01. features extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.linalg import norm\n",
    "import pickle\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "import math\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications.vgg19 import VGG19, preprocess_input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Flatten, Dense, Dropout, GlobalAveragePooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using vgg19 as backbone\n",
    "model = VGG19(\n",
    "    weights='imagenet',\n",
    "    include_top=False,\n",
    "    input_shape=(160, 160,3),\n",
    "    pooling='max'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "dataset = r'S:\\\\( F I X E D   P R O J E C T )\\\\dataset jeno'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature extraction function\n",
    "\n",
    "def extract_features(image_path, model):\n",
    "    input_shape = (160, 160, 3)\n",
    "    img = image.load_img(\n",
    "        image_path,\n",
    "        target_size=(input_shape[0], input_shape[1])\n",
    "    )\n",
    "\n",
    "    # turning image to array\n",
    "    img_array = image.img_to_array(img)\n",
    "\n",
    "    # expand image\n",
    "    expanded_img_array = np.expand_dims(img_array, axis=0)\n",
    "\n",
    "    # preprocess iamge\n",
    "    img_preprocess = preprocess_input(expanded_img_array)\n",
    "\n",
    "    # extracting\n",
    "    features_img = model.predict(img_preprocess)\n",
    "\n",
    "    # flatten features\n",
    "    flatten_img = features_img.flatten()\n",
    "    normalized_features = flatten_img/norm(flatten_img)\n",
    "\n",
    "    return normalized_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of file name\n",
    "extenstions = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG']\n",
    "\n",
    "def get_listfile(dir):\n",
    "    list_file = []\n",
    "\n",
    "    for root, directories, filenames in os.walk(dir):\n",
    "        for filename in filenames:\n",
    "            if any(ext in filename for ext in extenstions):\n",
    "                filepath = os.path.join(root, filename)\n",
    "\n",
    "                if os.path.exists(filepath):      # checking if path exist\n",
    "                    list_file.append(filepath)\n",
    "                else: \n",
    "                    print(filepath)\n",
    "    return list_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5481\n"
     ]
    }
   ],
   "source": [
    "filenames = sorted(get_listfile(dataset))\n",
    "print(len(filenames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zakis\\AppData\\Local\\Temp\\ipykernel_2468\\477404303.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for i in tqdm_notebook(range(len(filenames))):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d33c8e08b393485080dac1614bc50d80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5481 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 0s 19ms/step\n"
     ]
    }
   ],
   "source": [
    "# extract using vgg19\n",
    "\n",
    "vgg19_features = []\n",
    "\n",
    "for i in tqdm_notebook(range(len(filenames))):\n",
    "    vgg19_features.append(extract_features(filenames[i], model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(vgg19_features,\n",
    "        open('features_vgg19.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5481 images belonging to 1 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zakis\\AppData\\Local\\Temp\\ipykernel_2468\\109920220.py:12: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  imgdatagen_features = model.predict_generator(generator, num_epochs)\n"
     ]
    }
   ],
   "source": [
    "# extract using ImageDataGenerator\n",
    "\n",
    "dataset = r\"..\\\\dataset jeno\"\n",
    "batch_size = 128\n",
    "datagen = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
    "generator = datagen.flow_from_directory(dataset, target_size=(224,224), class_mode='binary', shuffle=False)\n",
    "\n",
    "num_imgs =len(generator.filenames)\n",
    "num_epochs = int(math.ceil(num_imgs/batch_size))\n",
    "\n",
    "imgdatagen_features = []\n",
    "imgdatagen_features = model.predict_generator(generator, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving ImageDataGenerator features\n",
    "\n",
    "for i, features in enumerate(imgdatagen_features):\n",
    "    imgdatagen_features = features/norm(features)\n",
    "\n",
    "imgdatagen_features = imgdatagen_features.reshape(len(imgdatagen_features), -1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = [dataset +'/'+s for s in generator.filenames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(generator.classes, open('classname.pickle', 'wb'))\n",
    "pickle.dump(filenames, open('filenames.pickle', 'wb'))\n",
    "pickle.dump(imgdatagen_features,\n",
    "            open('features_imgdatagen.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting with finetuned model\n",
    "\n",
    "train_samples = 5481\n",
    "num_class = 1\n",
    "img_width, img_height = 160, 160\n",
    "dataset = r'..\\\\dataset jeno'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5481 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_input,\n",
    "    rotation_range = 0.1,\n",
    "    zoom_range=0.1,\n",
    "    brightness_range=(1, 3),\n",
    "    vertical_flip=True,\n",
    ")\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    dataset,\n",
    "    target_size=(img_width, img_height),\n",
    "    shuffle=True,\n",
    "    batch_size=64,\n",
    "    interpolation='nearest',\n",
    "    seed=12345,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_maker():\n",
    "    base_model = VGG19(include_top=False,\n",
    "                           input_shape=(img_width, img_height, 3))\n",
    "    for layer in base_model.layers[:]:\n",
    "        layer.trainable = False\n",
    "    input = Input(shape=(img_width, img_height, 3))\n",
    "    custom_model = base_model(input)\n",
    "    custom_model = GlobalAveragePooling2D()(custom_model)\n",
    "    custom_model = Dense(64, activation='relu')(custom_model)\n",
    "    custom_model = Dropout(0.2)(custom_model)\n",
    "    predictions = Dense(num_class, activation='softmax')(custom_model)\n",
    "    return Model(inputs=input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_finetuned = model_maker()\n",
    "model_finetuned.compile(loss='cosine_similarity',\n",
    "                        optimizer=tf.keras.optimizers.Adam(0.0001),\n",
    "                        metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zakis\\AppData\\Local\\Temp\\ipykernel_2468\\1036803645.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  model_finetuned.fit_generator(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "8/8 [==============================] - 12s 985ms/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 2/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 3/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 4/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 5/25\n",
      "8/8 [==============================] - 11s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 6/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 7/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 8/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 9/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 10/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 11/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 12/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 13/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 14/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 15/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 16/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 17/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 18/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 19/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 20/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 21/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 22/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 23/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 24/25\n",
      "8/8 [==============================] - 9s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
      "Epoch 25/25\n",
      "8/8 [==============================] - 10s 1s/step - loss: 0.0000e+00 - acc: 0.0000e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x203ea89b7f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_finetuned.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=8,\n",
    "    epochs=25,\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_finetuned.save(\"./mode finetuned.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zakis\\AppData\\Local\\Temp\\ipykernel_2468\\2041645570.py:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  finetune_features = model_finetuned.predict_generator(train_generator, 25)\n"
     ]
    }
   ],
   "source": [
    "finetune_features = []\n",
    "finetune_features = model_finetuned.predict_generator(train_generator, 25)\n",
    "\n",
    "for i, finetune_feature in enumerate(finetune_features):\n",
    "    finetune_features[i] = finetune_feature/norm(finetune_feature)\n",
    "\n",
    "finetune_features = finetune_features.reshape(len(finetune_features), -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(\n",
    "    finetune_features,\n",
    "    open('./features_finetuned_vgg16.picke', 'wb')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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