{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 — Sestavení velké mapy\n",
    "\n",
    "Složíme otagované dlaždice (z notebooku 03) do jednoho velkého leteckého snímku HK\n",
    "se zakreslenými detekcemi vozidel.\n",
    "\n",
    "Výsledná mapa: **74 × 69 dlaždic × 256 px = 18 944 × 17 664 px**\n",
    "(přibližně 90 MB PNG nebo 15 MB JPEG)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import re\n",
    "from tqdm.notebook import tqdm\n",
    "import json\n",
    "\n",
    "ANNOTATED_DIR = Path(\"tiles_annotated\")\n",
    "assert ANNOTATED_DIR.exists(), \"Nejprve spusť 03_inference_tiles.ipynb!\"\n",
    "\n",
    "TILE_SIZE = 256\n",
    "\n",
    "# Zjistíme rozsah mřížky\n",
    "tile_files = sorted(ANNOTATED_DIR.glob(\"18_*.jpg\"))\n",
    "xs = []; ys = []\n",
    "for f in tile_files:\n",
    "    m = re.match(r\"18_(\\d+)_(\\d+)\\.jpg\", f.name)\n",
    "    if m:\n",
    "        xs.append(int(m.group(1)))\n",
    "        ys.append(int(m.group(2)))\n",
    "\n",
    "x_min, x_max = min(xs), max(xs)\n",
    "y_min, y_max = min(ys), max(ys)\n",
    "cols = x_max - x_min + 1\n",
    "rows = y_max - y_min + 1\n",
    "\n",
    "W = cols * TILE_SIZE\n",
    "H = rows * TILE_SIZE\n",
    "\n",
    "print(f\"Mřížka: {cols} × {rows} dlaždic\")\n",
    "print(f\"Výsledná mapa: {W} × {H} px\")\n",
    "print(f\"Odhadovaná velikost (JPEG 85%): ~{W*H*3/1e6/10:.0f} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sestavení mapy — plná rozlišení\n",
    "# Poznámka: ~18k × 17k px = ~3 GB v RAM → použijeme chunky nebo zmenšenou verzi\n",
    "\n",
    "OUTPUT_FULL = Path(\"hk_annotated_full.jpg\")\n",
    "OUTPUT_PREVIEW = Path(\"hk_annotated_preview.jpg\")\n",
    "PREVIEW_SCALE = 4  # 1/4 rozlišení pro preview\n",
    "\n",
    "# Zkontroluj dostupnou RAM\n",
    "import psutil\n",
    "ram_gb = psutil.virtual_memory().available / 1e9\n",
    "needed_gb = W * H * 3 / 1e9\n",
    "print(f\"Dostupná RAM: {ram_gb:.1f} GB, potřeba pro plnou mapu: {needed_gb:.1f} GB\")\n",
    "\n",
    "if needed_gb > ram_gb * 0.8:\n",
    "    print(\"⚠ Nedostatek RAM pro plnou mapu — generuji pouze preview\")\n",
    "    BUILD_FULL = False\n",
    "else:\n",
    "    print(\"RAM dostačuje — generuji plnou mapu\")\n",
    "    BUILD_FULL = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sestavení preview mapy (vždy)\n",
    "pw = W // PREVIEW_SCALE\n",
    "ph = H // PREVIEW_SCALE\n",
    "ts_small = TILE_SIZE // PREVIEW_SCALE\n",
    "\n",
    "canvas_preview = Image.new(\"RGB\", (pw, ph), (30, 30, 30))\n",
    "\n",
    "missing = 0\n",
    "for f in tqdm(tile_files, desc=\"Preview\"):\n",
    "    m = re.match(r\"18_(\\d+)_(\\d+)\\.jpg\", f.name)\n",
    "    if not m:\n",
    "        continue\n",
    "    xi = int(m.group(1)) - x_min\n",
    "    yi = int(m.group(2)) - y_min\n",
    "    try:\n",
    "        tile = Image.open(f).resize((ts_small, ts_small), Image.LANCZOS)\n",
    "        canvas_preview.paste(tile, (xi * ts_small, yi * ts_small))\n",
    "    except Exception as e:\n",
    "        missing += 1\n",
    "\n",
    "canvas_preview.save(OUTPUT_PREVIEW, quality=88, optimize=True)\n",
    "print(f\"Preview mapa: {OUTPUT_PREVIEW}  ({pw}×{ph} px)\")\n",
    "if missing:\n",
    "    print(f\"  Chybějící dlaždice: {missing}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sestavení plné mapy (pokud je dost RAM)\n",
    "if BUILD_FULL:\n",
    "    canvas = Image.new(\"RGB\", (W, H), (30, 30, 30))\n",
    "\n",
    "    for f in tqdm(tile_files, desc=\"Plná mapa\"):\n",
    "        m = re.match(r\"18_(\\d+)_(\\d+)\\.jpg\", f.name)\n",
    "        if not m:\n",
    "            continue\n",
    "        xi = int(m.group(1)) - x_min\n",
    "        yi = int(m.group(2)) - y_min\n",
    "        try:\n",
    "            tile = Image.open(f)\n",
    "            canvas.paste(tile, (xi * TILE_SIZE, yi * TILE_SIZE))\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "    canvas.save(OUTPUT_FULL, quality=88, optimize=True)\n",
    "    print(f\"Plná mapa: {OUTPUT_FULL}  ({W}×{H} px)\")\n",
    "    del canvas\n",
    "else:\n",
    "    print(\"Plná mapa přeskočena (nedostatek RAM).\")\n",
    "    print(f\"Pro generování použij: python3 assemble_map.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zobrazení preview mapy v notebooku\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "preview = Image.open(OUTPUT_PREVIEW)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 14))\n",
    "ax.imshow(preview)\n",
    "ax.set_title(f\"Hradec Králové — detekovaná vozidla\\n(preview {pw}×{ph} px)\", fontsize=14)\n",
    "ax.axis(\"off\")\n",
    "\n",
    "# Legenda\n",
    "legend_elements = [\n",
    "    mpatches.Patch(facecolor='#00DC00', edgecolor='white', label='car'),\n",
    "    mpatches.Patch(facecolor='#FFDC00', edgecolor='white', label='van'),\n",
    "    mpatches.Patch(facecolor='#DC0000', edgecolor='white', label='truck'),\n",
    "    mpatches.Patch(facecolor='#0078FF', edgecolor='white', label='bus'),\n",
    "]\n",
    "ax.legend(handles=legend_elements, loc='lower right', fontsize=12,\n",
    "          framealpha=0.8, title='Typy vozidel')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"hk_map_overview.png\", dpi=100, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Souhrn výsledků\n",
    "with open(\"detections.json\") as f:\n",
    "    all_detections = json.load(f)\n",
    "\n",
    "from collections import Counter\n",
    "CLASS_NAMES = [\"car\", \"van\", \"truck\", \"bus\"]\n",
    "counts = Counter()\n",
    "tiles_with_vehicles = 0\n",
    "\n",
    "for name, dets in all_detections.items():\n",
    "    if dets:\n",
    "        tiles_with_vehicles += 1\n",
    "    for d in dets:\n",
    "        counts[CLASS_NAMES[d[\"cls\"]]] += 1\n",
    "\n",
    "print(\"=\" * 40)\n",
    "print(\"SOUHRN — Hradec Králové\")\n",
    "print(\"=\" * 40)\n",
    "print(f\"Zpracované dlaždice:       {len(all_detections):6d}\")\n",
    "print(f\"Dlaždice s vozidly:        {tiles_with_vehicles:6d}\")\n",
    "print()\n",
    "print(\"Detekovaná vozidla:\")\n",
    "for cls in CLASS_NAMES:\n",
    "    print(f\"  {cls:10s}: {counts[cls]:6d}\")\n",
    "print(f\"  {'CELKEM':10s}: {sum(counts.values()):6d}\")\n",
    "print(\"=\" * 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sloupcový graf celkových počtů\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "bar_colors = [\"#00DC00\", \"#FFDC00\", \"#DC0000\", \"#0078FF\"]\n",
    "bars = ax.bar(CLASS_NAMES, [counts[c] for c in CLASS_NAMES], color=bar_colors, edgecolor=\"black\")\n",
    "\n",
    "for bar, cls in zip(bars, CLASS_NAMES):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5,\n",
    "            str(counts[cls]), ha=\"center\", va=\"bottom\", fontsize=12, fontweight=\"bold\")\n",
    "\n",
    "ax.set_title(\"Počet detekovaných vozidel — Hradec Králové\", fontsize=14)\n",
    "ax.set_ylabel(\"Počet vozidel\")\n",
    "ax.set_xlabel(\"Typ vozidla\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"vehicle_counts_chart.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  }
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