#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Unified preprocessing for 7 fundus-image classification datasets.

Per dataset it produces:
    <dataset_root>/
        train/<label>/<img>
        val/<label>/<img>
        test/<label>/<img>
        labels.csv   (columns: split,filepath,label,class_name,orig_id)
and DELETES everything classification-irrelevant (segmentation masks, disc/fovea
localization, image-quality columns, extra preprocessing versions, other modalities,
upload templates, helper code, original nested folders ...).

Split policy (decided with the user):
  * Datasets WITH a complete official labeled split -> keep it:
        AIROGS (train/val/test),  APTOS (train/val/test),  DeepDRiD (train/val/test)
  * Datasets WITHOUT one -> stratified 7:1:2 (seed=42):
        MMAC (official has no test -> pool train+val and re-split),
        ADAM, IDRiD, PAPILA(grouped by patient)
Class encodings:
  MMAC   0..4 myopic-maculopathy grade
  ADAM   0=Non-AMD 1=AMD
  AIROGS 0=NRG    1=RG          (release-crop version only)
  PAPILA 0=healthy 1=glaucoma   (diagnosis==2 "suspect" dropped; split by patient)
  IDRiD  0..4 DR grade
  APTOS  0..4 DR grade
  DeepDRiD 0..4 DR grade        (regular fundus only; per-eye label)

Run dry-run first (prints the plan, moves nothing), then with --execute.
"""
import os, sys, csv, random, shutil
from collections import defaultdict
import pandas as pd

ROOT = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Dataset"
SEED = 42
RATIOS = (0.7, 0.1, 0.2)
EXECUTE = "--execute" in sys.argv
KEEP = {"train", "val", "test", "labels.csv"}


# ----------------------------------------------------------------------------- helpers
def walk_index(root, exts=(".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp")):
    """Map basename-without-extension -> absolute path for every image under root."""
    idx = {}
    for dp, _, files in os.walk(root):
        for f in files:
            if f.lower().endswith(exts):
                idx[os.path.splitext(f)[0]] = os.path.join(dp, f)
    return idx


def stratified_split(pairs, ratios=RATIOS, seed=SEED):
    """pairs: list[(key,label)] -> dict key->split, stratified per label."""
    by = defaultdict(list)
    for k, l in pairs:
        by[l].append(k)
    out, rnd = {}, random.Random(seed)
    for l in sorted(by, key=str):
        ks = sorted(by[l])
        rnd.shuffle(ks)
        n = len(ks)
        ntr = int(round(n * ratios[0]))
        nva = int(round(n * ratios[1]))
        # guarantee non-empty test when a class is large enough
        if n >= 3 and ntr + nva >= n:
            nva = max(0, n - ntr - 1)
        for i, k in enumerate(ks):
            out[k] = "train" if i < ntr else ("val" if i < ntr + nva else "test")
    return out


def place(out_dir, records, do_clean_root=True):
    """records: list of dict(src, split, label, class_name, orig_id).
    Moves files into out_dir/<split>/<label>/, writes labels.csv, then whitelist-cleans."""
    # collision check
    seen, dup = set(), 0
    for r in records:
        key = (r["split"], str(r["label"]), os.path.basename(r["src"]))
        if key in seen:
            dup += 1
        seen.add(key)
    counts = defaultdict(int)
    for r in records:
        counts[(r["split"], r["label"])] += 1
    # report
    name = os.path.basename(out_dir.rstrip("/"))
    total = len(records)
    print(f"  [{name}] total={total}  duplicates={dup}")
    for sp in ("train", "val", "test"):
        per = {l: c for (s, l), c in sorted(counts.items()) if s == sp}
        if per:
            print(f"     {sp:5s} n={sum(per.values()):5d}  by_class={per}")
    if dup:
        print(f"  !! {dup} basename collisions in same split/label — aborting this dataset")
        return
    if not EXECUTE:
        return
    rows = []
    for r in records:
        dd = os.path.join(out_dir, r["split"], str(r["label"]))
        os.makedirs(dd, exist_ok=True)
        base = os.path.basename(r["src"])
        dst = os.path.join(dd, base)
        shutil.move(r["src"], dst)
        rows.append([r["split"], os.path.join(r["split"], str(r["label"]), base),
                     r["label"], r["class_name"], r["orig_id"]])
    with open(os.path.join(out_dir, "labels.csv"), "w", newline="") as f:
        w = csv.writer(f)
        w.writerow(["split", "filepath", "label", "class_name", "orig_id"])
        w.writerows(sorted(rows))
    if do_clean_root:
        for item in os.listdir(out_dir):
            if item in KEEP:
                continue
            p = os.path.join(out_dir, item)
            shutil.rmtree(p) if os.path.isdir(p) else os.remove(p)
    print(f"     -> moved & cleaned: {out_dir}")


# ----------------------------------------------------------------------------- datasets
def do_mmac():
    out = os.path.join(ROOT, "Myopia", "Classification_of_Myopic_Maculopathy")
    base = os.path.join(out, "1. Classification of Myopic Maculopathy")
    gt = os.path.join(base, "2. Groundtruths")
    label_map = {}
    for fn in ["1. MMAC2023_Myopic_Maculopathy_Classification_Training_Labels.csv",
               "2. MMAC2023_Myopic_Maculopathy_Classification_Validation_Labels.csv"]:
        df = pd.read_csv(os.path.join(gt, fn))
        for _, r in df.iterrows():
            label_map[str(r["image"])] = int(r["myopic_maculopathy_grade"])
    idx = walk_index(os.path.join(base, "1. Images"))
    recs, pairs = [], []
    for img, g in label_map.items():
        key = os.path.splitext(img)[0]
        if key not in idx:
            continue
        pairs.append((key, g))
    sp = stratified_split(pairs)
    for key, g in pairs:
        recs.append(dict(src=idx[key], split=sp[key], label=g,
                         class_name=f"grade_{g}", orig_id=key))
    return out, recs


def do_adam():
    out = os.path.join(ROOT, "AMD", "adamdataset")
    base = os.path.join(out, "ADAM", "Training400")
    pairs, srcs = [], {}
    for sub, lab, cname in [("Non-AMD", 0, "Non-AMD"), ("AMD", 1, "AMD")]:
        for dp, _, files in os.walk(os.path.join(base, sub)):
            for f in files:
                if f.lower().endswith((".jpg", ".jpeg", ".png")):
                    k = os.path.splitext(f)[0]
                    pairs.append((k, lab))
                    srcs[k] = (os.path.join(dp, f), lab, cname)
    sp = stratified_split(pairs)
    recs = [dict(src=srcs[k][0], split=sp[k], label=srcs[k][1],
                 class_name=srcs[k][2], orig_id=k) for k, _ in pairs]
    return out, recs


def do_airogs():
    out = os.path.join(ROOT, "Glaucoma", "eyepacs-airogs-light")
    base = os.path.join(out, "release-crop", "release-crop")
    split_map = {"train": "train", "validation": "val", "test": "test"}
    cls = {"NRG": (0, "NRG"), "RG": (1, "RG")}
    recs = []
    for osp, nsp in split_map.items():
        for c, (lab, cname) in cls.items():
            d = os.path.join(base, osp, c)
            if not os.path.isdir(d):
                continue
            for f in os.listdir(d):
                if f.lower().endswith((".jpg", ".jpeg", ".png")):
                    recs.append(dict(src=os.path.join(d, f), split=nsp, label=lab,
                                     class_name=cname, orig_id=os.path.splitext(f)[0]))
    return out, recs


def do_papila():
    out = os.path.join(ROOT, "Glaucoma", "papila-retinal-fundus-images")
    base = os.path.join(out, "PapilaDB-PAPILA-17f8fa7746adb20275b5b6a0d99dc9dfe3007e9f")
    idx = walk_index(os.path.join(base, "FundusImages"))
    # build per-image label, grouped by patient
    eye_lab = {}          # image_key -> (label, patient)
    for eye, suffix in [("od", "OD"), ("os", "OS")]:
        df = pd.read_excel(os.path.join(base, "ClinicalData", f"patient_data_{eye}.xlsx"),
                           header=None)
        for _, r in df.iterrows():
            pid = str(r[0]).strip()
            if not pid.startswith("#"):
                continue
            num = int(pid[1:])
            diag = r[3]
            try:
                diag = int(float(diag))
            except (ValueError, TypeError):
                continue
            if diag == 2:          # suspect -> drop
                continue
            lab = 0 if diag == 0 else 1
            key = f"RET{num:03d}{suffix}"
            if key in idx:
                eye_lab[key] = (lab, f"RET{num:03d}")
    # patient-level label = glaucoma if any kept eye glaucoma
    pat_lab = defaultdict(int)
    for k, (lab, pat) in eye_lab.items():
        pat_lab[pat] = max(pat_lab[pat], lab)
    sp = stratified_split(list(pat_lab.items()))
    recs = []
    for k, (lab, pat) in eye_lab.items():
        recs.append(dict(src=idx[k], split=sp[pat], label=lab,
                         class_name=("healthy" if lab == 0 else "glaucoma"), orig_id=k))
    return out, recs


def do_idrid():
    out = os.path.join(ROOT, "DR", "idrid-dataset")
    df = pd.read_csv(os.path.join(out, "idrid_labels.csv"))
    idx = walk_index(os.path.join(out, "Imagenes"))
    pairs, srcs = [], {}
    for _, r in df.iterrows():
        k = str(r["id_code"]).strip()
        if k not in idx:
            continue
        lab = int(r["diagnosis"])
        pairs.append((k, lab))
        srcs[k] = lab
    sp = stratified_split(pairs)
    recs = [dict(src=idx[k], split=sp[k], label=srcs[k],
                 class_name=f"grade_{srcs[k]}", orig_id=k) for k, _ in pairs]
    return out, recs


def do_aptos():
    out = os.path.join(ROOT, "DR", "aptos2019")
    recs = []
    for csvname, sp, imgdir in [("train_1.csv", "train", "train_images"),
                                ("valid.csv", "val", "val_images"),
                                ("test.csv", "test", "test_images")]:
        df = pd.read_csv(os.path.join(out, csvname))
        idx = walk_index(os.path.join(out, imgdir))
        for _, r in df.iterrows():
            k = str(r["id_code"]).strip()
            if k not in idx:
                continue
            lab = int(r["diagnosis"])
            recs.append(dict(src=idx[k], split=sp, label=lab,
                             class_name=f"grade_{lab}", orig_id=k))
    return out, recs


def do_deepdrid():
    out = os.path.join(ROOT, "DR", "deepdrid")
    base = os.path.join(out, "DeepDRiD-master", "regular_fundus_images")

    def eye_level(row, image_id):
        col = "left_eye_DR_Level" if "_l" in image_id else "right_eye_DR_Level"
        v = row.get(col)
        if pd.isna(v):
            v = row.get("patient_DR_Level")
        return None if pd.isna(v) else int(v)

    recs = []
    # train + val from per-image csv
    for sub, sp in [("regular-fundus-training", "train"), ("regular-fundus-validation", "val")]:
        d = os.path.join(base, sub)
        df = pd.read_csv(os.path.join(d, f"{sub}.csv"))
        idx = walk_index(os.path.join(d, "Images"))
        for _, r in df.iterrows():
            iid = str(r["image_id"]).strip()
            if iid not in idx:
                continue
            lab = eye_level(r, iid)
            if lab is None or lab < 0 or lab > 4:
                continue
            recs.append(dict(src=idx[iid], split=sp, label=lab,
                             class_name=f"grade_{lab}", orig_id=iid))
    # test from Online-Challenge labels.xlsx
    ev = os.path.join(base, "Online-Challenge1&2-Evaluation")
    dfx = pd.read_excel(os.path.join(ev, "Challenge1_labels.xlsx"))
    idx = walk_index(os.path.join(ev, "Images"))
    col = "DR_Levels" if "DR_Levels" in dfx.columns else dfx.columns[-1]
    for _, r in dfx.iterrows():
        iid = str(r["image_id"]).strip()
        if iid not in idx or pd.isna(r[col]):
            continue
        lab = int(r[col])
        if 0 <= lab <= 4:
            recs.append(dict(src=idx[iid], split="test", label=lab,
                             class_name=f"grade_{lab}", orig_id=iid))
    return out, recs


DATASETS = [("MMAC/Myopia", do_mmac), ("ADAM/AMD", do_adam),
            ("AIROGS/Glaucoma", do_airogs), ("PAPILA/Glaucoma", do_papila),
            ("IDRiD/DR", do_idrid), ("APTOS/DR", do_aptos), ("DeepDRiD/DR", do_deepdrid)]


def main():
    only = [a for a in sys.argv[1:] if not a.startswith("--")]
    print(f"=== prepare_datasets ({'EXECUTE' if EXECUTE else 'DRY-RUN'}) seed={SEED} ===")
    for name, fn in DATASETS:
        if only and name.split("/")[0].lower() not in [o.lower() for o in only]:
            continue
        print(f"\n### {name}")
        out, recs = fn()
        place(out, recs)
    print("\n=== done ===")


if __name__ == "__main__":
    main()