build_csv.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "M3dO8zwZa01E"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pytesseract\n",
        "from imutils import paths\n",
        "import os\n",
        "import pandas as pd\n",
        "from pytesseract import pytesseract\n",
        "\n",
        "# pytesseract.pytesseract.tesseract_cmd = \"C:\\Program Files\\Tesseract-OCR\\\\tesseract.exe\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "upq-KvplbZY2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf6914eb-3185-44d4-880f-d23f6385a02c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !apt install tesseract-ocr"
      ],
      "metadata": {
        "id": "-_j9HlK4bDUn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ZrM7Ceo7a01G"
      },
      "outputs": [],
      "source": [
        "imagePaths = list(paths.list_images(\"/content/drive/MyDrive/datad\"))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "IyUqDFFCa01H"
      },
      "outputs": [],
      "source": [
        "def prepro_angle(img):\n",
        "    filtered_image= cv2.bilateralFilter(img, 5, 50, 50)\n",
        "    ret,threshold_img=cv2.threshold(filtered_image,140,255,cv2.THRESH_BINARY)\n",
        "    element=np.ones((2,2),np.uint8)\n",
        "    threshold_img=cv2.erode(threshold_img,element)\n",
        "    return threshold_img\n",
        "\n",
        "def prepro_space(img):\n",
        "    filtered_image= cv2.bilateralFilter(img, 5, 50, 50)\n",
        "    ret,threshold_img=cv2.threshold(filtered_image,135,255,cv2.THRESH_BINARY_INV)\n",
        "    return threshold_img\n",
        "\n",
        "def prepro_pressure(img):\n",
        "    inverted=255-img\n",
        "    bilateral = cv2.bilateralFilter(inverted, 5, 50, 50)\n",
        "    ret,thresh=cv2.threshold(bilateral,110,255,cv2.THRESH_TOZERO)\n",
        "    return thresh\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NpzaqCxoa01H"
      },
      "outputs": [],
      "source": [
        "def line_spacing(threshold_img):\n",
        "    letter_size=average_letter_size=var_letter_size=0\n",
        "    TOP_MARGIN=LINE_SPACING=WORD_SPACING=0.0\n",
        "    lines=list()\n",
        "    def horizontal_projection ()->list:\n",
        "        (h,w)=np.shape(threshold_img)\n",
        "        print(h,w)\n",
        "        sum_rows=list()\n",
        "        for j in range (0,h-1):\n",
        "            row=threshold_img[j:j+1,0:w]\n",
        "            sum_rows.append(np.sum(row))\n",
        "        return sum_rows\n",
        "    hplist=horizontal_projection()\n",
        "\n",
        "    topMargincount=0\n",
        "    for sum in hplist:\n",
        "        if(sum<=255):\n",
        "            topMargincount=topMargincount+1\n",
        "        else:\n",
        "            break\n",
        "    lineTop=0\n",
        "    lineBottom=0\n",
        "    spaceTop=0\n",
        "    SpaceBottom=0\n",
        "    setLineTop=True\n",
        "    setSpaceTop=True\n",
        "    includeNextSpace=True\n",
        "    space_zero=list()\n",
        "    for i in range(0,len(hplist)):\n",
        "        if hplist[i]<3000:\n",
        "            if setSpaceTop:\n",
        "                spaceTop=i\n",
        "                setSpaceTop=False\n",
        "            SpaceBottom=i+1\n",
        "            if i<len(hplist)-1:\n",
        "                if(hplist[i+1]<3000):\n",
        "                    continue\n",
        "            if includeNextSpace:\n",
        "                space_zero.append(SpaceBottom-spaceTop)\n",
        "            else:\n",
        "                previous=0\n",
        "                if(len(space_zero)==0):\n",
        "                    previous=0\n",
        "                else:\n",
        "                    previous=space_zero.pop()\n",
        "                space_zero.append(previous+SpaceBottom-lineTop)\n",
        "            setSpaceTop=True\n",
        "        if hplist[i]>3000:\n",
        "            if setLineTop:\n",
        "                lineTop=i\n",
        "                setLineTop=False\n",
        "            lineBottom=i+1\n",
        "            if i<len(hplist)-1:\n",
        "                if hplist[i+1]>3000:\n",
        "                    continue\n",
        "                if(lineBottom-lineTop<20):\n",
        "                    includeNextSpace=False\n",
        "                    setLineTop=True\n",
        "                    continue\n",
        "            includeNextSpace=True\n",
        "            lines.append([lineTop,lineBottom])\n",
        "            setLineTop=True\n",
        "    threshold=15000\n",
        "    new_space_row_count=0\n",
        "    total_row_count=0\n",
        "    total_line_count=0\n",
        "    flag=False\n",
        "    for i,line in enumerate(lines):\n",
        "        segment=hplist[line[0]:line[1]]\n",
        "        for j,sum in enumerate(segment):\n",
        "            if sum<threshold:\n",
        "                new_space_row_count=new_space_row_count+1\n",
        "            else:\n",
        "                total_row_count=total_row_count+1\n",
        "                flag=True\n",
        "        if flag:\n",
        "            total_line_count=total_line_count+1\n",
        "            flag=False\n",
        "    total_space_row_count=new_space_row_count+np.sum(space_zero[1:-1])\n",
        "    #average_line_spacing=float(total_space_row_count)/total_line_count\n",
        "    var_line_spacing=new_space_row_count\n",
        "    if(len(space_zero)!=0 and len(space_zero)<=2):\n",
        "\n",
        "       var_line_spacing=var_line_spacing+space_zero[-1];\n",
        "    elif(len(space_zero)>2):\n",
        "        var_line_spacing=var_line_spacing+ np.var(space_zero[1:-1])\n",
        "\n",
        "    return var_line_spacing\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Coc1JJZNa01H"
      },
      "outputs": [],
      "source": [
        "def pen_pressure(img):\n",
        "    element=np.ones((3,3),np.uint8)\n",
        "    skel=np.zeros(np.shape(img),np.uint8)\n",
        "    original_variance=np.var(img)\n",
        "    while True:\n",
        "        open=cv2.erode(img,element)\n",
        "        temp=img-open\n",
        "        eroded=cv2.erode(img,element)\n",
        "        skel=np.bitwise_or(skel,temp)\n",
        "        img=eroded\n",
        "    # print('a')\n",
        "        if(np.all(img==0)):\n",
        "            break\n",
        "\n",
        "    thinned=np.var(skel)\n",
        "    pres=original_variance-thinned\n",
        "    return pres\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "U8p7AkZTa01I"
      },
      "outputs": [],
      "source": [
        "def slant_angle(threshold_img):\n",
        "    def getVerticalProjectionProfile(image):\n",
        "\n",
        "    # Convert black spots to ones\n",
        "    #image[image == 0]   = 1\n",
        "    # Convert white spots to zeros\n",
        "        image[image == 255] = 1\n",
        "\n",
        "        vertical_projection = np.sum(image, axis = 0)\n",
        "\n",
        "        return vertical_projection\n",
        "    s_function=np.zeros(9,np.float64)\n",
        "    count_=np.zeros(9,np.uint64)\n",
        "    data=pytesseract.image_to_data(threshold_img,output_type='dict')\n",
        "    boxes = len(data['level'])\n",
        "    words=[]\n",
        "    for i in range(boxes ):\n",
        "        words.append((data['left'][i], data['top'][i], data['width'][i], data['height'][i]))\n",
        "    angles=[ -45,-30, -15, -5, 0, 5, 15, 30,45]\n",
        "    for i in range (0,9):\n",
        "        angle=angles[i]\n",
        "        s_temp=0.0\n",
        "        count=0\n",
        "        for word in words:\n",
        "            (x,y,w,h)=word\n",
        "        #   if(x<0 or y<0):\n",
        "        #      continue\n",
        "            if(h<20 or w<20):\n",
        "                continue\n",
        "        #   if(w<0):\n",
        "        #      print(w)\n",
        "            original=threshold_img[x:x+h,y:y+w]\n",
        "            shift=(np.tan(angle*np.pi/180)*h)/2\n",
        "            pad_length=abs(int(shift))\n",
        "        #   if(pad_length<0):\n",
        "        #    print(pad_length)\n",
        "            x=int(h)\n",
        "            y=int(w+pad_length*2)\n",
        "            if(x<=0 or y<=0):\n",
        "                continue\n",
        "            blank_image=np.zeros((int(x),int(y),3),np.uint8)\n",
        "            new_image=cv2.cvtColor(blank_image,cv2.COLOR_BGR2GRAY)\n",
        "        #   if(y<w):\n",
        "        #      print(y,w)\n",
        "            if(w!=np.shape(original)[1] or h!=np.shape(original)[0]):\n",
        "                continue\n",
        "            new_image[:,pad_length:w+pad_length]=original\n",
        "            (h,w)=np.shape(new_image)[:2]\n",
        "            x1=w/2\n",
        "            y1=0\n",
        "            x2=w/4\n",
        "            y2=h\n",
        "            x3=3*w/4\n",
        "            y3=h\n",
        "            pts1=np.float32([[x1,y1],[x2,y2],[x3,y3]])\n",
        "            pts2=np.float32([[x1+shift,y1],[x2-shift,y2],[x3-shift,y3]])\n",
        "            M = cv2.getAffineTransform(pts1, pts2)\n",
        "            deslanted=cv2.warpAffine(new_image,M,(w,h))\n",
        "            #print(deslanted)\n",
        "            vp=getVerticalProjectionProfile(deslanted.copy())\n",
        "            #print(vp)\n",
        "            for vpk in vp:\n",
        "                if(vpk==0):\n",
        "                 continue\n",
        "                num_fgpixel=vpk\n",
        "            # print(num_fgpixel,h)\n",
        "                if num_fgpixel<h/3:\n",
        "                 continue\n",
        "\n",
        "                s_temp=s_temp+num_fgpixel/h\n",
        "                count=count+1\n",
        "        if(count==0):\n",
        "         continue\n",
        "\n",
        "        s_function[i]=s_function[i]+s_temp/count\n",
        "        count_[i]=count_[i]+1\n",
        "    index_max=np.argmax(s_function)\n",
        "    SLANT_ANGLE=angles[index_max]\n",
        "    return SLANT_ANGLE\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "kANlxuf-a01I"
      },
      "outputs": [],
      "source": [
        "def baseline_angle(dilated_img):\n",
        "    contours, hierarchy = cv2.findContours(dilated_img,\n",
        "    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
        "    angle_list=list()\n",
        "    for ctr in contours:\n",
        "            center,dim,angle=cv2.minAreaRect(ctr)\n",
        "            if(dim[1]<20): #as paper asked to ignore boxes with height less than 20 pixels\n",
        "             continue\n",
        "            if angle>80:\n",
        "                angle=90-angle\n",
        "            elif angle<-45:\n",
        "                angle=angle+90\n",
        "            angle_list.append(angle)\n",
        "    baseline_angle=np.var(angle_list)\n",
        "    return baseline_angle\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Guy0ctsea01I",
        "outputId": "081d6b1a-6e08-4c6e-f102-ba2185941867"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "cols=['Pen Pressure', 'Line Spacing','Slant Angle', 'Baseline angle','Category','Link']\n",
        "features=pd.DataFrame(columns=cols )\n",
        "for image in imagePaths:\n",
        "\n",
        "    lable = os.path.split(image)[1].split('_')[-1].split('.')[0]\n",
        "    val=bool(0)\n",
        "    if(lable==\"yes\"):\n",
        "        val=bool(1)\n",
        "\n",
        "    img=cv2.imread(image)\n",
        "\n",
        "    img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "    img=cv2.resize(img,(900,900))\n",
        "    img_ang=prepro_angle(img)\n",
        "    lst=[pen_pressure(prepro_pressure(img)),line_spacing(prepro_space(img)),slant_angle(img_ang),baseline_angle(img_ang),val,image]\n",
        "    features.loc[len(features.index)]=lst\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-raPxMTMa01J"
      },
      "outputs": [],
      "source": [
        "features.to_csv('features.csv',header=True,index=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isna(features).sum()"
      ],
      "metadata": {
        "id": "xW8aC9-zr-1v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t-zdoMSwsA3o"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "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": "3.10.11"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}