{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9813daa0-33db-4c7b-aca2-f8d483758a91",
   "metadata": {},
   "source": [
    "# 1.Objective"
   ]
  },
  {
   "cell_type": "raw",
   "id": "28ebca8c-0726-452c-9325-64a839f43f2b",
   "metadata": {},
   "source": [
    "Predicting Traffic Signals using CNN model with respect to images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd57302-8ce1-46f8-9a42-5e215c41fe6d",
   "metadata": {},
   "source": [
    "# 2.Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac796236-6c9d-4ff8-80ab-6db298b0f982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import time\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import os\n",
    "\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdca1dc3-c9d6-40b0-ad68-9a6ba1978ff9",
   "metadata": {},
   "source": [
    "# GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a388e34d-ace3-42bd-bcfb-5123ba3af4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NVIDIA GeForce RTX 4050 Laptop GPU'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.get_device_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2115498-ff87-41bf-a39c-4850320b10ce",
   "metadata": {},
   "source": [
    "## 3.Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4019def-8553-4801-bdbb-c0bcb8c347e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "classes = 43\n",
    "for i in range(classes):\n",
    "    path = os.path.join('traffic_lights/','Train',str(i))\n",
    "    images = os.listdir(path)\n",
    "\n",
    "    for a in images:\n",
    "        try:\n",
    "            image = Image.open(path + '/'+ a)\n",
    "            image = image.resize((30,30))\n",
    "            image = np.array(image)\n",
    "            X_train.append(image)\n",
    "            y_train.append(i)\n",
    "        except:\n",
    "            print(\"Error loading image\")\n",
    "\n",
    "\n",
    "# Converting lists into numpy arrays\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_train = X_train / 255.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6171bb7d-10ac-454a-9fff-b45f7b295121",
   "metadata": {},
   "source": [
    "## 4. Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e095f0a5-7e95-4eee-bb02-934ea0d1b665",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of classes 43\n",
      "Total Training images 39209 \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select 5 random classes\n",
    "random_classes = random.sample(range(classes), 5)\n",
    "\n",
    "# Select 1 random image from each of the selected classes\n",
    "random_images = []\n",
    "for class_id in random_classes:\n",
    "    class_indices = np.where(y_train == class_id)[0]  # Get indices of images in this class\n",
    "    random_image_index = random.choice(class_indices)  # Pick a random image index\n",
    "    random_images.append((X_train[random_image_index], class_id))  # Store image and class ID\n",
    "\n",
    "print(f\"Total Number of classes {classes}\")\n",
    "print(f\"Total Training images {len(X_train)} \")\n",
    "\n",
    "# Display the images\n",
    "plt.figure(figsize=(15, 5))\n",
    "for i, (image, class_id) in enumerate(random_images):\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plt.imshow(image)\n",
    "    plt.title(f\"Class: {class_id}\")\n",
    "    plt.axis('off')  # Hide axes\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e251f351-1abe-4d1e-8774-ab966a06498d",
   "metadata": {},
   "source": [
    "## 5. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6274b04-2303-4fa5-95e6-99a342d2779a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39209, 30, 30, 3) (39209,)\n",
      "cuda\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "\n",
    "X_train_tensor = torch.tensor(X_train, dtype=torch.float32).permute(0, 3, 1, 2)  # (N, C, H, W)\n",
    "y_train_tensor = torch.tensor(y_train, dtype=torch.float32)\n",
    "\n",
    "# Move to GPU if available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "\n",
    "X_train_tensor = X_train_tensor.to(device)\n",
    "y_train_tensor = y_train_tensor.to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2de54c-ae15-4549-91f6-0311f794b8ef",
   "metadata": {},
   "source": [
    "# 6. CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dae4c45f-3438-4835-bd82-9a303da41020",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ClassNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(ClassNet, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(3,6,3)\n",
    "        self.conv2 = nn.Conv2d(6,16,5)\n",
    "        self.maxpool1 = nn.MaxPool2d(2)\n",
    "        self.conv3 = nn.Conv2d(16,32,5)\n",
    "        self.maxpool2 = nn.MaxPool2d(2)\n",
    "\n",
    "        self.fc1 = nn.Linear(512,256)\n",
    "        self.dropout1 = nn.Dropout(0.5)\n",
    "        self.fc2 = nn.Linear(256,128)\n",
    "        self.dropout2 = nn.Dropout(0.5)\n",
    "        self.fc3 = nn.Linear(128,43)\n",
    "    def forward(self,input):\n",
    "\n",
    "        x = F.relu(self.conv1(input))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.maxpool1(x)\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = self.maxpool2(x)\n",
    "\n",
    "        x = torch.flatten(x,1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout1(x)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.dropout2(x)\n",
    "        output = self.fc3(x)\n",
    "\n",
    "        return output\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ead1542-afcb-4326-aeaa-c634d0b7d0ed",
   "metadata": {},
   "source": [
    "## 7. Cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d0e9bf6-4792-439e-a995-39f6f9d36945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize and move model to GPU\n",
    "model = ClassNet().to(device)\n",
    "\n",
    "# Define loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20cd284-5782-49ac-a4ad-4beb3bfd4794",
   "metadata": {},
   "source": [
    "## 8. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7df9b2c7-2013-4659-af32-699f05b45ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35, Loss: 3.1098715614418255\n",
      "Epoch 11/35, Loss: 0.10796286027987734\n",
      "Epoch 21/35, Loss: 0.05449174819453483\n",
      "Epoch 31/35, Loss: 0.04201330893281665\n",
      "Training complete!\n",
      "Time taken for training 28.92817997932434 sec\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "batch_size = 128\n",
    "epochs = 35\n",
    "\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "t0 = time.time()\n",
    "# Training loop\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    \n",
    "    for X_batch, y_batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        y_batch = y_batch.long()\n",
    "        outputs = model(X_batch)\n",
    "        loss = criterion(outputs, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "    if epoch%10 == 0:\n",
    "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {running_loss/len(train_loader)}\")\n",
    "\n",
    "print(\"Training complete!\")\n",
    "print(\"Time taken for training\",time.time()-t0,\"sec\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788a057c-c5eb-4824-bee2-9b47f6b5b921",
   "metadata": {},
   "source": [
    "## 9. Test Images loading and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4322053-25f0-4bba-a5e7-a2660f672092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Importing the test dataset\n",
    "y_test = pd.read_csv('traffic_lights/Test.csv')\n",
    "\n",
    "labels = y_test[\"ClassId\"].values\n",
    "imgs = y_test[\"Path\"].values\n",
    "\n",
    "data=[]\n",
    "\n",
    "for img in imgs:\n",
    "    image = Image.open('traffic_lights/'+img)\n",
    "    image = image.resize([30, 30])\n",
    "    data.append(np.array(image))\n",
    "\n",
    "X_test=np.array(data)\n",
    "X_test = X_test/255\n",
    "y_test = np.array(labels)\n",
    "X_test_tensor = torch.tensor(X_test, dtype=torch.float32).permute(0, 3, 1, 2)  # (N, C, H, W)\n",
    "y_test_tensor = torch.tensor(y_test, dtype=torch.long)  # Class indices\n",
    "\n",
    "# Move to GPU if available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "# Converting to GPU if available\n",
    "X_test_tensor = X_test_tensor.to(device)\n",
    "y_test_tensor = y_test_tensor.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3ed637-f13f-4874-ab1b-5448c1801b54",
   "metadata": {},
   "source": [
    "## 10. Model Evaluation on Test Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "632e9d3d-b171-4007-b332-7919cd9852b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 96.46%\n"
     ]
    }
   ],
   "source": [
    "# Evaluating model on test data\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "# Convert tensors back to numpy for accuracy calculation\n",
    "predicted = predicted.cpu().numpy()\n",
    "y_test = y_test_tensor.cpu().numpy()\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, predicted)\n",
    "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1dbe90-e1a7-4bb0-9782-763561f1259a",
   "metadata": {},
   "source": [
    "## 11. Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e24769e-7f95-4644-90be-0264f5ab9dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9658\n",
      "Recall: 0.9646\n",
      "F1-Score: 0.9645\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "precision = precision_score(y_test, predicted, average='weighted')\n",
    "recall = recall_score(y_test, predicted, average='weighted')\n",
    "f1 = f1_score(y_test, predicted, average='weighted')\n",
    "\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-Score: {f1:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51cdb12c-3603-4ccd-91e8-be47167b57b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cm = confusion_matrix(y_test, predicted)\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fc7579-d13e-4080-a6f4-bc88368a2fc4",
   "metadata": {},
   "source": [
    "## 12. Saving Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "278301a9-5679-4d02-ae15-1ec02dfc0e48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model weights saved to traffic_light_model_weights.pth\n"
     ]
    }
   ],
   "source": [
    "model_weights_path = 'traffic_light_model_weights.pth'\n",
    "\n",
    "torch.save(model.state_dict(), model_weights_path)\n",
    "\n",
    "print(f\"Model weights saved to {model_weights_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9002c8e5-f206-443c-bb6f-955801ff940c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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