{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "glasses=cv2.imread('Train/glasses.png',cv2.IMREAD_UNCHANGED)\n",
    "mustache=cv2.imread('Train/mustache.png',cv2.IMREAD_UNCHANGED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "glassesCasc=cv2.CascadeClassifier('Train/third-party/frontalEyes35x16.xml')\n",
    "noseCasc=cv2.CascadeClassifier('Train/third-party/Nose18x15.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap=cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    ret,frame=cap.read()\n",
    "    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "    eyes = glassesCasc.detectMultiScale(gray, 1.5, 5, 0)\n",
    "    for (x, y, w, h) in eyes:\n",
    "        glasses_resized = cv2.resize(glasses, (w, h))\n",
    "        alpha_channel = glasses_resized[:, :, 3] / 255.0\n",
    "        \n",
    "        # Create a mask for the glasses\n",
    "        glasses_mask = np.zeros_like(glasses_resized[:, :, 3])\n",
    "        \n",
    "        # Copy alpha channel to mask and apply threshold\n",
    "        glasses_mask[glasses_resized[:, :, 3] > 0] = 255\n",
    "        \n",
    "        # Overlay the glasses using the mask\n",
    "        for c in range(0, 3):\n",
    "            frame[y:y+h, x:x+w, c] = (1 - alpha_channel) * frame[y:y+h, x:x+w, c] + alpha_channel * glasses_resized[:, :, c]\n",
    "\n",
    "    nose=noseCasc.detectMultiScale(gray,1.3,5,0)\n",
    "    for (x, y, w, h) in nose:\n",
    "        mustache_resized = cv2.resize(mustache, (w, h))\n",
    "        alpha_channel = mustache_resized[:, :, 3] / 255.0\n",
    "        \n",
    "        mustache_mask = np.zeros_like(mustache_resized[:, :, 3])\n",
    "        \n",
    "        # Copy alpha channel to mask and apply threshold\n",
    "        mustache_mask[mustache_resized[:, :, 3] > 0] = 255\n",
    "\n",
    "        for c in range(0, 3):\n",
    "            frame[y:y+h, x:x+w, c] = (1 - alpha_channel) * frame[y:y+h, x:x+w, c] + alpha_channel * mustache_resized[:, :, c]\n",
    "\n",
    "    cv2.imshow('Webcam Feed', frame)\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
 ],
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