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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 | |
| def index(): | |
| return render_template('index.html') # Add your HTML file here | |
| 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) | |