{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8d9c166-c713-4e3e-900b-df2d6c6df3b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:TensorFlow GPU support is not available on native Windows for TensorFlow >= 2.11. Even if CUDA/cuDNN are installed, GPU will not be used. Please use WSL2 or the TensorFlow-DirectML plugin.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Sequential name=sequential, built=True>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model=load_model(\"Face_Detection_Model.h5\")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92f623a0-76d0-463e-a7d8-4783296ef2e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "test_image = image.load_img(\n",
    "    r\"C:\\Users\\abhis\\Desktop\\Deep Learning\\Deep Learning Project\\Face Detection Project\\images\\train\\Face\\00af34930b6f21da.jpg\",\n",
    "    target_size=(128,128))\n",
    "\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis=0)\n",
    "test_image = test_image / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6df15bba-8afd-479f-9e56-8abffffe46b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step\n",
      "[[0.12947011]]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(test_image)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "031710e6-077c-482d-aa39-9412ab838578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Face Detected\n"
     ]
    }
   ],
   "source": [
    "if pred [0][0]>0.5:\n",
    "    print(\"Face Not Detected\")\n",
    "else:\n",
    "    print(\"Face Detected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de1d4df7-38de-4bfd-8d82-605a8a904cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "test_image = image.load_img(\n",
    "    r\"C:\\Users\\abhis\\Desktop\\Deep Learning\\Deep Learning Project\\Face Detection Project\\images\\train\\No_Face\\001_0001.jpg\",\n",
    "    target_size=(128,128))\n",
    "\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis=0)\n",
    "test_image = test_image / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79dba6a6-479c-403a-bfa7-768e2be56564",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test_image)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80198a51-50d1-4a47-9595-54f5330f728b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if pred [0][0]>0.5:\n",
    "    print(\"Face Not Detected\")\n",
    "else:\n",
    "    print(\"Face Detected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70d6e1d0-4cca-470e-9bc0-9e5099139aaa",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa5e9f5-2fe5-4d84-9f12-dca2f7834335",
   "metadata": {},
   "source": [
    "**WEB CAM OR LIVE FACE DETECTING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571476e8-d9ab-4f1a-bad6-43238be047f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "#Load Face Detector\n",
    "face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
    "\n",
    "#Open Webcam\n",
    "cam = cv2.VideoCapture(1) #1 Means Portable Webcam. In Default Is 0 Means Inbuilt Camera.\n",
    "\n",
    "while True:\n",
    "    ret, frame = cam.read()\n",
    "\n",
    "    if not ret:\n",
    "        break\n",
    "    \n",
    "    #Convert To Grayscale\n",
    "    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    #Detect Faces\n",
    "    faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 5)\n",
    "\n",
    "    #If Face Detected\n",
    "    if len(faces)>0:\n",
    "        text = \"Face Detected\"\n",
    "    else:\n",
    "        text = \"Face Not Detected\"\n",
    "\n",
    "    cv2.putText(frame, text, (20,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)\n",
    "\n",
    "    #Draw Rectangle Around Faces\n",
    "    for (x,y,w,h) in faces:\n",
    "        cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)\n",
    "\n",
    "    cv2.imshow(\"Live Face Detection\", frame)\n",
    "\n",
    "    if cv2.waitKey(1) == 27:\n",
    "        break\n",
    "\n",
    "cam.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0764de-c404-4af1-86e1-1fbe751698db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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