agrobot_base/Python/OAK/datasets/multiespec_module/_7_split.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import os
import re
import json
import shutil
import random
import argparse


with open("config.json", "r", encoding="utf-8") as f:
    config = json.load(f)

RESOLUCAO = tuple(config.get("resolucao"))
pasta_origem = os.path.join("dataset", f"{RESOLUCAO[0]}x{RESOLUCAO[1]}", "group")
pasta_destino = os.path.join("dataset", "split")

TENSOR_EXT = ".npy"
MASK_NPY_SUFFIX = ".npy"

RE_ORIGINAL_PREFIX = re.compile(r"^original_(.+)$", re.IGNORECASE)
RE_AUGMENTED_FAMILY = re.compile(r"^augmented_(.+?)(?:_aug[a-zA-Z0-9]*_\d+)?$", re.IGNORECASE)
RE_AUG_SUFFIX = re.compile(r"_aug[a-zA-Z0-9]*_\d+$", re.IGNORECASE)


def garantir(p):
    os.makedirs(p, exist_ok=True)


def lista_grupos(root):
    if not os.path.isdir(root):
        return []
    out = []
    for g in sorted(os.listdir(root)):
        gdir = os.path.join(root, g)
        if not os.path.isdir(gdir):
            continue
        if os.path.isdir(os.path.join(gdir, "tensors")) and os.path.isdir(os.path.join(gdir, "masks")):
            out.append(g)
    return out


def listar_tensors(tensor_dir):
    if not os.path.isdir(tensor_dir):
        return []
    fs = []
    for f in os.listdir(tensor_dir):
        if f.lower().endswith(TENSOR_EXT):
            fs.append(f)
    return sorted(fs)


def mask_npy_from_tensor_name(tensor_name):
    base, _ = os.path.splitext(tensor_name)
    return base + MASK_NPY_SUFFIX


def classify_source_and_family(filename_no_ext):
    m = RE_ORIGINAL_PREFIX.match(filename_no_ext)
    if m:
        return "original", m.group(1)

    m = RE_AUGMENTED_FAMILY.match(filename_no_ext)
    if m:
        return "augmented", m.group(1)

    if RE_AUG_SUFFIX.search(filename_no_ext):
        fam = RE_AUG_SUFFIX.sub("", filename_no_ext)
        return "augmented", fam

    return "unknown", filename_no_ext


def build_family_index(tensor_dir, mask_dir):
    """
    family -> {
      "original": tensor_name or None,
      "augmented": [tensor_name, ...],
      "all": [...]
    }
    Só indexa se houver pelo menos mask .npy correspondente.
    """
    familias = {}
    tensors = listar_tensors(tensor_dir)

    for tensor_name in tensors:
        base_no_ext, _ = os.path.splitext(tensor_name)
        mask_npy_name = mask_npy_from_tensor_name(tensor_name)

        if not os.path.exists(os.path.join(mask_dir, mask_npy_name)):
            continue

        source, fam = classify_source_and_family(base_no_ext)
        d = familias.setdefault(fam, {"original": None, "augmented": [], "all": []})
        d["all"].append(tensor_name)

        if source == "original":
            d["original"] = tensor_name
        elif source == "augmented":
            d["augmented"].append(tensor_name)
        else:
            if d["original"] is None:
                d["original"] = tensor_name
            else:
                d["augmented"].append(tensor_name)

    return familias


def allocate_counts(n, p_train, p_val, p_test, min_train, min_val, min_test):
    n_train = int(round(n * p_train))
    n_val = int(round(n * p_val))
    n_test = n - n_train - n_val

    if n_test < 0:
        excesso = -n_test
        take_train = min(excesso, max(0, n_train))
        n_train -= take_train
        excesso -= take_train
        if excesso > 0:
            take_val = min(excesso, max(0, n_val))
            n_val -= take_val
            excesso -= take_val
        n_test = 0

    min_sum = min_train + min_val + min_test
    if n >= min_sum:
        n_train = max(n_train, min_train)
        n_val = max(n_val, min_val)
        n_test = max(n_test, min_test)

        total = n_train + n_val + n_test
        while total > n:
            if n_test > min_test:
                n_test -= 1
            elif n_val > min_val:
                n_val -= 1
            elif n_train > min_train:
                n_train -= 1
            else:
                break
            total = n_train + n_val + n_test

        while total < n:
            if n_train - min_train <= n_val - min_val:
                n_train += 1
            else:
                n_val += 1
            total = n_train + n_val + n_test
    else:
        n_train = min(n, max(1, min_train))
        resto = n - n_train
        n_val = max(0, min(resto, min_val))
        n_test = max(0, resto - n_val)

    diff = n - (n_train + n_val + n_test)
    if diff != 0:
        if diff > 0:
            take = min(diff, n - n_train)
            n_train += take
            diff -= take
            if diff > 0:
                n_val += diff
        else:
            diff = -diff
            take = min(diff, n_test)
            n_test -= take
            diff -= take
            if diff > 0:
                n_val -= diff

    return n_train, n_val, n_test


def copiar(
    nomes,
    src_tensor_dir,
    src_mask_dir,
    dst_tensor_dir,
    dst_mask_dir,
):
    """
    Copia:
    - tensor .npy
    - mask .npy obrigatória
    - mask .png opcional (debug)
    """
    garantir(dst_tensor_dir)
    garantir(dst_mask_dir)

    moved = 0
    for nome in nomes:
        tensor_src = os.path.join(src_tensor_dir, nome)
        mask_npy_name = mask_npy_from_tensor_name(nome)

        mask_npy_src = os.path.join(src_mask_dir, mask_npy_name)

        if not (os.path.exists(tensor_src) and os.path.exists(mask_npy_src)):
            continue

        shutil.copy2(tensor_src, os.path.join(dst_tensor_dir, nome))
        shutil.copy2(mask_npy_src, os.path.join(dst_mask_dir, mask_npy_name))

        moved += 1

    return moved


def split_group(group_name, p_train, p_val, p_test, seed, mins, caps_map=None):
    src_tensor_dir = os.path.join(pasta_origem, group_name, "tensors")
    src_mask_dir = os.path.join(pasta_origem, group_name, "masks")

    familias = build_family_index(src_tensor_dir, src_mask_dir)

    familias_originais = [fam for fam, d in familias.items() if d["original"] is not None]
    total_familias = len(familias_originais)

    if total_familias == 0:
        print(f"[{group_name}] 0 famílias com original, pulando.")
        return {"train": 0, "val": 0, "test": 0, "familias": 0}

    rng = random.Random(seed)
    rng.shuffle(familias_originais)

    n_tr, n_va, n_te = allocate_counts(
        total_familias, p_train, p_val, p_test,
        mins["train"], mins["val"], mins["test"]
    )

    fam_train = set(familias_originais[:n_tr])
    fam_val = set(familias_originais[n_tr:n_tr+n_va])
    fam_test = set(familias_originais[n_tr+n_va:n_tr+n_va+n_te])

    if caps_map and group_name in caps_map:
        cap = caps_map[group_name]
        if len(fam_train) > cap:
            fam_list = list(fam_train)
            rng.shuffle(fam_list)
            kept = set(fam_list[:cap])
            dropped = set(fam_list[cap:])
            fam_train = kept
            print(f"[{group_name}] cap-train-families={cap} → mantidas {len(kept)} famílias, descartadas {len(dropped)} do TRAIN")

    nomes_train, nomes_val, nomes_test = [], [], []

    for fam, d in familias.items():
        if fam in fam_train:
            if d["original"]:
                nomes_train.append(d["original"])
            if d["augmented"]:
                nomes_train.extend(d["augmented"])
        elif fam in fam_val:
            if d["original"]:
                nomes_val.append(d["original"])
        elif fam in fam_test:
            if d["original"]:
                nomes_test.append(d["original"])

    dst_train_tensor = os.path.join(pasta_destino, "train", "group", group_name, "tensors")
    dst_train_mask = os.path.join(pasta_destino, "train", "group", group_name, "masks")

    dst_val_tensor = os.path.join(pasta_destino, "val", "group", group_name, "tensors")
    dst_val_mask = os.path.join(pasta_destino, "val", "group", group_name, "masks")

    dst_test_tensor = os.path.join(pasta_destino, "test", "group", group_name, "tensors")
    dst_test_mask = os.path.join(pasta_destino, "test", "group", group_name, "masks")

    m_train = copiar(nomes_train, src_tensor_dir, src_mask_dir, dst_train_tensor, dst_train_mask)
    m_val = copiar(nomes_val, src_tensor_dir, src_mask_dir, dst_val_tensor, dst_val_mask)
    m_test = copiar(nomes_test, src_tensor_dir, src_mask_dir, dst_test_tensor, dst_test_mask)

    print(f"[{group_name}] famílias={total_familias} → train(tensors)={m_train}, val(tensors)={m_val}, test(tensors)={m_test}")
    return {"train": m_train, "val": m_val, "test": m_test, "familias": total_familias}


def main():
    ap = argparse.ArgumentParser(description="Split estratificado por grupo SEM vazamento (tensors/masks).")
    ap.add_argument("--train", type=float, default=0.70)
    ap.add_argument("--val", type=float, default=0.29)
    ap.add_argument("--test", type=float, default=0.01)
    ap.add_argument("--seed", type=int, default=42)

    ap.add_argument("--min-train", type=int, default=1)
    ap.add_argument("--min-val", type=int, default=1)
    ap.add_argument("--min-test", type=int, default=0)

    ap.add_argument("--resolucao", type=str, default=None,
                    help="Sobrescreve resolução no formato WxH (ex: 1024x800).")

    ap.add_argument("--cap-train-families", type=str, default="",
                    help="Mapa 'grupo:cap,...' para limitar famílias no TRAIN. Ex.: 'chao:350'")

    args = ap.parse_args()

    if args.resolucao:
        try:
            w, h = args.resolucao.lower().split("x")
            resolucao = (int(w), int(h))
        except Exception:
            resolucao = RESOLUCAO
    else:
        resolucao = RESOLUCAO

    def parse_cap_map(s):
        caps = {}
        if not s:
            return caps
        for item in s.split(","):
            k, v = item.strip().split(":")
            caps[k.strip()] = int(v)
        return caps

    caps_map = parse_cap_map(args.cap_train_families)

    global pasta_origem, pasta_destino
    pasta_origem = os.path.join("dataset", f"{resolucao[0]}x{resolucao[1]}", "group")
    pasta_destino = os.path.join("dataset", "split")

    soma = args.train + args.val + args.test
    if soma <= 0:
        raise ValueError("Soma de proporções deve ser > 0.")

    p_train = args.train / soma
    p_val = args.val / soma
    p_test = args.test / soma

    mins = {
        "train": max(0, args.min_train),
        "val": max(0, args.min_val),
        "test": max(0, args.min_test),
    }

    garantir(pasta_destino)

    grupos = lista_grupos(pasta_origem)
    if not grupos:
        print(f"[WARN] Nenhum grupo encontrado em: {pasta_origem}")
        return

    random.seed(args.seed)

    total_global = {"train": 0, "val": 0, "test": 0, "familias": 0}
    print(f"Grupos: {', '.join(grupos)}")
    print(f"Proporções normalizadas: train={p_train:.3f}, val={p_val:.3f}, test={p_test:.3f}")
    print(f"Mínimos por grupo (famílias): train={mins['train']} val={mins['val']} test={mins['test']}")

    for g in grupos:
        res = split_group(g, p_train, p_val, p_test, args.seed, mins, caps_map=caps_map)
        for k in total_global.keys():
            total_global[k] += res.get(k, 0)

    print("\nResumo global (tensors copiados):")
    print(f"  train: {total_global['train']}")
    print(f"  val:   {total_global['val']}")
    print(f"  test:  {total_global['test']}")
    print(f"  famílias (total): {total_global['familias']}")
    print("\n✅ Split sem vazamento concluído!")


if __name__ == "__main__":
    main()