Video_Player_Type.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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     "text": [
      "[INFO] starting video stream...\n",
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     "text": [
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     ]
    }
   ],
   "source": [
    "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 tensorflow_hub as hub\n",
    "import numpy as np\n",
    "import imutils\n",
    "import time\n",
    "import cv2\n",
    "import os\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import json\n",
    "\n",
    "def detect_and_predict_mask(frame, faceNet, maskNet):\n",
    "        (h, w) = frame.shape[:2]\n",
    "        blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),(104.0, 177.0, 123.0))\n",
    "        faceNet.setInput(blob)\n",
    "        detections = faceNet.forward()\n",
    "        print(detections.shape)\n",
    "        faces = []\n",
    "        global score\n",
    "        locs = []\n",
    "        preds = []\n",
    "      \n",
    "        for i in range(0, detections.shape[2]):\n",
    "            confidence = detections[0, 0, i, 2]\n",
    "            if confidence > 0.5:\n",
    "                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
    "                (startX, startY, endX, endY) = box.astype(\"int\")\n",
    "                (startX, startY) = (max(0, startX), max(0, startY))\n",
    "                (endX, endY) = (min(w - 1, endX), min(h - 1, endY))\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",
    "                faces.append(face)\n",
    "                locs.append((startX, startY, endX, endY))\n",
    "        if len(faces) > 0:\n",
    "                \n",
    "                faces = np.array(faces, dtype=\"float32\")\n",
    "                preds = maskNet.predict(faces, batch_size=32)\n",
    "                score = np.argmax(preds)\n",
    "              \n",
    "        return (locs, preds)\n",
    "#score =0\n",
    "\n",
    "prototxtPath = r\"face_detector\\deploy.prototxt\"\n",
    "weightsPath = r\"face_detector\\res10_300x300_ssd_iter_140000.caffemodel\"\n",
    "faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)\n",
    "maskNet = tf.keras.models.load_model((\"mask_MobileNet_detector.model\"), \n",
    "                                     custom_objects={'KerasLayer':hub.KerasLayer})\n",
    "                                     \n",
    "print(\"[INFO] starting video stream...\")\n",
    "vs = VideoStream(src=0).start()\n",
    "while True:\n",
    "        frame = vs.read()\n",
    "        frame = imutils.resize(frame, width=400)\n",
    "        (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)\n",
    "        \n",
    "        for (box, pred) in zip(locs, preds):\n",
    "# unpack the bounding box and predictions\n",
    "            (startX, startY, endX, endY) = box\n",
    "            pred=score \n",
    "            if score==0:\n",
    "                label=(\"cloth\")\n",
    "            elif score == 1:\n",
    "                label=(\"n95\")\n",
    "            elif score == 2:\n",
    "                label=(\"n95v\")\n",
    "            elif  score==3:\n",
    "                 label=(\"No Mask\")\n",
    "            else:\n",
    "                label=(\"srg\")\n",
    "        \n",
    "            if label == (\"No Mask\"):\n",
    "                    color = (0, 0, 255)\n",
    "            else:\n",
    "                color = (0, 225, 0)\n",
    "            cv2.putText(frame, label, (startX, startY - 10),\n",
    "            cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n",
    "            cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\n",
    "        cv2.imshow(\"Frame\", frame)\n",
    "        key = cv2.waitKey(1) & 0xFF\n",
    "        if key == ord(\"q\"):\n",
    "            break              \n",
    "cv2.destroyAllWindows()\n",
    "vs.stop()\n",
    "\n"
   ]
  },
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