{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01 — Příprava datasetu VisDrone\n",
    "\n",
    "**VisDrone2019-DET** je veřejně dostupný dataset leteckých snímků s anotacemi vozidel.\n",
    "Obsahuje třídy: `pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor`.\n",
    "\n",
    "My použijeme pouze vozidlové třídy a převedeme anotace do formátu YOLO.\n",
    "\n",
    "**Citace:**\n",
    "> Zhu, P., Wen, L., Du, D., et al. (2021). Detection and Tracking Meet Drones Challenge. *IEEE TPAMI*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Instalace závislostí\n",
    "%pip install ultralytics Pillow tqdm requests --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stahuji train.zip ...\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import shutil, os, zipfile, urllib.request\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "DATA_DIR = Path(\"data/visdrone\")\n",
    "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# VisDrone 2019 DET — stáhni ze stránek http://aiskyeye.com\n",
    "# nebo z Google Drive zrcadla:\n",
    "SPLITS = {\n",
    "    \"train\": \"https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip\",\n",
    "    \"val\":   \"https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip\",\n",
    "    \"test\":  \"https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip\",\n",
    "}\n",
    "\n",
    "def download_with_progress(url: str, dest: Path):\n",
    "    if dest.exists():\n",
    "        print(f\"  Už existuje: {dest.name}\")\n",
    "        return\n",
    "    print(f\"Stahuji {dest.name} ...\")\n",
    "    urllib.request.urlretrieve(url, dest)\n",
    "    print(f\"  OK → {dest}\")\n",
    "\n",
    "for split, url in SPLITS.items():\n",
    "    zip_path = DATA_DIR / f\"{split}.zip\"\n",
    "    download_with_progress(url, zip_path)\n",
    "    split_dir = DATA_DIR / split\n",
    "    if not split_dir.exists():\n",
    "        print(f\"Rozbaluji {zip_path.name} ...\")\n",
    "        with zipfile.ZipFile(zip_path) as zf:\n",
    "            zf.extractall(DATA_DIR)\n",
    "        print(f\"  Hotovo → {split_dir}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prozkoumání struktury datasetu\n",
    "for split in [\"train\", \"val\", \"test\"]:\n",
    "    d = DATA_DIR / split\n",
    "    if d.exists():\n",
    "        imgs = list(d.glob(\"images/*.jpg\"))\n",
    "        anns = list(d.glob(\"annotations/*.txt\"))\n",
    "        print(f\"{split:5s}: {len(imgs):4d} snímků, {len(anns):4d} anotací\")\n",
    "    else:\n",
    "        print(f\"{split}: chybí\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ukázkový anotační soubor — formát VisDrone:\n",
    "# <bbox_left>,<bbox_top>,<bbox_width>,<bbox_height>,<score>,<category>,<truncation>,<occlusion>\n",
    "import pandas as pd\n",
    "\n",
    "sample_ann = next((DATA_DIR / \"train\" / \"annotations\").glob(\"*.txt\"))\n",
    "df = pd.read_csv(sample_ann, header=None,\n",
    "                 names=[\"x\",\"y\",\"w\",\"h\",\"score\",\"cat\",\"trunc\",\"occ\"])\n",
    "print(f\"Soubor: {sample_ann.name}\")\n",
    "print(df.head(10))\n",
    "print(\"\\nRozdělení tříd v celém souboru:\", df[\"cat\"].value_counts().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "# VisDrone kategorie → náš mapovací slovník\n",
    "# 0=ignored, 1=pedestrian, 2=people, 3=bicycle, 4=car,\n",
    "# 5=van, 6=truck, 7=tricycle, 8=awning-tricycle, 9=bus, 10=motor, 11=others\n",
    "\n",
    "# Zachováme jen vozidlové třídy, přemapujeme na 4 naše třídy:\n",
    "# 0=car, 1=van, 2=truck, 3=bus\n",
    "VISDRONE_TO_YOLO = {\n",
    "    4: 0,   # car  → car\n",
    "    5: 1,   # van  → van\n",
    "    6: 2,   # truck → truck\n",
    "    9: 3,   # bus  → bus\n",
    "}\n",
    "CLASS_NAMES = [\"car\", \"van\", \"truck\", \"bus\"]\n",
    "\n",
    "YOLO_DIR = Path(\"data/yolo_visdrone\")\n",
    "\n",
    "def convert_split(split: str):\n",
    "    src_img = DATA_DIR / split / \"images\"\n",
    "    src_ann = DATA_DIR / split / \"annotations\"\n",
    "    dst_img = YOLO_DIR / split / \"images\"\n",
    "    dst_lbl = YOLO_DIR / split / \"labels\"\n",
    "    dst_img.mkdir(parents=True, exist_ok=True)\n",
    "    dst_lbl.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    ann_files = sorted(src_ann.glob(\"*.txt\"))\n",
    "    skipped = 0\n",
    "\n",
    "    for ann_path in tqdm(ann_files, desc=split):\n",
    "        img_path = src_img / ann_path.with_suffix(\".jpg\").name\n",
    "        if not img_path.exists():\n",
    "            skipped += 1\n",
    "            continue\n",
    "\n",
    "        img = Image.open(img_path)\n",
    "        iw, ih = img.size\n",
    "\n",
    "        rows = []\n",
    "        with open(ann_path) as f:\n",
    "            for line in f:\n",
    "                parts = line.strip().split(\",\")\n",
    "                if len(parts) < 6:\n",
    "                    continue\n",
    "                x, y, w, h = int(parts[0]), int(parts[1]), int(parts[2]), int(parts[3])\n",
    "                cat = int(parts[5])\n",
    "                if cat not in VISDRONE_TO_YOLO:\n",
    "                    continue\n",
    "                if w <= 0 or h <= 0:\n",
    "                    continue\n",
    "                yolo_cls = VISDRONE_TO_YOLO[cat]\n",
    "                cx = (x + w / 2) / iw\n",
    "                cy = (y + h / 2) / ih\n",
    "                nw = w / iw\n",
    "                nh = h / ih\n",
    "                rows.append(f\"{yolo_cls} {cx:.6f} {cy:.6f} {nw:.6f} {nh:.6f}\")\n",
    "\n",
    "        # Kopiuj obrázek a ulož label (i prázdný label je OK pro neg. sample)\n",
    "        shutil.copy(img_path, dst_img / img_path.name)\n",
    "        lbl_out = dst_lbl / ann_path.name\n",
    "        lbl_out.write_text(\"\\n\".join(rows))\n",
    "\n",
    "    print(f\"  {split}: {len(ann_files)-skipped} zpracováno, {skipped} přeskočeno\")\n",
    "\n",
    "for split in [\"train\", \"val\"]:\n",
    "    convert_split(split)\n",
    "print(\"Konverze hotova!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vytvoření YAML konfigurace pro YOLOv8\n",
    "import yaml\n",
    "\n",
    "cfg = {\n",
    "    \"path\": str(YOLO_DIR.resolve()),\n",
    "    \"train\": \"train/images\",\n",
    "    \"val\": \"val/images\",\n",
    "    \"names\": {i: n for i, n in enumerate(CLASS_NAMES)},\n",
    "    \"nc\": len(CLASS_NAMES),\n",
    "}\n",
    "\n",
    "yaml_path = YOLO_DIR / \"dataset.yaml\"\n",
    "with open(yaml_path, \"w\") as f:\n",
    "    yaml.dump(cfg, f, allow_unicode=True, sort_keys=False)\n",
    "\n",
    "print(f\"Dataset YAML: {yaml_path}\")\n",
    "print(yaml.dump(cfg, allow_unicode=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vizualizace ukázkových anotací\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import random\n",
    "\n",
    "COLORS = [\"#00FF00\", \"#FFFF00\", \"#FF0000\", \"#0080FF\"]  # car, van, truck, bus\n",
    "\n",
    "def show_sample(split=\"train\", n=4):\n",
    "    img_dir = YOLO_DIR / split / \"images\"\n",
    "    lbl_dir = YOLO_DIR / split / \"labels\"\n",
    "    imgs = [p for p in img_dir.glob(\"*.jpg\") if (lbl_dir / p.with_suffix(\".txt\").name).stat().st_size > 0]\n",
    "    samples = random.sample(imgs, min(n, len(imgs)))\n",
    "\n",
    "    fig, axes = plt.subplots(1, len(samples), figsize=(5*len(samples), 5))\n",
    "    if len(samples) == 1:\n",
    "        axes = [axes]\n",
    "\n",
    "    for ax, img_path in zip(axes, samples):\n",
    "        img = Image.open(img_path)\n",
    "        iw, ih = img.size\n",
    "        ax.imshow(img)\n",
    "        lbl_path = lbl_dir / img_path.with_suffix(\".txt\").name\n",
    "        for line in lbl_path.read_text().splitlines():\n",
    "            if not line.strip():\n",
    "                continue\n",
    "            cls, cx, cy, nw, nh = map(float, line.split())\n",
    "            cls = int(cls)\n",
    "            x = (cx - nw/2) * iw\n",
    "            y = (cy - nh/2) * ih\n",
    "            w = nw * iw\n",
    "            h = nh * ih\n",
    "            rect = patches.Rectangle((x, y), w, h, linewidth=1.5,\n",
    "                                      edgecolor=COLORS[cls], facecolor=\"none\")\n",
    "            ax.add_patch(rect)\n",
    "            ax.text(x, y-2, CLASS_NAMES[cls], color=COLORS[cls], fontsize=7)\n",
    "        ax.set_title(img_path.stem[:20], fontsize=8)\n",
    "        ax.axis(\"off\")\n",
    "\n",
    "    plt.suptitle(\"Ukázkové anotace (zelená=car, žlutá=van, červená=truck, modrá=bus)\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"sample_annotations.png\", dpi=150, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "show_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Statistiky datasetu\n",
    "from collections import Counter\n",
    "\n",
    "for split in [\"train\", \"val\"]:\n",
    "    lbl_dir = YOLO_DIR / split / \"labels\"\n",
    "    counter = Counter()\n",
    "    n_imgs = 0\n",
    "    for lbl in lbl_dir.glob(\"*.txt\"):\n",
    "        lines = [l for l in lbl.read_text().splitlines() if l.strip()]\n",
    "        for line in lines:\n",
    "            counter[int(line.split()[0])] += 1\n",
    "        n_imgs += 1\n",
    "    print(f\"\\n{split} ({n_imgs} snímků):\")\n",
    "    for cls_id, cnt in sorted(counter.items()):\n",
    "        print(f\"  {CLASS_NAMES[cls_id]:10s}: {cnt:6d}\")\n",
    "    print(f\"  {'CELKEM':10s}: {sum(counter.values()):6d}\")"
   ]
  }
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