temp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for /home/kushal/.keras-ocr/craft_mlt_25k.h5\n",
      "Looking for /home/kushal/.keras-ocr/crnn_kurapan.h5\n"
     ]
    }
   ],
   "source": [
    "# !pip install keras_ocr\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import keras_ocr\n",
    "import numpy as np\n",
    "import cv2\n",
    "pipeline = keras_ocr.pipeline.Pipeline()#read image from the an image path (a jpg/png file or an image url)\n",
    "def midpoint(x1, y1, x2, y2):\n",
    "    x_mid = int((x1 + x2)/2)\n",
    "    y_mid = int((y1 + y2)/2)\n",
    "    return (x_mid, y_mid)\n",
    "def inpaint_text(img_path, pipeline):\n",
    "    # read image\n",
    "    img = keras_ocr.tools.read(img_path)\n",
    "    # generate (word, box) tuples\n",
    "    words=[]\n",
    "    boxes=[]\n",
    "    prediction_groups = pipeline.recognize([img])\n",
    "    mask = np.zeros(img.shape[:2], dtype=\"uint8\")\n",
    "    for box in prediction_groups[0]:\n",
    "           x0, y0 = box[1][0]\n",
    "           x1, y1 = box[1][1]\n",
    "           x2, y2 = box[1][2]\n",
    "           x3, y3 = box[1][3]\n",
    "        #    box[1].tolist()\n",
    "        #    box[1].to(list)\n",
    "           words.append(box[0])\n",
    "           boxes.append(box[1].tolist())\n",
    "           x_mid0, y_mid0 = midpoint(x1, y1, x2, y2)\n",
    "           x_mid1, y_mi1 = midpoint(x0, y0, x3, y3)\n",
    "\n",
    "           thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 ))\n",
    "\n",
    "           cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255,\n",
    "           thickness)\n",
    "           img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)\n",
    "           img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "           cv2.imwrite(f'removed_text_data/{img_path}',img_rgb)\n",
    "\n",
    "    return(img_rgb,words,boxes,prediction_groups,mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "1/1 [==============================] - 2s 2s/step\n",
      "1/1 [==============================] - 8s 8s/step\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 23\u001b[0m\n\u001b[1;32m     19\u001b[0m image_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39mentry[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# label = int(entry['label'])\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# text = entry['text']\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# image_path = \"drive\"\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m imgs,words,boxes,prediction_group,mask\u001b[38;5;241m=\u001b[39m\u001b[43minpaint_text\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpipeline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     24\u001b[0m text_removed[\u001b[38;5;28mint\u001b[39m(entry[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m])]\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m : \u001b[38;5;28mint\u001b[39m(entry[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwords\u001b[39m\u001b[38;5;124m'\u001b[39m : words,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;66;03m# 'mask' : mask,\u001b[39;00m\n\u001b[1;32m     30\u001b[0m }\n",
      "Cell \u001b[0;32mIn[18], line 18\u001b[0m, in \u001b[0;36minpaint_text\u001b[0;34m(img_path, pipeline)\u001b[0m\n\u001b[1;32m     16\u001b[0m words\u001b[38;5;241m=\u001b[39m[]\n\u001b[1;32m     17\u001b[0m boxes\u001b[38;5;241m=\u001b[39m[]\n\u001b[0;32m---> 18\u001b[0m prediction_groups \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecognize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(img\u001b[38;5;241m.\u001b[39mshape[:\u001b[38;5;241m2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muint8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m box \u001b[38;5;129;01min\u001b[39;00m prediction_groups[\u001b[38;5;241m0\u001b[39m]:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_ocr/pipeline.py:63\u001b[0m, in \u001b[0;36mPipeline.recognize\u001b[0;34m(self, images, detection_kwargs, recognition_kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     recognition_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m     62\u001b[0m box_groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdetector\u001b[38;5;241m.\u001b[39mdetect(images\u001b[38;5;241m=\u001b[39mimages, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdetection_kwargs)\n\u001b[0;32m---> 63\u001b[0m prediction_groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecognizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecognize_from_boxes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbox_groups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbox_groups\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrecognition_kwargs\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m box_groups \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     67\u001b[0m     tools\u001b[38;5;241m.\u001b[39madjust_boxes(boxes\u001b[38;5;241m=\u001b[39mboxes, boxes_format\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mboxes\u001b[39m\u001b[38;5;124m\"\u001b[39m, scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m scale)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m scale \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m boxes\n\u001b[1;32m     70\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m boxes, scale \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(box_groups, scales)\n\u001b[1;32m     71\u001b[0m ]\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m     73\u001b[0m     \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(predictions, boxes))\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m predictions, boxes \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(prediction_groups, box_groups)\n\u001b[1;32m     75\u001b[0m ]\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_ocr/recognition.py:541\u001b[0m, in \u001b[0;36mRecognizer.recognize_from_boxes\u001b[0;34m(self, images, box_groups, **kwargs)\u001b[0m\n\u001b[1;32m    531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m    532\u001b[0m     X \u001b[38;5;241m=\u001b[39m X[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, np\u001b[38;5;241m.\u001b[39mnewaxis]\n\u001b[1;32m    533\u001b[0m predictions \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    534\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[1;32m    535\u001b[0m         [\n\u001b[1;32m    536\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39malphabet[idx]\n\u001b[1;32m    537\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m row\n\u001b[1;32m    538\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m idx \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblank_label_idx, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    539\u001b[0m         ]\n\u001b[1;32m    540\u001b[0m     )\n\u001b[0;32m--> 541\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprediction_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    542\u001b[0m ]\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [predictions[start:end] \u001b[38;5;28;01mfor\u001b[39;00m start, end \u001b[38;5;129;01min\u001b[39;00m start_end]\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/src/engine/training.py:2655\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   2653\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[1;32m   2654\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[0;32m-> 2655\u001b[0m     tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2656\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   2657\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:832\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    829\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    831\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 832\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    834\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    835\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:877\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    874\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    875\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 877\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    881\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    882\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1323\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1319\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1321\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1322\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1323\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1324\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1325\u001b[0m     args,\n\u001b[1;32m   1326\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1327\u001b[0m     executing_eagerly)\n\u001b[1;32m   1328\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/context.py:1486\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1484\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1486\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1487\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1488\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1489\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1490\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1491\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1492\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1493\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1494\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1495\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1496\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1500\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1501\u001b[0m   )\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import json\n",
    "# !cp -r drive/MyDrive/data1/img data/img\n",
    "\n",
    "text_removed={}\n",
    "i=int(0)\n",
    "with open(\"data/train.jsonl\", 'r') as f:\n",
    "    for line in f:\n",
    "        entry = json.loads(line)\n",
    "        if (i==100):\n",
    "            with open(\"text_removed.json\", 'w') as f:\n",
    "                  json.dump(text_removed, f)\n",
    "            i=0\n",
    "        else:\n",
    "            i+=1\n",
    "    # Iterate through each image entry in the JSON data\n",
    "        # print(entry)\n",
    "        # image_id = int(entry['id'])\n",
    "        image_path = \"data/\"+entry['img']\n",
    "        # label = int(entry['label'])\n",
    "        # text = entry['text']\n",
    "        # image_path = \"drive\"\n",
    "        imgs,words,boxes,prediction_group,mask=inpaint_text(image_path,pipeline)\n",
    "        text_removed[int(entry['id'])]={\n",
    "            'id' : int(entry['id']),\n",
    "            'words' : words,\n",
    "            'boxes' : boxes,\n",
    "            # 'prediction_group' : prediction_group,\n",
    "            # 'mask' : mask,\n",
    "        }\n"
   ]
  }
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