Detect Mask Video.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "JkD3DvyeNWzf"
   },
   "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,
   "metadata": {
    "id": "BAfVhSYfNa5Y"
   },
   "outputs": [],
   "source": [
    "def detect_and_predict_mask(frame, faceNet, maskNet):\n",
    "\t# Grab the dimensions of the frame and then construct a blob\n",
    "\t# from it\n",
    "\t(h, w) = frame.shape[:2]\n",
    "\tblob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),\n",
    "\t\t(104.0, 177.0, 123.0))\n",
    "\n",
    "\t# Pass the blob through the network and obtain the face detections\n",
    "\tfaceNet.setInput(blob)\n",
    "\tdetections = faceNet.forward()\n",
    "\tprint(detections.shape)\n",
    "\n",
    "\t# Initialize our list of faces, their corresponding locations,\n",
    "\t# and the list of predictions from our face mask network\n",
    "\tfaces = []\n",
    "\tlocs = []\n",
    "\tpreds = []\n",
    "\n",
    "\t# loop over the detections\n",
    "\tfor i in range(0, detections.shape[2]):\n",
    "\t\t# Extract the confidence (i.e., probability) associated with\n",
    "\t\t# the detection\n",
    "\t\tconfidence = detections[0, 0, i, 2]\n",
    "\n",
    "\t\t# Filter out weak detections by ensuring the confidence is\n",
    "\t\t# greater than the minimum confidence\n",
    "\t\tif confidence > 0.5:\n",
    "\t\t\t# Compute the (x, y)-coordinates of the bounding box for\n",
    "\t\t\t# the object\n",
    "\t\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
    "\t\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n",
    "\n",
    "\t\t\t# Ensure the bounding boxes fall within the dimensions of\n",
    "\t\t\t# the frame\n",
    "\t\t\t(startX, startY) = (max(0, startX), max(0, startY))\n",
    "\t\t\t(endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n",
    "\n",
    "\t\t\t# Extract the face ROI, convert it from BGR to RGB channel\n",
    "\t\t\t# ordering, resize it to 224x224, and preprocess it\n",
    "\t\t\tface = frame[startY:endY, startX:endX]\n",
    "\t\t\tface = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n",
    "\t\t\tface = cv2.resize(face, (224, 224))\n",
    "\t\t\tface = img_to_array(face)\n",
    "\t\t\tface = preprocess_input(face)\n",
    "\n",
    "\t\t\t# Add the face and bounding boxes to their respective\n",
    "\t\t\t# lists\n",
    "\t\t\tfaces.append(face)\n",
    "\t\t\tlocs.append((startX, startY, endX, endY))\n",
    "\n",
    "\t# Only make a predictions if at least one face was detected\n",
    "\tif len(faces) > 0:\n",
    "\t\t# for faster inference we'll make batch predictions on *all*\n",
    "\t\t# faces at the same time rather than one-by-one predictions\n",
    "\t\t# in the above `for` loop\n",
    "\t\tfaces = np.array(faces, dtype=\"float32\")\n",
    "\t\tpreds = maskNet.predict(faces, batch_size=32)\n",
    "\n",
    "\t# Return a 2-tuple of the face locations and their corresponding\n",
    "\t# locations\n",
    "\treturn (locs, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "f3Ja2UUYNf-Y"
   },
   "outputs": [],
   "source": [
    "# load our serialized face detector model from disk\n",
    "prototxtPath = r\"deploy.prototxt\"\n",
    "weightsPath = r\"res10_300x300_ssd_iter_140000.caffemodel\"\n",
    "faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "d8_7m3yQNtiA"
   },
   "outputs": [],
   "source": [
    "# load the face mask detector model from disk\n",
    "maskNet = load_model(\"mask_detector.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dC0x_kA2N-zF",
    "outputId": "6420084d-3e86-4a16-981b-f89e21ec31e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] starting video stream...\n"
     ]
    }
   ],
   "source": [
    "# Initialize the video stream\n",
    "print(\"[INFO] starting video stream...\")\n",
    "vs = VideoStream(src=0).start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "xT0mRP5jOBo4",
    "outputId": "a8eb2687-c90b-4e49-f0a9-4db0d1554dbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(1, 1, 200, 7)\n"
     ]
    }
   ],
   "source": [
    "# Loop over the frames from the video stream\n",
    "while True:\n",
    "\t# Grab the frame from the threaded video stream and resize it\n",
    "\t# to have a maximum width of 400 pixels\n",
    "\tframe = vs.read()\n",
    "\tframe = imutils.resize(frame, width=400)\n",
    "\n",
    "\t# Detect faces in the frame and determine if they are wearing a\n",
    "\t# face mask or not\n",
    "\t(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)\n",
    "\n",
    "\t# Loop over the detected face locations and their corresponding\n",
    "\t# locations\n",
    "\tfor (box, pred) in zip(locs, preds):\n",
    "\t\t# Unpack the bounding box and predictions\n",
    "\t\t(startX, startY, endX, endY) = box\n",
    "\t\t(mask, withoutMask) = pred\n",
    "\n",
    "\t\t# Determine the class label and color we'll use to draw\n",
    "\t\t# the bounding box and text\n",
    "\t\tlabel = \"Mask\" if mask > withoutMask else \"No Mask\"\n",
    "\t\tcolor = (0, 255, 0) if label == \"Mask\" else (0, 0, 255)\n",
    "\n",
    "\t\t# Include the probability in the label\n",
    "\t\tlabel = \"{}: {:.2f}%\".format(label, max(mask, withoutMask) * 100)\n",
    "\n",
    "\t\t# Display the label and bounding box rectangle on the output\n",
    "\t\t# frame\n",
    "\t\tcv2.putText(frame, label, (startX, startY - 10),\n",
    "\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n",
    "\t\tcv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\n",
    "\n",
    "\t# Show the output frame\n",
    "\tcv2.imshow(\"Frame\", frame)\n",
    "\tkey = cv2.waitKey(1) & 0xFF\n",
    "\n",
    "\t# If the `q` key was pressed, break from the loop\n",
    "\tif key == ord(\"q\"):\n",
    "\t\tbreak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleanup\n",
    "cv2.destroyAllWindows()\n",
    "vs.stop()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Detect Mask Video.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}