{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f175028c",
   "metadata": {},
   "source": [
    "## The \"Wild Khmer\" Dataset Creation Script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ed4f71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 10000 images from JSON\n",
      "Processing images and labels... This may take a while.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42c314cacca648d6a0e8b4df6093a9d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:28<00:00, 67.53it/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b0fef7ef288405c908d8e5ca92348fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading dataset shards:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Parquet files...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd6d7d400c8f46b5a10501516cbec7b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/92 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68cb344590af4837bb993bc6e0e1bef0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE ✅\n",
      "Wild Train file: wild_khmer_train.parquet\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "from datasets import Dataset, Features, Image as datasets_Image, Value\n",
    "\n",
    "# -------------------------\n",
    "# CONFIG\n",
    "# -------------------------\n",
    "JSON_PATH = \"info.json\" # Your CADT JSON file\n",
    "IMAGE_FOLDER = \"images/images\"              # Folder with your JPGs\n",
    "TRAIN_OUT = \"wild_khmer_train.parquet\"\n",
    "TEST_OUT = \"wild_khmer_test.parquet\"\n",
    "TEST_SPLIT = 0.1\n",
    "\n",
    "# -------------------------\n",
    "# LOAD DATA\n",
    "# -------------------------\n",
    "with open(JSON_PATH, 'r', encoding='utf-8') as f:\n",
    "    via_data = json.load(f)\n",
    "print(f\"Loaded {len(via_data)} images from JSON\")\n",
    "\n",
    "# -------------------------\n",
    "# DATA GENERATOR\n",
    "# -------------------------\n",
    "def generate_examples():\n",
    "    for key, data in tqdm(via_data.items()):\n",
    "        filename = data['filename']\n",
    "        image_path = os.path.join(IMAGE_FOLDER, filename)\n",
    "\n",
    "        if not os.path.exists(image_path):\n",
    "            continue\n",
    "\n",
    "        # 1. Get Image Dimensions for Normalization\n",
    "        try:\n",
    "            with Image.open(image_path) as img:\n",
    "                width, height = img.size\n",
    "                # Read raw bytes for embedding\n",
    "                with open(image_path, \"rb\") as f:\n",
    "                    img_bytes = f.read()\n",
    "        except Exception as e:\n",
    "            print(f\"Error loading {filename}: {e}\")\n",
    "            continue\n",
    "\n",
    "        # 2. Process Regions (Polygons -> Normalized Bounding Boxes)\n",
    "        regions_list = []\n",
    "        for region in data['regions']:\n",
    "            try:\n",
    "                # Extract coordinates\n",
    "                xs = region['shape_attributes']['all_points_x']\n",
    "                ys = region['shape_attributes']['all_points_y']\n",
    "                label = region['region_attributes']['label']\n",
    "\n",
    "                # Convert Polygon to Bounding Box (ymin, xmin, ymax, xmax)\n",
    "                xmin, xmax = min(xs), max(xs)\n",
    "                ymin, ymax = min(ys), max(ys)\n",
    "\n",
    "                # Normalize to 0-1000 scale (Qwen3-VL Standard)\n",
    "                n_xmin = int((xmin / width) * 1000)\n",
    "                n_xmax = int((xmax / width) * 1000)\n",
    "                n_ymin = int((ymin / height) * 1000)\n",
    "                n_ymax = int((ymax / height) * 1000)\n",
    "\n",
    "                # Format as a dictionary for the grounding task\n",
    "                regions_list.append({\n",
    "                    \"bbox_2d\": [n_ymin, n_xmin, n_ymax, n_xmax],\n",
    "                    \"label\": label\n",
    "                })\n",
    "            except KeyError:\n",
    "                continue # Skip regions without labels or points\n",
    "\n",
    "        # 3. Create the final text label (JSON string)\n",
    "        # This will be processed by your convert_to_conversation function\n",
    "        grounding_json = json.dumps({\"regions\": regions_list}, ensure_ascii=False)\n",
    "\n",
    "        yield {\n",
    "            \"image\": img_bytes,\n",
    "            \"text\": grounding_json\n",
    "        }\n",
    "\n",
    "# -------------------------\n",
    "# DATASET FEATURES\n",
    "# -------------------------\n",
    "features = Features({\n",
    "    \"image\": datasets_Image(),  # embedded image bytes\n",
    "    \"text\": Value(\"string\"),    # JSON string of boxes and text\n",
    "})\n",
    "\n",
    "# -------------------------\n",
    "# CREATE & SAVE\n",
    "# -------------------------\n",
    "print(\"Processing images and labels... This may take a while.\")\n",
    "ds = Dataset.from_generator(generate_examples, features=features)\n",
    "\n",
    "# Shuffle and split\n",
    "ds = ds.train_test_split(test_size=TEST_SPLIT)\n",
    "\n",
    "print(\"Saving Parquet files...\")\n",
    "ds[\"train\"].to_parquet(TRAIN_OUT)\n",
    "ds[\"test\"].to_parquet(TEST_OUT)\n",
    "\n",
    "print(\"DONE ✅\")\n",
    "print(f\"Wild Train file: {TRAIN_OUT}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f80720",
   "metadata": {},
   "source": [
    "## Open The Image Dataset For Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "57395385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2da1e3e954a45fdb05b47c8d38e66b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading dataset shards:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'PIL.JpegImagePlugin.JpegImageFile'>\n",
      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=960x1280 at 0x219E9F9BEC0>\n"
     ]
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "ds = Dataset.from_parquet(\"wild_khmer_train.parquet\")\n",
    "sample = ds[1][\"image\"]\n",
    "print(type(sample))\n",
    "print(sample)\n",
    "sample.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2307a106",
   "metadata": {},
   "source": [
    "## DUMMY DATASET ROW"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa809986",
   "metadata": {},
   "source": [
    "```python\n",
    "{\n",
    " 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x800>,\n",
    " 'text': '{\"regions\": [{\"bbox_2d\": [150, 200, 300, 800], \"label\": \"អាហារដ្ឋានមិត្តភាព\"}, {\"bbox_2d\": [850, 400, 920, 600], \"label\": \"012 345 678\"}]}'\n",
    "}\n",
    "```"
   ]
  }
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