TinyML_reshape.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TinyML_reshape",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theevildoof/TinyML_OV7670/blob/main/TinyML_reshape.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YEPP3zKhyuIj"
      },
      "outputs": [],
      "source": [
        "import numpy as np                   # advanced math library\n",
        "import matplotlib.pyplot as plt      # MATLAB like plotting routines\n",
        "import random                        # for generating random numbers\n",
        "\n",
        "from keras.datasets import mnist     # MNIST dataset is included in Keras\n",
        "from keras.models import Sequential  # Model type to be used\n",
        "\n",
        "from keras.layers.core import Dense, Dropout, Activation # Types of layers to be used in our model\n",
        "from keras.utils import np_utils                         # NumPy related tools\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.config.run_functions_eagerly(True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "print(\"X_train shape\", X_train.shape)\n",
        "print(\"y_train shape\", y_train.shape)\n",
        "print(\"X_test shape\", X_test.shape)\n",
        "print(\"y_test shape\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToJjbUjkyuuq",
        "outputId": "4980e719-c0b0-4b62-c37b-325c7950bed7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "X_train shape (60000, 28, 28)\n",
            "y_train shape (60000,)\n",
            "X_test shape (10000, 28, 28)\n",
            "y_test shape (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = (9,9) # Make the figures a bit bigger\n",
        "\n",
        "for i in range(9):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    num = random.randint(0, len(X_train))\n",
        "    plt.imshow(X_train[num], cmap='gray', interpolation='none')\n",
        "    plt.title(\"Class {}\".format(y_train[num]))\n",
        "    \n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "gVTWN9TTywGi",
        "outputId": "5dca8c34-bcef-4c70-ab28-202c0270b752"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# just a little function for pretty printing a matrix\n",
        "def matprint(mat, fmt=\"g\"):\n",
        "    col_maxes = [max([len((\"{:\"+fmt+\"}\").format(x)) for x in col]) for col in mat.T]\n",
        "    for x in mat:\n",
        "        for i, y in enumerate(x):\n",
        "            print((\"{:\"+str(col_maxes[i])+fmt+\"}\").format(y), end=\",\")\n",
        "        print(\"\")\n",
        "\n",
        "# now print!        \n",
        "matprint(X_train[num])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GvQRzENyyKn",
        "outputId": "279e52e5-617e-436c-aa2d-b6b1c5ae6c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  3, 13, 47,173,253,253,253,249,122,  5,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0, 16,126,252,252,252,252,252,252,253,252,164,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,157,252,252,224,175, 96, 96,153,236,252,238, 52,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0,17,221,220, 84, 20,  0,  0,  0,  0, 52,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 7,146, 98,  0,  0,  0,  0,  0,  0, 13,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0, 13,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0, 13,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0, 59,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,132,252,252, 84,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0, 93,121,121, 69,  0,190,252,217,  4,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0, 26,219,253,253,253,253,195,255,253, 96,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,118,252,245,130,153,250,252,253,252, 96,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,169,252,180,  0,  0,171,252,253,252,119, 12,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,169,252,193, 10,  0,229,252,253,252,252,164,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0, 83,252,252, 77, 99,246,252,221,226,252,238, 81,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0, 35,232,252,232,244,252,217, 29, 46,225,252,209, 18,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,140,252,252,252,252, 92,  0,  0,117,252,252,100, 29,188,19,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  9,128,155,155, 47,  2,  0,  0,  6,134,252,242,229,215,15,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 49,252,252,252,104, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  3, 98,224,114,  2, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n",
            "0,0,0,0,0,0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,0,0,0,0,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number28 = np.array([0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0, 35,130,255,199,146, 59, 18,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0, 15,114,248,253,253,253,253,253,205, 57,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  8,187,253,253,225,139,139,165,250,253,218, 24,  1,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,120,253,253,154,  0,  0,  0,  0, 40,229,253,253, 23,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,154,253,154,  6,  0,  0,  0,  0,  0, 93,228,253,194,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  1, 11, 93,240,233, 39,  0,  0,  0,  0,  0,  0,  0,123,253,221,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0, 94,253,253,212,253,220, 34,  0,  0,  0,  0,  0,  0, 86,253,200,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,222,253,108,  9, 80,167,168,  0,  0,  0,  0,  0,105,235,253, 76,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,215,253,157, 74,  7,  0,  9,  0,  0,  0,  0, 55,240,249,147,  3,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0, 45,253,253,253,197, 80,  5,  0,  0,  0, 62,236,253,165,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  2, 72,230,253,253,253,198, 73,  0, 57,235,253,193, 17,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0, 12, 67,103, 99,224,250,238,246,253,236, 62,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  6,193,253,253,253, 48,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0, 14,236,253,253,253,128,  4,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0, 10,180,253,204,128,233,253,172,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,115,253,240, 74,  0, 19,221,245, 83,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,195,253,199,  0,  0,  0, 35,201,243, 36,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,195,253,107,  0,  0,  8, 33,197,245, 37,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,195,253,237,179,179,196,253,253,188,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0, 29,136,214,253,253,253,253,195, 62,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
        "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0])"
      ],
      "metadata": {
        "id": "aeJRqeG6zjMF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from skimage.transform import resize\n",
        "t = number28.reshape(28,28)\n",
        "print(t.shape)\n",
        "number112 = resize(t, (112, 112))\n",
        "print(number112.dtype)\n",
        "\"\"\"\n",
        "for i in range(0,112):\n",
        "  for j in range(0,112):\n",
        "    if number112[i][j] < 10e-20:\n",
        "      number112[i][j] = 0\n",
        "      /\n",
        "\"\"\"\n",
        "for i in range(0,112):\n",
        "  for j in range(0,112):\n",
        "      number112[i][j] = number112[i][j] * 10e+19\n",
        "      if number112[i][j] < 10:\n",
        "       number112[i][j] = 0\n",
        "    \n",
        "np.amax(number112)\n",
        "number112 = number112 /12\n",
        "np.amax(number112)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFuChq9ny4Ys",
        "outputId": "0733a0db-7784-4419-cc85-a25a57512bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n",
            "float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "229.0377089375628"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(number112, cmap='gray', interpolation='none')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "CEK8YIx1zmu-",
        "outputId": "9a111ad8-7315-4030-8850-dc86fd5649f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3c9d03e410>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(t, cmap='gray', interpolation='none')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "Y7SPZXSyz191",
        "outputId": "8a066225-85ba-4de7-94f4-e84a05792371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3c9cfad3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# just a little function for pretty printing a matrix\n",
        "def matprint(mat, fmt=\"g\"):\n",
        "    col_maxes = [max([len((\"{:\"+fmt+\"}\").format(x)) for x in col]) for col in mat.T]\n",
        "    for x in mat:\n",
        "        for i, y in enumerate(x):\n",
        "            print((\"{:\"+str(col_maxes[i])+fmt+\"}\").format(y), end=\",\")\n",
        "        print(\"\")\n",
        "\n",
        "# now print!        \n",
        "matprint(number112)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njvT7fRF2SDK",
        "outputId": "b527b7ea-ce0f-4051-e94d-586b4d0e6e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
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            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,      0,       0,       0,       0,      0,       0,      0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,       0,      0,      0,       0,      0,      0,      0,      0,       0,      0,       0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,      0,       0,      0,       0,       0,      0,      0,      0,      0,      0,      0,      0,       0,       0,       0,      0,       0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number112.shape"
      ],
      "metadata": {
        "id": "U-LcuBBd2074",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3348bab-ed8d-483a-a26d-a4ac6c4cb72a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(112, 112)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number28new = np.array( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 23, 20, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 49, 143, 223, 196, 149, 73, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 126, 228, 252, 257, 252, 248, 242, 193, 67, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 176, 247, 254, 213, 156, 149, 175, 236, 256, 204, 53, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 119, 246, 248, 156, 34, 15, 14, 18, 69, 216, 259, 221, 50, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 21, 166, 246, 160, 25, 0, 0, 0, 0, 4, 107, 225, 259, 177, 14, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 4, 31, 115, 229, 234, 86, 0, 0, 0, 0, 0, 0, 13, 142, 252, 209, 21, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 3, 107, 223, 230, 214, 237, 192, 50, 0, 0, 0, 0, 0, 8, 124, 245, 186, 19, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 20, 201, 251, 147, 44, 95, 154, 127, 7, 0, 0, 0, 4, 116, 224, 235, 91, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 19, 192, 254, 178, 89, 25, 16, 13, 0, 0, 0, 4, 80, 224, 242, 149, 10, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 67, 227, 256, 244, 190, 94, 25, 0, 0, 4, 82, 218, 248, 163, 21, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 4, 91, 210, 245, 244, 227, 184, 90, 30, 84, 219, 256, 188, 38, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 3, 28, 76, 114, 127, 201, 234, 231, 244, 263, 218, 74, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 47, 205, 266, 273, 250, 92, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 44, 227, 264, 260, 253, 145, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 173, 251, 208, 158, 218, 239, 163, 18, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 120, 245, 236, 92, 17, 52, 201, 227, 98, 4, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 184, 256, 192, 24, 0, 0, 65, 205, 213, 40, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 192, 250, 146, 28, 18, 22, 70, 206, 225, 42, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 173, 247, 232, 186, 181, 199, 240, 249, 178, 19, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 140, 201, 226, 230, 232, 233, 184, 65, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 21, 25, 25, 25, 25, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
        "\n",
        "])\n",
        "\n",
        "number28new = number28new.reshape(28,28)\n",
        "for i in range(0,28):\n",
        "  for j in range(0,28):\n",
        "      if number28new[i][j] < 35:\n",
        "       number28new[i][j] = 0\n",
        "\n"
      ],
      "metadata": {
        "id": "ZuDWXcijJo37"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(number28new, cmap='gray', interpolation='none')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "4uzzxlKJZ_8v",
        "outputId": "c12e5f5d-116b-4817-d19d-64906d069df8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3c9cf95410>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# just a little function for pretty printing a matrix\n",
        "def matprint(mat, fmt=\"g\"):\n",
        "    col_maxes = [max([len((\"{:\"+fmt+\"}\").format(x)) for x in col]) for col in mat.T]\n",
        "    for x in mat:\n",
        "        for i, y in enumerate(x):\n",
        "            print((\"{:\"+str(col_maxes[i])+fmt+\"}\").format(y), end=\",\")\n",
        "        print(\"\")\n",
        "\n",
        "# now print!        \n",
        "matprint(number28new)"
      ],
      "metadata": {
        "id": "F0Y14FkTaIYw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14c8b3b4-c534-4071-e7fa-4156ff470d50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0, 49,143,223,196,149, 73,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,126,228,252,257,252,248,242,193, 67,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,176,247,254,213,156,149,175,236,256,204, 53,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,119,246,248,156,  0,  0,  0,  0, 69,216,259,221, 50,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,166,246,160,  0,  0,  0,  0,  0,  0,107,225,259,177,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,115,229,234, 86,  0,  0,  0,  0,  0,  0,  0,142,252,209,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,107,223,230,214,237,192, 50,  0,  0,  0,  0,  0,  0,124,245,186,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,201,251,147, 44, 95,154,127,  0,  0,  0,  0,  0,116,224,235, 91,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,192,254,178, 89,  0,  0,  0,  0,  0,  0,  0, 80,224,242,149,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0, 67,227,256,244,190, 94,  0,  0,  0,  0, 82,218,248,163,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0, 91,210,245,244,227,184, 90,  0, 84,219,256,188, 38,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0, 76,114,127,201,234,231,244,263,218, 74,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0, 47,205,266,273,250, 92,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0, 44,227,264,260,253,145,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,173,251,208,158,218,239,163,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,120,245,236, 92,  0, 52,201,227, 98,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,184,256,192,  0,  0,  0, 65,205,213, 40,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,192,250,146,  0,  0,  0, 70,206,225, 42,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,173,247,232,186,181,199,240,249,178,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0, 39,140,201,226,230,232,233,184, 65,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n",
            "0,0,0,0,0,0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,0,0,0,0,0,0,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "crop_test = np.array([116, 141, 144, 139, 133, 126, 126, 120, 113, 131, 138, 124, 118, 109, 136, 127, 114, 141, 116, 147, 144, 131, 135, 132, 84, 81, 95, 103, \n",
        "154, 142, 153, 123, 129, 112, 137, 111, 114, 120, 123, 122, 118, 122, 137, 120, 125, 119, 114, 119, 131, 105, 125, 181, 128, 132, 97, 130, \n",
        "154, 137, 111, 126, 116, 128, 130, 118, 119, 124, 110, 118, 127, 108, 131, 105, 109, 112, 119, 119, 110, 102, 113, 144, 150, 140, 132, 130, \n",
        "157, 133, 128, 136, 133, 125, 105, 106, 121, 110, 122, 121, 127, 136, 129, 116, 110, 136, 120, 128, 132, 122, 135, 132, 139, 152, 127, 113, \n",
        "124, 128, 129, 135, 129, 117, 109, 105, 104, 108, 135, 123, 141, 139, 121, 121, 122, 129, 133, 124, 109, 110, 124, 129, 127, 119, 137, 139, \n",
        "131, 128, 129, 130, 128, 131, 110, 116, 105, 130, 116, 144, 148, 116, 114, 114, 122, 134, 135, 121, 126, 131, 114, 113, 124, 136, 141, 152, \n",
        "127, 129, 123, 136, 134, 127, 121, 109, 125, 121, 140, 154, 123, 114, 117, 126, 143, 137, 118, 114, 141, 121, 118, 138, 136, 146, 145, 137, \n",
        "126, 119, 127, 119, 124, 115, 110, 128, 111, 139, 143, 133, 108, 122, 115, 136, 148, 126, 112, 118, 139, 142, 144, 143, 154, 143, 120, 121, \n",
        "138, 131, 124, 135, 118, 119, 128, 118, 137, 153, 124, 113, 115, 128, 145, 148, 130, 119, 119, 121, 136, 165, 151, 132, 121, 121, 132, 143, \n",
        "132, 123, 119, 126, 141, 149, 119, 137, 142, 123, 124, 134, 119, 145, 157, 127, 115, 122, 132, 148, 165, 166, 131, 133, 117, 113, 122, 137, \n",
        "123, 124, 108, 109, 115, 120, 145, 147, 128, 120, 116, 120, 158, 174, 132, 119, 127, 125, 160, 168, 158, 135, 125, 109, 107, 122, 117, 141, \n",
        "118, 116, 106, 99, 113, 117, 123, 131, 118, 138, 120, 141, 185, 147, 119, 139, 134, 152, 171, 157, 123, 113, 99, 105, 106, 125, 146, 182, \n",
        "110, 113, 108, 109, 104, 111, 123, 115, 119, 132, 150, 162, 151, 104, 112, 129, 157, 159, 155, 120, 102, 102, 112, 104, 150, 176, 188, 181, \n",
        "120, 114, 108, 106, 109, 108, 113, 129, 134, 149, 174, 120, 119, 114, 116, 143, 171, 148, 119, 114, 102, 114, 125, 137, 175, 175, 138, 131, \n",
        "98, 89, 95, 92, 93, 95, 97, 113, 121, 138, 147, 112, 114, 120, 131, 153, 154, 134, 119, 97, 102, 150, 155, 177, 170, 118, 114, 99, \n",
        "95, 96, 91, 76, 96, 92, 84, 88, 111, 115, 110, 105, 104, 119, 159, 155, 137, 125, 109, 103, 121, 145, 171, 172, 147, 112, 144, 137, \n",
        "96, 93, 89, 82, 92, 94, 91, 102, 110, 124, 115, 104, 105, 139, 168, 155, 124, 115, 95, 89, 106, 165, 173, 153, 126, 107, 172, 202, \n",
        "103, 96, 88, 90, 101, 103, 103, 98, 101, 109, 125, 108, 128, 176, 168, 137, 103, 104, 107, 113, 141, 165, 191, 163, 123, 122, 150, 186, \n",
        "95, 87, 85, 85, 89, 92, 90, 95, 99, 102, 97, 109, 166, 189, 154, 114, 105, 106, 124, 130, 111, 183, 199, 179, 155, 125, 126, 113, \n",
        "93, 92, 94, 92, 73, 93, 97, 125, 89, 91, 110, 136, 180, 193, 141, 107, 99, 118, 166, 144, 108, 177, 197, 173, 179, 173, 125, 110, \n",
        "96, 91, 94, 84, 78, 76, 83, 77, 81, 91, 108, 140, 191, 189, 122, 111, 107, 137, 180, 125, 103, 176, 190, 169, 179, 193, 193, 191, \n",
        "102, 99, 90, 93, 84, 83, 82, 87, 91, 101, 129, 149, 189, 151, 113, 92, 124, 158, 185, 122, 115, 159, 164, 150, 158, 177, 182, 176, \n",
        "97, 106, 104, 81, 76, 97, 93, 93, 102, 97, 127, 190, 199, 118, 94, 110, 145, 191, 195, 145, 130, 172, 181, 184, 193, 194, 193, 192, \n",
        "122, 117, 126, 110, 113, 108, 118, 129, 132, 129, 128, 159, 158, 112, 97, 125, 149, 157, 162, 129, 138, 146, 139, 148, 147, 142, 141, 149, \n",
        "121, 122, 117, 115, 102, 113, 122, 131, 140, 106, 164, 185, 148, 132, 117, 133, 167, 158, 173, 122, 137, 165, 173, 161, 160, 168, 160, 177, \n",
        "95, 112, 108, 97, 105, 107, 106, 112, 111, 116, 167, 197, 141, 108, 119, 130, 194, 174, 199, 129, 129, 190, 197, 185, 181, 190, 192, 187, \n",
        "129, 129, 138, 130, 128, 130, 132, 119, 132, 137, 135, 157, 124, 118, 123, 160, 174, 153, 175, 105, 128, 175, 152, 118, 139, 152, 147, 121, \n",
        "108, 102, 103, 102, 109, 105, 117, 130, 151, 160, 177, 191, 131, 109, 103, 173, 189, 190, 167, 91, 111, 205, 197, 176, 190, 205, 203, 122])"
      ],
      "metadata": {
        "id": "Fp8sJyHUXBxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_test = crop_test.reshape(28,28)"
      ],
      "metadata": {
        "id": "vHMId13jxxS_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(crop_test, cmap='gray', interpolation='none')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "LbaLm1bOx3fp",
        "outputId": "9e78f968-2335-45c3-80ae-5b19a4ee2654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3c9ccd9510>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "crop_big = np.array([84, 156, 156, 156, 124, 156, 156, 145, 145, 105, 134, 134, 124, 124, 124, 134, 134, 113, 124, 124, 113, 102, 156, 126, 202, 101, 124, 116, 128, 170, 162, 152, 141, 152, 110, 110, 164, 142, 110, 132, 132, 132, 132, 132, 132, 110, 100, 152, 141, 170, 106, 126, 62, 120, 102, 113, 156, 113, 116, 117, 181, 90, 102, 105, 105, 64, 64, 32, 84, 185, 109, 137, 62, 145, 156, 134, 134, 134, 166, 134, 134, 145, 145, 124, 124, 134, 134, 113, 145, 145, 156, 105, 137, 158, 128, 118, 129, 66, 88, 88, 88, 77, 77, 88, 88, 98, 109, 109, 98, 77, 77, 140, \n",
        "156, 84, 84, 84, 134, 145, 145, 145, 145, 156, 134, 145, 145, 145, 134, 124, 134, 124, 124, 124, 124, 166, 130, 192, 70, 145, 137, 117, 141, 130, 100, 89, 89, 89, 152, 100, 110, 132, 132, 142, 142, 142, 142, 132, 132, 110, 100, 152, 120, 128, 64, 73, 121, 105, 60, 113, 124, 116, 160, 162, 124, 84, 105, 106, 160, 98, 141, 109, 170, 126, 174, 88, 94, 137, 156, 156, 134, 156, 134, 134, 156, 145, 145, 124, 124, 134, 134, 145, 145, 134, 134, 105, 126, 158, 106, 129, 129, 77, 88, 88, 77, 77, 77, 88, 88, 77, 98, 98, 77, 98, 88, 129, \n",
        "156, 84, 156, 94, 134, 124, 145, 156, 156, 145, 145, 145, 145, 156, 156, 134, 134, 134, 134, 134, 145, 209, 45, 49, 113, 126, 117, 130, 100, 100, 110, 110, 110, 100, 121, 100, 100, 132, 132, 142, 142, 142, 142, 142, 132, 110, 100, 141, 128, 74, 73, 142, 116, 92, 156, 94, 126, 138, 224, 102, 177, 126, 152, 162, 142, 132, 132, 184, 162, 170, 116, 173, 138, 124, 84, 156, 124, 145, 145, 156, 156, 84, 156, 134, 134, 134, 134, 156, 145, 105, 94, 148, 169, 117, 117, 56, 66, 88, 88, 77, 77, 77, 77, 77, 77, 88, 88, 88, 88, 129, 129, 77, \n",
        "116, 84, 84, 124, 156, 124, 134, 145, 145, 166, 166, 145, 73, 177, 177, 156, 156, 134, 145, 156, 137, 66, 101, 156, 137, 117, 170, 100, 100, 110, 110, 110, 110, 110, 110, 110, 132, 132, 132, 142, 142, 142, 142, 142, 100, 110, 141, 170, 85, 126, 174, 160, 92, 124, 145, 137, 160, 124, 124, 116, 96, 149, 142, 132, 132, 121, 110, 110, 110, 152, 126, 174, 66, 94, 73, 94, 166, 166, 166, 166, 166, 188, 156, 156, 84, 94, 166, 166, 105, 177, 177, 148, 169, 190, 190, 56, 56, 77, 77, 77, 77, 88, 88, 88, 88, 140, 98, 98, 88, 88, 140, 140, \n",
        "94, 156, 156, 177, 145, 156, 156, 73, 145, 177, 177, 137, 166, 94, 105, 177, 156, 166, 166, 166, 117, 181, 60, 84, 96, 141, 110, 100, 100, 110, 100, 110, 110, 110, 110, 110, 110, 132, 132, 132, 142, 142, 142, 110, 100, 162, 130, 149, 106, 62, 98, 145, 134, 166, 105, 160, 130, 124, 105, 149, 130, 110, 110, 110, 132, 142, 110, 121, 100, 141, 85, 137, 98, 137, 84, 94, 94, 94, 94, 166, 126, 156, 156, 94, 94, 73, 73, 84, 105, 105, 116, 158, 158, 190, 190, 201, 129, 140, 140, 88, 88, 88, 98, 88, 150, 88, 98, 88, 88, 77, 77, 150, \n",
        "126, 177, 177, 177, 156, 145, 145, 145, 156, 177, 177, 166, 148, 126, 116, 105, 166, 105, 105, 177, 128, 152, 113, 105, 181, 162, 162, 100, 100, 110, 110, 110, 100, 100, 100, 100, 100, 100, 110, 110, 110, 110, 110, 100, 152, 109, 128, 74, 105, 194, 137, 96, 84, 105, 96, 192, 81, 126, 149, 162, 162, 121, 110, 110, 121, 110, 110, 121, 109, 170, 106, 73, 132, 105, 73, 105, 116, 126, 126, 94, 94, 126, 105, 84, 62, 94, 94, 105, 105, 116, 116, 126, 158, 201, 201, 201, 201, 150, 98, 98, 161, 161, 161, 98, 98, 98, 88, 77, 98, 150, 150, 109, \n",
        "166, 156, 145, 145, 145, 145, 145, 145, 145, 156, 84, 169, 116, 105, 105, 116, 145, 105, 105, 148, 88, 122, 177, 85, 152, 162, 162, 100, 100, 78, 100, 110, 89, 100, 100, 100, 100, 100, 110, 100, 100, 100, 141, 109, 170, 138, 85, 126, 102, 117, 41, 166, 166, 117, 120, 60, 166, 85, 224, 152, 152, 121, 121, 100, 100, 100, 78, 141, 98, 138, 126, 92, 180, 73, 116, 116, 116, 116, 116, 116, 116, 116, 137, 180, 126, 116, 116, 126, 105, 116, 116, 180, 212, 169, 212, 129, 140, 161, 161, 172, 172, 161, 109, 161, 161, 130, 58, 26, 161, 161, 172, 150, \n",
        "166, 156, 156, 145, 145, 134, 134, 166, 166, 145, 116, 166, 166, 156, 84, 84, 62, 73, 73, 148, 66, 60, 105, 149, 130, 152, 152, 141, 130, 141, 141, 141, 141, 162, 152, 152, 152, 152, 141, 141, 130, 130, 170, 128, 85, 137, 126, 92, 106, 94, 158, 169, 149, 69, 81, 156, 85, 170, 141, 152, 152, 89, 89, 141, 162, 162, 141, 149, 96, 105, 194, 138, 137, 137, 126, 126, 126, 148, 149, 109, 109, 181, 181, 149, 158, 126, 116, 126, 116, 126, 137, 158, 180, 201, 201, 97, 98, 161, 26, 100, 89, 161, 161, 161, 161, 109, 37, 98, 88, 150, 150, 161, \n",
        "166, 156, 156, 145, 145, 145, 145, 156, 156, 145, 94, 177, 177, 145, 116, 84, 73, 134, 105, 149, 80, 124, 126, 181, 120, 152, 141, 141, 141, 130, 141, 141, 141, 152, 152, 141, 141, 152, 141, 141, 120, 149, 96, 64, 94, 164, 109, 94, 84, 134, 105, 180, 37, 81, 62, 116, 106, 181, 141, 141, 141, 78, 78, 141, 130, 170, 117, 85, 94, 162, 158, 84, 134, 84, 126, 105, 126, 158, 170, 109, 80, 101, 152, 109, 106, 94, 84, 62, 94, 94, 84, 116, 116, 126, 180, 180, 180, 129, 129, 129, 129, 129, 108, 129, 129, 118, 140, 66, 129, 129, 140, 150, \n",
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        "134, 134, 124, 92, 102, 102, 102, 92, 102, 113, 113, 102, 102, 102, 102, 102, 102, 102, 113, 113, 92, 124, 124, 124, 124, 124, 145, 134, 145, 102, 113, 134, 134, 124, 156, 213, 177, 170, 164, 164, 164, 174, 196, 185, 185, 185, 217, 206, 153, 160, 166, 124, 102, 92, 92, 113, 134, 42, 60, 106, 121, 185, 206, 217, 228, 228, 228, 217, 217, 217, 217, 228, 196, 196, 185, 196, 130, 74, 88, 84, 100, 74, 110, 185, 156, 238, 238, 228, 228, 228, 217, 196, 196, 196, 196, 185, 196, 185, 206, 217, 217, 217, 217, 228, 228, 206, 206, 206, 174, 152, 74, 141, \n",
        "113, 102, 102, 81, 158, 81, 81, 180, 92, 102, 102, 102, 92, 92, 92, 102, 102, 92, 92, 102, 92, 102, 102, 92, 113, 102, 102, 102, 102, 113, 134, 124, 145, 145, 166, 141, 188, 181, 92, 164, 185, 196, 196, 196, 196, 196, 196, 206, 153, 88, 124, 185, 132, 81, 81, 92, 92, 114, 17, 74, 142, 196, 206, 217, 228, 228, 228, 228, 228, 217, 217, 228, 228, 206, 206, 142, 120, 53, 96, 156, 142, 128, 49, 124, 156, 238, 238, 228, 228, 217, 217, 196, 185, 185, 185, 196, 185, 206, 217, 217, 217, 238, 228, 228, 228, 238, 238, 206, 196, 141, 53, 120, \n",
        "124, 113, 113, 92, 81, 113, 113, 60, 81, 81, 81, 102, 70, 102, 113, 102, 102, 113, 113, 113, 113, 102, 81, 113, 92, 113, 113, 113, 124, 156, 113, 156, 156, 156, 166, 130, 166, 170, 153, 153, 153, 174, 185, 185, 185, 196, 196, 196, 100, 138, 174, 164, 124, 113, 124, 113, 113, 188, 141, 188, 162, 196, 196, 196, 196, 174, 196, 174, 174, 196, 196, 206, 196, 153, 100, 170, 64, 110, 84, 32, 124, 149, 49, 124, 124, 228, 228, 217, 217, 196, 185, 164, 174, 185, 185, 185, 185, 185, 196, 196, 185, 196, 196, 196, 196, 196, 196, 185, 142, 128, 134, 149, \n",
        "113, 92, 113, 92, 81, 81, 92, 124, 102, 145, 134, 102, 102, 124, 124, 124, 124, 124, 124, 124, 124, 113, 84, 102, 134, 124, 124, 124, 124, 156, 156, 134, 134, 156, 166, 130, 177, 170, 121, 153, 153, 164, 164, 164, 185, 185, 174, 153, 78, 85, 141, 70, 134, 134, 124, 102, 134, 102, 96, 60, 85, 181, 173, 100, 100, 110, 132, 142, 142, 142, 110, 110, 141, 181, 85, 105, 121, 138, 85, 28, 64, 120, 92, 153, 196, 196, 196, 185, 185, 174, 164, 142, 142, 132, 142, 153, 153, 164, 164, 174, 174, 174, 185, 185, 185, 185, 185, 164, 100, 74, 121, 64])"
      ],
      "metadata": {
        "id": "F1MZWKEMx7fq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_big = crop_big.reshape(112,112)\n",
        "plt.imshow(crop_big, cmap='gray', interpolation='none')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "PK5Kgtp_yrd2",
        "outputId": "63b1a559-28b8-48d0-fb02-7f0b996e50c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3c9ba96190>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "a1zCFRctyybI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}