demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Image processing class: accepts formula images, outputs LaTeX code and rendered images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bin/anaconda3/envs/unimernetv2_pip/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/Users/bin/anaconda3/envs/unimernetv2_pip/lib/python3.10/site-packages/torchtext/data/__init__.py:4: UserWarning: \n",
      "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
      "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
      "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import os\n",
    "import random\n",
    "import sys\n",
    "\n",
    "from IPython.display import display, Math\n",
    "from PIL import Image\n",
    "from rich import print as rprint\n",
    "from rich.panel import Panel\n",
    "from rich.rule import Rule\n",
    "from rich.table import Table\n",
    "from termcolor import colored\n",
    "import torch\n",
    "\n",
    "sys.path.insert(0, os.path.join(os.getcwd(), \"..\"))\n",
    "from unimernet.common.config import Config\n",
    "from unimernet.datasets.builders import *\n",
    "from unimernet.models import *\n",
    "from unimernet.processors import *\n",
    "import unimernet.tasks as tasks\n",
    "from unimernet.processors import load_processor\n",
    "\n",
    "class ImageProcessor:\n",
    "    \n",
    "    def __init__(self, cfg_path, image_dir):\n",
    "        self.cfg_path = cfg_path\n",
    "        self.image_dir = image_dir\n",
    "        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        self.model, self.vis_processor = self.load_model_and_processor()\n",
    "\n",
    "    def load_model_and_processor(self):\n",
    "        args = argparse.Namespace(cfg_path=self.cfg_path, options=None)\n",
    "        cfg = Config(args)\n",
    "        task = tasks.setup_task(cfg)\n",
    "        model = task.build_model(cfg).to(self.device)\n",
    "        vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)\n",
    "\n",
    "        return model, vis_processor\n",
    "\n",
    "    def process_single_image(self, image_path):\n",
    "        try:\n",
    "            raw_image = Image.open(image_path)\n",
    "        except IOError:\n",
    "            print(f\"Error: Unable to open image at {image_path}\")\n",
    "            return\n",
    "\n",
    "        resized_image = self.resize_image(raw_image)\n",
    "        image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)\n",
    "        output = self.model.generate({\"image\": image})\n",
    "        pred = output[\"pred_str\"][0]\n",
    "        self.print_result(0, image_path, resized_image, pred)\n",
    "        rprint(Rule(style=\"black\"))\n",
    "\n",
    "    def process_images(self):\n",
    "        image_names = os.listdir(self.image_dir)\n",
    "        image_paths = [os.path.join(self.image_dir, name) for name in image_names]\n",
    "\n",
    "        for id, image_path in enumerate(image_paths):\n",
    "            raw_image = Image.open(image_path)\n",
    "            resized_image = self.resize_image(raw_image)\n",
    "            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)\n",
    "            output = self.model.generate({\"image\": image})\n",
    "            pred = output[\"pred_str\"][0]\n",
    "            self.print_result(id, image_path, resized_image, pred)\n",
    "            rprint(Rule(style=\"black\"))\n",
    "\n",
    "    @staticmethod\n",
    "    def resize_image(image, max_len=600):\n",
    "        width, height = image.size\n",
    "        if max(width, height) > max_len :\n",
    "            if width > height:\n",
    "                scale = float(max_len) / width\n",
    "                width = max_len\n",
    "                height = int(height * scale)\n",
    "            else:\n",
    "                scale = float(max_len) / height\n",
    "                height = max_len\n",
    "                width = int(width * scale)\n",
    "\n",
    "        return image.resize((width, height))\n",
    "\n",
    "    @staticmethod\n",
    "    def print_result(id, image_path, raw_image, pred):\n",
    "        colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan']\n",
    "        chosen_color = random.choice(colors)\n",
    "\n",
    "        table = Table(show_header=True, header_style=chosen_color)\n",
    "        table.add_column(\"Sample ID\", style=\"dim\", width=12)\n",
    "        table.add_column(\"Image Path\", style=\"dim\", width=80)\n",
    "        table.add_row(str(id), image_path)\n",
    "        rprint(table)\n",
    "        print(colored(f\"{id}_1: Source image\", chosen_color), end=\" \")\n",
    "        display(raw_image)\n",
    "        print(colored(f'{id}_2: Rendered image from LaTeX', chosen_color), end=\" \")\n",
    "        render_katex(pred)\n",
    "        print(colored(f'{id}_3: Predicted LaTeX code', chosen_color), end=\" \")\n",
    "        pred_text_panel = Panel.fit(pred, title=\"Predicted LaTeX\", border_style=chosen_color)\n",
    "        rprint(pred_text_panel)\n",
    "\n",
    "def render_katex(latex_string, show=True):\n",
    "    display(Math(latex_string))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bin/anaconda3/envs/unimernetv2_pip/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:510: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomVisionEncoderDecoderModel init\n",
      "VariableUnimerNetModel init\n",
      "VariableUnimerNetPatchEmbeddings init\n",
      "VariableUnimerNetModel init\n",
      "VariableUnimerNetPatchEmbeddings init\n",
      "CustomMBartForCausalLM init\n",
      "CustomMBartDecoder init\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bin/anaconda3/envs/unimernetv2_pip/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:540: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
      "  warnings.warn(\n",
      "/Users/bin/anaconda3/envs/unimernetv2_pip/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:545: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"color: #000080; text-decoration-color: #000080\"> Sample ID    </span>┃<span style=\"color: #000080; text-decoration-color: #000080\"> Image Path                                                                       </span>┃\n",
       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0            </span>│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> /Users/bin/code/GoGoGo/UniMERNet/asset/test_imgs/0000001.png                     </span>│\n",
       "└──────────────┴──────────────────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[34m \u001b[0m\u001b[34mSample ID   \u001b[0m\u001b[34m \u001b[0m┃\u001b[34m \u001b[0m\u001b[34mImage Path                                                                      \u001b[0m\u001b[34m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[2m \u001b[0m\u001b[2m0           \u001b[0m\u001b[2m \u001b[0m│\u001b[2m \u001b[0m\u001b[2m/Users/bin/code/GoGoGo/UniMERNet/asset/test_imgs/0000001.png                    \u001b[0m\u001b[2m \u001b[0m│\n",
       "└──────────────┴──────────────────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m0_1: Source image\u001b[0m "
     ]
    },
    {
     "data": {
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",
      "image/png": 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M0YgZ0tGuKBBi6khsrEhPLmaQkF4JcDp9C6QCeLptqiQF+rakF2pL+fR4XEMYjLa4TuxPhLk/xiClSHfuFX8AEu028XPp2aSP0EWeSKjDzaH4VfCUsJor+RvZ4bX+tgDEW8MJkSsCCZ0qMkfgfTCSpwzhXpVxDEQZDS3BAFeJP9yZKxoIDTmfuJpiRNGoQXUPJ2gljk5YTjUKCccMR2IJRwi3lxNCYZ7YdxRO/2k0jPdZK+pwoYyt4aUjPgJgx8MdcOywcXqcLnNAhLk5POK0f/w8Ejg24PeNU36Dgfk64rv5SJbt/S7s6m/XIZC+8IHTWcIA2HPgKPZJuiANmGZoYlTNfQJFae2tLcx0l5NqHR7HnaieaoRB+Rc3qDii4z3VHUCJ/8PIJPHhu2gFY7jxcAVd8ECZgzupPe4AzpFsejRHidmBYrgwvNyOqjV6vMl4llAi46KtAlelCCf0ZJkjMhjw9vaZ7WGcyr1W/XhpKFyvpqy3k515GARvGnrAqZYCmDzAoprKLN+QPGQFz5G/Tu5HHsHcL64m9oAz3q4jTKnzfu0AMOtPDFWWFYImHk4Q8aGOIKmNTmL9cCzZ4ewPwc7vNriAC5Hx4kM1bb65sPuaZOGCHgeOPq76YB5n8zXZ+BnSg2URrpNuHas+Lni7eGey1ez3LbYeqZy5tOO2c9j35fN4Zifudcs6v9gBrNv61iiXRK951LipjrX23Gn32sk2HPppigr3c34EDwCeLp+tv1D3ePTfx+PfgodWAnMG3tAryiC0ek4rT1TczF3thcDS6owwAh19ctfIuCO+bQkG3A11JQkDW3wGNy/GhlBY049Fr7iqvf7Rd5Xn+pA2I07sHpPIG/fwx65mTj3Pd3rNicWVBhQbUXhR/l644xoSzbD3Joklj77pm9j2/cRjFh1Bz/axSeLp+ruHFnc2n6v8YGTXLWdLl4we//Mh8ObQbwg/vq4PiTpitcZyySzrXNe5/Pn8LQ/b3I9aPofL4e0PDk69ttiufMtZ7M+yxbF7y042rA4g7EbcWAD3qeseJFt8+asZANv3YkXzw5d14KJkkt6YDM9BPkxU2MpkMN9QuxPuSBLqLUd71VVYbYZhfhagQWEDqqtkAiXH3LumjAs4LtblVXyM5wpva3DkzhikzR3ra/iYwFVeyuEoPbglCq7SxbK0QaC0dhWn+WeXuXigKVc8fox7tiVSX92tLiVPigJ2A4hkQkLsa8FKQgnLTnUOtHmc26YNPIdKnFFlqb1RP42QiYuyuW7uxATKSxKcVElRkSIOmOF5gjxl5o+cSqga2pSVA74CFXt49SYLJqRmdPiCQ0AA7Jl/mMp3FcLcTe/ymYqtTKnv5Cq+L+hmDqpGT17KVCLoU6e3aGB1VTJMIDPjk4kpXtb2Y5W4PJnv5yeJM+8Co/bkvmc7w8ETlSh5uLVVCc7FbR2KG/gyS9Ih9g2+mnQoy34MWvujwlSYWWfv48MBVbleWdWuHh5AXioSKM3d6it4GO4jJNQzZ1yCa7mGArkA8PxhDErLLXAl14McROzQP2epRgnAzT/+sCeY6TPHkuDOH07DeSIa8eBDs2yUtFfqoQoaYCVkMqbe7bdzVFoEyhThSfsoB0o1/kcuArHYD+/heIz1EVLjwVz6CRDKxMg947GZbZwHBowiJ0poKX3IcR0u0yBNXFpBkyxE+szbalR9WDiDVMuNohIWPqb1aC80gdODpZVIQvb5Ni2Ewa7xoB4gg4/LfP8rKUmeTT1LZuixGVXCPftOXb+pU9+wT4GAqRfqrXr0lVzgMkXyPPryRt1FgmWsCpVHIFsbWQGqfpwyjuhLGTnYzXCnxkgznOEZDL/rYNcdKbpFC+hDThX6gcc0rCIGUaVNW4B4LM4zVI0zDpdBquVGUd19qBDCpQJlQRMsn8FIHcHMhEpKW2cf4wEXWqqWk05eOJQRYxwojN7ToaCRlkLMvJ2nZyiZ91WXdoZcenwQcSi87iNaxax2/AWuDWC7wx/1jAdYB5/nqOhoBcC9A5ei8hkd0iRn1QEFzagIZbKbb+Y/2iiw10HUQPXZ+WrmOVEzEnOa4qlNj9PaCyTKjezuEIoE+LH55AkebzdMN8q6CG34gomAmUer2IfgOM1eR8Dp1p04F/nvDn2Y2XejDaHwOHaco47mUwH339gDilIn2oD07S5dzm1ktQ0POqWNc9zw4+cmaKMG+aOjOhiUN0XYZtYx/bVHv0PhVHE0q/TaV+8U3V0dR8qwPR36ElRa0niiomuakEtcTayFcJ3N7w5gHYoby+XNLESl6j6qhrwlCy+LBWP8WR0YD5Zcfw1EO03kgYSoZf499ARr7vZrr2u65C7qxe/vqYkZ5i5J3jas0CRpJ/Nk9hAa9mYzObHKgUWlnaVyIY9LfjQAj9r1IlQaSsZ7s+jqfCpk0XlhkIT8mNLU/nGFoKmm1KdC+e9FFlXvwFqSMkri8MYyPZlVHNPdr+k9Icu0OPQsWzO0JoZQQb9ATe+YZORqA2hEWf2pUE+o9Lr7VwXPllcA3Sl9xiZYiuPou4dax7jz5797rSe7O/eJ7LjxaLtA43WfSrOJITQiLVR5pS/KRnh4qgN55ZTJr7TWl2ydP78PFcvnf5Awd1+xZkosYyYUF+J35AnDuhqja9IhImBKLGMSgfPBw2J+e3Kxaoy+SYflS2hTUFQRYF6ebMKk3Gdfsf2XQmuqWPpmGz7kwkwtc316JpyOAHy2lKZNiUWHQpNQnIO/NhjbWhM0cQYiULW/SnhN9WU+2/NYBkz/S6KRVx8Cq96mxDJ+zqXp5bjjAuomPKN4Ge/CpMkagZTIfDklaP2mM0X+N/emS+Fzm3fp7vKAOpU33Jb33Py+nI5aaGLh1AO2L1FUHxasCMGo8ZL3VSnuee4VGAe9Aq7xplRyeNzG1ZpJ48X1bOCApMnLD+kV03OnV+PZBeoPN8b7wq+4hwAu1RhT/3wHLB427s00o0Z1cuSQo005EKOcN6rUEitW/rq/Q4exjhy7l13qOhreUCpMcLBVFrl25dfetDcXVw+ED0wZ1WqLbBp76EB0qy6jr54vBQ31kRP9BRLXFWr0kJsgq5vo/PieD7+qYpB99W2hraSwbUeAnbGzKfbs2ITTWvporYsX+ziqc8VKDFgIpFLA5Qtdu8yMLCJ+rMmu3cxoC0ysO+8XfD+b/bCxh/vOfDQaykouic7atB/XmV9//LT368pw4xILiyh6IrKd2J3duQptiDuRcIhIrEoh+Zy3QXVaGOnWDT4HGlGcJ7KdEEqjBRGngkaDJ6cy8/pahNnX3ryeFRCIw8S6GFfba3iwbmIpq20MfyOJOS2gnWsR+38Jb20DLjzo3RPUp9z6Zl7rGt6Lq1jwx24trN0K8rmkf0g3rE6RwgJ364uIuj7xDPlqSf02tGTdF7Fpl/p1IOxoUJdYY088wl0w/JCmTEK+GVETpLiifpmQ/AH2cPm14AsUiuOS9ovhY+by4eY3Cexr8AkpW71SoVZSU1dHJqoZNqq8IxuKMMmc7/PgbgK4BLdpofCh+x9+Ze0DKTd7a3EVq/6D7G2DtE5FBhtv0ZbkPKf+emUwfJ1AxP/aMcQGyYSyVdagnX/AFx08DOpd6+iE5Fcuz9VUu3vqW02Aye3t0QaAxOLVsIfWrT7vSPeQloMebRVZqO7cc7lkcS0B/8djmqto+ak7WlUst4QLcEGgBYuyFpTUYQmssI+z7HtAQbuOXbXE/zKrdRj/cu8s3a2NGzWDBVZB9beDHVXkQvPzmaDo90VG5xW4sGM9tG0z8MEN/R1ASUZOL0Ie69lfU0+Uo8kzudIdyyF7ccf3cOs/OPUaLYuybw9pq6C6QgRVJuL5aI/9NYL1SamGhFCkTrXaDX1G/fzjj+t/fUwh+vd4Rgi6cqcX9COS0M241YF+f88oaWkVS7zNZgLbqYwOs6LepfreGGfVEXuPO3LTasMUVQEzJgwc0TWkFhxVALdFAtIR4y0rSFgA3NPLhqAfkXOjPJ0b0O+w6cYxEKydY92hHkd8FWkHX0U9wLcwldk4xvh2ghSHi6MPh4pL/g6VSJLG74F2Wk2KVdxewpGYUx8pM1fwfuhFMBTCNKit5rgL1ECNGSyZ6LVH/Yj48VB/L7AZiNULGdWFytad2TuWXbjvZC3M6UZNtd2s0/uTxw5gV2ZF3xAQa/J4C5gDMSkFfWH+iHZ17c3QLTlX6BRw1awTkN9MU+b22WcxxIUhZSHJN5fAH3Ve3oDmfgGZL/FCD6R764Y5P+zmMPTDDo41qIe7x5KOWAPcy1jfQ1d3vvKJouagw3DVZbNqZchKqKyn3b9uznMOc6Ol1lNQQkmTeeSfp6x+wTP7grunw0IR6cDcjqI19tJvU5eHX17pSpyWhCSs98UxrHlVfoX5bkVO524qR27LEZFxv/3r4HbMWxu/62kL0j7azEis3I9bfeS8Z38ovIrY+0ZiYz1sVFOscqCzO2g9hsS29ApEhOvHaJt5PwCN6+im9yc9/KE8HEHc1iAH49ZGhwgF6ZCN6MGasG6Wx70H2zgTFQe+UOCo5UfIlr3F753RB1t07wtaSs7kkwftyVx70RP7ovun46Ii+Kp3OGnjyt/3/xUOevPQL+1VzdIcpCjYxl99pVw1N1AT4wmoxCLtvuN/7QHOTrJ8BL8mxW+4dqTcwbLzadkOO9AVgzcvBF27Rlq8IVTL2Kj8U192G0n0lPXgIzO1xjrOKk/4WpRPlkwCQY42YYSADxQYt6C+UzavBr1heyGqb9rNph9Y2WYqjVbEj7KlGW1CtKFtH1ARMVcbz380U7+RtnJz8mwT05z9Nea7FMhq2XTyV03uA69cBaMcKGndrs45ez9pS7GMvd+3SgbHIdayNHD04o5xoCzAv+L8MluARfugFZCqXT52FFbL0vre6GorietG5xWxPoI5qnobPPUiq9Ter185NwoZn/QJVfmAu+Pnto+H71VaBZZAJse1AyGxgm/QUF4eLxCi325H93ckQJ3Nk0cfqLFKeRf6ONS/+FbJRYU5g/rZ/6M+zVT4w4ZwzeNV+/t3qZaWWLlAUsEWgV3YUFgbcgvpCVQcBqvyU3b+j0VZ4MQCqqD4DethTXCaxtslhG/ck8WOsKJ1K/a2huULv2vnh6B7xeNoyZUVRLKL64IIaPo3pGSA4PN/9g2Hw8pOWqiO4+2f1k2H8tPJqOCJ40dDOWy2dnmyFDMf4GNVmF5bMYqtYEGtazz6+OC7gZPpv5qz3dKd8KLeOI9aPYw79VtnNUJQ9bHmgXp8a2k2N6sOSHP3ZJx/HE4tm2akC7pmXXUmz39YPQ7nf82ZfXL3/1GFv5ZHp4i4UskILm7NyLO7Py+fBOQN0OifQHiNSq0OJzqTiW25oCxugCv8SuZ2J2K2UioH0+MYlECQUX/toiFECEDH8VeuDwB4YkM3WpC8Ydlk1MM1UR+IxYnJDuB9aG7Z9c/hKh7n1hbAxR3ZKq3NaENExFn2UvOK7l8TKa5GGBR3qefF1EPFEZYMDJK1CZ18NJEXxbW0xNITBzyhO6oW0T7BGc4OkMD35a3kgAFdow4sUll823EOZZv7Vw2XomEK9ppPM3kH5w8DID95PKiOfQ8e+lWrsPJaXz64ev5zZ/jSps728E5T8hzHOyFcEA9zuLK8DVlvSAe6fz8unPH36b6C+og51lQXTw7OGw5vWiRNTMH6QSwRG1JR3QYSFnY1f01GSo6lN6kVP6j6cKk6I6FMfhPeWM0ReCE9+Jp4y2i9iVVxMXTuXOnX+9O7EKr0pujhPHhytITGmIGWMBx9Y+C0NoN5UHoz2OJ0JtLZVPAJHDl/NjguIU3yDslpW+/Js2ep2+zetKBq93SYV+BmPbyo4q0hdd2hQ8I9OKEnHwrhTUzip/kpeR3BdgUQZYQLwYNrtKke4vW+R7JBlAR5JVr17qkwr0B0PfBFLyNWXHH3yjiLJDYeieSHiMD6qAFU0KPPEAUn/YDq6p9f+joA5x4jfdjsup0oJynd7bYZF+DbDgfYwfJUtiebluNxoKtWDaNl/zIBp+ulaJPOK3nlayt7iiwc4Z3SX26Qf0YxYUn8hbHE+FP4g+jjUH0Qo3mKkH966pYULqvFmwHwFsJEPH6/B6cEFixR2iBYcjxDSwB48KNrwJMAHvxTmn7CikS992oplw6z3z78yeYPLFS8fEVEPOwBq98Mhh8t9ZH/dX26ogwVLIBJfWYSSjzeEAJATExwe7iVrLj8xQpCVod1Bom8LgQNr5qT76/9Tk+a1MJPnECyfZETAOv/GL2PnsULVrRzlZcXtaOH9KIGoNUvXBwx1kdq4SdFq0IezLK/iLX2T/2/lGL+kj0wFbb8BqQfbv88HNzYfrfh8+Av1Oqs1C8HpJFI0BV+lHRaddg/k/PJyS1VUytW5d/i5q7ycFy3brdYsu6XE1azeMBpysNjGWP0Xooo/5zB3Q852Q6m2N/2S4geOrcFYz9YMTc+bPPOg7VvI6mD1+B2hJbN8LdIbcXm+PMe86eOBHw/SUWdDQLbj4urqZhGkIi1XfPZuLnDUWpxhFSRQzhsH2260C7n8shZkGylkBbBwgX/tGin+HYe9tkBiy9bIfZFt2b/mvvpOlgP2mezWjQkJdXhiuRH8DO/WNQgaUBvppOJpNIGMfwGX1E/8p1aKasdA5RJpESTEVjJxTwlnn9bjGOSPQEPJMhfeewjGV4bS/wdJvboJqmndnBphpqmqdUcO/K5BYQwe8AeIUePb5EPGEhuPCFNbqdQprJM10MNcsgpcyLKSFCReBcBdFNEzAxE//zaU/1Hl6qnG2SZV1NEhB5WFZFBEApJg0giFTc00K/qMz3doD679FUsIOwKlXid4YZLXXiAQKAy5BXkzqZPdLUzLYrSJmFXVGe8vGA54Me1CyLKpBO8sgCbcELOCQwk9dRbZ3Th0m7jdjiTBkjA7IFD2LdSVQ/zvirDUNqBIIk3QIg4bqXEnkR53cg9teWNG0eRWnsBfaeYA/KGEkIeT0vnhbL8F9q7bud6E0tXlYlkcdgmnalhgJbdH0DklQEVQhRCXsw01UKWhDR1QuOCXQlfinz0349Nbo+t6TOtyT6ev6HWtfv5d/CUHqXwLzaf0gSp33WAX9M0wY4wKcoKAzcrjLBmncAPyXphhLmWiiIuDBQkQvBBcU8tkSFW+xwovjpGn/qzpKs+n8biLS2x0oCo2Nixq/VkGV6OFyvV/NNRj0Bgxn386WzU2mZq8umokpIg8UUZtPEb3/hlnHbNLdu8X+MuhN00Z1rIJLCYP64otLOQqdC89ItMarYja4B/EMyGG8YEfS794jjHsI5+aXDHo8IxaHn17zb3fveKQsJgn/ZP06/lnDxXjZpu4c1ujtfmtuv+4upGS/sl9PTDnvtfZ4+UQbSQ4970s7Mh36/JdcfgqBoRprvbNqLx8opbWsVqaiQ9m2qI7Kjn6J/FR1Ns2zXF6CWxeXG18iUJkGmYTYtAS0ssjO3P15p2aCarFxkBw4mFK2tzm7CWfoYD6gxs9CxGn8GpyfTfj4DhxIoIbL24Wj2orKsSNdM8lCXQ/kNauh9JfmlNdWEZBpSF5dVlTzQ+GtFKJqKFRMDw4n3C0DnF6hs8DW9c3zWPfdzKx5qfevGi2qbdCucArjl7F+De1+a5AmzQlzblb6W5BRRsb6VHzwS3hAgYTizG93JwsHi7Kjc9Y5ac66BxBcOipE1LrBrg4q6nj7CzOd1k+8YD4PbLwsFv6tPSY2yC/+UIGL4Uaj4nbLnmwHA9w2uoYH7/Cs2KNHk9ZrrwVU5vXZBC/KeK4yBdsevTL0x5RQWlhe4bSyyNYbt31GAZTNJZIYOD/wu6r2l3HK8lWxq6gf6u2eVsoFjXf4RGZyamBUZAfSnMj6zD2vDadai4kOEwxV0RfUv0pg8cMEdx8a4gPBTqSeJuyweGQ6h8f6Gzz2hrSOGRcYX+47pgf6+pSpUpQ+xUB4hFr83MiZYFobKiPHWPq5hl7K3AbTXLg1RO2HZth56+9Mb6tsPYZPox+B+0pvZvR4BOrNsH3ggEl776GVSnHS/t6y5/fOnCAJhYHPkfnT7M+Er4iwsQF1w/r4CJlbhg6SLJkms/C0HDF3d/a1P8/XD/bP+C++V4ayqxspN9b6ZzZWi1VPeR21K7+58MXmLUFy5HIvuuMRQBy3fOns1pO9eQio4Mi7/qNtEBvqstq5uVjtAENFsEqKcVP3sXUWvPwM0amyS4PQWu4Lh4ri16X9PjkPnocco0j5U4XthlPiQzvA/j0tU+8M+S77gOr8N/8EEm6hZpd5xgRAunoecc431Pq2X6qfOt++XrlyJJ0RDwJcauUhHPaHEJFSotLGprTt9N0OY730x2QxPaHBGgK5bsckSYJ+iNvuR3x1Aa87noRoO8M7oetpq6ckF/+KMThB9M2Qa3Af57p6ZuHgY/8tuN6SoA8OYSktFNCRHUbh383RLuevh+OrjRRVdGxNbOP3sRZno3zp6gRs+/EN77Ho1X1TDrpf1IUhmxpqisFOfUxPlRBZVSMu2bMQJ0Ys3+Z26ngOBp/rAvMidU0wTfaglba8trMLFQE8cobmVggCcSgbziLkKYdJvM2X9jCpUVN+rQOgtwgrY96owIA00y7c6yb6E/Qw07ZRG4fwn7gi3kO4YlzvNVcXkBVkmZvbigNG2MPUPBRDZzBOjE6npkz7k94MYmP9YOeeC+CpeKQAf4KAFnFxcWMOJRFX03NKEBVqbKUgfzSla/DFCw6o/IDvMNf0q9mvCVYvuhlQwrNekA11EszWWO+FwnmNRF+fr+eZfFyAQ9cwSoiVTe9/w+tmjDpYP09YQmUB8YDi96RLN3BG1hC+jcEXBBrgokdrJNTA7SmyUCO9WPuQqoP3rVUmGwvLEHR7//ST0D0SFvHf3Yc5zTZvZvLxXkT3CoLXVptrEpuj4Gls1km7Y6/kxA80WAqlii3UOG2dktrb650FH1fEEBxkPdkm/yT2kYpRoDJ/RQMqxYABye1tbnrhSVq/ggWyV8AwsnQaUCd0qYdSBplrAXvxiBykz3nmqhPsphHfdHs+84+sQg5sBaJzC0Y9TeN9lUYiIYKA6m9qLYjPxwDqg9H9jI8o1SN+2fSwSoxOJnNQyFU2oWDm9PCfnwxo/8HvFjJk5BgQv8Gff5OX2p7mZGrh8I0ympBLR9e2Ps6/BVCOf8bJ2rZaDCm1KBbyAT14N6Nx7oNXXnUA8AzlesIrJUrcBKCd67tS5sAqsIglcjv3CGI5wec2QeNepyCFDNtztFwT0O1JIMJ1iwZIlTXRlyE9ncEaCmCOfFb+tpkZS9yAyA8B7750oyzTnrRi5Smg2Lyw9OPdnvKzox3H786ZORto+q5gOzpeK1pb6FRfBKM3rHJkXkLPVgw8Zu8457TQgc1q5ZPc4x5e+lC9UyA5T/4luf93diVcD/3voTTFH49/JfRUcNIlSuZ1k7BtLZ7O9PgLobGw48D45VMvNOV8mEPOcIUM+8K25w+WVmoE0g8p96y57TmnfcKni4PM23IlskduhjReCD314N91WXxQJhT3Q9lF4vFFh0bgOpzItCX3K6kSKscZEyryFwQkFtbCUH8+5PgI1v6sJTt7DlYN6GOwmKmRvtAYjcfhK0GTw/HL554/hUz5/svmnUaeGbIRMTd5YdCWtU06Tw/CJAJRbyKMaJ9EFkPc8C0K9hrLNQ1bWHg94i51GKWSAt2BQyS5IAItqYBGheLqdUVJqGdu9sHRfBIm/IEplxue3hJbo4T8hRcH0dAPjp2GHfwx5GpGzJ/YY27/DPoTQ3tX8rAtSlEPXHmH+40lL/XbQNEtaNa/cnUMLP7aip54hPO9DKK0Dz9A/hSVvD23YgT8wYB6Vs0ZGi3OGCiWy9fu1m//5kitO7T9k3azCemrxRPWa9qibB84sAdbuhUY+yW4+KIgIGNqr3jAoc0FBmnIsy75iNvb572JgytnpfHhCvHzy9MUWT/LlGgC44jXm1/bXoVuU2x8bUnlUOf7AqN8qHbKXND3Pm9KDrol6jDo6d7hyTbG1cUa8Hk6AJETA6sQRv1FaOhGucZm5ZsGIFGNMHx7Uqqeb2nFaN6XLfiUktbrvMqTE9k/z5RsDoxILvAbN9vl2zehMBpYRVoA0KlmeWmr8Wqg3r8h4TKoTEKlFXZEKaLwJPkVjNNwiGZw78GpLBGiBtusr4Rq0QufAOr6n92xFoaYn1NMcP7+WaWkuNgFHnfEsdvGlcLTcCLa1iKYHq/0WeNWQPkznefZ7aSel1hW+P5/QRRXJGbtnfzoghYPcfmPVgf1zJCOsWqtLSKlYAsHR9LqE6uO5Y2tM7ajj2xx/1T2/GalG+5HBUA6tEC+RkHFx1XQt76dmWVrHcgJkxJ3njcS+fN02fo8KqDrr2krvw60fgsyHphEJX2CRE4vqbEH7zxNLuHqqFJzSXq8ThRxXOoHEjwtcpXtz/zbMM8DlALS2xnt/bZtw82cIjF+OHNr2tlViYSJm0xvUw1Od6lhh5IayJDSEetmDrhMRwKy89wn/+fr+fO+errSuWgupza7sAKytHIz8K63HYAuGWllhSY++8a8dSgR4Z5fPUMPUIqRpB1J2ttfap2qKaH9J87/ciFbVlmuZq7uxbR7QTSwqlHMZHVViPWFtl2W5YHRVlYJobcAu8HQSVMFbFlxlsaYn1CIiL0GM4T9eUsXfQxUUeNrix4wkLA3/v0C4PDv8D2Sf0pQH7SBTXfNtpSDISM2FWY3ZGPE+Y3KkLNE1L9/VHHtxRYr16rbGJ+LePWAKwp1/jFH2f7RlojQOFhTHZwVqT5E95oMqbQQEME+yvCEc/f/iR1ooB6iGleb5CKMrOGW+BNBSvXrVCh9XSEku7mKAxEq3kXEA4LAnycked51+q3gn6ln6shtJvzr3ySX19Wu8M4E+vx36Mfe81Ik3Yuk24x+3HqMJYD2L1lyIaShzJJJanhNi8vGRYS0ssfeErPBy6cepU+Mel3+zVWnkD8K33GsbKRp+H54jXXnzywBw/6OVBJdalqDXd9PmX7yqfJDgeOIaWC4MRWZNkEUKcRf1owStFvCyJtWNku/LoiXw8plbnBL8Xd/rfzStgPxd/232lM/3uQdGJ8XrzCj8Ys1WgvJGkTiyyKJfd6+b6SmWS1sHAJW+LanruvNeJu9+/F8QH5QndnLXHe36EnTbUzDzH3Cw+wMfCnLpuZ0sH6+2x/rBcDjh1fbQV8tNCHLSxV4lvaRVLzw3S6lnKG+bwolGeutxGO/yVhRsoqNMAzeOpOlNCidDeZQ6TexY6pXQew1xacIBaYFmP16pDfNeDZuHBVxjaBIklgu5oCf/KNs2JePGHqecrHR+f8jMDWgGQIeqsO8aGWurDYANFqJSUohqmOjX7TKxpdDTozTSU18pULK5k4pC2eK9w31+un36sBYtjnAK1oFeLbWmJpdD3JXR+0mxL+G/cPv5E/G/zulLXIeDptFzfnDgv1Cd5Rjxa2IvhwSJosR+D1SRD9+TmrN04pp0mKroZpN9EU/Xl5FraGssTKMSskUzjwamRxge1OZkA70OuO6iuDP1PFrFaNArq/iu92sTwzSXR3UFCcOUKVR/98H/UlprU3bA9bmFT/5DXIlg9ZpBVGuipqfmKcS0tsV6zqUxlDXG9hQ0AjxOCeCleAOQ86K0utSHdVjewmugB+QDeD4OtdMmHdbSKGU0hQv7LtBQNQIspqukC5Jm2VNG0GhMJs521HchFA00N7gK3/4yJpHUuc1/gP3PRo2hGQj0/zdjJU7j2Wrb6yEi2tVAvp0Tvwp1BZsk8y8qURHHV7R6013UfrHijE5ksNKaPqE6vPY1da28WZgFS9tZPGEDoydPKEos4py3beqvManbd7Rxs4JyzcrbMjXfpTncyvHTNojAnmmUQfazCq+syrq5Bcd57ljsYLdirHsnL9mGPrvI62zM0XzGypSUWWHjizMnpLEEOWpd8Vb5ofGLUWLvHCRGWhUCdWBYb167xckA2vYY0ll91d4sFS2U3rUMsQLe1WfXkwyqK9Eel72FXnaypxLJfecFdbM0yChXksS7lpuMgqmBBcK7ZthMu6Hap/VzN/BrHvWUrLvg/4iPHMnfvUl+oU3tHnDV0JB5r5m8PuVe0MX9i3zIOMeadhn3hbEORlcJ5b8DRDdJpHj9raiQm1SGgWz86sRb3mqOpwsJds+ipgyZsW+sCweyHAw3eDaiTaSYQeHI7Fy25bCcRGY7cZsyLQTtpqY092sMmPd3fmaS0t18GzGOkqbb0ZeRbXMUCvbfMn7x7EEsszVA9Ia6SGRmjteTdu2sBRrEJM/SqpfIN5hWACz7N5uOjydOcuVrwpFgrG2mlV48wsJB4UQfb+3zf6YvSDPWeI2sPpIYUkKzRI8MT3R11nfAIqLAcfgZ91qZdgyS31fdItHw3Olgt/ZbPtsQjanVoR0WUgdDhd61a1/9OfdbXo4iXl9TqEalgWVZfov4xtSQlxehWJ57t3YoJN40WlVQomZYFWC8mS9N4bXGV4dsbtOrLQ7S8SyGK3dixhiKIy4WZD3pp1wMtC58zmX2naWGarPlUTR5xqbuLnOEanKPz1Z6uaqOIpfIbr7fReo1qbdpQlOZeFnk9rX8jh6Np8RJwLW/xbkTQHp+1bd3Y1OdkAN9AI3xpqpTeV3oGkVdDTUETOFmMzK6zTk1kcaRMzxN0cGMRvMzQS5lY8A4m+mhvai04Ai9pYrXgiJqGRkSgJS7eTVPzCkTAlFivwCS2xEMwJVZLnJVXYEymxHoFJrElHoIpsVrirLwCYzIl1iswiS3xEP4f5mH5wtkq7KsAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=600x136>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m0_2: Rendered image from LaTeX\u001b[0m "
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\begin{array} { r l } { \\mathrm { M i n i m i s e ~ } } & { { } J ( u . ; s , y ) = \\mathbb { E } \\left[ \\int _ { s } ^ { T } \\left( u _ { t } ^ { 2 } + 1 \\right) d t - \\ln \\left( \\cosh \\left( X _ { T } \\right) \\right) \\right] } \\\\ { \\mathrm { s u b j e c t ~ t o ~ } } & { { } \\left\\{ \\begin{array} { l l } { d X _ { t } = 2 u _ { t } d t + \\sqrt { 2 } d W _ { t } , t \\in [ s , T ] } \\\\ { X _ { s } = y } \\\\ { u _ { t } \\in [ - 1 , 1 ] , \\quad t \\in [ s , T ] } \\end{array} \\right. } \\end{array}$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m0_3: Predicted LaTeX code\u001b[0m "
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭──────────────────────────────────────────────── Predicted LaTeX ────────────────────────────────────────────────╮</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> \\begin{array} { r l } { \\mathrm { M i n i m i s e ~ } } &amp; { { } J ( u . ; s , y ) = \\mathbb { E } \\left[ \\int _ <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> { s } ^ { T } \\left( u _ { t } ^ { 2 } + 1 \\right) d t - \\ln \\left( \\cosh \\left( X _ { T } \\right) \\right)      <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> \\right] } \\\\ { \\mathrm { s u b j e c t ~ t o ~ } } &amp; { { } \\left\\{ \\begin{array} { l l } { d X _ { t } = 2 u _  <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> { t } d t + \\sqrt { 2 } d W _ { t } , t \\in [ s , T ] } \\\\ { X _ { s } = y } \\\\ { u _ { t } \\in [ - 1 , 1 ] ,   <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> \\quad t \\in [ s , T ] } \\end{array} \\right. } \\end{array}                                                       <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
       "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────────\u001b[0m\u001b[34m Predicted LaTeX \u001b[0m\u001b[34m───────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
       "\u001b[34m│\u001b[0m \\begin{array} { r l } { \\mathrm { M i n i m i s e ~ } } & { { } J ( u . ; s , y ) = \\mathbb { E } \\left[ \\int _ \u001b[34m│\u001b[0m\n",
       "\u001b[34m│\u001b[0m { s } ^ { T } \\left( u _ { t } ^ { 2 } + 1 \\right) d t - \\ln \\left( \\cosh \\left( X _ { T } \\right) \\right)      \u001b[34m│\u001b[0m\n",
       "\u001b[34m│\u001b[0m \\right] } \\\\ { \\mathrm { s u b j e c t ~ t o ~ } } & { { } \\left\\{ \\begin{array} { l l } { d X _ { t } = 2 u _  \u001b[34m│\u001b[0m\n",
       "\u001b[34m│\u001b[0m { t } d t + \\sqrt { 2 } d W _ { t } , t \\in [ s , T ] } \\\\ { X _ { s } = y } \\\\ { u _ { t } \\in [ - 1 , 1 ] ,   \u001b[34m│\u001b[0m\n",
       "\u001b[34m│\u001b[0m \\quad t \\in [ s , T ] } \\end{array} \\right. } \\end{array}                                                       \u001b[34m│\u001b[0m\n",
       "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[30m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "root_path = os.path.abspath(os.getcwd())\n",
    "config_path = os.path.join(root_path, \"configs/demo.yaml\")\n",
    "image_directory = os.path.join(root_path, \"asset/test_imgs\")\n",
    "\n",
    "processor = ImageProcessor(config_path, image_directory)\n",
    "\n",
    "# Process a single image located at the specified path\n",
    "processor.process_single_image(os.path.join(image_directory, '0000001.png'))\n",
    "\n",
    "# Uncomment the following line to process all images in the specified directory\n",
    "# processor.process_images()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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