Defect Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "outdoor-bacteria",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">ML For Defect Analysis</h1> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crucial-microwave",
   "metadata": {},
   "source": [
    "## 1. Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "proud-apparel",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "sunset-canon",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Copying files: 732 files [00:00, 838.90 files/s]\n"
     ]
    }
   ],
   "source": [
    "#Split the data into train, validation & test\n",
    "\n",
    "import splitfolders  \n",
    "\n",
    "input_folder = 'Data'\n",
    "\n",
    "# Split with a ratio.\n",
    "# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.\n",
    "#Train, val, test\n",
    "splitfolders.ratio(input_folder, output=\"Data_split\", \n",
    "                   seed=42, ratio=(.75, .2, .05), \n",
    "                   group_prefix=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "mechanical-hypothesis",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = os.path.join(os.getcwd(), 'Data_split\\\\train')\n",
    "validation_dir = os.path.join(os.getcwd(), 'Data_split\\\\val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "exposed-interview",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory with our training 'proper' pictures\n",
    "train_proper_dir = os.path.join(train_dir, 'proper')\n",
    "\n",
    "# Directory with our training 'defective' pictures\n",
    "train_defective_dir = os.path.join(train_dir, 'defective')\n",
    "\n",
    "# Directory with our validation 'proper' pictures\n",
    "validation_proper_dir = os.path.join(validation_dir, 'proper')\n",
    "\n",
    "# Directory with our validation 'defective' pictures\n",
    "validation_defective_dir = os.path.join(validation_dir, 'defective')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "adjustable-healing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nmodel = tf.keras.models.Sequential([\\n    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\\n    tf.keras.layers.MaxPooling2D(2, 2),\\n    \\n    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\\n    tf.keras.layers.MaxPooling2D(2,2),\\n    \\n    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\\n    tf.keras.layers.MaxPooling2D(2,2),\\n    \\n    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\\n    tf.keras.layers.MaxPooling2D(2,2),\\n    tf.keras.layers.Dropout(0.5),\\n    \\n    tf.keras.layers.Flatten(),\\n    tf.keras.layers.Dense(512, activation='relu'),\\n    tf.keras.layers.Dense(1, activation='sigmoid')\\n])\\n\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model has already been trained. Run this codeblock only to re-train the model. \n",
    "\n",
    "'''\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    \n",
    "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Dropout(0.5),\n",
    "    \n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "surprising-growing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n#Compiling the model\\n\\nmodel.compile(loss='binary_crossentropy',\\n              optimizer=RMSprop(lr=1e-4),\\n              metrics=['accuracy'])\\n\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model has already been trained. Run this codeblock only to re-train the model. \n",
    "'''\n",
    "#Compiling the model\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=RMSprop(lr=1e-4),\n",
    "              metrics=['accuracy'])\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "unlimited-float",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# Image Augmentation\\n\\ntrain_datagen = ImageDataGenerator(\\n      rescale=1./255,\\n      rotation_range=40,\\n      width_shift_range=0.2,\\n      height_shift_range=0.2,\\n      shear_range=0.2,\\n      zoom_range=0.2,\\n      horizontal_flip=True,\\n      fill_mode='nearest')\\n\\ntest_datagen = ImageDataGenerator(rescale=1./255)\\n\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model has already been trained. Run this codeblock only to re-train the model. \n",
    "\n",
    "'''\n",
    "# Image Augmentation\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=40,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      shear_range=0.2,\n",
    "      zoom_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "rising-kennedy",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# Flow training images in batches of 16 using train_datagen generator\\ntrain_generator = train_datagen.flow_from_directory(\\n        train_dir,  # This is the source directory for training images\\n        target_size=(150, 150),  # All images will be resized to 150x150\\n        batch_size=16,\\n        class_mode='binary')\\n\\n# Flow validation images in batches of 16 using test_datagen generator\\nvalidation_generator = test_datagen.flow_from_directory(\\n        validation_dir,\\n        target_size=(150, 150),\\n        batch_size=16,\\n        class_mode='binary')\\n\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model has already been trained. Run this codeblock only to re-train the model. \n",
    "\n",
    "'''\n",
    "# Flow training images in batches of 16 using train_datagen generator\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,  # This is the source directory for training images\n",
    "        target_size=(150, 150),  # All images will be resized to 150x150\n",
    "        batch_size=16,\n",
    "        class_mode='binary')\n",
    "\n",
    "# Flow validation images in batches of 16 using test_datagen generator\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=16,\n",
    "        class_mode='binary')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "efficient-advisory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "50/50 - 28s - loss: 0.6771 - acc: 0.5602 - val_loss: 0.6132 - val_acc: 0.7055\n",
      "Epoch 2/20\n",
      "50/50 - 27s - loss: 0.6269 - acc: 0.6738 - val_loss: 0.6532 - val_acc: 0.6370\n",
      "Epoch 3/20\n",
      "50/50 - 28s - loss: 0.5918 - acc: 0.7143 - val_loss: 0.5945 - val_acc: 0.6986\n",
      "Epoch 4/20\n",
      "50/50 - 30s - loss: 0.5846 - acc: 0.6967 - val_loss: 0.6047 - val_acc: 0.6781\n",
      "Epoch 5/20\n",
      "50/50 - 28s - loss: 0.5836 - acc: 0.7173 - val_loss: 0.5945 - val_acc: 0.6849\n",
      "Epoch 6/20\n",
      "50/50 - 30s - loss: 0.5642 - acc: 0.7111 - val_loss: 0.6082 - val_acc: 0.7055\n",
      "Epoch 7/20\n",
      "50/50 - 30s - loss: 0.5553 - acc: 0.7356 - val_loss: 0.5847 - val_acc: 0.7329\n",
      "Epoch 8/20\n",
      "50/50 - 35s - loss: 0.5416 - acc: 0.7431 - val_loss: 0.6069 - val_acc: 0.7260\n",
      "Epoch 9/20\n",
      "50/50 - 34s - loss: 0.5535 - acc: 0.7312 - val_loss: 0.5511 - val_acc: 0.7192\n",
      "Epoch 10/20\n",
      "50/50 - 35s - loss: 0.5308 - acc: 0.7399 - val_loss: 0.5909 - val_acc: 0.7055\n",
      "Epoch 11/20\n",
      "50/50 - 36s - loss: 0.5516 - acc: 0.7243 - val_loss: 0.5463 - val_acc: 0.7534\n",
      "Epoch 12/20\n",
      "50/50 - 32s - loss: 0.5009 - acc: 0.7601 - val_loss: 0.6318 - val_acc: 0.7534\n",
      "Epoch 13/20\n",
      "50/50 - 28s - loss: 0.5002 - acc: 0.7581 - val_loss: 0.5631 - val_acc: 0.7740\n",
      "Epoch 14/20\n",
      "50/50 - 28s - loss: 0.5024 - acc: 0.7437 - val_loss: 0.5623 - val_acc: 0.7603\n",
      "Epoch 15/20\n",
      "50/50 - 29s - loss: 0.5116 - acc: 0.7556 - val_loss: 0.4795 - val_acc: 0.7603\n",
      "Epoch 16/20\n",
      "50/50 - 25s - loss: 0.4721 - acc: 0.7739 - val_loss: 0.5860 - val_acc: 0.7397\n",
      "Epoch 17/20\n",
      "50/50 - 24s - loss: 0.4918 - acc: 0.7701 - val_loss: 0.5130 - val_acc: 0.7603\n",
      "Epoch 18/20\n",
      "50/50 - 25s - loss: 0.4826 - acc: 0.7613 - val_loss: 0.4881 - val_acc: 0.7534\n",
      "Epoch 19/20\n",
      "50/50 - 30s - loss: 0.4444 - acc: 0.7845 - val_loss: 0.5835 - val_acc: 0.7466\n",
      "Epoch 20/20\n",
      "50/50 - 28s - loss: 0.4709 - acc: 0.7845 - val_loss: 0.5764 - val_acc: 0.7671\n"
     ]
    }
   ],
   "source": [
    "# The model has already been trained. Run this codeblock only to re-train the model. \n",
    "'''\n",
    "\n",
    "history = model.fit(\n",
    "      train_generator,\n",
    "      steps_per_epoch=50,  # 2000 images = batch_size * steps\n",
    "      epochs=20,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=30,  # 1000 images = batch_size * steps\n",
    "      verbose=2)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "distinct-daughter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This will work only if you have run the previous codeblocks\n",
    "\n",
    "'''\n",
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "announced-pilot",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the model\n",
    "\n",
    "#model.save('model.h5')   Current best model has train_acc = 82.75 and val_acc = 84.93                "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thermal-adult",
   "metadata": {},
   "source": [
    "## 2. Loading The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "indirect-release",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from IPython.display import Image, display\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "new_model = load_model('model_tacc8275_val8493.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "patent-mason",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aware-healthcare",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (5).jpg')\n",
    "display(Image(filename=img_path))\n",
    "\n",
    "listOfImageNames = [img_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "partial-grade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfrom IPython.display import Image, display\\n\\nlistOfImageNames = [os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (1).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (2).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (3).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (4).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (5).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (6).jpg'),\\n                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (7).jpg'),\\n                    ]\\n\\nfor imageName in listOfImageNames:\\n    display(Image(filename=imageName))\\n\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For multiple images\n",
    "'''\n",
    "from IPython.display import Image, display\n",
    "\n",
    "listOfImageNames = [os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (1).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (2).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (3).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (4).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (5).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (6).jpg'),\n",
    "                    os.path.join(os.getcwd(), 'Data\\\\Defective\\\\Defective (7).jpg'),\n",
    "                    ]\n",
    "\n",
    "for imageName in listOfImageNames:\n",
    "    display(Image(filename=imageName))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "marine-angel",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "varied-fellow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.]\n",
      "Bottle is Defective\n"
     ]
    }
   ],
   "source": [
    "#for single image\n",
    "\n",
    "path = img_path\n",
    "img = image.load_img(path, target_size=(150, 150))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "images = np.vstack([x])\n",
    "classes = new_model.predict(images, batch_size=10)\n",
    "print(classes[0])\n",
    "if classes[0]<0.5:\n",
    "    print(\"Bottle is Defective\")\n",
    "else:\n",
    "    print(\"Bottle is Proper\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "wireless-mouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor fn in listOfImageNames:\\n \\n    path = fn\\n    img = image.load_img(path, target_size=(150, 150))\\n    x = image.img_to_array(img)\\n    x = np.expand_dims(x, axis=0)\\n\\n    images = np.vstack([x])\\n    classes = new_model.predict(images, batch_size=10)\\n    print(classes[0])\\n    if classes[0]>0.5:\\n        print(\"Bottle is proper\")\\n    else:\\n        print(\"Bottle is defective\")\\n'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for multiple images\n",
    "\n",
    "'''\n",
    "for fn in listOfImageNames:\n",
    " \n",
    "    path = fn\n",
    "    img = image.load_img(path, target_size=(150, 150))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "\n",
    "    images = np.vstack([x])\n",
    "    classes = new_model.predict(images, batch_size=10)\n",
    "    print(classes[0])\n",
    "    if classes[0]>0.5:\n",
    "        print(\"Bottle is proper\")\n",
    "    else:\n",
    "        print(\"Bottle is defective\")\n",
    "'''"
   ]
  }
 ],
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