Prepros_simple&double_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prepros_simple&double_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPeEfmMPT0AEy/xUfaFDm1z",
      "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/Morilla123/Curso/blob/main/Prepros_simple%26double_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import librares"
      ],
      "metadata": {
        "id": "cWQ7xJfmoxQU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wFDMkTeioV8A"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install opencv-python-headless=='4.1.2.30'\n",
        "!pip install easyocr\n",
        "!pip install imutils\n",
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import imutils\n",
        "import easyocr\n",
        "\n",
        "import numpy as np                   # math library\n",
        "import matplotlib.pyplot as plt      \n",
        "import random                        # for generating random numbers\n",
        "from google.colab.patches import cv2_imshow\n",
        "import tensorflow as tf\n",
        "import seaborn as sns\n",
        "import cv2\n",
        "import imutils\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  \n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, Flatten\n",
        "from tensorflow.keras.layers import (\n",
        "    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense\n",
        ")"
      ],
      "metadata": {
        "id": "KS5M2lFwoyCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## test"
      ],
      "metadata": {
        "id": "zRfcMQZLowgc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('/content/09.jpg')\n",
        "cv2_imshow(img)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "WL0aTkMiX1DY",
        "outputId": "5d39b538-468d-4e82-b536-42c001104148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=317x118 at 0x7F66B13EC9D0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(118, 317, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('/content/11.jpg')\n",
        "hight,width,_ = img.shape \n",
        "img = img[0:hight,int(width/2):width]\n",
        "dim = (hight,hight)\n",
        "img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # es posible que esta linea no la necesitemos\n",
        "img = cv2.Canny(img, 250, 250)"
      ],
      "metadata": {
        "id": "RkvEnBfHw2eE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Xh0cT6z2w2Ug",
        "outputId": "4fd2fe3e-cf68-4845-ca77-97cd0c15d10d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=78x78 at 0x7F66B01FD8D0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(78, 78)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(image)\n",
        "print(img.shape)  # tener en cuenta dimension"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "id": "mA5gd3I0ZtJj",
        "outputId": "151aacde-514d-483c-9f61-955e44f1636e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=322x86 at 0x7F66B01FDDD0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(104, 104)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = cv2.imread('/content/9.jpg')\n",
        "\n",
        "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "blur = cv2.medianBlur(gray, 5)\n",
        "sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])\n",
        "sharpen = cv2.filter2D(blur, -1, sharpen_kernel)\n",
        "\n",
        "thresh = cv2.threshold(sharpen,160,255, cv2.THRESH_BINARY_INV)[1]\n",
        "kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))\n",
        "close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)\n",
        "\n",
        "cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "cnts = cnts[0] if len(cnts) == 2 else cnts[1]\n",
        "\n",
        "min_area = 1200\n",
        "max_area = 15000\n",
        "image_number = 0\n",
        "for c in cnts:\n",
        "    area = cv2.contourArea(c)\n",
        "    if area > min_area and area < max_area:\n",
        "        x,y,w,h = cv2.boundingRect(c)\n",
        "        ROI = image[y:y+h, x:x+w]\n",
        "        cv2.imwrite('ROI_{}.png'.format(image_number), ROI)\n",
        "        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)\n",
        "        image_number += 1\n",
        "\n",
        "cv2_imshow(image)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "id": "m5ydXQzfZtAr",
        "outputId": "2d476708-8f0d-4366-b563-73c44aef3e24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=322x86 at 0x7F66B0242ED0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(104, 104)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "T0D4yknXZsyF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def angle_cos(p0, p1, p2):\n",
        "    d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')\n",
        "    return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )"
      ],
      "metadata": {
        "id": "MX7YVhr4NkGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_area = 100\n",
        "max_area = 1500"
      ],
      "metadata": {
        "id": "DUjEcibrZcZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_squares(img):\n",
        "    img = cv2.GaussianBlur(img, (5, 5), 0)\n",
        "    squares = []\n",
        "    for gray in cv2.split(img):\n",
        "        for thrs in range(0, 255, 26):\n",
        "            if thrs == 0:\n",
        "                bin = cv2.Canny(gray, 0, 50, apertureSize=5)\n",
        "                bin = cv2.dilate(bin, None)\n",
        "            else:\n",
        "                _retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)\n",
        "\n",
        "            contours, _hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
        "            \n",
        "            for cnt in contours:\n",
        "                cnt_len = cv2.arcLength(cnt, True)\n",
        "                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)\n",
        "                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):\n",
        "                    cnt = cnt.reshape(-1, 2)\n",
        "                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in range(4)])\n",
        "                    #print(cnt)\n",
        "                    a = (cnt[1][1] - cnt[0][1])\n",
        "\n",
        "                    if max_cos < 0.1 and a < img.shape[0]*0.8:\n",
        "\n",
        "                        squares.append(cnt)\n",
        "    return squares"
      ],
      "metadata": {
        "id": "n3PzU-mIMBjA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# img = cv2.imread('/content/07.jpg')\n",
        "squares = find_squares(img)\n",
        "cv2.drawContours(img, squares, -1, (0, 255, 0), 3)\n",
        "cv2_imshow(img)\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "YoA8AesSMrH2",
        "outputId": "90667b01-1f69-486d-be4c-84ee48d835f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=317x317 at 0x7F66B0AEA6D0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(317, 317)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnts = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "cnts = imutils.grab_contours(cnts)\n",
        "\n",
        "for c in cnts:\n",
        "\n",
        "    peri = cv2.arcLength(c, True)\n",
        "    approx = cv2.approxPolyDP(c, 0.02 * peri, True)\n",
        "\n",
        "    if len(approx) == 4:\n",
        "        (x, y, w, h) = cv2.boundingRect(approx)\n",
        "        ar = w / float(h)\n",
        "        shape = \"square\" if ar >= 0.95 and ar <= 1.05 else \"rectangle\"\n",
        "        print('Cuatro puntos')\n",
        "        # print((x, y, w, h))\n",
        "\n",
        "    elif len(approx) != 4:\n",
        "        shape = \"pentagon\"\n",
        "        print('Distinto puntos')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYjYzc6cw2CN",
        "outputId": "9415572a-526d-47c2-b47f-7ee67b255c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distinto puntos\n",
            "Distinto puntos\n",
            "Cuatro puntos\n",
            "Distinto puntos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "approx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diznG6qIw1Pl",
        "outputId": "fcd0d16f-d325-42b3-a915-5f93ea4845e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[23,  7]],\n",
              "\n",
              "       [[23, 76]],\n",
              "\n",
              "       [[80, 77]],\n",
              "\n",
              "       [[81,  9]]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(np.asarray(img))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "8BNnRODiyM-d",
        "outputId": "8598c25f-c7c0-4777-97e6-895a15eed357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f66b022dc50>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "cv2.line(img,(approx[0][0][0],approx[0][0][1]),(approx[1][0][0],approx[1][0][1]),(0,0,0),13)\n",
        "cv2.line(img,(approx[1][0][0],approx[1][0][1]),(approx[2][0][0],approx[2][0][1]),(0,0,0),13)\n",
        "cv2.line(img,(approx[2][0][0],approx[2][0][1]),(approx[3][0][0],approx[3][0][1]),(0,0,0),13)\n",
        "cv2.line(img,(approx[3][0][0],approx[3][0][1]),(approx[0][0][0],approx[0][0][1]),(0,0,0),13)"
      ],
      "metadata": {
        "id": "h1Y5t9W_D4Kt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(np.asarray(img))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "BBMSbihHENIs",
        "outputId": "37489e42-ec66-4341-dc43-4d45a8b5f9da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f66b0ac7310>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# test "
      ],
      "metadata": {
        "id": "OsHPQu4GEaJb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CKQp_hE2ELjX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}