"""
Gleason_CNN polygon CSV'sini doğrular ve görsel karşılaştırma üretir.

Kullanım:
    python validate_polygons.py                       # 3 rastgele patch
    python validate_polygons.py --n 5 --seed 7
    python validate_polygons.py --creator test_pathologist1 --n 2

Çıktılar:
  - Konsol: istatistikler (label dağılımı, creator dağılımı, nokta sayıları)
  - PNG:     validate_<image_name>.png  (maske + orijinal + polygon overlay)
"""

import argparse
import json
import random
import sys
from pathlib import Path

import cv2
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.patches import Polygon as MplPolygon
from PIL import Image

ORIGIN_DIR = Path("origin")
CSV_PATH   = Path("polygons.csv")

# Palette PNG'deki sınıf renklerini taklit et (görselleştirme için)
CLASS_COLORS = {
    "Benign": "#00cc44",
    "G3":     "#4488ff",
    "G4":     "#ffdd00",
    "G5":     "#ff2222",
}

# Her kaynak için maske klasörü ve isim öneki
SOURCE_INFO = {
    "train":             ("Gleason_masks_train",             "mask_"),
    "test_pathologist1": ("Gleason_masks_test_pathologist1", "mask1_"),
    "test_pathologist2": ("Gleason_masks_test_pathologist2", "mask2_"),
}

# Maske palette indeks → label
MASK_LABEL = {0: "Benign", 1: "G3", 2: "G4", 3: "G5"}

# ZT klasörü bulma: image_name'in ilk üç alt kısmı TMA kimliği
def find_image_path(image_name: str) -> Path | None:
    parts = image_name.split("_")
    for n in range(len(parts), 0, -1):
        tma_id = "_".join(parts[:n])
        candidate = ORIGIN_DIR / tma_id / f"{image_name}.jpg"
        if candidate.exists():
            return candidate
    return None


def find_mask_path(image_name: str, creator: str) -> Path | None:
    folder_name, prefix = SOURCE_INFO[creator]
    candidate = ORIGIN_DIR / folder_name / f"{prefix}{image_name}.png"
    return candidate if candidate.exists() else None


# ---------------------------------------------------------------------------
# İstatistikler
# ---------------------------------------------------------------------------

def print_stats(df: pd.DataFrame):
    print(f"\n{'='*60}")
    print(f"Toplam polygon satırı : {len(df):,}")
    print(f"\nLabel dağılımı:")
    for label, cnt in df["label"].value_counts().items():
        print(f"  {label:8s}: {cnt:6,}")
    print(f"\nCreator dağılımı:")
    for creator, cnt in df["creator"].value_counts().items():
        print(f"  {creator:25s}: {cnt:6,}")
    lengths = df["polygon"].apply(lambda s: len(json.loads(s)) - 1)
    print(f"\nPolygon nokta sayısı (kapanış hariç):")
    print(f"  min={lengths.min()}  max={lengths.max()}  "
          f"medyan={lengths.median():.0f}  ort={lengths.mean():.1f}")
    print(f"{'='*60}\n")


# ---------------------------------------------------------------------------
# Görselleştirme
# ---------------------------------------------------------------------------

def draw_polygons(ax, image_rgb, polys: list[tuple[str, list]], title: str):
    ax.imshow(image_rgb)
    ax.set_title(title, fontsize=8)
    ax.axis("off")
    legend = {}
    for label, pts in polys:
        color = CLASS_COLORS.get(label, "#ffffff")
        arr = np.array(pts[:-1])
        patch = MplPolygon(arr, closed=True, facecolor=color,
                           alpha=0.35, edgecolor=color, linewidth=1.5)
        ax.add_patch(patch)
        if label not in legend:
            legend[label] = mpatches.Patch(color=color, label=label)
    if legend:
        ax.legend(handles=list(legend.values()), loc="upper right",
                  fontsize=7, framealpha=0.7)


def visualize_sample(image_name: str, creator: str, df: pd.DataFrame):
    mask_path = find_mask_path(image_name, creator)
    img_path  = find_image_path(image_name)

    mask_rgb = (
        np.array(Image.open(mask_path).convert("RGB"))
        if mask_path else np.zeros((200, 200, 3), dtype=np.uint8)
    )
    image_rgb = (
        cv2.cvtColor(cv2.imread(str(img_path)), cv2.COLOR_BGR2RGB)
        if img_path else np.zeros_like(mask_rgb)
    )

    rows = df[(df["image_name"] == image_name) & (df["creator"] == creator)]
    polys = [(r["label"], json.loads(r["polygon"])) for _, r in rows.iterrows()]
    n_pts = sum(len(p) - 1 for _, p in polys)

    fig, axes = plt.subplots(1, 3, figsize=(18, 6))
    fig.suptitle(f"{image_name}  [creator={creator}]", fontsize=9)

    axes[0].imshow(mask_rgb)
    axes[0].set_title("Maske (palette → RGB)")
    axes[0].axis("off")

    axes[1].imshow(image_rgb)
    axes[1].set_title("Orijinal görüntü")
    axes[1].axis("off")

    draw_polygons(
        axes[2], image_rgb, polys,
        f"Polygonlar  ({len(polys)} adet  /  {n_pts} nokta)"
    )

    plt.tight_layout()
    out_path = f"validate_{image_name[:50]}.png"
    plt.savefig(out_path, dpi=110, bbox_inches="tight")
    plt.close(fig)
    print(f"  Kaydedildi: {out_path}  "
          f"({len(polys)} polygon, {n_pts} nokta)")


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--csv",     default=str(CSV_PATH))
    parser.add_argument("--n",       type=int, default=3,
                        help="Gösterilecek örnek sayısı")
    parser.add_argument("--seed",    type=int, default=42)
    parser.add_argument("--creator", default=None,
                        help="Filtrele: train / test_pathologist1 / test_pathologist2")
    args = parser.parse_args()

    csv_path = Path(args.csv)
    if not csv_path.exists():
        sys.exit(f"CSV bulunamadı: {csv_path}")

    df = pd.read_csv(csv_path)

    # Temel yapı kontrolleri
    required = {"image_name", "label", "polygon", "creator"}
    missing = required - set(df.columns)
    if missing:
        sys.exit(f"CSV'de eksik kolonlar: {missing}")

    invalid_labels = set(df["label"].unique()) - {"Benign", "G3", "G4", "G5"}
    if invalid_labels:
        print(f"UYARI: Beklenmeyen label değerleri: {invalid_labels}")

    invalid_creators = set(df["creator"].unique()) - set(SOURCE_INFO.keys())
    if invalid_creators:
        print(f"UYARI: Beklenmeyen creator değerleri: {invalid_creators}")

    print_stats(df)

    subset = df if args.creator is None else df[df["creator"] == args.creator]
    unique_images = subset["image_name"].unique().tolist()

    if not unique_images:
        sys.exit("Görselleştirme için uygun görüntü bulunamadı.")

    # Polygon sayısı fazla olanları tercih et (içerik zengini)
    by_count = (
        subset.groupby("image_name")
        .size()
        .sort_values(ascending=False)
    )
    candidates = by_count.head(50).index.tolist()
    random.seed(args.seed)
    selected = random.sample(candidates, min(args.n, len(candidates)))

    print(f"Görselleştirilen {len(selected)} örnek:\n")
    for img_name in selected:
        creator = df.loc[df["image_name"] == img_name, "creator"].iloc[0]
        visualize_sample(img_name, creator, df)

    print("\nGörüntüler validate_*.png olarak kaydedildi.")


if __name__ == "__main__":
    import os
    os.chdir(Path(__file__).parent)
    main()