Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "07560318",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from PIL import Image\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c13a4b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN architecture\n",
    "class TrafficSignClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(TrafficSignClassifier, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(128 * 4 * 4, 128)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(128, 43)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = nn.functional.relu(self.conv1(x))\n",
    "        x = self.pool1(x)\n",
    "        x = nn.functional.relu(self.conv2(x))\n",
    "        x = self.pool2(x)\n",
    "        x = nn.functional.relu(self.conv3(x))\n",
    "        x = self.pool3(x)\n",
    "        x = x.view(-1, 128 * 4 * 4)\n",
    "        x = nn.functional.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aac55952",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the custom dataset class\n",
    "class TrafficSignDataset(Dataset):\n",
    "    def __init__(self, dataframe, transform):\n",
    "        self.images = []\n",
    "        self.labels = []\n",
    "        self.transform = transform\n",
    "        \n",
    "        for index, row in dataframe.iterrows():\n",
    "            img = Image.open(row['Path'])\n",
    "            img = np.array(img)\n",
    "            self.images.append(img)\n",
    "            self.labels.append(row['ClassId'])\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        image = self.images[index]#.transpose((2, 0, 1))\n",
    "        #image = torch.tensor(image)\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return (image, torch.LongTensor([self.labels[index]])) #'''torch.FloatTensor'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "218c6aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the training and testing data from CSV files\n",
    "train_df = pd.read_excel('Train.xlsx')\n",
    "test_df = pd.read_excel('Test.xlsx')\n",
    "\n",
    "transform=transforms.Compose([transforms.ToPILImage(), transforms.Resize((32,32)), transforms.ToTensor()])\n",
    "\n",
    "# Create the custom dataset objects and data loaders\n",
    "train_dataset = TrafficSignDataset(train_df, transform)\n",
    "test_dataset = TrafficSignDataset(test_df, transform)\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, )\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True,)\n",
    "\n",
    "# Instantiate the CNN model and define the loss function and optimizer\n",
    "model = TrafficSignClassifier()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "224a461f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 loss: 2.424\n",
      "Epoch 2 loss: 1.248\n",
      "Epoch 3 loss: 0.696\n",
      "Epoch 4 loss: 0.467\n",
      "Epoch 5 loss: 0.351\n",
      "Epoch 6 loss: 0.277\n",
      "Epoch 7 loss: 0.226\n",
      "Epoch 8 loss: 0.189\n",
      "Epoch 9 loss: 0.165\n",
      "Epoch 10 loss: 0.142\n",
      "Epoch 11 loss: 0.123\n",
      "Epoch 12 loss: 0.110\n",
      "Epoch 13 loss: 0.095\n",
      "Epoch 14 loss: 0.090\n",
      "Epoch 15 loss: 0.080\n",
      "Epoch 16 loss: 0.074\n",
      "Epoch 17 loss: 0.070\n",
      "Epoch 18 loss: 0.059\n",
      "Epoch 19 loss: 0.055\n",
      "Epoch 20 loss: 0.055\n",
      "Epoch 21 loss: 0.048\n",
      "Epoch 22 loss: 0.043\n",
      "Epoch 23 loss: 0.040\n",
      "Epoch 24 loss: 0.043\n",
      "Epoch 25 loss: 0.042\n",
      "Epoch 26 loss: 0.034\n",
      "Epoch 27 loss: 0.033\n",
      "Epoch 28 loss: 0.033\n",
      "Epoch 29 loss: 0.031\n",
      "Epoch 30 loss: 0.036\n"
     ]
    }
   ],
   "source": [
    "# Train the model on the training data\n",
    "for epoch in range(30):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(train_loader, 0):\n",
    "        inputs, labels = data\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels.squeeze())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bd03e9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 93.14%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the testing data\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        images, labels = data\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels.squeeze()).sum().item()\n",
    "\n",
    "accuracy = 100 * correct / total\n",
    "print('Test accuracy: %.2f%%' % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c7f14d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 loss: 2.502\n",
      "Epoch 2 loss: 0.966\n",
      "Epoch 3 loss: 0.570\n",
      "Epoch 4 loss: 0.387\n",
      "Epoch 5 loss: 0.303\n",
      "Epoch 6 loss: 0.247\n",
      "Epoch 7 loss: 0.210\n",
      "Epoch 8 loss: 0.186\n",
      "Epoch 9 loss: 0.161\n",
      "Epoch 10 loss: 0.135\n",
      "Epoch 11 loss: 0.120\n",
      "Epoch 12 loss: 0.100\n",
      "Epoch 13 loss: 0.093\n",
      "Epoch 14 loss: 0.083\n",
      "Epoch 15 loss: 0.072\n",
      "Epoch 16 loss: 0.064\n",
      "Epoch 17 loss: 0.061\n",
      "Epoch 18 loss: 0.055\n",
      "Epoch 19 loss: 0.047\n",
      "Epoch 20 loss: 0.046\n",
      "Epoch 21 loss: 0.045\n",
      "Epoch 22 loss: 0.040\n",
      "Epoch 23 loss: 0.037\n",
      "Epoch 24 loss: 0.031\n",
      "Epoch 25 loss: 0.034\n",
      "Epoch 26 loss: 0.032\n",
      "Epoch 27 loss: 0.031\n",
      "Epoch 28 loss: 0.029\n",
      "Epoch 29 loss: 0.024\n",
      "Epoch 30 loss: 0.025\n",
      "Test accuracy: 93.94%\n"
     ]
    }
   ],
   "source": [
    "# ignore this cell\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "\n",
    "# Define the CNN architecture\n",
    "class TrafficSignClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(TrafficSignClassifier, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.fc1 = nn.Linear(128 * 4 * 4, 128)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(128, 43)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = nn.functional.relu(self.conv1(x))\n",
    "        x = self.pool1(x)\n",
    "        x = nn.functional.relu(self.conv2(x))\n",
    "        x = self.pool2(x)\n",
    "        x = nn.functional.relu(self.conv3(x))\n",
    "        x = self.pool3(x)\n",
    "        x = x.view(-1, 128 * 4 * 4)\n",
    "        x = nn.functional.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "    \n",
    "# Define the custom dataset class \n",
    "class TrafficSignDataset(Dataset):\n",
    "    def __init__(self, dataframe, transform):\n",
    "        self.images = []\n",
    "        self.labels = []\n",
    "        self.transform = transform\n",
    "        \n",
    "        for index, row in dataframe.iterrows():\n",
    "            img = Image.open(row['Path'])\n",
    "            img = np.array(img)\n",
    "            self.images.append(img)\n",
    "            self.labels.append(row['ClassId'])\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        image = self.images[index]#.transpose((2, 0, 1))\n",
    "        #image = torch.tensor(image)\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "        return (image, torch.LongTensor([self.labels[index]])) #'''torch.FloatTensor'''\n",
    "    \n",
    "# Load the training and testing data from CSV files\n",
    "train_df = pd.read_excel('Train.xlsx')\n",
    "test_df = pd.read_excel('Test.xlsx')\n",
    "\n",
    "transform=transforms.Compose([transforms.ToPILImage(), transforms.Resize((32,32)), transforms.ToTensor()])\n",
    "\n",
    "# Create the custom dataset objects and data loaders\n",
    "train_dataset = TrafficSignDataset(train_df, transform)\n",
    "test_dataset = TrafficSignDataset(test_df, transform)\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, )\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True,)\n",
    "\n",
    "# Instantiate the CNN model and define the loss function and optimizer\n",
    "model = TrafficSignClassifier()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# Train the model on the training data\n",
    "for epoch in range(30):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(train_loader, 0):\n",
    "        inputs, labels = data\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels.squeeze())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))\n",
    "    \n",
    "# Evaluate the model on the testing data\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        images, labels = data\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels.squeeze()).sum().item()\n",
    "\n",
    "accuracy = 100 * correct / total\n",
    "print('Test accuracy: %.2f%%' % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f2942c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Randomly select 5 images from the test set\n",
    "indices = np.random.choice(len(test_dataset), 15)\n",
    "\n",
    "# Define a dictionary to map class IDs to class names\n",
    "class_names = {\n",
    "          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 for vehicles over 3.5 metric tons',\n",
    "          11:'Right-of-way at the next intersection',\n",
    "          12:'Priority road',\n",
    "          13:'Yield',\n",
    "          14:'Stop',\n",
    "          15:'No vehicles',\n",
    "          16:'Vehicles over 3.5 metric tons prohibted',\n",
    "          17:'No entry',\n",
    "          18:'General caution',\n",
    "          19:'Dangerous curve to the left',\n",
    "          20:'Dangerous curve to the 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 of all speed and 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 of no passing by vehicles over 3.5 metric tons'}\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "# Visualize the selected images and their predicted labels\n",
    "\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "\n",
    "for i, index in enumerate(indices):\n",
    "    # Get the image and label\n",
    "    image, label = test_dataset[index]\n",
    "    \n",
    "    # Get the predicted label from the model\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        output = model(image.unsqueeze(0))\n",
    "        predicted_label = torch.argmax(output, dim=1).item()\n",
    "    \n",
    "    # Convert the image to a numpy array and display it\n",
    "    image = image.permute(1, 2, 0).numpy()\n",
    "    ax = fig.add_subplot(3, 5, i+1)\n",
    "    ax.imshow(image)\n",
    "    \n",
    "    # Set the title of the plot to the actual and predicted label\n",
    "    actual_label = class_names[label.item()]\n",
    "    predicted_label = class_names[predicted_label]\n",
    "    ax.set_title(f'Actual: {actual_label}\\nPredicted: {predicted_label}')\n",
    "    \n",
    "    # Turn off the axis labels\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "266c534d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Roi.X1</th>\n",
       "      <th>Roi.Y1</th>\n",
       "      <th>Roi.X2</th>\n",
       "      <th>Roi.Y2</th>\n",
       "      <th>ClassId</th>\n",
       "      <th>Path</th>\n",
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       "      <th>0</th>\n",
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       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Train/20/00020_00000_00000.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
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       "      <td>23</td>\n",
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       "      <td>Train/20/00020_00000_00001.png</td>\n",
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       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Train/20/00020_00000_00002.png</td>\n",
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       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>Train/20/00020_00000_00003.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>Train/20/00020_00000_00004.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Width  Height  Roi.X1  Roi.Y1  Roi.X2  Roi.Y2  ClassId  \\\n",
       "0     27      26       5       5      22      20       20   \n",
       "1     28      27       5       6      23      22       20   \n",
       "2     29      26       6       5      24      21       20   \n",
       "3     28      27       5       6      23      22       20   \n",
       "4     28      26       5       5      23      21       20   \n",
       "\n",
       "                             Path  \n",
       "0  Train/20/00020_00000_00000.png  \n",
       "1  Train/20/00020_00000_00001.png  \n",
       "2  Train/20/00020_00000_00002.png  \n",
       "3  Train/20/00020_00000_00003.png  \n",
       "4  Train/20/00020_00000_00004.png  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b622e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     2250\n",
       "1     2220\n",
       "13    2160\n",
       "12    2100\n",
       "38    2070\n",
       "10    2010\n",
       "4     1980\n",
       "5     1860\n",
       "25    1500\n",
       "9     1470\n",
       "7     1440\n",
       "3     1410\n",
       "8     1410\n",
       "11    1320\n",
       "18    1200\n",
       "35    1200\n",
       "17    1110\n",
       "14     780\n",
       "31     780\n",
       "33     689\n",
       "15     630\n",
       "26     600\n",
       "28     540\n",
       "23     510\n",
       "30     450\n",
       "16     420\n",
       "6      420\n",
       "34     420\n",
       "22     390\n",
       "36     390\n",
       "40     360\n",
       "20     360\n",
       "21     330\n",
       "39     300\n",
       "29     270\n",
       "24     270\n",
       "32     240\n",
       "27     240\n",
       "41     240\n",
       "42     240\n",
       "37     210\n",
       "0      210\n",
       "19     210\n",
       "Name: ClassId, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['ClassId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "274595b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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