load_own_images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "34d9c58b-0d1e-4a61-8b77-3db916272bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio.v3 as iio\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3ba962a-43e2-4d30-b9bf-92c76aa80219",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_own_dataset = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb83fa5d-cc30-41c6-94d6-8314086a1102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading ...  my_own_images/2828_my_own_3.png\n",
      "loading ...  my_own_images/2828_my_own_2.png\n",
      "loading ...  my_own_images/2828_my_own_5.png\n",
      "loading ...  my_own_images/2828_my_own_4.png\n",
      "loading ...  my_own_images/2828_my_own_6.png\n"
     ]
    }
   ],
   "source": [
    "for image_file_name in glob.glob('my_own_images/2828_my_own_?.png'):\n",
    "    print (\"loading ... \", image_file_name)\n",
    "    label = int(image_file_name[-5:-4])\n",
    "    img_array = iio.imread(image_file_name, mode=\"F\")\n",
    "    img_data = 255.0 - img_array.reshape(784)\n",
    "    img_data = (img_data / 255.0 * 0.99) + 0.01\n",
    "    record = np.append(label, img_data)\n",
    "    our_own_dataset.append(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9da79b9c-bdfe-4988-ba20-76908d036e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115f26ff0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = our_own_dataset[0][1:].reshape(28, 28)\n",
    "plt.imshow(im, cmap=\"Greys\", interpolation=\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9f304952-18f4-4925-af4c-b8fc6418f59d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x116157f50>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im2 = ndim.rotate(im, -10, cval=0.01, reshape=False)\n",
    "plt.imshow(im2, cmap=\"Greys\", interpolation=\"None\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}