Extract_Line_Table.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46a82f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022/09/15 15:27:15] ppocr DEBUG: Namespace(alpha=1.0, benchmark=False, beta=1.0, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/home/mingyang/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer', cls_thresh=0.9, cpu_threads=10, crop_res_save_dir='./output', det=True, det_algorithm='DB', det_db_box_thresh=0.6, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.5, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_fce_box_type='poly', det_limit_side_len=960, det_limit_type='max', det_model_dir='/home/mingyang/.paddleocr/whl/det/en/en_PP-OCRv3_det_infer', det_pse_box_thresh=0.85, det_pse_box_type='quad', det_pse_min_area=16, det_pse_scale=1, det_pse_thresh=0, det_sast_nms_thresh=0.2, det_sast_polygon=False, det_sast_score_thresh=0.5, draw_img_save_dir='./inference_results', drop_score=0.5, e2e_algorithm='PGNet', e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_limit_side_len=768, e2e_limit_type='max', e2e_model_dir=None, e2e_pgnet_mode='fast', e2e_pgnet_score_thresh=0.5, e2e_pgnet_valid_set='totaltext', enable_mkldnn=True, fourier_degree=5, gpu_mem=500, help='==SUPPRESS==', image_dir=None, image_orientation=False, ir_optim=True, kie_algorithm='LayoutXLM', label_list=['0', '180'], lang='en', layout=True, layout_dict_path=None, layout_model_dir=None, layout_nms_threshold=0.5, layout_score_threshold=0.5, max_batch_size=10, max_text_length=25, merge_no_span_structure=True, min_subgraph_size=15, mode='structure', ocr=True, ocr_order_method=None, ocr_version='PP-OCRv3', output='./output', precision='fp32', process_id=0, rec=True, rec_algorithm='SVTR_LCNet', rec_batch_num=6, rec_char_dict_path='/dvmm-filer2/users/mingyang/miniconda3/envs/DeepRule/lib/python3.6/site-packages/paddleocr/ppocr/utils/en_dict.txt', rec_image_shape='3, 48, 320', rec_model_dir='/home/mingyang/.paddleocr/whl/rec/en/en_PP-OCRv3_rec_infer', recovery=False, save_crop_res=False, save_log_path='./log_output/', save_pdf=False, scales=[8, 16, 32], ser_dict_path='../train_data/XFUND/class_list_xfun.txt', ser_model_dir=None, shape_info_filename=None, show_log=True, sr_batch_num=1, sr_image_shape='3, 32, 128', sr_model_dir=None, structure_version='PP-Structurev2', table=True, table_algorithm='TableAttn', table_char_dict_path=None, table_max_len=488, table_model_dir=None, total_process_num=1, type='ocr', use_angle_cls=True, use_dilation=False, use_gpu=False, use_mp=False, use_onnx=False, use_pdserving=False, use_space_char=True, use_tensorrt=False, use_xpu=False, vis_font_path='./doc/fonts/simfang.ttf', warmup=False)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "from IPython.display import Image, display\n",
    "from paddleocr import PaddleOCR,draw_ocr\n",
    "from tqdm import tqdm\n",
    "ocr = PaddleOCR(use_angle_cls=True, lang='en', use_gpu=False, ocr_version=\"PP-OCRv3\", enable_mkldnn=True) # need to run only once to download and load model into memory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d06ca56",
   "metadata": {},
   "source": [
    "## Line Table Extraction\n",
    "In this section, we develop code to extract the data table from the Line Image. \n",
    "### Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "feabaf57",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = \"test2019\"\n",
    "data_path = '/dvmm-filer2/projects/mingyang/semafor/chart_table/data/line/annotations'\n",
    "test_data_path = '/'.join([data_path, \"instancesLine(1023)_{}.json\".format(split)])\n",
    "image_path = '/dvmm-filer2/projects/mingyang/semafor/chart_table/data/line/images/{}'.format(split)\n",
    "\n",
    "with open(test_data_path, 'r') as f:\n",
    "    test_annotation = json.load(f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "284217cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['licenses', 'images', 'annotations', 'categories'])\n"
     ]
    }
   ],
   "source": [
    "print(test_annotation.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bcde079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "3072\n",
      "7441\n"
     ]
    }
   ],
   "source": [
    "print(type(test_annotation['images']))\n",
    "print(type(test_annotation['annotations']))\n",
    "print(type(test_annotation['categories']))\n",
    "\n",
    "print(len(test_annotation['images']))\n",
    "print(len(test_annotation['annotations']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87362453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'file_name': 'f4225b4df53bb3fdebc333923ffae00e_Z2VucHJvay1zemZvLnJ1CTMxLjMxLjE5Ni4yMDU=.xls-2-0.png', 'height': 608, 'width': 891, 'id': 116745}\n",
      "{'image_id': 116745, 'category_id': 0, 'bbox': [127.0, 276.0, 185.0, 229.0, 243.0, 209.0, 300.0, 129.0, 358.0, 207.0, 416.0, 210.0, 474.0, 235.0, 532.0, 127.0, 590.0, 263.0, 647.0, 233.0, 705.0, 287.0, 763.0, 250.0, 821.0, 356.0], 'area': 0, 'id': 278989}\n",
      "{'supercategory': 'Series', 'id': 0, 'name': 'Series'}\n"
     ]
    }
   ],
   "source": [
    "print(test_annotation['images'][0])\n",
    "print(test_annotation['annotations'][0])\n",
    "print(test_annotation['categories'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "544fd60c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'image_id': 116773, 'category_id': 0, 'bbox': [357.0, 190.0, 348.0, 186.0, 348.0, 186.0, 334.0, 185.0, 321.0, 186.0, 321.0, 186.0, 312.0, 187.0, 303.0, 176.0, 303.0, 176.0, 285.0, 176.0, 276.0, 165.0, 276.0, 165.0, 267.0, 159.0, 258.0, 150.0, 258.0, 150.0, 249.0, 140.0, 240.0, 131.0, 240.0, 131.0, 231.0, 122.0, 222.0, 101.0, 222.0, 101.0, 213.0, 89.0, 204.0, 82.0, 204.0, 82.0, 195.0, 70.0, 186.0, 64.0, 186.0, 64.0, 177.0, 58.0, 168.0, 58.0, 168.0, 58.0, 159.0, 59.0, 150.0, 58.0, 150.0, 58.0, 141.0, 56.0, 132.0, 54.0, 132.0, 54.0, 123.0, 55.0, 114.0, 56.0, 114.0, 56.0, 103.0, 55.0, 96.0, 53.0, 96.0, 53.0, 87.0, 47.0, 78.0, 45.0, 78.0, 45.0, 69.0, 45.0, 60.0, 46.0, 60.0, 46.0, 51.0, 54.0, 42.0, 54.0], 'area': 0, 'id': 279042}, {'image_id': 116773, 'category_id': 0, 'bbox': [357.0, 206.0, 348.0, 204.0, 348.0, 204.0, 334.0, 203.0, 321.0, 203.0, 321.0, 203.0, 312.0, 204.0, 303.0, 195.0, 303.0, 195.0, 285.0, 195.0, 276.0, 187.0, 276.0, 187.0, 267.0, 182.0, 258.0, 175.0, 258.0, 175.0, 249.0, 166.0, 240.0, 154.0, 240.0, 154.0, 231.0, 145.0, 222.0, 127.0, 222.0, 127.0, 213.0, 113.0, 204.0, 106.0, 204.0, 106.0, 195.0, 93.0, 186.0, 81.0, 186.0, 81.0, 177.0, 74.0, 168.0, 72.0, 168.0, 72.0, 159.0, 73.0, 150.0, 71.0, 150.0, 71.0, 141.0, 70.0, 132.0, 68.0, 132.0, 68.0, 123.0, 69.0, 114.0, 70.0, 114.0, 70.0, 103.0, 69.0, 96.0, 67.0, 96.0, 67.0, 87.0, 62.0, 78.0, 59.0, 78.0, 59.0, 69.0, 57.0, 60.0, 58.0, 60.0, 58.0, 51.0, 64.0, 42.0, 68.0], 'area': 0, 'id': 279043}]\n"
     ]
    }
   ],
   "source": [
    "## Line Annotation\n",
    "#image_info = [x['id']: {'file_name':x['file_name'], 'height':x['height'], 'width':x['width']} for x in test_annotation['images']]\n",
    "#good sample: [28]\n",
    "sample_info = test_annotation['images'][28]\n",
    "sample_id = sample_info['id']\n",
    "#Find all the annotations:\n",
    "\n",
    "#Display the image\n",
    "sample_img_path = '/'.join([image_path, sample_info['file_name']])\n",
    "display(Image(filename=sample_img_path))\n",
    "\n",
    "#Map Annotations to Image ID\n",
    "AnnotationMap = {}\n",
    "for x in test_annotation['annotations']:\n",
    "    img_id = x['image_id']\n",
    "    AnnotationMap[img_id] = AnnotationMap.get(img_id, []) + [x]\n",
    "    \n",
    "sample_annotation = AnnotationMap[sample_id]\n",
    "print(sample_annotation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d3f114",
   "metadata": {},
   "source": [
    "### Detect Text Elements of the Image\n",
    "Extract the Text Elements from Line Image. \n",
    "\n",
    "1. **Extract Legend**\n",
    "    - Disregard the Numerical Values\n",
    "    - Disregard the bounding box that is not Vertically or Horizontally aligned\n",
    "    \n",
    "2. **Extract X-labels**\n",
    "   - Filter the texts that is above the 1/2 of the height (assume the x-axies is at the bottom)\n",
    "   - Using sliding line in vertical direction and record the intersected bounding box. \n",
    "   - The maximum number of intersection is considered as the x-labels\n",
    "   \n",
    "3. **Extract Y-labels** \n",
    "   - Filter the texts that is above the 1/2 of the height (assume the y-axies is to the left side)\n",
    "   - Using sliding line in vertical direction and record the intersected bounding box. \n",
    "   - The maximum number of intersection is considered as the y-labels\n",
    "\n",
    "\n",
    "2. **Extract Title**\n",
    "    - Detected Text that is closest to the Top. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "dded3892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[33.0, 7.0], [60.0, 7.0], [60.0, 19.0], [33.0, 19.0]], ['90.0', 0.9999209642410278]], [[[217.0, 13.0], [279.0, 13.0], [279.0, 25.0], [217.0, 25.0]], ['B-FACTOR', 0.9646163582801819]], [[[334.0, 17.0], [363.0, 17.0], [363.0, 33.0], [334.0, 33.0]], ['85,5', 0.9301401376724243]], [[[33.0, 38.0], [60.0, 38.0], [60.0, 52.0], [33.0, 52.0]], ['85.0', 0.9998229146003723]], [[[33.0, 71.0], [60.0, 71.0], [60.0, 84.0], [33.0, 84.0]], ['008', 0.7841935753822327]], [[[34.0, 104.0], [59.0, 104.0], [59.0, 116.0], [34.0, 116.0]], ['75.0', 0.9991638660430908]], [[[32.0, 136.0], [61.0, 136.0], [61.0, 151.0], [32.0, 151.0]], ['70.0', 0.9999606609344482]], [[[34.0, 169.0], [59.0, 169.0], [59.0, 182.0], [34.0, 182.0]], ['65.0', 0.999873697757721]], [[[33.0, 200.0], [60.0, 200.0], [60.0, 214.0], [33.0, 214.0]], ['60.0', 0.9996654987335205]], [[[32.0, 233.0], [61.0, 233.0], [61.0, 247.0], [32.0, 247.0]], ['55.0', 0.9998172521591187]], [[[108.0, 235.0], [136.0, 235.0], [136.0, 254.0], [108.0, 254.0]], ['57.0', 0.9998224973678589]], [[[33.0, 267.0], [60.0, 267.0], [60.0, 280.0], [33.0, 280.0]], ['50.0', 0.9999395608901978]], [[[70.0, 282.0], [146.0, 281.0], [146.0, 293.0], [70.0, 294.0]], ['123456', 0.9936718344688416]], [[[147.0, 283.0], [425.0, 283.0], [425.0, 294.0], [147.0, 294.0]], ['7 8 9 10111213141516171819202122232425262728', 0.9756883978843689]]]\n"
     ]
    }
   ],
   "source": [
    "#Get the OCR of this image\n",
    "ocr_dir = \"/dvmm-filer2/projects/mingyang/semafor/chart_table/data/line/ocr_results/test2019/{}.json\".format(sample_id)\n",
    "with open(ocr_dir, \"r\") as f:\n",
    "    ocr_texts = json.load(f)\n",
    "\n",
    "print(ocr_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ea413ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[217.0, 13.0], [279.0, 13.0], [279.0, 25.0], [217.0, 25.0]], ['B-FACTOR', 0.9646163582801819]], [[[334.0, 17.0], [363.0, 17.0], [363.0, 33.0], [334.0, 33.0]], ['85,5', 0.9301401376724243]]]\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def ocr_preprocessing(ocr_texts):\n",
    "    filtered_ocr_texts = []\n",
    "    thred = 0.8\n",
    "    for line in ocr_texts:\n",
    "        if line[1][1] > thred:\n",
    "            filtered_ocr_texts.append(line)\n",
    "    return filtered_ocr_texts\n",
    "\n",
    "def hv_alignment(a, b):\n",
    "    #compare the bounding box a and b to check if they are horizontal or vertically aligned\n",
    "    align_thred = 5\n",
    "    align_diff = [abs(a[0][0]-b[0][0]), abs(a[0][1]-b[0][1]), abs(a[2][0]-b[1][0]), abs(a[2][1]-b[2][1])]\n",
    "    if min(align_diff) < align_thred:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "\n",
    "#Extract xlabels\n",
    "def extract_xlabels(ocr_texts, sample_info):\n",
    "    img_height = int(sample_info['height'])\n",
    "    #print(img_height)\n",
    "    x_label_candidates = []\n",
    "    \n",
    "    #Only consider the texts under the middle point. \n",
    "    starting_point = int(0.5*img_height)\n",
    "    #moving pace is the min of the bounding box size\n",
    "    step = int(min([x[0][2][1]-x[0][0][1] for x in ocr_texts]))\n",
    "    \n",
    "    intersected_list = []\n",
    "    for l_pos in range(starting_point, img_height, step):\n",
    "        #find the intersected texts\n",
    "        intersected_texts = [x for x in ocr_texts if x[0][0][1] < l_pos and x[0][2][1] > l_pos]\n",
    "        intersected_list.append(intersected_texts)\n",
    "    intersected_list = sorted(intersected_list, key=len)\n",
    "    x_label_candidates = intersected_list[-1]   \n",
    "        \n",
    "    return x_label_candidates\n",
    "\n",
    "#Extract ylabels\n",
    "def extract_ylabels(ocr_texts, sample_info):\n",
    "    img_width = int(sample_info['width'])\n",
    "    #print(img_height)\n",
    "    y_label_candidates = []\n",
    "    \n",
    "    #Only consider the texts under the middle point. \n",
    "    end_point = int(0.5*img_width)\n",
    "    #moving pace is the min of the bounding box width\n",
    "    step = int(0.5*min([x[0][2][0]-x[0][0][0] for x in ocr_texts]))\n",
    "    \n",
    "    \n",
    "    intersected_list = []\n",
    "    for l_pos in range(0, end_point, step):\n",
    "        #find the intersected texts\n",
    "        intersected_texts = [x for x in ocr_texts if x[0][0][0] < l_pos and x[0][2][0] > l_pos]\n",
    "        intersected_list.append(intersected_texts)\n",
    "    \n",
    "    intersected_list = sorted(intersected_list, key=len)\n",
    "    #print(intersected_list[-1])\n",
    "    y_label_candidates = intersected_list[-1]  \n",
    "    #Get rid of y_labels that are not numbers\n",
    "    \n",
    "    final_y_label_candidates = []\n",
    "    for y_label in y_label_candidates:\n",
    "        try:\n",
    "            y_label_text = y_label[1][0]\n",
    "            y_label_value = float(y_label[1][0])\n",
    "            final_y_label_candidates.append(y_label)\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "    return final_y_label_candidates\n",
    "def estimate_range(y_labels):\n",
    "    \"\"\"\n",
    "    Make sure the order of the value is following the order of the position. \n",
    "    \"\"\"\n",
    "    #sorted(intersected_list, key=len)(key=x[0][0][1])\n",
    "    y_labels.sort(key=lambda x: x[0][0][1])\n",
    "    \n",
    "    y_max = max([float(y[1][0]) for y in y_labels])\n",
    "    y_max_candidate = float(y_labels[0][1][0])\n",
    "    while y_max != y_max_candidate:\n",
    "        #pop the first item\n",
    "        del y_labels[0]\n",
    "        y_max = max([float(y[1][0]) for y in y_labels])\n",
    "        y_max_candidate = float(y_labels[0][1][0])\n",
    "    y_max_pos = 0.5*(y_labels[0][0][0][1]+y_labels[0][0][2][1])\n",
    "    \n",
    "    y_min = min([float(y[1][0]) for y in y_labels])\n",
    "    y_min_candidate = float(y_labels[-1][1][0])\n",
    "    while y_min != y_min_candidate:\n",
    "        #pop the first item\n",
    "        del y_labels[-1]\n",
    "        y_min = min([float(y[1][0]) for y in y_labels])\n",
    "        y_min_candidate = float(y_labels[-1][1][0])\n",
    "    y_min_pos = 0.5*(y_labels[-1][0][0][1]+y_labels[-1][0][2][1])\n",
    "    \n",
    "    #y_values = [float(y[1][0]) for y in y_labels]\n",
    "    y_scale = (y_max - y_min)/(0.5*(y_labels[-1][0][0][1]+y_labels[-1][0][2][1]) - 0.5*(y_labels[0][0][0][1]+y_labels[0][0][2][1]))\n",
    "    return y_max, y_min, y_scale, y_max_pos, y_min_pos\n",
    "\n",
    "#Extract Legend\n",
    "def extract_legend(ocr_texts):\n",
    "    numerical_pattern = \"(\\d+(\\.\\d+))\"\n",
    "\n",
    "    #Step 1, Get rid of Numerical Value\n",
    "    legend_candidates = []\n",
    "    for candidate in ocr_texts:\n",
    "        candidate_text = candidate[1][0]\n",
    "        if not re.search(numerical_pattern, candidate_text):\n",
    "            legend_candidates.append(candidate)\n",
    "\n",
    "    #print(legend_candidates)\n",
    "    #Step 2, Group Text based on their Vertical or Horizontal Alignment.\n",
    "    align_map = {}\n",
    "    for i_a,c_a in enumerate(legend_candidates):\n",
    "        align_map[i_a] = []\n",
    "        i_b = i_a+1\n",
    "        for c_b in legend_candidates[i_a+1:]:\n",
    "            if hv_alignment(c_a[0],c_b[0]):\n",
    "                align_map[i_a].append(i_b)\n",
    "            i_b +=1\n",
    "    \n",
    "    legend_groups = []\n",
    "    legend_group = []\n",
    "\n",
    "    for k,v in align_map.items():\n",
    "        if k in legend_group:\n",
    "            continue\n",
    "        if v:\n",
    "            legend_group += v + [k]\n",
    "            for x in v:\n",
    "                legend_group += align_map[x]\n",
    "            legend_group = list(set(legend_group))\n",
    "            legend_groups.append(legend_group)\n",
    "            legend_group = []\n",
    "    if len(legend_groups) == 0:\n",
    "        return []\n",
    "    #Get the final legends\n",
    "    if len(legend_groups) == 1 and len(legend_groups[0]) > 0:\n",
    "        return [legend_candidates[x] for x in legend_groups[0]]\n",
    "    else:\n",
    "        #Pick the one with the maximum elements or maybe the group that has the mapped elements as the gt sectors\n",
    "        legend_groups.sort(key=len)\n",
    "        return [legend_candidates[x] for x in legend_groups[-1]]\n",
    "\n",
    "def expand_legend_box(detected_legends):\n",
    "    #Assume the mark is to the left\n",
    "    for c_legend in detected_legends:\n",
    "        width = c_legend[0][2][0]-c_legend[0][0][0]\n",
    "        expand_width = int(0.2*width)\n",
    "        c_legend[0][0][0] -= expand_width\n",
    "        c_legend[0][3][0] -= expand_width\n",
    "        \n",
    "ocr_texts = ocr_preprocessing(ocr_texts)\n",
    "#Get the x__labels\n",
    "x_labels = extract_xlabels(ocr_texts, sample_info)\n",
    "\n",
    "#Get the y_labels\n",
    "#print(\"\\n\")\n",
    "y_labels = extract_ylabels(ocr_texts, sample_info)\n",
    "\n",
    "#Estimate the Data Range Value\n",
    "y_max, y_min, y_scale, y_max_pos, y_min_pos= estimate_range(y_labels)\n",
    "\n",
    "#Get the legends\n",
    "filtered_ocr_texts = []\n",
    "for c_ocr in ocr_texts:\n",
    "    if c_ocr in x_labels or c_ocr in y_labels:\n",
    "        continue\n",
    "    filtered_ocr_texts.append(c_ocr)\n",
    "\n",
    "detected_legends = extract_legend(filtered_ocr_texts)\n",
    "\n",
    "print(detected_legends)\n",
    "#Augment the length of detected_legends\n",
    "expand_legend_box(detected_legends)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b24090",
   "metadata": {},
   "source": [
    "### Read Data Values\n",
    "In this step, we compute the value of each point based on its locations compared tot he y_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2d1b08a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# def determinant(v1, v2):\n",
    "#   return v1[0]*v2[1] - v1[1]*v2[0]\n",
    "\n",
    "# def dotproduct(v1, v2):\n",
    "#   return sum((a*b) for a, b in zip(v1, v2))\n",
    "\n",
    "# def length(v):\n",
    "#   return math.sqrt(dotproduct(v, v))\n",
    "\n",
    "# def angle(v1, v2):\n",
    "#   return math.atan2(determinant(v1,v2), dotproduct(v1, v2))\n",
    "\n",
    "def compute_unit(p1,p2):\n",
    "    distance = [p1[0] - p2[0], -(p1[1] - p2[1])]\n",
    "    norm = math.sqrt(distance[0] ** 2 + distance[1] ** 2)\n",
    "    direction = [distance[0] / norm, distance[1] / norm]\n",
    "    return direction\n",
    "\n",
    "def compute_distance(p1,p2):\n",
    "    distance = (p1[0] - p2[0])**2 + (p1[1] - p2[1])**2\n",
    "    return distance\n",
    "    \n",
    "\n",
    "for line in sample_annotation:\n",
    "    line_pts = [(line['bbox'][i],line['bbox'][i+1])for i in range(0,len(line['bbox']),2)]\n",
    "    line_values = [0]*len(line_pts)\n",
    "    for i,pt in enumerate(line_pts):\n",
    "        pt_value = y_scale*(y_min_pos - pt[1]) + y_min\n",
    "        line_values[i] = pt_value\n",
    "    line['values'] = line_values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc927e4",
   "metadata": {},
   "source": [
    "### Map the Legend or the Column Text\n",
    "In this final step we will map the legend text or the other detected Text to each line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5799560a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W7AC2NMtbgE9OsC1jGXE+ianRfKl4DXB3Vb1nyaaZORfD+jBL5yLJ05KsbpZ/EvgVFr+r+BxwRVNtqs/DEVP9VA9A83jX3/D/7/T/swk36bgk+RkWr/Jh8TXYH56VPiydiwF4iMW5GD4BfBR4JvAfwKuqamq/PB3ShwtZvLXwf/NJHLlXPo2S/BLwBeBO4AdN8dtYvEc+E+fiKH3YzIyciyQvYPHL21UsXjR/tKre1fyN3wCsBf4V+K2q+u/JtfTYpj74JUndmvZbPZKkjhn8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPfO/rKfkRnu6x/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(75.0, 116.0), (88.0, 87.0), (101.0, 162.0), (113.0, 148.0), (126.0, 226.0), (139.0, 217.0), (151.0, 129.0), (164.0, 188.0), (177.0, 158.0), (189.0, 100.0), (202.0, 113.0), (215.0, 126.0), (227.0, 171.0), (240.0, 129.0), (253.0, 129.0), (266.0, 155.0), (278.0, 132.0), (291.0, 142.0), (304.0, 61.0), (316.0, 181.0), (329.0, 67.0), (342.0, 145.0), (354.0, 41.0), (367.0, 90.0), (380.0, 74.0), (392.0, 142.0), (405.0, 136.0), (418.0, 188.0)]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from collections import Counter\n",
    "\n",
    "def get_dominant_color(image, k=4, image_processing_size = None):\n",
    "    \"\"\"\n",
    "    takes an image as input\n",
    "    returns the dominant color of the image as a list\n",
    "    \n",
    "    dominant color is found by running k means on the \n",
    "    pixels & returning the centroid of the largest cluster\n",
    "\n",
    "    processing time is sped up by working with a smaller image; \n",
    "    this resizing can be done with the image_processing_size param \n",
    "    which takes a tuple of image dims as input\n",
    "\n",
    "    >>> get_dominant_color(my_image, k=4, image_processing_size = (25, 25))\n",
    "    [56.2423442, 34.0834233, 70.1234123]\n",
    "    \"\"\"\n",
    "    #resize image if new dims provided\n",
    "    if image_processing_size is not None:\n",
    "        image = cv2.resize(image, image_processing_size, \n",
    "                            interpolation = cv2.INTER_AREA)\n",
    "    \n",
    "    #reshape the image to be a list of pixels\n",
    "    image = image.reshape((image.shape[0] * image.shape[1], 3))\n",
    "\n",
    "    #cluster and assign labels to the pixels \n",
    "    clt = KMeans(n_clusters = k)\n",
    "    labels = clt.fit_predict(image)\n",
    "\n",
    "    #count labels to find most popular\n",
    "    label_counts = Counter(labels)\n",
    "    \n",
    "    \n",
    "    #subset out most popular centroid\n",
    "    #dominant_color = clt.cluster_centers_[label_counts.most_common(1)[0][0]]\n",
    "    dominant_colors = clt.cluster_centers_\n",
    "    return list(dominant_colors)\n",
    "\n",
    "#If Legend Exist and the Length Matched\n",
    "#if detected_legends and len(detected_legends) == len(sample_annotation):\n",
    "#Get the dominant Color for each Legends\n",
    "legends = {}\n",
    "sample_image = cv2.imread(sample_img_path)\n",
    "sample_image = cv2.cvtColor(sample_image, cv2.COLOR_BGR2RGB)\n",
    "for i, dl in enumerate(detected_legends):\n",
    "    legends[i] = {'bbox': dl[0], 'text': dl[1]}\n",
    "    start_point = (int(dl[0][0][0]), int(dl[0][0][1]))\n",
    "    end_point = (int(dl[0][2][0]), int(dl[0][2][1]))\n",
    "    \n",
    "    cropped_image = sample_image[start_point[1]:end_point[1], start_point[0]:end_point[0]]\n",
    "    plt.imshow(cropped_image)\n",
    "    plt.show()\n",
    "#     print(cropped_image[5,3])\n",
    "#     dominant_colors = get_dominant_color(cropped_image, k=3)\n",
    "#     print(dominant_colors)\n",
    "    legends[i]['cropped_image'] = cropped_image\n",
    "\n",
    "\n",
    "#Find the Color for Each Line, locate a center point, assume the legend will be there\n",
    "for line in sample_annotation:\n",
    "    line_pts = [(line['bbox'][i],line['bbox'][i+1])for i in range(0,len(line['bbox']),2)]\n",
    "    line['column_value'] = [\"None\"]*len(line_pts)\n",
    "    pick_point = line_pts[int(0.5*len(line_pts))]\n",
    "    pick_point = (int(pick_point[0]), int(pick_point[1]))\n",
    "    \n",
    "    sample_image_hsv = cv2.cvtColor(sample_image, cv2.COLOR_RGB2HSV)\n",
    "    pick_point_color = sample_image_hsv[pick_point[1],pick_point[0]]\n",
    "    \n",
    "    pp_color_min = np.array([max(0, pick_point_color[0]-5),max(50,pick_point_color[1]-10),max(50, pick_point_color[2]-10)], np.uint8)\n",
    "    pp_color_max = np.array([pick_point_color[0]+5,min(255, pick_point_color[1]+10),min(255, pick_point_color[2]+10)], np.uint8)\n",
    "    \n",
    "    line['row_value'] = \"None\"\n",
    "    for k, cl in legends.items():\n",
    "        cl_image = cl[\"cropped_image\"]\n",
    "        cl_hsv_image = cv2.cvtColor(cl_image, cv2.COLOR_RGB2HSV)\n",
    "        frame_threshed = cv2.inRange(cl_hsv_image, pp_color_min, pp_color_max)\n",
    "        \n",
    "\n",
    "        #might satisty two saturation\n",
    "        \n",
    "        if np.count_nonzero(frame_threshed)/(frame_threshed.shape[0]*frame_threshed.shape[1]) > 0.01:\n",
    "            #Find a Map\n",
    "            line['row_value'] = cl['text'][0]\n",
    "            break\n",
    "     #Map the line column values \n",
    "    print(line_pts)\n",
    "    for i, pt in enumerate(line_pts):\n",
    "        pt_x = pt[0]\n",
    "        thred = 5 #maybe set it to the length of the bbox\n",
    "        for x_label in x_labels:\n",
    "            x_label_text = x_label[1][0]\n",
    "            c_x = 0.5*(x_label[0][0][0]+x_label[0][2][0])\n",
    "            if abs(c_x-pt_x) < thred:\n",
    "                line['column_value'][i] = x_label_text\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "                             \n",
    "        \n",
    "\n",
    "                             \n",
    "    \n",
    "#plt.imshow('Binary',thresh_img)\n",
    "#print(sample_annotation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6a26dcb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None']\n",
      "None\n",
      "[74.18426103646833, 78.63723608445298, 67.12092130518234, 69.27063339731286, 57.2936660268714, 58.675623800383875, 72.18809980806142, 63.12859884836852, 67.73512476007677, 76.64107485604606, 74.64491362763916, 72.64875239923225, 65.73896353166987, 72.18809980806142, 72.18809980806142, 68.1957773512476, 71.7274472168906, 70.1919385796545, 82.62955854126679, 64.20345489443378, 81.70825335892513, 69.73128598848368, 85.70057581573896, 78.17658349328215, 80.63339731285988, 70.1919385796545, 71.11324376199616, 63.12859884836852]\n"
     ]
    }
   ],
   "source": [
    "display(Image(filename=sample_img_path))\n",
    "for result in sample_annotation:\n",
    "    print(result['column_value'])\n",
    "    print(result['row_value'])\n",
    "    print(result['values'])\n",
    "#     for c, v in zip(result['column_value'], result['values']):\n",
    "#         print(f\"({c},{result['row_value']}): {v}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "525ea515",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chart2table",
   "language": "python",
   "name": "chart2table"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}