MTCNNWQ_facial_recognition (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u1Pj_BMa6U0B"
   },
   "source": [
    "# MTCNN\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2iLzJaOirxl0",
    "outputId": "44b6edb4-0e13-405a-b4e7-e2554cf0e807"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mtcnn in c:\\users\\hieu\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (0.1.1)\n",
      "Requirement already satisfied: keras>=2.0.0 in c:\\users\\hieu\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from mtcnn) (2.6.0)\n",
      "Requirement already satisfied: opencv-python>=4.1.0 in c:\\users\\hieu\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from mtcnn) (4.5.5.62)\n",
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     ]
    }
   ],
   "source": [
    "!pip install mtcnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in c:\\users\\hieu\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (21.2.4)\n",
      "Collecting pip\n",
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      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 21.2.4\n",
      "    Uninstalling pip-21.2.4:\n",
      "      Successfully uninstalled pip-21.2.4\n",
      "Successfully installed pip-22.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Building wheels for collected packages: clang\n",
      "  Building wheel for clang (setup.py): started\n",
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      "  Created wheel for clang: filename=clang-5.0-py3-none-any.whl size=30701 sha256=3cdc198d6c2318f1f347dbe8ebdbe18a955164092474a919bf435c6d099e6c04\n",
      "  Stored in directory: c:\\users\\hieu\\appdata\\local\\pip\\cache\\wheels\\3a\\ce\\7a\\27094f689461801c934296d07078773603663dfcaca63bb064\n",
      "Successfully built clang\n",
      "Installing collected packages: flatbuffers, clang\n",
      "  Attempting uninstall: flatbuffers\n",
      "    Found existing installation: flatbuffers 20210226132247\n",
      "    Uninstalling flatbuffers-20210226132247:\n",
      "      Successfully uninstalled flatbuffers-20210226132247\n",
      "Successfully installed clang-5.0 flatbuffers-1.12\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading pandas-1.4.1-cp39-cp39-win_amd64.whl (10.5 MB)\n",
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      "Installing collected packages: pandas\n",
      "Successfully installed pandas-1.4.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting keras==2.6.0Note: you may need to restart the kernel to use updated packages.\n",
      "  Downloading keras-2.6.0-py2.py3-none-any.whl (1.3 MB)\n",
      "     ---------------------------------------- 1.3/1.3 MB 16.5 MB/s eta 0:00:00\n",
      "Installing collected packages: keras\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.8.0\n",
      "    Uninstalling keras-2.8.0:\n",
      "      Successfully uninstalled keras-2.8.0\n",
      "Successfully installed keras-2.6.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pip install keras==2.6.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8.0\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-1.0.2-cp39-cp39-win_amd64.whl (7.2 MB)\n",
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      "Building wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py): started\n",
      "  Building wheel for sklearn (setup.py): finished with status 'done'\n",
      "  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1315 sha256=6a7644f5e80527a2cf72552e1bf23e0bbfd790484620cf3f2b574c71bf23982b\n",
      "  Stored in directory: c:\\users\\hieu\\appdata\\local\\pip\\cache\\wheels\\e4\\7b\\98\\b6466d71b8d738a0c547008b9eb39bf8676d1ff6ca4b22af1c\n",
      "Successfully built sklearn\n",
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      "Successfully installed joblib-1.1.0 scikit-learn-1.0.2 sklearn-0.0 threadpoolctl-3.1.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.5.1-cp39-cp39-win_amd64.whl (7.2 MB)\n",
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      "Installing collected packages: pillow, kiwisolver, fonttools, cycler, matplotlib\n",
      "Successfully installed cycler-0.11.0 fonttools-4.29.1 kiwisolver-1.3.2 matplotlib-3.5.1 pillow-9.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tqdm\n",
      "  Downloading tqdm-4.62.3-py2.py3-none-any.whl (76 kB)\n",
      "     ---------------------------------------- 76.2/76.2 KB 4.4 MB/s eta 0:00:00\n",
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      "Installing collected packages: tqdm\n",
      "Successfully installed tqdm-4.62.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "q5ayHdew8B50",
    "outputId": "fa9702f4-ad9f-4068-f34c-e16d128075c6"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.applications.mobilenet import MobileNet, preprocess_input\n",
    "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dropout, Dense,BatchNormalization, Flatten, MaxPool2D\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, Callback\n",
    "from keras.layers import Conv2D, Reshape\n",
    "#from keras.utils import Sequence\n",
    "from keras.backend import epsilon\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "#from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
    "import cv2\n",
    "from tqdm.notebook import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "CzwyMPxS73Zv"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: /physical_device:GPU:0   Type: GPU\n"
     ]
    }
   ],
   "source": [
    "#GPU\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    print(\"Name:\", gpu.name, \"  Type:\", gpu.device_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[name: \"/device:CPU:0\"\n",
       " device_type: \"CPU\"\n",
       " memory_limit: 268435456\n",
       " locality {\n",
       " }\n",
       " incarnation: 16321485363489434885\n",
       " xla_global_id: -1,\n",
       " name: \"/device:GPU:0\"\n",
       " device_type: \"GPU\"\n",
       " memory_limit: 11302404096\n",
       " locality {\n",
       "   bus_id: 1\n",
       "   links {\n",
       "   }\n",
       " }\n",
       " incarnation: 6135553955223861757\n",
       " physical_device_desc: \"device: 0, name: NVIDIA GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2\"\n",
       " xla_global_id: 416903419]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "\n",
    "device_lib.list_local_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Js2Py\n",
      "  Downloading Js2Py-0.71-py3-none-any.whl (1.0 MB)\n",
      "     ---------------------------------------- 1.0/1.0 MB 13.1 MB/s eta 0:00:00\n",
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      "Collecting pyjsparser>=2.5.1\n",
      "  Downloading pyjsparser-2.7.1.tar.gz (24 kB)\n",
      "  Preparing metadata (setup.py): started\n",
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      "Building wheels for collected packages: pyjsparser\n",
      "  Building wheel for pyjsparser (setup.py): started\n",
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      "  Created wheel for pyjsparser: filename=pyjsparser-2.7.1-py3-none-any.whl size=26004 sha256=63a7670a1c05b4a40421a2d48d828a5242a66fa11b87714b7fc5a2511e1a7d86\n",
      "  Stored in directory: c:\\users\\hieu\\appdata\\local\\pip\\cache\\wheels\\f0\\70\\61\\f42dc45dcf0fbe8c495ce579b04730787081499bfb5b8bc60e\n",
      "Successfully built pyjsparser\n",
      "Installing collected packages: pyjsparser, tzdata, pytz-deprecation-shim, tzlocal, Js2Py\n",
      "Successfully installed Js2Py-0.71 pyjsparser-2.7.1 pytz-deprecation-shim-0.1.0.post0 tzdata-2021.5 tzlocal-4.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Js2Py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oSukhDDh7dER",
    "tags": []
   },
   "source": [
    "## Section 2 - import data\n",
    "Run the following cell as is to mount Google Drive.\n",
    "\n",
    "Upload all necessary content for this practical into your Google Drive's Data/D7 folder.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "LeiWW7cS0Ip5"
   },
   "outputs": [],
   "source": [
    "folder = 'final project\\\\data/data\\\\__MACOSX\\\\CelebA_StyleGan'\n",
    "train_folder = folder + '\\\\real'\n",
    "test_folder=folder + '\\\\fake'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'final project\\\\data/data\\\\__MACOSX\\\\CelebA_StyleGan\\\\real'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EC6qq4DTdzBR"
   },
   "source": [
    "Data prepare \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vk5by8v_7m-d"
   },
   "source": [
    "##  Declare a List of Functions \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Uewvppe-vTvi"
   },
   "outputs": [],
   "source": [
    "#@title\n",
    "import numpy as np\n",
    "import cv2\n",
    "from IPython.display import Image, display\n",
    "#import cv2_imshow\n",
    "from mtcnn import MTCNN\n",
    "\n",
    "import tensorflow \n",
    "from tensorflow import keras\n",
    "\n",
    "\n",
    "# Loads an image from a file using OpenCV.\n",
    "# NOTE: OpenCV loads an image in BGR format by default,\n",
    "#       so we must convert it back to the RGB format.\n",
    "#\n",
    "def load_image(filename):\n",
    "    img = cv2.imread(filename,1)\n",
    "    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "\n",
    "# Draw a bounding box over the image with a \n",
    "# text.\n",
    "#\n",
    "def draw_box(img, x1, y1, x2, y2, text): \n",
    "    img = cv2.rectangle(img,(x1,y1),(x2,y2),(255,255,0),2)\n",
    "\n",
    "    if text != \"\":\n",
    "        img = cv2.rectangle(img,(x1,y1),(x2,y1 + 12),(255,255,0),-1)\n",
    "        img = cv2.putText(img, text, (x1, y1 + 10), cv2.FONT_HERSHEY_PLAIN, 0.7, (0,0,0), 1, cv2.LINE_AA)\n",
    "\n",
    "    return img\n",
    "\n",
    "\n",
    "# Crops out parts of an image based on a list of bounding\n",
    "# boxes. The cropped faces are also resized to 160x160 in\n",
    "# preparation for passing it to FaceNet to compute the\n",
    "# face embeddings.\n",
    "#\n",
    "def crop_faces_to_160x160(img, bounding_boxes): \n",
    "    cropped_faces = []\n",
    "\n",
    "    for (x,y,w,h) in bounding_boxes:\n",
    "        cropped_face = img[y:y+h, x:x+w]\n",
    "        cropped_face = cv2.resize(cropped_face, (160, 160), interpolation=cv2.INTER_CUBIC)\n",
    "        cropped_faces.append(cropped_face)\n",
    "    \n",
    "    return np.array(cropped_faces)\n",
    "\n",
    "\n",
    "# Shows an image in Colab\n",
    "#\n",
    "def show_image(img):\n",
    "    cv2.imshow(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))\n",
    "\n",
    "\n",
    "\n",
    "from IPython.display import display, Javascript\n",
    "from js2py import eval_js\n",
    "from base64 import b64decode\n",
    "\n",
    "def take_photo(filename='photo.jpg', quality=0.8):\n",
    "  js = Javascript('''\n",
    "    async function takePhoto(quality) {\n",
    "      const div = document.createElement('div');\n",
    "      const capture = document.createElement('button');\n",
    "      capture.textContent = 'Capture';\n",
    "      div.appendChild(capture);\n",
    "\n",
    "      const video = document.createElement('video');\n",
    "      video.style.display = 'block';\n",
    "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
    "\n",
    "      document.body.appendChild(div);\n",
    "      div.appendChild(video);\n",
    "      video.srcObject = stream;\n",
    "      await video.play();\n",
    "\n",
    "      // Resize the output to fit the video element.\n",
    "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
    "\n",
    "      // Wait for Capture to be clicked.\n",
    "      await new Promise((resolve) => capture.onclick = resolve);\n",
    "\n",
    "      const canvas = document.createElement('canvas');\n",
    "      canvas.width = video.videoWidth;\n",
    "      canvas.height = video.videoHeight;\n",
    "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
    "      stream.getVideoTracks()[0].stop();\n",
    "      div.remove();\n",
    "      return canvas.toDataURL('image/jpeg', quality);\n",
    "    }\n",
    "    ''')\n",
    "  display(js)\n",
    "  data = eval_js('takePhoto({})'.format(quality))\n",
    "  binary = b64decode(data.split(',')[1])\n",
    "  with open(filename, 'wb') as f:\n",
    "    f.write(binary)\n",
    "  return filename\n",
    "\n",
    "\n",
    "def launch_camera(prompt, filename):\n",
    "\n",
    "    print (prompt)\n",
    "    try:\n",
    "      filename1 = take_photo(filename)\n",
    "      print('Saved to ' + filename1)\n",
    "      \n",
    "      # Show the image which was just taken.\n",
    "      #show_image(filename1)\n",
    "\n",
    "    except Exception as err:\n",
    "      # Errors will be thrown if the user does not have a webcam or if they do not\n",
    "      # grant the page permission to access it.\n",
    "      print(str(err))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "q0Hq8JgS84Mq"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Loads the MTCNN pre-trained model for facial detection\n",
    "\n",
    "face_detector_mtcnn = MTCNN()\n",
    "\n",
    "def detect_faces_with_mtcnn(img):\n",
    "\n",
    "\n",
    "    bounding_boxes = []\n",
    "    detected_faces = face_detector_mtcnn.detect_faces(img)\n",
    "    for detected_face in detected_faces:\n",
    "        bounding_boxes.append(detected_face[\"box\"])\n",
    "\n",
    "    return bounding_boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_image(image_path):\n",
    "    detector = MTCNN() \n",
    "    img=cv2.imread(image_path)\n",
    "    data=detector.detect_faces(img)\n",
    "    biggest=0\n",
    "    if data !=[]:\n",
    "        for faces in data:\n",
    "            box=faces['box']            \n",
    "            # calculate the area in the image\n",
    "            area = box[3]  * box[2]\n",
    "            if area>biggest:\n",
    "                biggest=area\n",
    "                bbox=box \n",
    "        bbox[0]= 0 if bbox[0]<0 else bbox[0]\n",
    "        bbox[1]= 0 if bbox[1]<0 else bbox[1]\n",
    "        img=img[bbox[1]: bbox[1]+bbox[3],bbox[0]: bbox[0]+ bbox[2]] \n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert from bgr to rgb\n",
    "        return (True, img) \n",
    "    else:\n",
    "        return (False, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mtcnn import MTCNN\n",
    "import cv2\n",
    "import os\n",
    "def crop_image(source_dir, dest_dir, mode):\n",
    "    if os.path.isdir(dest_dir)==False:\n",
    "        os.mkdir(dest_dir)\n",
    "    detector = MTCNN()\n",
    "    source_list=os.listdir(source_dir)\n",
    "    uncropped_file_list=[]\n",
    "    for f in source_list:\n",
    "        f_path=os.path.join(source_dir, f)\n",
    "        dest_path=os.path.join(dest_dir,f)\n",
    "        img=cv2.imread(f_path)\n",
    "        data=detector.detect_faces(img)\n",
    "        if data ==[]:\n",
    "            uncropped_file_list.append(f_path)\n",
    "        else:\n",
    "            if mode==1:  #detect the box with the largest area\n",
    "                for i, faces in enumerate(data): # iterate through all the faces found\n",
    "                    box=faces['box']  # get the box for each face                \n",
    "                    biggest=0                    \n",
    "                    area = box[3]  * box[2]\n",
    "                    if area>biggest:\n",
    "                        biggest=area\n",
    "                        bbox=box \n",
    "                bbox[0]= 0 if bbox[0]<0 else bbox[0]\n",
    "                bbox[1]= 0 if bbox[1]<0 else bbox[1]\n",
    "                img=img[bbox[1]: bbox[1]+bbox[3],bbox[0]: bbox[0]+ bbox[2]] \n",
    "                cv2.imwrite(dest_path, img)\n",
    "            else:\n",
    "                for i, faces in enumerate(data): # iterate through all the faces found\n",
    "                    box=faces['box']\n",
    "                    if box !=[]:\n",
    "                        # return all faces found in the image\n",
    "                        box[0]= 0 if box[0]<0 else box[0]\n",
    "                        box[1]= 0 if box[1]<0 else box[1]\n",
    "                        cropped_img=img[box[1]: box[1]+box[3],box[0]: box[0]+ box[2]]\n",
    "                        fname=os.path.splitext(f)[0]\n",
    "                        fext=os.path.splitext(f)[1]\n",
    "                        fname=fname + str(i) + fext\n",
    "                        save_path=os.path.join(dest_dir,fname )\n",
    "                        cv2.imwrite(save_path, cropped_img)  \n",
    "       \n",
    "    return uncropped_file_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import os\n",
    "\n",
    "path = r'final project'\n",
    "\n",
    "# Check current working directory.\n",
    "directory = os.getcwd()\n",
    "print(\"Current working directory %s\" % directory)\n",
    "\n",
    "# Now change the directory\n",
    "#os.chdir( path )\n",
    "\n",
    "# Check current working directory.\n",
    "#directory = os.getcwd()\n",
    "\n",
    "#print(\"Directory changed successfully %s\" % directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###MTCNN Fake face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\099267.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\099445.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\099459.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\099475.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\099483.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_137_0001.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_139_0110.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_142_1111.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_43_1100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_57_0010.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_70_0011.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\easy_82_0001.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_110_1110.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_141_0100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_197_1100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_234_1100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_54_0001.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\hard_82_0011.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_188_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_18_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_277_1100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_328_0011.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_334_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_342_1110.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_352_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_385_1100.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_408_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_413_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_41_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_42_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_432_1111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/fake\\mid_72_1111.jpg\n"
     ]
    }
   ],
   "source": [
    "source_dir='D:/final project/final project/data/data/CelebA_StyleGan/fake' # directory with files to crop\n",
    "dest_dir='D:/final project/final project/fake' # directory where cropped images get stored\n",
    "uncropped_files_list=crop_image(source_dir, dest_dir,1) # mode=1 means 1 face per image\n",
    "for f in uncropped_files_list:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\01038.jpg\n",
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      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00743.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00761.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00764.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00796.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00798.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00804.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00806.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00807.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00814.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00834.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00850.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00851.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00867.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00870.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00876.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00880.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00885.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00887.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00894.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00895.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00901.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00917.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00935.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00937.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00942.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00946.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00951.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00961.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00962.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00969.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00970.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00980.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_00990.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01005.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01034.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01047.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01048.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01077.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan/real\\real_01081.jpg\n"
     ]
    }
   ],
   "source": [
    "##MTCNN fore real\n",
    "source_dir='D:/final project/final project/data/data/CelebA_StyleGan/real' # directory with files to crop\n",
    "dest_dir='D:/final project/final project/real' # directory where cropped images get stored\n",
    "uncropped_files_list=crop_image(source_dir, dest_dir,1) # mode=1 means 1 face per image\n",
    "for f in uncropped_files_list:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052005.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052023.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052111.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052188.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052236.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052256.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052277.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052278.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052289.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052297.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052303.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052332.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052375.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052383.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052403.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052447.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052451.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052506.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052536.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052539.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052555.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052561.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052577.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052605.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052607.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052654.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052721.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052732.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052738.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052764.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052785.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052790.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052819.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052827.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052841.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052854.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052864.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052922.jpg\n",
      "D:/final project/final project/data/data/CelebA_StyleGan_testset/fake\\052974.jpg\n"
     ]
    }
   ],
   "source": [
    "source_dir='D:/final project/final project/data/data/CelebA_StyleGan_testset/fake' # directory with files to crop\n",
    "dest_dir='D:/final project/final project/test/fake' # directory where cropped images get stored\n",
    "uncropped_files_list=crop_image(source_dir, dest_dir,1) # mode=1 means 1 face per image\n",
    "for f in uncropped_files_list:\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##MTCNN fore real face test\n",
    "source_dir='D:/final project/final project/data/data/CelebA_StyleGan_testset/real' # directory with files to crop\n",
    "dest_dir='D:/final project/final project/test/real' # directory where cropped images get stored\n",
    "uncropped_files_list=crop_image(source_dir, dest_dir,1) # mode=1 means 1 face per image\n",
    "for f in uncropped_files_list:\n",
    "    print(f)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "07-Feb_WQ_facial_recognition.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tf_gpu",
   "language": "python",
   "name": "tf_gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}