{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a9a579c-df1c-4742-a0ed-e3db27bdc3e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import os\n",
    "import imageio\n",
    "from skimage import color, transform\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99e5b7bf-4438-477c-9880-008fb864ae56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "image_folder = 'data/Cro_Training_Set/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da337206-82a6-416c-b5db-04466695a7b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_letter(x):\n",
    "    if x == 'A':\n",
    "        y = 0\n",
    "    if x == 'B':\n",
    "        y = 1\n",
    "    if x == 'C':\n",
    "        y = 2\n",
    "    if x == 'D':\n",
    "        y = 3\n",
    "    if x == 'E':\n",
    "        y = 4\n",
    "    return y\n",
    "def add_to_dataset(x,y,z,l):\n",
    "    y.at[z,'Label'] = l\n",
    "    for i in range(0,1024):\n",
    "        y.at[z,docnames[i+1]] = x[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d28e8d0-9386-4ccd-a7bf-f19e9d59d6f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docnames = [\"Pixel \" + str(i) for i in range(1024)]\n",
    "docnames.insert(0, 'Label')\n",
    "df1 = pd.DataFrame(columns = docnames) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce03500d-7d48-493f-a00c-94c1d79bf02d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "i = 0\n",
    "for filename in os.listdir(image_folder):\n",
    "    file_path = os.path.join(image_folder, filename)\n",
    "\n",
    "    # Check if the file is a PNG file\n",
    "    if filename.lower().endswith('.bmp'):\n",
    "        # Extract the single letter from the filename (adjust the index accordingly)\n",
    "        single_letter = filename[0]\n",
    "\n",
    "        # Read the image and convert it to a NumPy array\n",
    "        img_array = imageio.v2.imread(file_path)\n",
    "\n",
    "        # Resize the image to 32x32\n",
    "        resized_img_array = transform.resize(img_array, (32, 32))\n",
    "\n",
    "        # Convert the RGB image to grayscale\n",
    "        gray_img_array = color.rgb2gray(resized_img_array)\n",
    "\n",
    "        # Flatten the image to 1024\n",
    "        flattened_img_array = gray_img_array.flatten()\n",
    "        \n",
    "        label = convert_letter(single_letter)\n",
    "        add_to_dataset(flattened_img_array,df1,i,label)\n",
    "        i = i + 1\n",
    "\n",
    "        # Append the processed image data to the list\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02056706-60c3-4390-990a-9f84bb56c049",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1.to_csv('cro_data_test.csv', index = False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66da76d9-6286-4e1e-b91e-007f43642c14",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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