script.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.6"
    },
    "colab": {
      "name": "Final CC.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz8YGsPH9Epj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "\n",
        "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
        "cv2.imshow(\"Digits 1\", cc1)\n",
        "cv2.waitKey(0)\n",
        "cc2 = cv2.imread('creditcard_digits2.jpg', 0)\n",
        "cv2.imshow(\"Digits 2\", cc2)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaPQO_Pv9Epm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"Digits 2 Thresholded\", th2)\n",
        "cv2.waitKey(0)\n",
        "    \n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fuku05vV9Ept",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np \n",
        "import random\n",
        "import cv2\n",
        "from scipy.ndimage import convolve\n",
        "\n",
        "def DigitAugmentation(frame, dim = 32):\n",
        "    frame = cv2.resize(frame, None, fx=2, fy=2, interpolation = cv2.INTER_CUBIC)\n",
        "    frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)\n",
        "    random_num = np.random.randint(0,9)\n",
        "\n",
        "    if (random_num % 2 == 0):\n",
        "        frame = add_noise(frame)\n",
        "    if(random_num % 3 == 0):\n",
        "        frame = pixelate(frame)\n",
        "    if(random_num % 2 == 0):\n",
        "        frame = stretch(frame)\n",
        "    frame = cv2.resize(frame, (dim, dim), interpolation = cv2.INTER_AREA)\n",
        "\n",
        "    return frame \n",
        "\n",
        "def add_noise(image):\n",
        "    prob = random.uniform(0.01, 0.05)\n",
        "    rnd = np.random.rand(image.shape[0], image.shape[1])\n",
        "    noisy = image.copy()\n",
        "    noisy[rnd < prob] = 0\n",
        "    noisy[rnd > 1 - prob] = 1\n",
        "    return noisy\n",
        "\n",
        "def pixelate(image):\n",
        "    dim = np.random.randint(8,12)\n",
        "    image = cv2.resize(image, (dim, dim), interpolation = cv2.INTER_AREA)\n",
        "    image = cv2.resize(image, (16, 16), interpolation = cv2.INTER_AREA)\n",
        "    return image\n",
        "\n",
        "def stretch(image):\n",
        "    ran = np.random.randint(0,3)*2\n",
        "    if np.random.randint(0,2) == 0:\n",
        "        frame = cv2.resize(image, (32, ran+32), interpolation = cv2.INTER_AREA)\n",
        "        return frame[int(ran/2):int(ran+32)-int(ran/2), 0:32]\n",
        "    else:\n",
        "        frame = cv2.resize(image, (ran+32, 32), interpolation = cv2.INTER_AREA)\n",
        "        return frame[0:32, int(ran/2):int(ran+32)-int(ran/2)]\n",
        "    \n",
        "def pre_process(image, inv = False):\n",
        "    try:\n",
        "        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    except:\n",
        "        gray_image = image\n",
        "        pass\n",
        "    \n",
        "    if inv == False:\n",
        "        _, th2 = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
        "    else:\n",
        "        _, th2 = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "    resized = cv2.resize(th2, (32,32), interpolation = cv2.INTER_AREA)\n",
        "    return resized"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeF5soYE9Epv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"cc1\", th2)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "region = [(0, 0), (35, 48)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "for i in range(0,1): \n",
        "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "    for j in range(0,10):\n",
        "        roi2 = DigitAugmentation(roi)\n",
        "        roi_otsu = pre_process(roi2, inv = False)\n",
        "        cv2.imshow(\"otsu\", roi_otsu)\n",
        "        cv2.waitKey(0)\n",
        "        \n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNTdMvEk9Epy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
        "\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"cc1\", th2)\n",
        "cv2.imshow(\"creditcard_digits1\", cc1)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "region = [(2, 19), (50, 72)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "for i in range(0,10):   \n",
        "    if i > 0:\n",
        "        top_left_x = top_left_x + 59\n",
        "        bottom_right_x = bottom_right_x + 59\n",
        "\n",
        "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "    print(\"Augmenting Digit - \", str(i))\n",
        "    for j in range(0,2000):\n",
        "        roi2 = DigitAugmentation(roi)\n",
        "        roi_otsu = pre_process(roi2, inv = True)\n",
        "        cv2.imwrite(\"./credit_card/train/\"+str(i)+\"./_1_\"+str(j)+\".jpg\", roi_otsu)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IHivb0P9Ep2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"cc1\", th2)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "region = [(0, 0), (35, 48)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "for i in range(0,10):   \n",
        "    if i > 0:\n",
        "        top_left_x = top_left_x + 35\n",
        "        bottom_right_x = bottom_right_x + 35\n",
        "\n",
        "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "    print(\"Augmenting Digit - \", str(i))\n",
        "    for j in range(0,2000):\n",
        "        roi2 = DigitAugmentation(roi)\n",
        "        roi_otsu = pre_process(roi2, inv = False)\n",
        "        cv2.imwrite(\"./credit_card/train/\"+str(i)+\"./_2_\"+str(j)+\".jpg\", roi_otsu)\n",
        "        cv2.imshow(\"otsu\", roi_otsu)\n",
        "        print(\"-\")\n",
        "        cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1eD9Dah9Ep4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
        "\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"cc1\", th2)\n",
        "cv2.imshow(\"creditcard_digits1\", cc1)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "region = [(2, 19), (50, 72)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "for i in range(0,10):   \n",
        "    if i > 0:\n",
        "        top_left_x = top_left_x + 59\n",
        "        bottom_right_x = bottom_right_x + 59\n",
        "\n",
        "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "    print(\"Augmenting Digit - \", str(i))\n",
        "    for j in range(0,2000):\n",
        "        roi2 = DigitAugmentation(roi)\n",
        "        roi_otsu = pre_process(roi2, inv = True)\n",
        "        cv2.imwrite(\"./credit_card/test/\"+str(i)+\"./_1_\"+str(j)+\".jpg\", roi_otsu)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPtlETcM9Ep6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
        "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
        "cv2.imshow(\"cc1\", th2)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "\n",
        "region = [(0, 0), (35, 48)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "for i in range(0,10):   \n",
        "    if i > 0:\n",
        "        top_left_x = top_left_x + 35\n",
        "        bottom_right_x = bottom_right_x + 35\n",
        "\n",
        "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "    print(\"Augmenting Digit - \", str(i))\n",
        "    for j in range(0,2000):\n",
        "        roi2 = DigitAugmentation(roi)\n",
        "        roi_otsu = pre_process(roi2, inv = False)\n",
        "        cv2.imwrite(\"./credit_card/test/\"+str(i)+\"./_2_\"+str(j)+\".jpg\", roi_otsu)\n",
        "        cv2.imshow(\"otsu\", roi_otsu)\n",
        "        print(\"-\")\n",
        "        cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9V_UbQW9Ep9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "8125de22-fe76-436f-eb5f-f0933994003d"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
        "from keras import optimizers\n",
        "import keras\n",
        "\n",
        "input_shape = (32, 32, 3)\n",
        "img_width = 32\n",
        "img_height = 32\n",
        "num_classes = 10\n",
        "nb_train_samples = 10000\n",
        "nb_validation_samples = 2000\n",
        "batch_size = 16\n",
        "epochs = 1\n",
        "\n",
        "train_data_dir = './credit_card/train'\n",
        "validation_data_dir = './credit_card/test'\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255)\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale = 1./255,              \n",
        "      rotation_range = 10,           \n",
        "      width_shift_range = 0.25,      \n",
        "      height_shift_range = 0.25,     \n",
        "      shear_range=0.5,\n",
        "      zoom_range=0.5,\n",
        "      horizontal_flip = False,       \n",
        "      fill_mode = 'nearest')         \n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_data_dir,\n",
        "        target_size = (img_width, img_height),\n",
        "        batch_size = batch_size,\n",
        "        class_mode = 'categorical')\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        validation_data_dir,\n",
        "        target_size = (img_width, img_height),\n",
        "        batch_size = batch_size,\n",
        "        class_mode = 'categorical',\n",
        "        shuffle = False)    "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ceabbcb068ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mtarget_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg_width\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_height\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         class_mode = 'categorical')\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m validation_generator = validation_datagen.flow_from_directory(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/preprocessing/image.py\u001b[0m in \u001b[0;36mflow_from_directory\u001b[0;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation)\u001b[0m\n\u001b[1;32m    465\u001b[0m             \u001b[0mfollow_links\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_links\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m             \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m         )\n\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/preprocessing/image.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)\u001b[0m\n\u001b[1;32m    148\u001b[0m             \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m             dtype=dtype)\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/directory_iterator.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0msubdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                     \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './credit_card/train'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OmQxNI09EqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(20, (5, 5),\n",
        "                 padding = \"same\", \n",
        "                 input_shape = input_shape))\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
        "\n",
        "model.add(Conv2D(50, (5, 5),\n",
        "                 padding = \"same\"))\n",
        "model.add(Activation(\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(500))\n",
        "model.add(Activation(\"relu\"))\n",
        "\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation(\"softmax\"))\n",
        "           \n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = keras.optimizers.Adadelta(),\n",
        "              metrics = ['accuracy'])\n",
        "    \n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt2HWjTH9EqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.optimizers import RMSprop\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "                   \n",
        "checkpoint = ModelCheckpoint(\"/home/deeplearningcv/DeepLearningCV/Trained Models/creditcard.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"min\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "\n",
        "earlystop = EarlyStopping(monitor = 'val_loss', \n",
        "                          min_delta = 0, \n",
        "                          patience = 3,\n",
        "                          verbose = 1,\n",
        "                          restore_best_weights = True)\n",
        "\n",
        "callbacks = [earlystop, checkpoint]\n",
        "\n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = RMSprop(lr = 0.001),\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "nb_train_samples = 20000\n",
        "nb_validation_samples = 4000\n",
        "epochs = 5\n",
        "batch_size = 16\n",
        "\n",
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch = nb_train_samples // batch_size,\n",
        "    epochs = epochs,\n",
        "    callbacks = callbacks,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = nb_validation_samples // batch_size)\n",
        "\n",
        "model.save(\"/home/deeplearningcv/DeepLearningCV/Trained Models/creditcard.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bm3UPFkj9EqE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import imutils\n",
        "from skimage.filters import threshold_adaptive\n",
        "import os\n",
        "\n",
        "def order_points(pts):\n",
        "    rect = np.zeros((4, 2), dtype = \"float32\")\n",
        "    s = pts.sum(axis = 1)\n",
        "    rect[0] = pts[np.argmin(s)]\n",
        "    rect[2] = pts[np.argmax(s)]\n",
        "    diff = np.diff(pts, axis = 1)\n",
        "    rect[1] = pts[np.argmin(diff)]\n",
        "    rect[3] = pts[np.argmax(diff)]\n",
        "    return rect\n",
        "\n",
        "def four_point_transform(image, pts):\n",
        "    rect = order_points(pts)\n",
        "    (tl, tr, br, bl) = rect\n",
        "    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n",
        "    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n",
        "    maxWidth = max(int(widthA), int(widthB))\n",
        "    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n",
        "    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n",
        "    maxHeight = max(int(heightA), int(heightB))\n",
        "\n",
        "    dst = np.array([\n",
        "        [0, 0],\n",
        "        [maxWidth - 1, 0],\n",
        "        [maxWidth - 1, maxHeight - 1],\n",
        "        [0, maxHeight - 1]], dtype = \"float32\")\n",
        "\n",
        "    M = cv2.getPerspectiveTransform(rect, dst)\n",
        "    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n",
        "\n",
        "    return warped\n",
        "\n",
        "def doc_Scan(image):\n",
        "    orig_height, orig_width = image.shape[:2]\n",
        "    ratio = image.shape[0] / 500.0\n",
        "\n",
        "    orig = image.copy()\n",
        "    image = imutils.resize(image, height = 500)\n",
        "    orig_height, orig_width = image.shape[:2]\n",
        "    Original_Area = orig_height * orig_width\n",
        "    \n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    gray = cv2.GaussianBlur(gray, (5, 5), 0)\n",
        "    edged = cv2.Canny(gray, 75, 200)\n",
        "\n",
        "    cv2.imshow(\"Image\", image)\n",
        "    cv2.imshow(\"Edged\", edged)\n",
        "    cv2.waitKey(0)\n",
        "    _, contours, hierarchy  = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
        "    contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]\n",
        "    \n",
        "    for c in contours:\n",
        "\n",
        "        area = cv2.contourArea(c)\n",
        "        if area < (Original_Area/3):\n",
        "            print(\"Error Image Invalid\")\n",
        "            return(\"ERROR\")\n",
        "        peri = cv2.arcLength(c, True)\n",
        "        approx = cv2.approxPolyDP(c, 0.02 * peri, True)\n",
        "\n",
        "        if len(approx) == 4:\n",
        "            screenCnt = approx\n",
        "            break\n",
        "\n",
        "    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)\n",
        "    cv2.imshow(\"Outline\", image)\n",
        "\n",
        "    warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)\n",
        "    cv2.resize(warped, (640,403), interpolation = cv2.INTER_AREA)\n",
        "    cv2.imwrite(\"credit_card_color.jpg\", warped)\n",
        "    warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)\n",
        "    warped = warped.astype(\"uint8\") * 255\n",
        "    cv2.imshow(\"Extracted Credit Card\", warped)\n",
        "    cv2.waitKey(0)\n",
        "    cv2.destroyAllWindows()\n",
        "    return warped"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHC6_E4j9EqG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsJ2Kc4h9EqI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image = cv2.imread('test_card.jpg')\n",
        "image = doc_Scan(image)\n",
        "\n",
        "region = [(55, 210), (640, 290)]\n",
        "\n",
        "top_left_y = region[0][1]\n",
        "bottom_right_y = region[1][1]\n",
        "top_left_x = region[0][0]\n",
        "bottom_right_x = region[1][0]\n",
        "\n",
        "roi = image[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
        "cv2.imshow(\"Region\", roi)\n",
        "cv2.imwrite(\"credit_card_extracted_digits.jpg\", roi)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPsSV1As9EqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "import keras\n",
        "\n",
        "classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/creditcard.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xpvLICP9EqM",
        "colab_type": "text"
      },
      "source": [
        "# Let's test on our extracted image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ek3iEaPM9EqN",
        "colab_type": "code",
        "colab": {},
        "outputId": "e5fbac70-9689-4c3a-cac2-155c6af46de6"
      },
      "source": [
        "def x_cord_contour(contours):\n",
        "    if cv2.contourArea(contours) > 10:\n",
        "        M = cv2.moments(contours)\n",
        "        return (int(M['m10']/M['m00']))\n",
        "    else:\n",
        "        pass\n",
        "\n",
        "img = cv2.imread('credit_card_extracted_digits.jpg')\n",
        "orig_img = cv2.imread('credit_card_color.jpg')\n",
        "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "cv2.imshow(\"image\", img)\n",
        "cv2.waitKey(0)\n",
        "\n",
        "blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n",
        "#cv2.imshow(\"blurred\", blurred)\n",
        "#cv2.waitKey(0)\n",
        "\n",
        "edged = cv2.Canny(blurred, 30, 150)\n",
        "#cv2.imshow(\"edged\", edged)\n",
        "#cv2.waitKey(0)\n",
        "\n",
        "_, contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\n",
        "contours = sorted(contours, key=cv2.contourArea, reverse=True)[:13] #Change this to 16 to get all digits\n",
        "contours = sorted(contours, key = x_cord_contour, reverse = False)\n",
        "\n",
        "full_number = []\n",
        "\n",
        "for c in contours:\n",
        "    (x, y, w, h) = cv2.boundingRect(c)    \n",
        "    if w >= 5 and h >= 25 and cv2.contourArea(c) < 1000:\n",
        "        roi = blurred[y:y + h, x:x + w]\n",
        "        cv2.imshow(\"ROI1\", roi)\n",
        "        roi_otsu = pre_process(roi, True)\n",
        "        cv2.imshow(\"ROI2\", roi_otsu)\n",
        "        roi_otsu = cv2.cvtColor(roi_otsu, cv2.COLOR_GRAY2RGB)\n",
        "        roi_otsu = keras.preprocessing.image.img_to_array(roi_otsu)\n",
        "        roi_otsu = roi_otsu * 1./255\n",
        "        roi_otsu = np.expand_dims(roi_otsu, axis=0)\n",
        "        image = np.vstack([roi_otsu])\n",
        "        label = str(classifier.predict_classes(image, batch_size = 10))[1]\n",
        "        print(label)\n",
        "        (x, y, w, h) = (x+region[0][0], y+region[0][1], w, h)\n",
        "        cv2.rectangle(orig_img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
        "        cv2.putText(orig_img, label, (x , y + 90), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n",
        "        cv2.imshow(\"image\", orig_img)\n",
        "        cv2.waitKey(0) \n",
        "        \n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "3\n",
            "5\n",
            "5\n",
            "2\n",
            "2\n",
            "0\n",
            "3\n",
            "2\n",
            "3\n",
            "9\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}