{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d70291b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "from imutils.video import VideoStream\n",
    "import numpy as np\n",
    "import imutils\n",
    "import time\n",
    "import cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d59f4c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_and_predict_mask(frame, faceNet, maskNet):\n",
    "    # grab the dimensions of the frame and then construct a blob from it\n",
    "    (h, w) = frame.shape[:2]\n",
    "    blob = cv2.dnn.blobFromImage(frame, 1.0, (224,224),(104.0,177.0,123.0) )\n",
    "    \n",
    "    # pass the blob through the network and obtain the face detections\n",
    "    faceNet.setInput(blob)\n",
    "    detections = faceNet.forward()\n",
    "    print(detections.shape)\n",
    "    \n",
    "    # initialize our list of faces, their corresponding locations, and the list of predictions from our face mask network\n",
    "    faces = []\n",
    "    locs = []\n",
    "    preds = []\n",
    "    # loop over the detections\n",
    "    for i in range(0,detections.shape[2]):\n",
    "        # extract the confidence (i.e., probability) associated with the detection\n",
    "        confidence = detections[0,0,i,2]\n",
    "        \n",
    "        # filter out weak detections by ensuring the confidence is greater than minimum confidence\n",
    "        if confidence > 0.5:\n",
    "            # compute the (x, y)-cordinates of the bounding box for the object\n",
    "            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
    "            (startX, startY, endX, endY) = box.astype(\"int\")\n",
    "            \n",
    "            # ensure the bounding boxes fall within the dimensions of the frame\n",
    "            (startX , startY) = (max(0,startX) , max(0,startY))\n",
    "            (endX, endY) = (min(w-1,endX) , min(h-1,endY))\n",
    "            \n",
    "            # extract the face ROI, convert it from BGR to RGB channel ordering, resize it to 224x224, and preprocess it face=frame[startY:endY, startX:endX]\n",
    "            # bounding mask only for face detected\n",
    "            face = frame[startY:endY , startX:endX]\n",
    "            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n",
    "            face = cv2.resize(face, (224,224))\n",
    "            face = img_to_array(face)\n",
    "            face = preprocess_input(face)\n",
    "            \n",
    "            # add the face and bounding boxes to their respective lists\n",
    "            faces.append(face)\n",
    "            locs.append((startX, startY, endX, endY))\n",
    "    \n",
    "    # only make a predictions if at least one face was detected\n",
    "    if len(faces) > 0:\n",
    "        # far faster inference we'll make batch predictions on *all* faces at the same time rather than one-by-one predictions in the above 'for' loop\n",
    "        faces = np.array(faces,dtype=\"float32\")\n",
    "        preds = maskNet.predict(faces, batch_size=32)\n",
    "        \n",
    "    # return a 2-tuple of the face locations and their corresponding locations\n",
    "    return (locs, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74bfdab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load our serialized face detector model from disk\n",
    "prototxtPath = r\"deploy.prototxt.txt\"\n",
    "weightsPath = r\"res10_300x300_ssd_iter_140000.caffemodel\"\n",
    "faceNet = cv2.dnn.readNet(prototxtPath,weightsPath)\n",
    "\n",
    "# load the face mask detector model from disk\n",
    "maskNet = load_model(\"mask_detector.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52cc9df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# initialize the video stream\n",
    "print(\"[INFO] starting video stream...\")\n",
    "vs = VideoStream(src=0).start()\n",
    "\n",
    "# initialize the video stream\n",
    "print(\"[INFO] starting video stream...\")\n",
    "vs = VideoStream(src=0).start()\n",
    "# loop over the frames from the video stream\n",
    "while True:\n",
    "    # grab the frame from the threaded video stream and resize it to have a max width of 400 pixels\n",
    "    frame = vs.read()\n",
    "    frame = imutils.resize(frame,width=400)\n",
    "    \n",
    "    # detect faces in the frame and determine if they are wearing a face mask or not\n",
    "    (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)\n",
    "    \n",
    "    # loop over the detected face locations and their correspondings locations\n",
    "    for (box, pred) in zip(locs, preds):\n",
    "        # unpack the bounding box and predictions\n",
    "        (startX, startY, endX, endY) = box\n",
    "        (mask, withoutMask) = pred\n",
    "        \n",
    "        # determine the class label and color we'll use to draw the bounding box and text\n",
    "        label = \"Mask\" if mask> withoutMask else \"No Mask\"\n",
    "        color = (0,255,0) if label==\"Mask\" else (0,0,255)\n",
    "        \n",
    "        # include the probability in the label\n",
    "        label = \"{}: {:.2f}%\".format(label,max(mask, withoutMask) *100)\n",
    "        \n",
    "        # display the label and bounding box rectangle on the output frame\n",
    "        cv2.putText(frame,label,(startX,startY-10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n",
    "        cv2.rectangle(frame, (startX,startY), (endX,endY),color,2)\n",
    "        \n",
    "    # show the output frame\n",
    "    cv2.imshow(\"Frame\",frame)\n",
    "    key = cv2.waitKey(1) & 0xFF\n",
    "    \n",
    "    # if the 'q' key was pressed, break from the loop\n",
    "    if key == ord(\"q\"):\n",
    "        break\n",
    "        \n",
    "        \n",
    "# do a bit of cleanup\n",
    "cv2.destroyAllWindows()\n",
    "vs.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b404a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vs.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7fe320b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a24deadc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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