UI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "Using gpu device 0: GeForce GTX 980M (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5103)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = load_model('vgg16_gender2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean = np.load('summ.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def model_predict(model, img, mean, im_height = 224, im_width = 224, rounded = False):\n",
    "    image_final = np.zeros((1,224,224,3))\n",
    "    img = cv2.resize(img, (im_width, im_height))\n",
    "    img = img - mean\n",
    "    image_final[0,:,:,:] = img\n",
    "    image_final = np.swapaxes(image_final, 1, 3)\n",
    "    image_final = np.swapaxes(image_final, 2, 3)\n",
    "    \n",
    "    prediction = model.predict(image_final)\n",
    "    return str(prediction)\n",
    "\n",
    "\n",
    "def recognize_person():\n",
    "    gender = \"\"\n",
    "    face_cascade = cv2.CascadeClassifier('../Capture-Face-master/face_detection_xml/face_cascade_classifier.xml')\n",
    "    video_capture = cv2.VideoCapture(0)\n",
    "    face_detected = False\n",
    "    while True:\n",
    "        # Capture frame-by-frame\n",
    "        ret, frame = video_capture.read()\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
    "        for (x, y, w, h) in faces:\n",
    "            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
    "            image = frame[max(0, y - 40): min(frame.shape[0], y+h + 40), max(0, x - 40):min(frame.shape[1], x+w + 40)]\n",
    "            cv2.imwrite('face.png', image)\n",
    "            gender = model_predict(model, image, mean)\n",
    "            face_detected = True\n",
    "        if face_detected:\n",
    "            cv2.putText(image, gender, (image.shape[1]/5, image.shape[0]/5), cv2.FONT_HERSHEY_SIMPLEX, 2, 255)\n",
    "            cv2.imshow(\"Prediction\", image)\n",
    "            raw_input(\"Enter To continue\")\n",
    "            face_detected = False\n",
    "        cv2.imshow('img', frame)\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "    #When everything is done, release the capture\n",
    "    video_capture.release()\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "recognize_person()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "()\n"
     ]
    }
   ],
   "source": [
    "img = cv2.imread('man-08.jpg')\n",
    "face_cascade = cv2.CascadeClassifier('../Capture-Face-master/face_detection_xml/face_cascade_classifier.xml')\n",
    "faces = face_cascade.detectMultiScale(img, 1.3, 5)\n",
    "print faces\n",
    "for (x, y, w, h) in faces:\n",
    "    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
    "    img = img[max(0, y - 70): min(img.shape[0], y+h + 70), max(0, x - 70):min(img.shape[1], x+w + 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "error",
     "evalue": "..\\..\\..\\opencv-3.1.0\\modules\\imgproc\\src\\imgwarp.cpp:3229: error: (-215) ssize.area() > 0 in function cv::resize\n",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-ea62c0325cbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimage_final\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m224\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m224\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m224\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m224\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimage_final\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mimage_final\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_final\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: ..\\..\\..\\opencv-3.1.0\\modules\\imgproc\\src\\imgwarp.cpp:3229: error: (-215) ssize.area() > 0 in function cv::resize\n"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "image_final = np.zeros((1,224,224,3))\n",
    "img = cv2.resize(img, (224, 224))\n",
    "img = img - mean\n",
    "image_final[0,:,:,:] = img\n",
    "image_final = np.swapaxes(image_final, 1, 3)\n",
    "image_final = np.swapaxes(image_final, 2, 3)\n",
    "\n",
    "prediction = model.predict(image_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.95320815]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}