Facial_Reco.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import face_recognition\n",
    "import easygui as eg\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Directory containing known faces directories\n",
    "knownf_dir = \"known faces\"\n",
    "\n",
    "#Directory containing unknown faces\n",
    "unknownf_dir = \"unknown faces\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"LOADING KNOWN FACES\")\n",
    "\n",
    "#Just some variables\n",
    "known_facesf,known_namesf,answer=[],[],'n'\n",
    "\n",
    "#Traversing through known faces directory\n",
    "for name in os.listdir(knownf_dir):\n",
    "    print(\"Folder Name -->\",name)\n",
    "    \n",
    "    #Skip if you already has the encoding data stored in the directory for the person\n",
    "    answer = input(\"Want to skip(y/n):\")\n",
    "    \n",
    "    #If you choose not to skip this loop will create encoding data \n",
    "    if(answer == 'n'):\n",
    "        \n",
    "        #Creating a file named ed.txt to save encoding data\n",
    "        handle = open(f\"{knownf_dir}/{name}/ed.txt\",'w')\n",
    "        \n",
    "        for filename in os.listdir(f\"{knownf_dir}/{name}\"):\n",
    "            print(filename)\n",
    "            \n",
    "            #Try opening the image file to create encoding for if present in recognisable formats else skip\n",
    "            try:\n",
    "                image = face_recognition.load_image_file(f\"{knownf_dir}/{name}/{filename}\")\n",
    "            except:\n",
    "                continue\n",
    "            \n",
    "            #This next line creates encoding of the face at location 0\n",
    "            #Increase the number of jitters for better encoding data\n",
    "            encoding = face_recognition.face_encodings(image,num_jitters=1)[0]\n",
    "            \n",
    "            known_facesf.append(encoding)\n",
    "            known_namesf.append(name)\n",
    "            \n",
    "            #Saving the encoding data with name of the person in ed.txt file \n",
    "            handle.write(str(encoding))\n",
    "            handle.write('\\n')\n",
    "            handle.write(str(name))\n",
    "            handle.write('\\n')\n",
    "        \n",
    "        handle.close()\n",
    "print(\"DONE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to retrieve the facial encoding data present in directories \n",
    "def pack():\n",
    "    \n",
    "    #Just emptying some variables\n",
    "    known_facesf,known_namesf=[],[]\n",
    "    \n",
    "    #Traversing through known faces directory\n",
    "    for name in os.listdir(knownf_dir):\n",
    "        \n",
    "        #Try to find the ed.txt file in the directory or else continue \n",
    "        try:\n",
    "            \n",
    "            #Open ed.txt in read mode\n",
    "            handle = open(f\"{knownf_dir}/{name}/ed.txt\",'r') \n",
    "            \n",
    "            y=np.array([])\n",
    "            #Loop too split the data as required to append\n",
    "            for a in handle:  \n",
    "                for file in a.split():\n",
    "                    file = file.lstrip('[').rstrip(']')\n",
    "                    \n",
    "                    '''Try to append the floating point value of the data if possible to numpy.array or else add it to the list of arrays.\n",
    "                        Or more simply if the value in file variable is a number it is the part of the encoding and if it isn't the number which means it is the name of the person.\n",
    "                        Then it is the end of the faces encoding data'''\n",
    "                    try:\n",
    "                        y = np.append(y,float(file))\n",
    "                    except:\n",
    "                        known_namesf.append(name)\n",
    "                        known_facesf.append(y)\n",
    "                        y=np.array([])\n",
    "            handle.close()\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "        #Loop to remove empty numpy.arrays if any\n",
    "        for i in known_facesf:\n",
    "            if i.size == 0:\n",
    "                known_facesf.pop(known_facesf.index(i))\n",
    "                \n",
    "    #Returns the encoding data of the various known faces and their names respectively\n",
    "    return known_facesf,known_namesf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to lower the size of the image if you have larger image files\n",
    "#Not necessarily rerquired\n",
    "#Copied this function from stack overflow can't find from where now so cant mention the person \n",
    "\n",
    "def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):\n",
    "    # initialize the dimensions of the image to be resized and  grab the image size\n",
    "    dim = None\n",
    "    (h, w) = image.shape[:2]\n",
    "\n",
    "    # if both the width and height are None, then return the original image\n",
    "    if width is None and height is None:\n",
    "        return image\n",
    "\n",
    "    # check to see if the width is None\n",
    "    if width is None:\n",
    "        # calculate the ratio of the height and construct the dimensions\n",
    "        r = height / float(h)\n",
    "        dim = (int(w * r), height)\n",
    "\n",
    "    # otherwise, the height is None\n",
    "    else:\n",
    "        # calculate the ratio of the width and construct the dimensions\n",
    "        r = width / float(w)\n",
    "        dim = (width, int(h * r))\n",
    "\n",
    "    # resize the image\n",
    "    resized = cv2.resize(image, dim, interpolation = inter)\n",
    "\n",
    "    # return the resized image\n",
    "    return resized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Processing Unknown Faces\")\n",
    "\n",
    "#Assigning encoding data and names to variables\n",
    "known_facesf,known_namesf=pack()\n",
    "\n",
    "#Enter y if you also want to name the persons for further improvements \n",
    "#Enter n if you just want to see the results with the previous encoding data\n",
    "ans = input(\"Want to improve results by giving names too(y/n)?\\n\")\n",
    "\n",
    "for filename in os.listdir(unknownf_dir):\n",
    "    print(filename)\n",
    "    \n",
    "    #Loading image file\n",
    "    image = face_recognition.load_image_file(f\"{unknownf_dir}/{filename}\")\n",
    "    \n",
    "    #Uncommment the below funciton call only if required and choose the parameters wisely\n",
    "    #image = image_resize(image,800,600)\n",
    "    \n",
    "    #Fucntion call to find the location of the face in the image\n",
    "    #Increase or decrease the number_of_times_to_unsample value to improve accuracy\n",
    "    #Test for various models like hog or cnn for better results\n",
    "    locations = face_recognition.face_locations(image,number_of_times_to_upsample=2, model=\"hog\")\n",
    "    \n",
    "    #Function call to get encoding for the face \n",
    "    encodings = face_recognition.face_encodings(image, locations)\n",
    "    \n",
    "    #Function call to convert from RGB color space to BGR \n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "    \n",
    "    print(f'found {len(encodings)} face(s)')\n",
    "    \n",
    "    face_no=0\n",
    "    write_names=[]    \n",
    "    \n",
    "    for face_encoding,face_location in zip(encodings,locations):\n",
    "        \n",
    "        #Function call to compare the face encodings and return the results\n",
    "        #Change the tolerance accordingly to get the desired results\n",
    "        results = face_recognition.compare_faces(known_facesf, face_encoding, tolerance = 0.45)\n",
    "        \n",
    "        match = None\n",
    "        \n",
    "        if ans=='n':\n",
    "            #If a match is found in the image\n",
    "            if True in results:\n",
    "                \n",
    "                #Find the name of the person at the particular index\n",
    "                match = known_namesf[results.index(True)]\n",
    "                \n",
    "                print(f\"Match found:{match}\") \n",
    "                \n",
    "                #To draw a hollow rectangle around the face of the person in the image\n",
    "                ''' (face_location[3], face_location[0])_____________\n",
    "                                                       |             |\n",
    "                                                       |    face     |\n",
    "                                                       |             |\n",
    "                                                       |_____________|(face_location[1],face_location[2])'''\n",
    "                top_left = (face_location[3], face_location[0])\n",
    "                bottom_right = (face_location[1],face_location[2])\n",
    "                color = (200, 200, 200)\n",
    "                cv2.rectangle(image,top_left,bottom_right,color, thickness = 2)\n",
    "                \n",
    "                #To draw a filled rectangle for containing the name of the person\n",
    "                top_left = (face_location[3]-2,face_location[2])\n",
    "                bottom_right = (face_location[1]+2,face_location[2]+20)\n",
    "                cv2.rectangle(image,top_left,bottom_right,color,cv2.FILLED)\n",
    "                \n",
    "                #To write the name at the specified co-ordinates\n",
    "                cv2.putText(image,match,(face_location[3], face_location[2]+15),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,0,0),thickness = 1)\n",
    "        \n",
    "        if ans=='y':\n",
    "            #To draw a hollow rectangle around the face of the person in the image\n",
    "            top_left = (face_location[3], face_location[0])\n",
    "            bottom_right = (face_location[1],face_location[2])\n",
    "            color = (200, 200, 200)\n",
    "            cv2.rectangle(image,top_left,bottom_right,color, thickness = 2)\n",
    "            \n",
    "            #If the face data of the person matches from any of the available face encoding data  \n",
    "            if True in results:\n",
    "                \n",
    "                #Find the name of the person at the particular index\n",
    "                match = known_namesf[results.index(True)]\n",
    "                \n",
    "                print(f\"Match found:{match}\")\n",
    "                \n",
    "                #To draw a filled rectangle for containing the name of the person\n",
    "                top_left = (face_location[3]-2,face_location[2])\n",
    "                bottom_right = (face_location[1]+2,face_location[2]+20)\n",
    "                cv2.rectangle(image,top_left,bottom_right,color,cv2.FILLED)\n",
    "                \n",
    "                #To write the name at the specified co-ordinates \n",
    "                cv2.putText(image,match,(face_location[3], face_location[2]+15),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,0,0),thickness = 1)\n",
    "            \n",
    "            #If the face data of the person doesn't matches from any of the available face encoding data\n",
    "            else:\n",
    "                face_no+=1\n",
    "                \n",
    "                #To draw a filled rectangle for containing the number of the person\n",
    "                top_left = (face_location[3]-2,face_location[2])\n",
    "                bottom_right = (face_location[1]+2,face_location[2]+20)\n",
    "                cv2.rectangle(image,top_left,bottom_right,color,cv2.FILLED)\n",
    "                \n",
    "                #To write the number at the specified co-ordinates \n",
    "                cv2.putText(image,str(face_no),(face_location[3], face_location[2]+15),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,0,0),thickness = 1)\n",
    "                \n",
    "                write_names.append(face_encoding)\n",
    "    \n",
    "    #Function call to show the image with all changes\n",
    "    cv2.imshow(filename,image)\n",
    "    \n",
    "    if ans=='y':\n",
    "        \n",
    "        #Loop asking to enter names of the people at different numbers\n",
    "        for i in range(face_no):\n",
    "            \n",
    "            #Function call for gui enter box\n",
    "            nm = eg.enterbox(\"Enter name of the person at number \"+str(i+1)+\" or press s for skipping\\n\")\n",
    "            \n",
    "            #If name is left empty or entered s(skip)\n",
    "            if nm=='s' or nm == \"\" :\n",
    "                continue\n",
    "                \n",
    "            #If the name of the person is entered \n",
    "            else:\n",
    "                \n",
    "                path = os.path.join(knownf_dir,nm)\n",
    "                \n",
    "                #Loop to check if the name already exists in directories\n",
    "                #If exists append in the existing ed.txt file or create a new one if ed.txt doesn't exist  \n",
    "                if os.path.exists(path):\n",
    "                    print(\"\\nDirectory already exists\\n\")                    \n",
    "                    path = os.path.join(path,'ed.txt')\n",
    "                    handle = open(path,'a')\n",
    "                    \n",
    "                #If path doesn't create it and make a new ed.txt file for encoding data\n",
    "                else:\n",
    "                    os.mkdir(path)\n",
    "                    print(\"\\nNew directory created\\n\")\n",
    "                    path = os.path.join(path,'ed.txt')\n",
    "                    handle = open(path,'w')\n",
    "                    \n",
    "                #Saving the encoding data with name of the person in ed.txt file \n",
    "                handle.write(str(write_names[i]))\n",
    "                handle.write('\\n')\n",
    "                handle.write(str(nm))\n",
    "                handle.write('\\n')\n",
    "                handle.close()\n",
    "        #Again calling the pack function so as new values too can get append into the variables\n",
    "        known_facesf,known_namesf=pack()\n",
    "    \n",
    "    #Function to wait for a keyboard event\n",
    "    cv2.waitKey(0)\n",
    "    \n",
    "    #Destroy the image window after the previous event \n",
    "    cv2.destroyWindow(filename)\n",
    "print(\"Done\")"
   ]
  }
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