{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "faceProto = 'opencv_face_detector.pbtxt'\n",
    "faceModel = 'opencv_face_detector_uint8.pb'\n",
    "\n",
    "ageProto = 'age_deploy.prototxt'\n",
    "ageModel = 'age_net.caffemodel'\n",
    "\n",
    "genderProto = 'gender_deploy.prototxt'\n",
    "genderModel = 'gender_net.caffemodel'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "faceNet = cv2.dnn.readNet(faceModel, faceProto)\n",
    "ageNet = cv2.dnn.readNet(ageModel, ageProto)\n",
    "genderNet = cv2.dnn.readNet(genderModel, genderProto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']\n",
    "genderList = ['Male', 'Female']\n",
    "MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def faceBox(faceNet, frame):\n",
    "    frameWidth = frame.shape[1]\n",
    "    frameHeight = frame.shape[0]\n",
    "\n",
    "    blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), [104, 117, 123], swapRB = False)\n",
    "    faceNet.setInput(blob)\n",
    "    detection = faceNet.forward()\n",
    "    #print(detection.shape)\n",
    "    bboxs = []\n",
    "\n",
    "    for i in range(detection.shape[2]):\n",
    "        confidence = detection[0,0,i,2]\n",
    "        if confidence > 0.7:\n",
    "            x1 = int(detection[0,0,i,3] * frameWidth)\n",
    "            y1 = int(detection[0,0,i,4] * frameHeight)\n",
    "\n",
    "            x2 = int(detection[0,0,i,5] * frameWidth)\n",
    "            y2 = int(detection[0,0,i,6] * frameHeight)\n",
    "\n",
    "            bboxs.append([x1, y1, x2, y2])\n",
    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 1)\n",
    "    return frame, bboxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cProfile import label\n",
    "padding = 20\n",
    "\n",
    "video = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    ret, frame = video.read()\n",
    "    frame, bboxs = faceBox(faceNet, frame)\n",
    "\n",
    "    for bbox in bboxs:\n",
    "        #face = frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]\n",
    "        face = frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]\n",
    "        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB = False)\n",
    "        genderNet.setInput(blob)\n",
    "        genderPred = genderNet.forward()\n",
    "        gender = genderList[genderPred[0].argmax()]\n",
    "\n",
    "\n",
    "        ageNet.setInput(blob)\n",
    "        agePred = ageNet.forward()\n",
    "        age = ageList[agePred[0].argmax()]\n",
    "\n",
    "\n",
    "        label = \"{},{}\".format(gender, age)\n",
    "        #cv2.rectangle(frame, (bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0,255, 255), -1)\n",
    "        cv2.putText(frame, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "\n",
    "\n",
    "    cv2.imshow('Ang & Gender Recognition', frame)\n",
    "    k = cv2.waitKey(1)\n",
    "\n",
    "    if k == ord('q'):\n",
    "        break\n",
    "\n",
    "video.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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