crimevision.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEebeTH1mXWh",
        "outputId": "b35a81a0-c776-4ec8-f410-4f72bd932f3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OoshUBZmYj5",
        "outputId": "13136f2e-4309-411b-f7ea-3864c179b5e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!unzip '/content/drive/MyDrive/archive.zip'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJ30aFCUKZzj"
      },
      "source": [
        "Importing libaraies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lj1cx6cFsExf"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yOm5L877DaVu"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qy0H_AjSDrof"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D,Dropout,MaxPooling2D,Conv2D, Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.applications import DenseNet121\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from IPython.display import clear_output\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWmXBxHaKfXO"
      },
      "source": [
        "creating training and testing dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bBLDmTyKF2kG"
      },
      "outputs": [],
      "source": [
        "train_dir='/content/Train'\n",
        "test_dir='/content/Test'\n",
        "Seed =10\n",
        "img_height=64\n",
        "img_width=64\n",
        "img_shape=(img_height,img_width)\n",
        "batch_size = 128\n",
        "epochs= 5\n",
        "learn_rate=0.00003"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fFkpLvWsGTgM"
      },
      "outputs": [],
      "source": [
        "crime_types=os.listdir(train_dir)\n",
        "n=len(crime_types)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxTflldkI3E6"
      },
      "outputs": [],
      "source": [
        "crimes={}\n",
        "train=test=0\n",
        "for clss in crime_types:\n",
        "  num=len(os.listdir(os.path.join(train_dir,clss)))\n",
        "  train+=num\n",
        "  test+=len(os.listdir(os.path.join(test_dir,clss)))\n",
        "  crimes[clss]=num"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXsd89afK9El"
      },
      "source": [
        "data visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SbAg_wANSEym",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "d49c5640-abdc-43d4-acef-001e3edff2d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7fb5f60a5e10>,\n",
              "  <matplotlib.patches.Wedge at 0x7fb5f60a4eb0>],\n",
              " [Text(-1.1615504401079835, 0.3013313377080951, 'Training data'),\n",
              "  Text(1.1615504189484738, -0.3013314192722449, 'test data')],\n",
              " [Text(-0.5807752200539917, 0.15066566885404753, '91.9%'),\n",
              "  Text(0.5807752094742369, -0.1506657096361224, '8.1%')])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "plt.pie(x=np.array([train,test]),autopct=\"%0.1f%%\", explode=[0.1,0.1], labels=[\"Training data\",\"test data\"],pctdistance=0.5,colors=[\"yellow\",\"green\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J0_Qe7-ATKAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "0cc7407f-d483-4ee0-e1a1-6a06c772aa50"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.bar(list(crimes.keys()),list(crimes.values()),width=0.4,align=\"center\",edgecolor=[\"red\"],color=[\"orange\"])\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel(\"Reported crimes\")\n",
        "plt.ylabel(\"Number of reported crimes\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1PBUE-YlfkZv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c68e31a0-70dc-4e0a-85f7-5a6d87fc40da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Arson',\n",
              " 'Burglary',\n",
              " 'Vandalism',\n",
              " 'Explosion',\n",
              " 'NormalVideos',\n",
              " 'Abuse',\n",
              " 'Robbery',\n",
              " 'Arrest',\n",
              " 'RoadAccidents',\n",
              " 'Shooting',\n",
              " 'Stealing',\n",
              " 'Shoplifting',\n",
              " 'Assault',\n",
              " 'Fighting']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "list(crimes.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lMNk3H8UNQ2",
        "outputId": "927ba31e-9b69-4873-aae6-d495b0b02e56"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1266345 files belonging to 14 classes.\n",
            "Using 1013076 files for training.\n"
          ]
        }
      ],
      "source": [
        "train_set=image_dataset_from_directory(train_dir,\n",
        "                                       label_mode=\"categorical\",\n",
        "                                       batch_size=batch_size,\n",
        "                                       image_size=(224,224),\n",
        "                                       shuffle=True,\n",
        "                                       seed=Seed,\n",
        "                                       validation_split=0.2,\n",
        "                                       subset=\"training\",\n",
        "                                       )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WoISJowoVOgs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7d597d9-29f3-46dc-db10-0a1bd1c07b81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1266345 files belonging to 14 classes.\n",
            "Using 253269 files for validation.\n"
          ]
        }
      ],
      "source": [
        "val_set=image_dataset_from_directory(train_dir,\n",
        "                                       label_mode=\"categorical\",\n",
        "                                       batch_size=batch_size,\n",
        "                                       image_size=(224,224),\n",
        "                                       shuffle=True,\n",
        "                                       seed=Seed,\n",
        "                                       validation_split=0.2,\n",
        "                                       subset=\"validation\",\n",
        "                                       )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9QKqH-oWq0m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0b461d2-efbd-436a-c009-ee8767a1c6da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 111308 files belonging to 14 classes.\n"
          ]
        }
      ],
      "source": [
        "test_set=image_dataset_from_directory(test_dir,\n",
        "                                      label_mode=\"categorical\",\n",
        "                                      class_names=None,\n",
        "                                      batch_size=batch_size,\n",
        "                                      image_size=(224,224),\n",
        "                                      shuffle=False,\n",
        "                                      seed=Seed,\n",
        "                                      )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZruTurDLE00"
      },
      "source": [
        "model building\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pAESZ6USXKpS"
      },
      "outputs": [],
      "source": [
        "def transfer_learning():\n",
        "  base_model=DenseNet121(include_top=False,input_shape=(224,224,3),weights=\"imagenet\")\n",
        "  thr=149\n",
        "  for layers in base_model.layers[:thr]:\n",
        "    layers.trainable=False\n",
        "  for layers in base_model.layers[thr:]:\n",
        "    layers.trainable=False\n",
        "  return base_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hHPImbpaYLl-"
      },
      "outputs": [],
      "source": [
        "def create_model():\n",
        "  model=Sequential()\n",
        "\n",
        "  base_model=transfer_learning()\n",
        "  model.add(base_model)\n",
        "\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(256,activation=\"relu\"))\n",
        "  model.add(Dropout(0.2))\n",
        "\n",
        "  model.add(Dense(512,activation=\"relu\"))\n",
        "  model.add(Dropout(0.2))\n",
        "\n",
        "  model.add(Dense(1024,activation=\"relu\"))\n",
        "\n",
        "  model.add(Dense(n,activation=\"softmax\"))\n",
        "\n",
        "  model.summary()\n",
        "\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zr66I00YZf6j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48bdd4b0-ab50-4365-f710-892a0102d28c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "29084464/29084464 [==============================] - 0s 0us/step\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " densenet121 (Functional)    (None, 7, 7, 1024)        7037504   \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1024)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               262400    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               131584    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1024)              525312    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 14)                14350     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,971,150\n",
            "Trainable params: 933,646\n",
            "Non-trainable params: 7,037,504\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model=create_model()\n",
        "\n",
        "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYM3qhhPLOaj"
      },
      "source": [
        "model training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yhqjb6qSZwMc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86896db6-558c-48bb-e3ca-c7e940152547"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "7915/7915 [==============================] - 3595s 451ms/step - loss: 0.2922 - accuracy: 0.9161 - val_loss: 0.0955 - val_accuracy: 0.9732\n",
            "Epoch 2/5\n",
            "7915/7915 [==============================] - 3587s 453ms/step - loss: 0.1484 - accuracy: 0.9570 - val_loss: 0.0537 - val_accuracy: 0.9860\n",
            "Epoch 3/5\n",
            "7915/7915 [==============================] - 3590s 453ms/step - loss: 0.1245 - accuracy: 0.9647 - val_loss: 0.0418 - val_accuracy: 0.9888\n",
            "Epoch 4/5\n",
            "7915/7915 [==============================] - 3594s 454ms/step - loss: 0.1128 - accuracy: 0.9683 - val_loss: 0.0358 - val_accuracy: 0.9905\n",
            "Epoch 5/5\n",
            "7915/7915 [==============================] - 3603s 455ms/step - loss: 0.1063 - accuracy: 0.9708 - val_loss: 0.0349 - val_accuracy: 0.9908\n"
          ]
        }
      ],
      "source": [
        "history=model.fit(x=train_set,validation_data=val_set,epochs=epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8bcYvt9Wbvc6"
      },
      "outputs": [],
      "source": [
        "model.save('crime_vision.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7wjR6Z7LVPR"
      },
      "source": [
        "model testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qS3Lz-Y2diMb"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model.load_weights('/content/crime_vision.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0YMSetrCd6Bo"
      },
      "outputs": [],
      "source": [
        "y_true= np.array([])\n",
        "for x,y in test_set:\n",
        "  y_true=np.concatenate([y_true,np.argmax(y.numpy(),axis=-1)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iwxOQG2TectY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4958f67d-3004-45de-8dc5-72fb9836a7e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  0., ..., 13., 13., 13.])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "y_true"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ryYkvvvveOCn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e11c7cf6-8339-4753-e95b-efdd9c097acc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "870/870 [==============================] - 318s 364ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.99983232e-02, 5.73431924e-02, 2.12578125e-05, ...,\n",
              "        7.11872303e-08, 1.47607017e-04, 8.64453614e-02],\n",
              "       [2.32983450e-03, 5.90169393e-02, 1.05944884e-04, ...,\n",
              "        4.57320448e-06, 4.51269792e-04, 8.85519832e-02],\n",
              "       [2.97827665e-02, 2.03254726e-02, 8.66446862e-05, ...,\n",
              "        4.07931999e-08, 5.48658572e-05, 2.91068237e-02],\n",
              "       ...,\n",
              "       [9.97288059e-03, 1.36954542e-02, 4.29670513e-03, ...,\n",
              "        1.08748488e-02, 1.65991876e-02, 1.70602649e-02],\n",
              "       [1.17110496e-03, 3.19995149e-03, 4.68374958e-04, ...,\n",
              "        1.42179790e-03, 1.50859926e-03, 2.60921987e-03],\n",
              "       [4.03818529e-04, 1.43140217e-03, 2.94161728e-04, ...,\n",
              "        1.63266144e-03, 7.99658708e-04, 1.26704236e-03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "y_pred=model.predict(test_set)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras.utils as image\n",
        "from tensorflow.keras.preprocessing.image import load_img"
      ],
      "metadata": {
        "id": "jC37TFlpDoWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_cHfsLFeX04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "e6e4763a-0e3b-4eb3-8b4f-402e1066343e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 3s 3s/step\n",
            "7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Abuse'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "img= image.load_img(\"/content/Test/Abuse/Abuse028_x264_0.png\",target_size=(224,224))\n",
        "x=image.img_to_array(img)\n",
        "x=np.expand_dims(x,axis=0)\n",
        "pred=np.argmax(model.predict(x))\n",
        "op=['Fighting','Arrest','Vandalism','Assault','Stealing','Arson','NormalVideos','Abuse','Explosion','Robbery','Burglary','Shooting','Shoplifting','RoadAccidents']\n",
        "print(pred)\n",
        "op[pred]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UNREPX5mnKXI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "fcfb6fd7-22fd-43dc-f64b-2ca8bc521810"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 99ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Abuse'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "img= image.load_img(\"/content/Test/Abuse/Abuse028_x264_0.png\",target_size=(64,64))\n",
        "x=image.img_to_array(img)\n",
        "x=np.expand_dims(x,axis=0)\n",
        "pred=np.argmax(model.predict(x))\n",
        "op=['Fighting','Arrest','Vandalism','Assault','Stealing','Arson','NormalVideos','Abuse','Explosion','Robbery','Burglary','Shooting','Shoplifting','RoadAccidents']\n",
        "op[pred]"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}