computer_vision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('training.csv')\n",
    "# df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'images/train_images'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Code\\bootcamp\\MLE\\VertexAI\\Project1-Cv\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3790\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3789\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3790\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3791\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'images/train_images'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\Code\\bootcamp\\MLE\\VertexAI\\Project1-Cv\\computer_vision.ipynb Cell 3\u001b[0m line \u001b[0;36m5\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m labels \u001b[39m=\u001b[39m []\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mfor\u001b[39;00m index, row \u001b[39min\u001b[39;00m df\u001b[39m.\u001b[39miterrows():\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     image \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mimread(row[\u001b[39m'\u001b[39;49m\u001b[39mimages/train_images\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     \u001b[39m# You can apply any preprocessing you need here (e.g., resizing, normalization, etc.)\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     \u001b[39m# image = cv2.resize(image, (desired_width, desired_height))\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Code/bootcamp/MLE/VertexAI/Project1-Cv/computer_vision.ipynb#W2sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     image \u001b[39m=\u001b[39m image \u001b[39m/\u001b[39m \u001b[39m255.0\u001b[39m  \u001b[39m# Normalize pixel values to the range [0, 1]\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Code\\bootcamp\\MLE\\VertexAI\\Project1-Cv\\venv\\Lib\\site-packages\\pandas\\core\\series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1037\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[key]\n\u001b[0;32m   1039\u001b[0m \u001b[39melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m-> 1040\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_value(key)\n\u001b[0;32m   1042\u001b[0m \u001b[39m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[0;32m   1043\u001b[0m \u001b[39m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[0;32m   1044\u001b[0m \u001b[39mif\u001b[39;00m is_iterator(key):\n",
      "File \u001b[1;32mc:\\Code\\bootcamp\\MLE\\VertexAI\\Project1-Cv\\venv\\Lib\\site-packages\\pandas\\core\\series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1153\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[label]\n\u001b[0;32m   1155\u001b[0m \u001b[39m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1156\u001b[0m loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex\u001b[39m.\u001b[39;49mget_loc(label)\n\u001b[0;32m   1158\u001b[0m \u001b[39mif\u001b[39;00m is_integer(loc):\n\u001b[0;32m   1159\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_values[loc]\n",
      "File \u001b[1;32mc:\\Code\\bootcamp\\MLE\\VertexAI\\Project1-Cv\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3797\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3792\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(casted_key, \u001b[39mslice\u001b[39m) \u001b[39mor\u001b[39;00m (\n\u001b[0;32m   3793\u001b[0m         \u001b[39misinstance\u001b[39m(casted_key, abc\u001b[39m.\u001b[39mIterable)\n\u001b[0;32m   3794\u001b[0m         \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(x, \u001b[39mslice\u001b[39m) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m casted_key)\n\u001b[0;32m   3795\u001b[0m     ):\n\u001b[0;32m   3796\u001b[0m         \u001b[39mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3797\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3798\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3799\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3800\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3801\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3802\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'images/train_images'"
     ]
    }
   ],
   "source": [
    "images = []\n",
    "labels = []\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    image = cv2.imread(row['images/train_images'])\n",
    "    # You can apply any preprocessing you need here (e.g., resizing, normalization, etc.)\n",
    "    # image = cv2.resize(image, (desired_width, desired_height))\n",
    "    image = image / 255.0  # Normalize pixel values to the range [0, 1]\n",
    "    images.append(image)\n",
    "    labels.append(row['label'])\n",
    "\n",
    "X = np.array(images)\n",
    "y = np.array(labels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}