based on style and weather (dummy function for example)
return "Suggested outfit: Casual wear suitable for sunny weather"
# Route to handle the display of the mirror and recommendations
@app.route('/')
def index():
return render_template('index.html') # Add your HTML file here
@app.route('/capture', methods=['POST'])
def capture():
# Capture an image from the webcam
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
if not ret:
return "Failed to capture image", 400
# Process the captured frame to detect faces and provide recommendations
frame = detect_faces(frame)
# Extract personalized recommendations (example placeholders)
skin_care = get_skin_care_recommendation(frame)
makeup = get_makeup_recommendation(frame)
health = get_health_recommendation(frame)
fashion = get_fashion_recommendation(frame)
# Return recommendations as response
recommendations = {
'skin_care': skin_care,
'makeup': makeup,
'health': health,
'fashion': fashion
}
cap.release()
return recommendations
if __name__ == "__main__":
app.run(debug=True)
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