face_recognition_using_dlib.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "face_recognition_using_dlib.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMG3nkYVUSB4uFTcfrux3xC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hamidsadeghi68/face-clustering/blob/main/face_recognition_using_dlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **install requirements (نصب نیازمندی‌ها)**"
      ],
      "metadata": {
        "id": "6kkSnwR53c-z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "face_recognition python package installation on **google colab** machine:"
      ],
      "metadata": {
        "id": "SSU21-jzq-P5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eG9Imqk9r1dg",
        "outputId": "9edff240-2c3e-4d9b-b44d-1d6320d0c535"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting face_recognition\n",
            "  Downloading face_recognition-1.3.0-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from face_recognition) (1.19.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from face_recognition) (7.1.2)\n",
            "Requirement already satisfied: dlib>=19.7 in /usr/local/lib/python3.7/dist-packages (from face_recognition) (19.18.0)\n",
            "Collecting face-recognition-models>=0.3.0\n",
            "  Downloading face_recognition_models-0.3.0.tar.gz (100.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 100.1 MB 21 kB/s \n",
            "\u001b[?25hRequirement already satisfied: Click>=6.0 in /usr/local/lib/python3.7/dist-packages (from face_recognition) (7.1.2)\n",
            "Building wheels for collected packages: face-recognition-models\n",
            "  Building wheel for face-recognition-models (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for face-recognition-models: filename=face_recognition_models-0.3.0-py2.py3-none-any.whl size=100566185 sha256=7842b8c3b127d536a1682fe6b22f7e5299c71517bd19a8d206df7d2f2904aa40\n",
            "  Stored in directory: /root/.cache/pip/wheels/d6/81/3c/884bcd5e1c120ff548d57c2ecc9ebf3281c9a6f7c0e7e7947a\n",
            "Successfully built face-recognition-models\n",
            "Installing collected packages: face-recognition-models, face-recognition\n",
            "Successfully installed face-recognition-1.3.0 face-recognition-models-0.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install face_recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **download sample images (دانلود تصاویر نمونه)**"
      ],
      "metadata": {
        "id": "RtyYfYh83sXE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "downloading images from my github page and saveing in **google colab** machine:"
      ],
      "metadata": {
        "id": "WEnCz9KDrEpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir sample_images\n",
        "!wget https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/hamid1.jpeg -P sample_images\n",
        "!wget https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/hamid2.jpeg -P sample_images\n",
        "!wget https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/Zidane.jpg -P sample_images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAhF7sS9tCEq",
        "outputId": "0b23ebc8-e641-42f2-8c3c-418a81017be8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-15 06:18:21--  https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/hamid1.jpeg\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/hamid1.jpeg [following]\n",
            "--2021-12-15 06:18:21--  https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/hamid1.jpeg\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 28125 (27K) [image/jpeg]\n",
            "Saving to: ‘sample_images/hamid1.jpeg’\n",
            "\n",
            "hamid1.jpeg         100%[===================>]  27.47K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2021-12-15 06:18:21 (16.5 MB/s) - ‘sample_images/hamid1.jpeg’ saved [28125/28125]\n",
            "\n",
            "--2021-12-15 06:18:21--  https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/hamid2.jpeg\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/hamid2.jpeg [following]\n",
            "--2021-12-15 06:18:21--  https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/hamid2.jpeg\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15430 (15K) [image/jpeg]\n",
            "Saving to: ‘sample_images/hamid2.jpeg’\n",
            "\n",
            "hamid2.jpeg         100%[===================>]  15.07K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-12-15 06:18:21 (70.6 MB/s) - ‘sample_images/hamid2.jpeg’ saved [15430/15430]\n",
            "\n",
            "--2021-12-15 06:18:21--  https://github.com/hamidsadeghi68/face-clustering/raw/main/sample_images/Zidane.jpg\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/Zidane.jpg [following]\n",
            "--2021-12-15 06:18:22--  https://raw.githubusercontent.com/hamidsadeghi68/face-clustering/main/sample_images/Zidane.jpg\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13099 (13K) [image/jpeg]\n",
            "Saving to: ‘sample_images/Zidane.jpg’\n",
            "\n",
            "Zidane.jpg          100%[===================>]  12.79K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-12-15 06:18:22 (70.5 MB/s) - ‘sample_images/Zidane.jpg’ saved [13099/13099]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **A test on face_recognition (face_recognition آزمایشی بر روی)**"
      ],
      "metadata": {
        "id": "M3lufHkxdFkS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Face datection:"
      ],
      "metadata": {
        "id": "F84ii6gOsrVE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import face_recognition\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "image1 = cv2.imread('./sample_images/hamid2.jpeg')\n",
        "img_rgb = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)\n",
        "boxes = face_recognition.face_locations(img_rgb, model=\"cnn\")\n",
        "\n",
        "# show the detected face\n",
        "left, top, right, bottom = boxes[0]\n",
        "cv2.rectangle(image1, (left, top), (right, bottom), (0, 255, 0), 2)\n",
        "cv2_imshow(image1)"
      ],
      "metadata": {
        "id": "Wof7z1AGuQYi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "99a4eeff-c055-4e53-c354-c64d7d1fb4b9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=212x250 at 0x7F6A121A63D0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature (template/embedding/encoding) extraction:"
      ],
      "metadata": {
        "id": "tjyVNplTsvjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_vector = face_recognition.face_encodings(img_rgb, boxes)\n",
        "print(feature_vector[0].shape)\n",
        "print(feature_vector[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKJQL7xlsx5D",
        "outputId": "bebb8ed8-5743-4edf-cd75-25f37da58189"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(128,)\n",
            "[-0.02045706  0.15042466  0.03269475 -0.02357545 -0.17419897  0.09307384\n",
            " -0.00799133 -0.03271943  0.17159624 -0.01575185  0.13862367 -0.03790281\n",
            " -0.22802609 -0.04170798 -0.10471036  0.1096641  -0.19145189 -0.15742636\n",
            " -0.11192769 -0.03354044  0.06392464  0.05940554  0.09194073 -0.0088202\n",
            " -0.06007581 -0.41690075 -0.03165367 -0.09010144  0.1699463  -0.16640314\n",
            " -0.021313   -0.00894671 -0.26618916 -0.0886266   0.00586014  0.04485692\n",
            "  0.01181514 -0.05792664  0.15733653  0.01166666 -0.18861113  0.11950141\n",
            "  0.01856809  0.29564151  0.14729053  0.09806047 -0.02869514 -0.03797424\n",
            "  0.05148821 -0.24848008  0.14535514  0.15711442  0.09477043  0.00387629\n",
            "  0.05959967 -0.12550691 -0.02278725  0.07650007 -0.20917316  0.09355518\n",
            "  0.11670212  0.06138049  0.01776947 -0.07989103  0.16040871  0.05047429\n",
            " -0.08205549 -0.15052746  0.01633147 -0.19894809  0.04020127  0.0164561\n",
            " -0.06143245 -0.10875759 -0.24563441  0.08059094  0.43941984  0.13830394\n",
            " -0.13624105  0.00171526 -0.06038583 -0.02222279  0.02186016  0.01277005\n",
            " -0.15377426  0.07516038 -0.09630792  0.09748204  0.13719574 -0.01174386\n",
            " -0.05829361  0.27117515 -0.02002851 -0.00870901  0.02895118  0.03421772\n",
            " -0.08907512  0.01835165 -0.11624138  0.04093263  0.02175708 -0.1251291\n",
            " -0.01279594  0.07181066 -0.17057388  0.05106916 -0.06457405 -0.03139916\n",
            "  0.01034275  0.03597221 -0.19333616 -0.08165742  0.1938335  -0.28462207\n",
            "  0.21479145  0.22482231  0.04498396  0.1016549   0.11366243  0.03870535\n",
            "  0.07285721  0.02208577 -0.09131837 -0.09409939  0.06005147 -0.07707501\n",
            "  0.04388683  0.0493203 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Face recongnition test (آزمایش بازشناسی چهره)**"
      ],
      "metadata": {
        "id": "gHqWwy94euRk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enroll function (تابع ثبت‌نام):"
      ],
      "metadata": {
        "id": "rZCWqh6HepYU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def enroll(img_path):\n",
        "  # This function takes an (single face) image dir and returns its template (feature vector)\n",
        "  \n",
        "  # read image file:\n",
        "  img = cv2.imread(img_path)\n",
        "  # convert BGR (OpenCV default) into RGB [R:Red, G:Green, B:Blue]\n",
        "  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "  # face detection (localization):\n",
        "  boxes = face_recognition.face_locations(img_rgb, model=\"cnn\")\n",
        "\n",
        "  # feature (template/embedding/encoding) exteraction:\n",
        "  encodings = face_recognition.face_encodings(img_rgb, boxes)\n",
        "\n",
        "  return encodings[0] # to do: multi-face images must be considered"
      ],
      "metadata": {
        "id": "hL__ZhOZaKPH"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Enrolling two images (hamid1.jpeg and Zidan.jpg) as gallery:"
      ],
      "metadata": {
        "id": "UIJSsRStrYH3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = './sample_images'\n",
        "imgs_names = ['hamid1.jpeg', 'Zidane.jpg']\n",
        "\n",
        "gallery_templates = [] # create an empty list for known subjects (gallery)\n",
        "for file_name in imgs_names:\n",
        "    template = enroll(os.path.join(data_dir, file_name))\n",
        "    gallery_templates.append(template)"
      ],
      "metadata": {
        "id": "oaf8ic6gQWZL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.   Comparing a probe image (hamid2.jpg) with gallery images:"
      ],
      "metadata": {
        "id": "LZTIQX1ZtLgO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image1 = cv2.imread('./sample_images/hamid2.jpeg')\n",
        "img_rgb = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)\n",
        "boxes = face_recognition.face_locations(img_rgb, model=\"cnn\")\n",
        "probe_template = face_recognition.face_encodings(img_rgb, boxes)\n",
        "\n",
        "ditances = face_recognition.face_distance(gallery_templates, probe_template[0])\n",
        "print(ditances) # the smallest index (with a proper distance threshold) can be used for identification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0E2Pl26tOdG",
        "outputId": "5b31072b-df0e-4d9b-a018-536a728d671f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.33802979 0.68673537]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **references (منابع)**\n",
        "\n",
        "http://dlib.net/\n",
        "\n",
        "https://github.com/ageitgey/face_recognition\n",
        "\n",
        "https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/"
      ],
      "metadata": {
        "id": "tsgWkPYB36Z1"
      }
    }
  ]
}