Mask Testing using images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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 cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prototxtPath=os.path.sep.join([r'E:\\Mask-Detection-and-Recognition-using-Deep-Learning-Keras\\FaceDetection_Classifier','deploy.prototxt'])\n",
    "weightsPath=os.path.sep.join([r'E:\\Mask-Detection-and-Recognition-using-Deep-Learning-Keras\\FaceDetection_Classifier','res10_300x300_ssd_iter_140000.caffemodel'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prototxtPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weightsPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "net=cv2.dnn.readNet(prototxtPath,weightsPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=load_model(r'E:\\Mask-Detection-and-Recognition-using-Deep-Learning-Keras\\Trained_model\\mobilenet_v2.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image=cv2.imread(r'E:\\Mask-Detection-and-Recognition-using-Deep-Learning-Keras\\example_images\\example_03.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(h,w)=image.shape[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(h,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob=cv2.dnn.blobFromImage(image,1.0,(300,300),(104.0,177.0,123.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.setInput(blob)\n",
    "detections=net.forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loop over the detections\n",
    "for i in range(0,detections.shape[2]):\n",
    "    confidence=detections[0,0,i,2]\n",
    "    \n",
    "    \n",
    "    if confidence>0.5:\n",
    "        #we need the X,Y coordinates\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",
    "        \n",
    "        #extract the face ROI, convert it from BGR to RGB channel, resize it to 224,224 and preprocess it\n",
    "        face=image[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",
    "        face=np.expand_dims(face,axis=0)\n",
    "        \n",
    "        (mask,withoutMask)=model.predict(face)[0]\n",
    "        \n",
    "        #determine the class label and color we will 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 boxes\n",
    "        cv2.putText(image,label,(startX,startY-10),cv2.FONT_HERSHEY_SIMPLEX,0.45,color,2)\n",
    "        cv2.rectangle(image,(startX,startY),(endX,endY),color,2)\n",
    "        \n",
    "        \n",
    "        \n",
    "cv2.imshow(\"OutPut\",image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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