fire-detection-rgb.ipynb

{
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   "outputs": [],
   "source": [
    "import keras\n",
    "keras.__version__\n",
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
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   "source": [
    "# Ignore  the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# data visualisation and manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    " \n",
    "#configure\n",
    "# sets matplotlib to inline and displays graphs below the corressponding cell.\n",
    "%matplotlib inline  \n",
    "style.use('fivethirtyeight')\n",
    "sns.set(style='whitegrid',color_codes=True)\n",
    "\n",
    "#model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_curve,roc_auc_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#preprocess.\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "#dl libraraies\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# specifically for cnn\n",
    "from tensorflow.keras.layers import Dropout, Flatten,Activation\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "from tensorflow.keras.layers import InputLayer\n",
    " \n",
    "import tensorflow as tf\n",
    "import random as rn\n",
    "\n",
    "# specifically for manipulating zipped images and getting numpy arrays of pixel values of images.\n",
    "import cv2                  \n",
    "import numpy as np  \n",
    "from tqdm import tqdm\n",
    "import os                   \n",
    "from random import shuffle  \n",
    "from zipfile import ZipFile\n",
    "from PIL import Image\n",
    "\n",
    "from tensorflow.keras.applications import VGG16\n",
    "\n",
    "# import tf.keras as keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "os.listdir('../input/fire-dataset/fire_dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "source": [
    "def make_train_data(label,DIR):\n",
    "    for img in tqdm(os.listdir(DIR)):\n",
    "        path = os.path.join(DIR,img)\n",
    "        img = Image.open(path)\n",
    "        img = img.resize((IMG_SIZE,IMG_SIZE))\n",
    "        img =img.convert('RGB')\n",
    "        img= np.array(img)\n",
    "        X.append(img)\n",
    "        Z.append(str(label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "X=[]\n",
    "Z=[]\n",
    "IMG_SIZE=256\n",
    "FIRE='../input/fire-dataset/fire_dataset/fire_images'\n",
    "NOTFIRE='../input/fire-dataset/fire_dataset/non_fire_images'\n",
    "\n",
    "make_train_data('1',FIRE)\n",
    "make_train_data('0',NOTFIRE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "X=np.array(X)\n",
    "fig,ax=plt.subplots(2,5)\n",
    "plt.subplots_adjust(bottom=0.3, top=0.7, hspace=0)\n",
    "fig.set_size_inches(10,10)\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range (5):\n",
    "        l=rn.randint(0,len(Z))\n",
    "        ax[i,j].imshow(X[l])\n",
    "        ax[i,j].set_title(Z[l])\n",
    "        ax[i,j].set_aspect('equal')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T07:39:22.310373Z",
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     "shell.execute_reply.started": "2022-04-21T07:39:22.310334Z"
    }
   },
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "Y=le.fit_transform(Z)\n",
    "# print(Y)\n",
    "Y=to_categorical(Y,2)\n",
    "print(Y[800])\n",
    "# non fire-0\n",
    "# fire-1\n",
    "print('RGB value of image',X[0][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T07:39:22.322456Z",
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   "outputs": [],
   "source": [
    "#example preprocessing\n",
    "\n",
    "\n",
    "path=\"../input/fire-dataset/fire_dataset/fire_images/fire.1.png\"\n",
    "import math\n",
    "x=Image.open(path)\n",
    "x=x.resize((IMG_SIZE,IMG_SIZE))\n",
    "x =x.convert('RGB')\n",
    "plt.imshow(x)\n",
    "arr=np.array(x)\n",
    "print(arr.shape)\n",
    "#segmentaion HSI\n",
    "\n",
    "for i in range(IMG_SIZE):\n",
    "    for j in range(IMG_SIZE):\n",
    "        r=arr[i,j,0]\n",
    "        g=arr[i,j,1]\n",
    "        b=arr[i,j,2]\n",
    "        if(r>g and g>b ):\n",
    "            pass\n",
    "        else:\n",
    "            arr[i,j]=[0,0,0]\n",
    "        \n",
    "# res=np.zeros((IMG_SIZE,IMG_SIZE,3))\n",
    "# for i in range(IMG_SIZE):\n",
    "#     for j in range(IMG_SIZE):\n",
    "#         s=np.sum(arr[i,j])\n",
    "#         r=arr[i,j,0]/s\n",
    "#         g=arr[i,j,1]/s\n",
    "#         b=arr[i,j,2]/s\n",
    "#         res[i,j,2]=(s*1.0)/3\n",
    "#         res[i,j,1]=(1-(3.0 * min([r,g,b])))*100\n",
    "#         d=math.sqrt((r-g)*(r-g)+(r-b)*(g-b))\n",
    "#         val= (0.5*(r-g+r-b))/(d+0.0000001)\n",
    "#         theta = math.degrees(math.acos(val))\n",
    "#         if(b<=g):\n",
    "#             res[i,j,0]=(theta)\n",
    "#         else:\n",
    "#             res[i,j,0]=(360-theta)\n",
    "#         d=math.sqrt((r-g)*(r-g)+(r-b)*(g-b))\n",
    "#         val= (0.5*(r-g+r-b))/(d+0.0000001)\n",
    "#         theta = math.degrees(math.acos(val))\n",
    "#         if(b<=g):\n",
    "#             res[i,j,0]=(theta)\n",
    "#         else:\n",
    "#             res[i,j,0]=(360-theta)\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-21T07:39:22.638170Z"
    }
   },
   "outputs": [],
   "source": [
    "!ls '../input/fire-dataset/fire_dataset/fire_images/fire.1.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-21T07:39:23.560668Z",
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   },
   "outputs": [],
   "source": [
    "def segmentation(arr):\n",
    "    for i in range(IMG_SIZE):\n",
    "        for j in range(IMG_SIZE):\n",
    "            r=arr[i,j,0]\n",
    "            g=arr[i,j,1]\n",
    "            b=arr[i,j,2]\n",
    "            if(r>g and g>b and r>200):\n",
    "                pass\n",
    "            else:\n",
    "                arr[i,j]=[0,0,0]\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "X_new=[]\n",
    "for mat in X:\n",
    "    X_new.append(segmentation(mat))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "X_new=np.array(X_new)\n",
    "fig,ax=plt.subplots(2,5)\n",
    "plt.subplots_adjust(bottom=0.3, top=0.7, hspace=0)\n",
    "fig.set_size_inches(10,10)\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range (5):\n",
    "        l=rn.randint(0,len(Z))\n",
    "        ax[i,j].imshow(X_new[l])\n",
    "        ax[i,j].set_title(Z[l])\n",
    "        ax[i,j].set_aspect('equal')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X_new,Y,test_size=0.25,random_state=1337)\n",
    "\n",
    "np.random.seed(42)\n",
    "rn.seed(42)\n",
    "#tf.set_random_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-21T07:43:54.602034Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model=VGG16(include_top=False, weights='imagenet',input_shape=(IMG_SIZE,IMG_SIZE,3), pooling='max')\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-21T07:43:59.900938Z"
    }
   },
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(base_model)\n",
    "model.add(Dropout(0.20))\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "model.add(Dense(1024,activation='relu'))\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(Dense(2,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "\n",
    "epochs=32\n",
    "batch_size=128\n",
    "base_model.trainable=True \n",
    "# simple early stopping\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=15)\n",
    "model.compile(optimizer=Adam(lr=1e-5),loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-21T07:44:00.181785Z"
    }
   },
   "outputs": [],
   "source": [
    "History = model.fit(x_train, y_train, epochs=epochs, validation_data=(x_test,y_test),callbacks=[es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-21T07:46:51.712300Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save('../working/model2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def representative_data_gen():\n",
    "    for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(10):\n",
    "        yield [input_value]\n",
    "\n",
    "modelName = \"fireClassification\"\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter.representative_dataset = representative_data_gen\n",
    "# Ensure that if any ops can't be quantized, the converter throws an error\n",
    "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
    "# Set the input and output tensors to uint8 (APIs added in r2.3)\n",
    "converter.inference_input_type = tf.uint8\n",
    "converter.inference_output_type = tf.uint8\n",
    "\n",
    "tflite_model_quant = converter.convert()\n",
    "open(str(modelName+\".tflite\"), \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-04-21T05:35:42.303606Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(History.history['accuracy'])\n",
    "plt.plot(History.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-04-21T05:35:42.304795Z",
     "iopub.status.idle": "2022-04-21T05:35:42.305415Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(History.history['loss'])\n",
    "plt.plot(History.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-04-21T05:35:42.306703Z",
     "iopub.status.idle": "2022-04-21T05:35:42.307318Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "path=\"../input/fire-dataset/fire_dataset/fire_images/fire.2.png\"\n",
    "x=Image.open(path)\n",
    "x=x.resize((IMG_SIZE,IMG_SIZE))\n",
    "x=x.convert('RGB')\n",
    "plt.imshow(x)\n",
    "x = img_to_array(x)\n",
    "x= segmentation(x)\n",
    "# plt.imshow(x)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "array = model.predict(x)\n",
    "print(array)\n",
    "result = array[0]\n",
    "answer = np.argmax(result)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-04-21T05:35:42.308466Z",
     "iopub.status.idle": "2022-04-21T05:35:42.309092Z"
    }
   },
   "outputs": [],
   "source": [
    "count=0\n",
    "for x in X_new:\n",
    "#     plt.imshow(x)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    array = model.predict(x)\n",
    "#     print(array)\n",
    "    result = array[0]\n",
    "    answer = np.argmax(result)\n",
    "    if answer :\n",
    "        count+=1\n",
    "#     print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-04-21T05:35:42.310206Z",
     "iopub.status.idle": "2022-04-21T05:35:42.310814Z"
    }
   },
   "outputs": [],
   "source": [
    "print(count,X_new.shape[0]-count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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