Upload task1.ipynb

task1.ipynb

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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&amp;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",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}