trafic_sign_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/muxair080/Big-Mart-Sales/blob/main/trafic_sign_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "a5m1S59AKYoY"
      },
      "outputs": [],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcxaEXHYKhxD",
        "outputId": "03572cec-3008-4e31-cdeb-d7ef3b612449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "gtsrb-german-traffic-sign.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d meowmeowmeowmeowmeow/gtsrb-german-traffic-sign"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "DTP_ZFBCKhuP"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('/content/gtsrb-german-traffic-sign.zip')\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "x9vCAxNiKhqr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "from PIL import Image\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout,BatchNormalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkjYmsLeLeHy"
      },
      "source": [
        "# Convert images to arrays"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "HdPTye_qKhnL"
      },
      "outputs": [],
      "source": [
        "data = []\n",
        "labels = []\n",
        "classes = 43\n",
        "cur_path = os.getcwd()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D879q9bALxNO"
      },
      "source": [
        "# Preprocess Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "JbWMYaqKNpJR"
      },
      "outputs": [],
      "source": [
        "# /content/train/0/00000_00000_00000.png"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "CZQvm-dILtG0"
      },
      "outputs": [],
      "source": [
        "for i in range(classes):\n",
        "  path = os.path.join(cur_path, 'train', str(i))\n",
        "  # print(path)\n",
        "  images = os.listdir(path)\n",
        "  # print(images)\n",
        "  for img in images:\n",
        "    try:\n",
        "      image = Image.open(path+'//'+img)\n",
        "      image = image.resize((30,30))\n",
        "      image = np.array(image)\n",
        "      data.append(image)\n",
        "      labels.append(i)\n",
        "    except Exception as e:\n",
        "      print(e)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47kUPOzMRhpZ",
        "outputId": "a51aab6a-f476-4a7b-c17c-16722e7efb6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39209, 30, 30, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "d = np.array(data)\n",
        "d.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1EieJjkPlOt"
      },
      "source": [
        "# List to numpy arrays"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "CKpsLkMeLuKl"
      },
      "outputs": [],
      "source": [
        "data = np.array(data)\n",
        "labels = np.array(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bcjtjf-GRTBV",
        "outputId": "ec01290b-762c-40af-fb07-40e270d50eea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "         ...,\n",
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              "         [ 40,  34,  34],\n",
              "         [ 43,  36,  36]],\n",
              "\n",
              "        [[ 37,  33,  26],\n",
              "         [ 37,  32,  24],\n",
              "         [ 31,  28,  20],\n",
              "         ...,\n",
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              "         [ 41,  36,  35],\n",
              "         [ 42,  36,  37]]],\n",
              "\n",
              "\n",
              "       [[[ 24,  23,  24],\n",
              "         [ 24,  23,  25],\n",
              "         [ 24,  23,  24],\n",
              "         ...,\n",
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              "         [ 19,  18,  19],\n",
              "         [ 21,  20,  20]],\n",
              "\n",
              "        [[ 23,  23,  24],\n",
              "         [ 22,  22,  24],\n",
              "         [ 22,  22,  23],\n",
              "         ...,\n",
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              "         [ 21,  20,  21]],\n",
              "\n",
              "        [[ 23,  23,  24],\n",
              "         [ 23,  23,  24],\n",
              "         [ 23,  23,  24],\n",
              "         ...,\n",
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              "         [ 22,  21,  22]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[ 26,  25,  23],\n",
              "         [ 31,  29,  25],\n",
              "         [ 30,  26,  24],\n",
              "         ...,\n",
              "         [ 25,  23,  22],\n",
              "         [ 27,  25,  22],\n",
              "         [ 28,  26,  22]],\n",
              "\n",
              "        [[ 22,  22,  20],\n",
              "         [ 27,  26,  23],\n",
              "         [ 28,  25,  23],\n",
              "         ...,\n",
              "         [ 25,  22,  20],\n",
              "         [ 24,  22,  19],\n",
              "         [ 25,  23,  19]],\n",
              "\n",
              "        [[ 22,  20,  19],\n",
              "         [ 23,  22,  20],\n",
              "         [ 25,  23,  21],\n",
              "         ...,\n",
              "         [ 22,  20,  19],\n",
              "         [ 22,  20,  18],\n",
              "         [ 24,  21,  19]]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dG1auVS0Ps0X"
      },
      "source": [
        "# Save Labels & Data in files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "kBm9pyyrPHDV"
      },
      "outputs": [],
      "source": [
        "np.save('/content/data/data', data)\n",
        "np.save('/content/data/labels', labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBvOsXDGQFo7"
      },
      "source": [
        "# Load Data & Labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "IpbL73xaQBwV"
      },
      "outputs": [],
      "source": [
        "data = np.load('/content/data/data.npy')\n",
        "labels = np.load('/content/data/labels.npy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2zuZ8gFRBir",
        "outputId": "39a882e4-cd06-4b96-f258-e3fb106f750e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(39209, 30, 30, 3) (39209,)\n"
          ]
        }
      ],
      "source": [
        "print(data.shape, labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "QrcNawYOREjf"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(data, \n",
        "                  labels,test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xM1xrIAkSlDg"
      },
      "source": [
        "# Convert labels to onehot encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "kLHB0SrmSnzj"
      },
      "outputs": [],
      "source": [
        "Y_train = to_categorical(Y_train, 43)\n",
        "Y_test = to_categorical(Y_test, 43)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbTKBsc3TZf-",
        "outputId": "193e5768-8365-43eb-ecf2-d3bce76d8d21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30, 30, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "input_shape=X_train.shape[1:]\n",
        "input_shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YneX4iDtTAhR"
      },
      "source": [
        "# Build CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "AeT2k96FSib7"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu',\n",
        "                input_shape=X_train.shape[1:]))\n",
        "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(rate=0.25))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(rate=0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(rate=0.5))\n",
        "model.add(Dense(43, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "o17K76GzS7rx"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGqDq6_JVlbU",
        "outputId": "682ae160-5365-4eac-e966-23188cc112df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 2.2680 - accuracy: 0.4102 - val_loss: 0.8593 - val_accuracy: 0.7445\n",
            "Epoch 2/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.9574 - accuracy: 0.7092 - val_loss: 0.4469 - val_accuracy: 0.8699\n",
            "Epoch 3/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.6233 - accuracy: 0.8117 - val_loss: 0.3460 - val_accuracy: 0.8908\n",
            "Epoch 4/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.4803 - accuracy: 0.8523 - val_loss: 0.1793 - val_accuracy: 0.9490\n",
            "Epoch 5/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.4121 - accuracy: 0.8773 - val_loss: 0.2178 - val_accuracy: 0.9406\n",
            "Epoch 6/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.3619 - accuracy: 0.8914 - val_loss: 0.1204 - val_accuracy: 0.9651\n",
            "Epoch 7/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.3452 - accuracy: 0.8988 - val_loss: 0.1020 - val_accuracy: 0.9726\n",
            "Epoch 8/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.3003 - accuracy: 0.9113 - val_loss: 0.0964 - val_accuracy: 0.9695\n",
            "Epoch 9/20\n",
            "981/981 [==============================] - 7s 7ms/step - loss: 0.2912 - accuracy: 0.9160 - val_loss: 0.0921 - val_accuracy: 0.9718\n",
            "Epoch 10/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.2845 - accuracy: 0.9182 - val_loss: 0.1463 - val_accuracy: 0.9568\n",
            "Epoch 11/20\n",
            "981/981 [==============================] - 5s 6ms/step - loss: 0.2823 - accuracy: 0.9180 - val_loss: 0.0930 - val_accuracy: 0.9726\n",
            "Epoch 12/20\n",
            "981/981 [==============================] - 5s 6ms/step - loss: 0.2694 - accuracy: 0.9233 - val_loss: 0.0681 - val_accuracy: 0.9791\n",
            "Epoch 13/20\n",
            "981/981 [==============================] - 5s 6ms/step - loss: 0.2671 - accuracy: 0.9245 - val_loss: 0.0607 - val_accuracy: 0.9804\n",
            "Epoch 14/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.2519 - accuracy: 0.9276 - val_loss: 0.1343 - val_accuracy: 0.9614\n",
            "Epoch 15/20\n",
            "981/981 [==============================] - 7s 7ms/step - loss: 0.2618 - accuracy: 0.9270 - val_loss: 0.0698 - val_accuracy: 0.9793\n",
            "Epoch 16/20\n",
            "981/981 [==============================] - 7s 8ms/step - loss: 0.2499 - accuracy: 0.9294 - val_loss: 0.1036 - val_accuracy: 0.9684\n",
            "Epoch 17/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.2456 - accuracy: 0.9302 - val_loss: 0.0676 - val_accuracy: 0.9797\n",
            "Epoch 18/20\n",
            "981/981 [==============================] - 6s 6ms/step - loss: 0.2600 - accuracy: 0.9287 - val_loss: 0.0652 - val_accuracy: 0.9796\n",
            "Epoch 19/20\n",
            "981/981 [==============================] - 5s 5ms/step - loss: 0.2522 - accuracy: 0.9309 - val_loss: 0.0588 - val_accuracy: 0.9814\n",
            "Epoch 20/20\n",
            "981/981 [==============================] - 5s 5ms/step - loss: 0.2378 - accuracy: 0.9351 - val_loss: 0.0761 - val_accuracy: 0.9774\n"
          ]
        }
      ],
      "source": [
        "histroy  = model.fit(X_train, Y_train, batch_size=32, \n",
        "                     epochs=20, validation_data=(X_test, Y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EI0QNOPJepRH"
      },
      "source": [
        "#  Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zVm5PAkser31",
        "outputId": "5266704a-3233-4059-db63-42b396d5d52d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(0)\n",
        "plt.plot(histroy.history['accuracy'], label='training accuracy')\n",
        "plt.plot(histroy.history['val_accuracy'], label='val accuracy')\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing Data"
      ],
      "metadata": {
        "id": "ztfiKJprhvwH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tst = pd.read_csv('Test.csv')\n",
        "label = tst['ClassId'].values\n",
        "imgs = tst['Path'].values\n",
        "print(label)\n",
        "print(imgs)\n",
        "data = []\n",
        "for img in imgs:\n",
        "  image = Image.open(img)\n",
        "  image = image.resize((30,30))\n",
        "  data.append(np.array(image))\n",
        "\n",
        "X_test = np.array(data)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtlR62iEn2UF",
        "outputId": "4cfeaa4a-2940-47dc-c784-4ecc897579d3"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16  1 38 ...  6  7 10]\n",
            "['Test/00000.png' 'Test/00001.png' 'Test/00002.png' ... 'Test/12627.png'\n",
            " 'Test/12628.png' 'Test/12629.png']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TpGZKBeicQ4",
        "outputId": "fcdb702e-5b22-499c-9aac-71e6c2427ab3"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "395/395 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([16,  1, 38, ..., 32,  7, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EdM5JVbjA9R",
        "outputId": "84eed78f-6a6f-4c8a-850e-4dd2f05d6dbf"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accuracy on Testing Data"
      ],
      "metadata": {
        "id": "lwJpiDcnjqZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "U28SZZkWjpX7"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(label, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5bc1-6BjxT9",
        "outputId": "e3b51628-da87-47c5-bcb1-cbd69db6298c"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9448931116389548"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = { 0:'Speed limit (20km/h)',\n",
        "            1:'Speed limit (30km/h)', \n",
        "            2:'Speed limit (50km/h)', \n",
        "            3:'Speed limit (60km/h)', \n",
        "            4:'Speed limit (70km/h)', \n",
        "            5:'Speed limit (80km/h)', \n",
        "            6:'End of speed limit (80km/h)', \n",
        "            7:'Speed limit (100km/h)', \n",
        "            8:'Speed limit (120km/h)', \n",
        "            9:'No passing', \n",
        "            10:'No passing veh over 3.5 tons', \n",
        "            11:'Right-of-way at intersection', \n",
        "            12:'Priority road', \n",
        "            13:'Yield', \n",
        "            14:'Stop', \n",
        "            15:'No vehicles', \n",
        "            16:'Veh > 3.5 tons prohibited', \n",
        "            17:'No entry', \n",
        "            18:'General caution', \n",
        "            19:'Dangerous curve left', \n",
        "            20:'Dangerous curve right', \n",
        "            21:'Double curve', \n",
        "            22:'Bumpy road', \n",
        "            23:'Slippery road', \n",
        "            24:'Road narrows on the right', \n",
        "            25:'Road work', \n",
        "            26:'Traffic signals', \n",
        "            27:'Pedestrians', \n",
        "            28:'Children crossing', \n",
        "            29:'Bicycles crossing', \n",
        "            30:'Beware of ice/snow',\n",
        "            31:'Wild animals crossing', \n",
        "            32:'End speed + passing limits', \n",
        "            33:'Turn right ahead', \n",
        "            34:'Turn left ahead', \n",
        "            35:'Ahead only', \n",
        "            36:'Go straight or right', \n",
        "            37:'Go straight or left', \n",
        "            38:'Keep right', \n",
        "            39:'Keep left', \n",
        "            40:'Roundabout mandatory', \n",
        "            41:'End of no passing', \n",
        "            42:'End no passing veh > 3.5 tons' }"
      ],
      "metadata": {
        "id": "INVk3R3Gk5Rh"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "def test_on_img(img):\n",
        "  data = []\n",
        "  image = Image.open(img)\n",
        "  image = image.resize((30,30))\n",
        "  data.append(np.array(image))\n",
        "  X_test = np.array(data)\n",
        "  Y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "  return image, Y_pred\n"
      ],
      "metadata": {
        "id": "q2vefAF-k7yM"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# /content/Test/00002.png\n",
        "plot, prediction = test_on_img(r'/content/Test/00002.png')\n",
        "s = [str(i) for i in prediction]\n",
        "a = int(\"\".join(s))\n",
        "# print(a)\n",
        "print(\"Predicted traffic sign is  : \", classes[a])\n",
        "plt.imshow(plot)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ltnmbpd2li4x"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPr01wHhe9Q07Hj/UHw89Cz",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}