02. detect_mask_image.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4d45daa",
   "metadata": {},
   "outputs": [],
   "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",
    "import numpy as np\n",
    "import argparse\n",
    "import cv2\n",
    "import os\n",
    "import os \n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba701964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tensorflow.python.keras.engine.training.Model object at 0x7fc1649880d0>\n"
     ]
    }
   ],
   "source": [
    "# load our serialized face detector model from disk\n",
    "# initialise the pretrained face detector model provided by OpenCV\n",
    "prototxtPath = \"deploy.prototxt\"\n",
    "weightsPath = \"res10_300x300_ssd_iter_140000.caffemodel\"\n",
    "\n",
    "# loading the pretrained face detector model using readNet() into the memory\n",
    "faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)\n",
    "\n",
    "\n",
    "# load the face mask detector model created previously into the memory\n",
    "maskNet = load_model(\"mask_detector.model\")\n",
    "print(maskNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d34d081b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 540, 3) 360 540\n"
     ]
    }
   ],
   "source": [
    "image = cv2.imread('examples/image03.jpg')\n",
    "orig = image.copy()\n",
    "(h, w) = image.shape[:2]\n",
    "print(image.shape,h,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d4af154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[117. 117. 117. ... 130. 130. 130.]\n",
      "   [117. 117. 117. ... 130. 130. 130.]\n",
      "   [117. 117. 117. ... 130. 130. 130.]\n",
      "   ...\n",
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      "   [ 26.  32.  34. ... 120. 120. 120.]]\n",
      "\n",
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      "   [ 45.  45.  45. ...  58.  58.  58.]\n",
      "   [ 45.  45.  45. ...  58.  58.  58.]\n",
      "   ...\n",
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      "   [-20. -14. -12. ...  51.  51.  51.]]\n",
      "\n",
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      "   ...\n",
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      "   [ 63.  70.  70. ... 108. 108. 108.]\n",
      "   [ 63.  69.  71. ... 108. 108. 108.]]]]\n",
      "[INFO] computing face detections...\n",
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     ]
    }
   ],
   "source": [
    "# construct a blob from the image\n",
    "blob=cv2.dnn.blobFromImage(image, 1.0, (300, 300),(104.0, 177.0, 123.0))\n",
    "print(blob)\n",
    "\n",
    "# pass the blob through the network and obtain the face detections\n",
    "print(\"[INFO] computing face detections...\")\n",
    "faceNet.setInput(blob)\n",
    "detections = faceNet.forward()\n",
    "print(detections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74c8d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2934639  0.18077049 0.51950324 0.54103553]\n",
      "[158.47049832  65.07737517 280.53174734 194.77279186]\n"
     ]
    }
   ],
   "source": [
    "# loop over the detections\n",
    "for i in range(0, detections.shape[2]):\n",
    "\t# extract the confidence (i.e., probability) associated with\n",
    "\t# the detection\n",
    "\tconfidence = detections[0, 0, i, 2]\n",
    "\t#print(confidence)\n",
    "    \n",
    "\t# filter out weak detections by ensuring the confidence is greater than the minimum confidence\n",
    "\tif confidence > 0.5:\n",
    "\t# compute the (x, y)-coordinates of the bounding box for the object\n",
    "\t\tprint(detections[0, 0, i, 3:7])   \n",
    "\n",
    "\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
    "\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n",
    "\t\tprint(box)\n",
    "\n",
    "\t\t\t# ensure the bounding boxes fall within the dimensions of the frame\n",
    "\t\t(startX, startY) = (max(0, startX), max(0, startY))\n",
    "\t\t(endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n",
    "\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\tface = image[startY:endY, startX:endX]\n",
    "\t\tface = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n",
    "\t\tface = cv2.resize(face, (224, 224))\n",
    "\t\tface = img_to_array(face)\n",
    "\t\tface = preprocess_input(face)\n",
    "\t\tface = np.expand_dims(face, axis=0)\n",
    "\n",
    "\n",
    "\t\t# pass the face through the model to determine if the face has a mask or not\n",
    "\t\t(mask, withoutMask) = maskNet.predict(face)[0]\n",
    "\n",
    "\n",
    "\t\t# determine the class label and color we'll use to draw 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",
    "\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 frame\n",
    "\t\tcv2.putText(image, label, (startX, startY - 10),\n",
    "\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n",
    "\t\tcv2.rectangle(image, (startX, startY), (endX, endY), color, 2)\n",
    "\n",
    "\n",
    "# show the output image\n",
    "cv2.imshow(\"Output\", image)\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9323ac3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac091da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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