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{
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"id": "536c48a1",
"metadata": {},
"outputs": [],
"source": [
"#!pip install pytesseract\n",
"from PIL import Image\n",
"import pandas as pd\n",
"import pytesseract\n",
"from pytesseract import Output\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "611fd576-62e6-406a-83ed-6d0a8497e34d",
"metadata": {},
"outputs": [],
"source": [
"#!pip install pyarrow"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f97b4939",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_parquet('./testing.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "afd89e19-9348-414e-a951-4e36dfa3fb60",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>image</th>\n",
" <th>ocr_annotation_texts</th>\n",
" <th>image_height</th>\n",
" <th>image_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
" <td>71 2 84 11 \\n43 7 57 9 PROJECT BRIEF\\n14 11 19...</td>\n",
" <td>1000</td>\n",
" <td>762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
" <td>3 3 11 10 B&W\\n77 3 87 10 QUALITY\\n15 4 74 9 Q...</td>\n",
" <td>1000</td>\n",
" <td>762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
" <td>12 11 15 13 TO:\\n24 11 34 13 R. B. SPELL\\n64 1...</td>\n",
" <td>1000</td>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
" <td>28 6 73 9 SPORTS MARKETING ENTERPRISES DOCUMEN...</td>\n",
" <td>1000</td>\n",
" <td>795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\...</td>\n",
" <td>18 8 25 9 S.P. Zolot\\n2 8 5 9 TO:\\n60 8 73 10 ...</td>\n",
" <td>1000</td>\n",
" <td>754</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" image \\\n",
"0 b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\... \n",
"1 b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\... \n",
"2 b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\... \n",
"3 b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\... \n",
"4 b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\... \n",
"\n",
" ocr_annotation_texts image_height \\\n",
"0 71 2 84 11 \\n43 7 57 9 PROJECT BRIEF\\n14 11 19... 1000 \n",
"1 3 3 11 10 B&W\\n77 3 87 10 QUALITY\\n15 4 74 9 Q... 1000 \n",
"2 12 11 15 13 TO:\\n24 11 34 13 R. B. SPELL\\n64 1... 1000 \n",
"3 28 6 73 9 SPORTS MARKETING ENTERPRISES DOCUMEN... 1000 \n",
"4 18 8 25 9 S.P. Zolot\\n2 8 5 9 TO:\\n60 8 73 10 ... 1000 \n",
"\n",
" image_width \n",
"0 762 \n",
"1 762 \n",
"2 754 \n",
"3 795 \n",
"4 754 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "22dc4078-3524-4a32-9f72-1f9e8e3588ca",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import io\n",
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "68630cb9",
"metadata": {},
"outputs": [],
"source": [
"pytesseract.pytesseract.tesseract_cmd = r'/opt/homebrew/bin/tesseract'\n",
"\n",
"\n",
"\n",
"class ocr:\n",
" def __init__(self, df):\n",
" self.df = df\n",
" def get_data(self, n):\n",
" image = Image.open(io.BytesIO(self.df.iloc(0)[n]['image']))\n",
" image_height = self.df.iloc(0)[n]['image_height']\n",
" image_width = self.df.iloc(0)[n]['image_width']\n",
" image_data = pytesseract.image_to_data(image, output_type=Output.DICT)\n",
" txt = ''\n",
" \n",
" for i in range(len(image_data['level'])):\n",
" if image_data['text'][i] == '':\n",
" continue\n",
" txt += image_data['text'][i]\n",
" txt += ' ' + str(image_data['left'][i]) + ' ' + str(image_data['top'][i]) + ' ' + str(image_data['width'][i]) + ' ' + str(image_data['height'][i]) + ' '\n",
" return image, image_height, image_width, image_data, txt\n",
" def sort_bounding_boxes(self, img_data, img_height, img_width):\n",
" n_boxes = len(img_data['text'])\n",
" if n_boxes == 0:\n",
" return []\n",
" #print(n_boxes)\n",
" boxes = []\n",
" for i in range(n_boxes):\n",
" box = {\n",
" 'index': i,\n",
" 'text': img_data['text'][i],\n",
" 'left': img_data['left'][i],\n",
" 'top': img_data['top'][i],\n",
" 'width': img_data['width'][i],\n",
" 'height': img_data['height'][i]\n",
" }\n",
" boxes.append(box)\n",
" \n",
" \n",
" sorted_boxes = sorted(boxes, key=lambda x: (x['top'], x['left']))\n",
" \n",
" final_boxes = []\n",
" for i in range(len(sorted_boxes)):\n",
" if (\n",
" sorted_boxes[i]['left'] >= img_width * 0.9\n",
" or sorted_boxes[i]['top'] >= img_height * 0.9\n",
" ):\n",
" continue\n",
" else:\n",
" final_boxes.append(sorted_boxes[i])\n",
" return final_boxes\n",
" def ocr_parse(self, img_data, img_height, img_width, width_threshold_percent=2, height_threshold_percent=1):\n",
" parsed_boxes = []\n",
"\n",
" if not img_data:\n",
" return parsed_boxes\n",
"\n",
" current_box = img_data[0]\n",
" img_width = max(current_box['left'] + current_box['width'], 1)\n",
" img_height = max(current_box['top'] + current_box['height'], 1)\n",
" current_text = current_box['text']\n",
"\n",
" for i in range(1, len(img_data)):\n",
" width_threshold = img_width * width_threshold_percent / 100\n",
" height_threshold = img_height * height_threshold_percent / 100\n",
"\n",
" if (\n",
" img_data[i]['left'] - (current_box['left'] + current_box['width']) <= width_threshold\n",
" and abs(img_data[i]['top'] - current_box['top']) <= height_threshold\n",
" ):\n",
" current_box['width'] = img_data[i]['left'] + img_data[i]['width'] - current_box['left']\n",
" current_box['height'] = max(current_box['height'], img_data[i]['top'] + img_data[i]['height'] - current_box['top'])\n",
" current_text += ' ' + img_data[i]['text']\n",
" else:\n",
" current_box['text'] = current_text\n",
" parsed_boxes.append(current_box)\n",
" current_box = img_data[i]\n",
" current_text = current_box['text']\n",
"\n",
" current_box['text'] = current_text\n",
" parsed_boxes.append(current_box)\n",
"\n",
" return parsed_boxes\n",
" def view(self, n):\n",
" image, img_height, img_width, img_data, text = self.get_data(n)\n",
" img_str = self.df.iloc(0)[n]['image']\n",
" nparr = np.fromstring(img_str, np.uint8)\n",
" img_np = cv2.imdecode(nparr, flags=1)\n",
" \n",
" n_boxes = len(img_data['level'])\n",
" for i in range(n_boxes):\n",
" (x, y, w, h) = (img_data['left'][i], img_data['top'][i], img_data['width'][i], img_data['height'][i])\n",
" \n",
" sorted_boxes = self.sort_bounding_boxes(img_data, img_height, img_width)\n",
" parsed_boxes = self.ocr_parse(sorted_boxes, img_width, img_height)\n",
" \n",
" for box in parsed_boxes:\n",
" (x, y, w, h) = (box['left'], box['top'], box['width'], box['height'])\n",
" cv2.rectangle(img_np, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
" cv2.imwrite('result.png', img_np)\n",
" txt = ''\n",
" for j in range(len(parsed_boxes)):\n",
" if parsed_boxes[j]['text'] == '':\n",
" continue\n",
" txt += parsed_boxes[j]['text'].strip(' ')\n",
" txt += ' ' + str(parsed_boxes[j]['left']) + ' ' + str(parsed_boxes[j]['top']) + ' ' + str(parsed_boxes[j]['width']) + ' ' + str(parsed_boxes[j]['height']) + ' '\n",
" print(txt)\n",
" return txt"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "1a66c36d-f835-467e-97d9-597c288280b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 0 718 1000 PROJECT BRIEF 329 76 107 11 DATE: 107 110 49 28 June 2, 1990 DATE: 179 110 -28 28 June 1, 195 116 49 10 1990 263 117 107 15 BRAND: 112 143 46 31 General Merchandising 178 144 174 13 ITEM: 394 147 33 10 Package Fixture Circle-K Nonspecific 452 148 -184 26 Convenient Storés 451 162 140 12 SUMMARY OF PROJECT: 110 184 155 14 See Attached 285 187 99 11 SUPPLIERS BEING CONSIDERED: 109 254 222 12 chicago show 107 280 101 71 Display 108 301 58 21 Equation 374 301 -133 21 Chicago Display 107 336 125 15 Robert Nielson & Associates 107 364 224 13 FUNDING: 106 406 64 44 1990 Customized Merchandising 107 434 240 16 Services 106 461 65 11 SPNS - 198 463 -10 9 SIGNATURE: 104 560 92 14 REQUESTING MANAGER 102 599 170 14 MERCHANDISING MANAGER 101 641 189 57 GROUP PRODUCT DIRECTOR 101 686 184 12 — DEPARTMENT PURCHASING 282 721 -99 28 — 353 729 14 2 RETURN TO: 98 771 82 14 REQUESTING 199 772 83 13 | MANAGER 376 773 -27 10 4514cbta 98 827 66 10 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/fv/pf_pqm6s2ds43tstk6b5q3wm0000gn/T/ipykernel_72369/3881488818.py:86: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
" nparr = np.fromstring(img_str, np.uint8)\n"
]
}
],
"source": [
"ocr_obj = ocr(df)\n",
"text = ocr_obj.view(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cb71096",
"metadata": {},
"outputs": [],
"source": [
"text"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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"version": "3.11.5"
}
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|