564 lines
17 KiB
Python
564 lines
17 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Split estratificado por GRUPO com val/test só do ORIGINAL e garantia de NÃO VAZAMENTO.
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Lê de:
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MODELO/dataset/<WxH>/group/<grupo>/{images,masks,(masks2),(labels)}
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Escreve em:
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MODELO/dataset/split/<split>/group/<grupo>/{images,masks,(masks2),(labels)}
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Definições:
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- Família = todas as variações da mesma base original:
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original_<base>.*
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augmented_<base>_aug_XX.*
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- Val/Test: somente original_<base>.
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- Train: original_<base> + todos augmented_<base>_aug_XX.
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Labels:
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- Ativados por config['dual_head_label'].
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- Copia labels .json/.txt e .npy quando existirem.
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- O pareamento é feito pelo mesmo base name da imagem.
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Uso:
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python _7_split_grouped_noleak_with_labels.py
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python _7_split_grouped_noleak_with_labels.py --train 0.7 --val 0.29 --test 0.01 --seed 42
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python _7_split_grouped_noleak_with_labels.py --strict-label
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python _7_split_grouped_noleak_with_labels.py --cap-train-families "navegavel:300,naonavegavel_navegavel:800"
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"""
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import os
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import re
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import json
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import shutil
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import random
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import argparse
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from typing import Dict, List, Optional
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# ===================== CONFIG =====================
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with open("config.json", "r", encoding="utf-8") as f:
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config = json.load(f)
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MODELO = config.get("camera")
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USE_MASKS2 = bool(config.get("dual_head_mask", config.get("dual_head", False)))
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USE_LABELS = bool(config.get("dual_head_label", False))
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RESOLUCAO = tuple(config.get("resolucao"))
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pasta_origem = os.path.join(MODELO, "dataset", f"{RESOLUCAO[0]}x{RESOLUCAO[1]}", "group")
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pasta_destino = os.path.join(MODELO, "dataset", "split")
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IMG_EXTS = (".jpg", ".jpeg", ".png")
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MSK_EXT = ".png"
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LABEL_EXTS = (".json", ".txt", ".npy")
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RE_ORIGINAL_PREFIX = re.compile(r"^original_(.+)$", re.IGNORECASE)
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RE_AUGMENTED_FAMILY = re.compile(r"^augmented_(.+?)(?:_aug_\d+)?$", re.IGNORECASE)
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RE_AUG_SUFFIX = re.compile(r"_aug_\d+$", re.IGNORECASE)
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# ===================== HELPERS =====================
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def garantir(p):
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os.makedirs(p, exist_ok=True)
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def lista_grupos(root):
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if not os.path.isdir(root):
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return []
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out = []
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for g in sorted(os.listdir(root)):
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gdir = os.path.join(root, g)
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if not os.path.isdir(gdir):
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continue
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if os.path.isdir(os.path.join(gdir, "images")) and os.path.isdir(os.path.join(gdir, "masks")):
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out.append(g)
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return out
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def listar_imagens(img_dir):
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if not os.path.isdir(img_dir):
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return []
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fs = []
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for f in os.listdir(img_dir):
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ext = os.path.splitext(f.lower())[1]
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if ext in IMG_EXTS:
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fs.append(f)
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return sorted(fs)
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def mask_from_image_name(img_name):
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base, _ = os.path.splitext(img_name)
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return base + MSK_EXT
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def mask2_from_image_name(img_name):
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base, _ = os.path.splitext(img_name)
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return base + MSK_EXT
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def label_candidates_from_image_name(img_name):
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base, _ = os.path.splitext(img_name)
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return [base + ext for ext in LABEL_EXTS]
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def classify_source_and_family(filename_no_ext):
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"""
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Retorna (source, family_key)
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source ∈ {original, augmented, unknown}
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family_key = base original sem prefixo/sufixo.
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"""
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m = RE_ORIGINAL_PREFIX.match(filename_no_ext)
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if m:
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return "original", m.group(1)
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m = RE_AUGMENTED_FAMILY.match(filename_no_ext)
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if m:
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return "augmented", m.group(1)
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if RE_AUG_SUFFIX.search(filename_no_ext):
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fam = RE_AUG_SUFFIX.sub("", filename_no_ext)
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return "augmented", fam
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return "unknown", filename_no_ext
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def has_any_label(label_dir: str, img_name: str) -> bool:
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if not label_dir or not os.path.isdir(label_dir):
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return False
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return any(os.path.exists(os.path.join(label_dir, cand)) for cand in label_candidates_from_image_name(img_name))
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def build_family_index(img_dir, msk_dir, label_dir=None, require_label=False):
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"""
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Constrói índice de famílias a partir de img_dir/msk_dir/labels.
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Retorna:
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dict family -> {
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original: str|None,
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augmented: [str],
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all: [str]
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}
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Os nomes são arquivos de imagem.
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"""
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familias = {}
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imgs = listar_imagens(img_dir)
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for img_name in imgs:
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base_no_ext, _ = os.path.splitext(img_name)
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mask_name = mask_from_image_name(img_name)
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if not os.path.exists(os.path.join(msk_dir, mask_name)):
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continue
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if require_label and not has_any_label(label_dir, img_name):
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continue
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source, fam = classify_source_and_family(base_no_ext)
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d = familias.setdefault(fam, {"original": None, "augmented": [], "all": []})
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d["all"].append(img_name)
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if source == "original":
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d["original"] = img_name
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elif source == "augmented":
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d["augmented"].append(img_name)
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else:
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if d["original"] is None:
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d["original"] = img_name
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else:
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d["augmented"].append(img_name)
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return familias
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def allocate_counts(n, p_train, p_val, p_test, min_train, min_val, min_test):
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n_train = int(round(n * p_train))
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n_val = int(round(n * p_val))
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n_test = n - n_train - n_val
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if n_test < 0:
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excesso = -n_test
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take_train = min(excesso, max(0, n_train))
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n_train -= take_train
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excesso -= take_train
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if excesso > 0:
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take_val = min(excesso, max(0, n_val))
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n_val -= take_val
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excesso -= take_val
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n_test = 0
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min_sum = min_train + min_val + min_test
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if n >= min_sum:
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n_train = max(n_train, min_train)
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n_val = max(n_val, min_val)
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n_test = max(n_test, min_test)
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total = n_train + n_val + n_test
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while total > n:
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if n_test > min_test:
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n_test -= 1
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elif n_val > min_val:
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n_val -= 1
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elif n_train > min_train:
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n_train -= 1
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else:
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break
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total = n_train + n_val + n_test
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while total < n:
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if n_train - min_train <= n_val - min_val:
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n_train += 1
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else:
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n_val += 1
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total = n_train + n_val + n_test
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else:
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n_train = min(n, max(1, min_train))
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resto = n - n_train
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n_val = max(0, min(resto, min_val))
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n_test = max(0, resto - n_val)
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diff = n - (n_train + n_val + n_test)
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if diff != 0:
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if diff > 0:
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take = min(diff, n - n_train)
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n_train += take
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diff -= take
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if diff > 0:
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n_val += diff
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else:
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diff = -diff
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take = min(diff, n_test)
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n_test -= take
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diff -= take
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if diff > 0:
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n_val -= diff
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return n_train, n_val, n_test
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def copiar_labels_para_item(nome, src_label_dir, dst_label_dir):
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if not src_label_dir or not dst_label_dir or not os.path.isdir(src_label_dir):
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return 0
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garantir(dst_label_dir)
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copied = 0
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for cand in label_candidates_from_image_name(nome):
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src = os.path.join(src_label_dir, cand)
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if not os.path.exists(src):
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continue
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shutil.copy2(src, os.path.join(dst_label_dir, cand))
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copied += 1
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return copied
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def copiar(
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nomes,
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src_img_dir,
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src_msk_dir,
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dst_img_dir,
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dst_msk_dir,
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src_msk2_dir=None,
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dst_msk2_dir=None,
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src_label_dir=None,
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dst_label_dir=None,
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strict_label=False,
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):
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garantir(dst_img_dir)
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garantir(dst_msk_dir)
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use_msk2 = bool(src_msk2_dir and dst_msk2_dir and os.path.isdir(src_msk2_dir))
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use_labels = bool(src_label_dir and dst_label_dir and os.path.isdir(src_label_dir))
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if use_msk2:
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garantir(dst_msk2_dir)
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if use_labels:
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garantir(dst_label_dir)
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moved = 0
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skipped_no_label = 0
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for nome in nomes:
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mask_name = mask_from_image_name(nome)
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src_img = os.path.join(src_img_dir, nome)
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src_msk = os.path.join(src_msk_dir, mask_name)
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if not (os.path.exists(src_img) and os.path.exists(src_msk)):
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continue
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if strict_label and use_labels and not has_any_label(src_label_dir, nome):
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skipped_no_label += 1
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continue
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shutil.copy2(src_img, os.path.join(dst_img_dir, nome))
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shutil.copy2(src_msk, os.path.join(dst_msk_dir, mask_name))
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if use_msk2:
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m2_name = mask2_from_image_name(nome)
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src_m2 = os.path.join(src_msk2_dir, m2_name)
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if os.path.exists(src_m2):
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shutil.copy2(src_m2, os.path.join(dst_msk2_dir, m2_name))
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if use_labels:
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copied = copiar_labels_para_item(nome, src_label_dir, dst_label_dir)
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if strict_label and copied == 0:
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skipped_no_label += 1
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continue
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moved += 1
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if skipped_no_label > 0:
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print(f"[WARN] {skipped_no_label} itens pulados por falta de label.")
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return moved
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# ===================== SPLIT =====================
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def split_group(group_name, p_train, p_val, p_test, seed, mins, caps_map=None, strict_label=False):
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src_img_dir = os.path.join(pasta_origem, group_name, "images")
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src_msk_dir = os.path.join(pasta_origem, group_name, "masks")
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src_msk2_dir = os.path.join(pasta_origem, group_name, "masks2")
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src_label_dir = os.path.join(pasta_origem, group_name, "labels")
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use_msk2 = USE_MASKS2 and os.path.isdir(src_msk2_dir)
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use_labels = USE_LABELS and os.path.isdir(src_label_dir)
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if USE_LABELS and not use_labels:
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msg = f"[{group_name}] dual_head_label=true, mas labels/ não existe."
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if strict_label:
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print(f"[WARN] {msg} Pulando grupo.")
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return {"train": 0, "val": 0, "test": 0, "familias": 0}
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print(f"[WARN] {msg} Split seguirá sem copiar labels.")
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familias = build_family_index(
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src_img_dir,
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src_msk_dir,
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label_dir=src_label_dir if use_labels else None,
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require_label=bool(strict_label and use_labels),
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)
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familias_originais = [fam for fam, d in familias.items() if d["original"] is not None]
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total_familias = len(familias_originais)
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if total_familias == 0:
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print(f"[{group_name}] 0 famílias com original, pulando.")
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return {"train": 0, "val": 0, "test": 0, "familias": 0}
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rng = random.Random(seed)
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rng.shuffle(familias_originais)
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n_tr, n_va, n_te = allocate_counts(
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total_familias,
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p_train,
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p_val,
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p_test,
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mins["train"],
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mins["val"],
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mins["test"],
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)
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fam_train = set(familias_originais[:n_tr])
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fam_val = set(familias_originais[n_tr : n_tr + n_va])
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fam_test = set(familias_originais[n_tr + n_va : n_tr + n_va + n_te])
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if caps_map and group_name in caps_map:
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cap = caps_map[group_name]
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if len(fam_train) > cap:
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fam_list = list(fam_train)
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rng.shuffle(fam_list)
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kept = set(fam_list[:cap])
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dropped = set(fam_list[cap:])
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fam_train = kept
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print(
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f"[{group_name}] cap-train-families={cap} → "
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f"mantidas {len(kept)} famílias, descartadas {len(dropped)} do TRAIN"
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)
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nomes_train, nomes_val, nomes_test = [], [], []
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for fam, d in familias.items():
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if fam in fam_train:
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if d["original"]:
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nomes_train.append(d["original"])
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if d["augmented"]:
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nomes_train.extend(d["augmented"])
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elif fam in fam_val:
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if d["original"]:
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nomes_val.append(d["original"])
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elif fam in fam_test:
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if d["original"]:
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nomes_test.append(d["original"])
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dest_train_img = os.path.join(pasta_destino, "train", "group", group_name, "images")
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dest_train_msk = os.path.join(pasta_destino, "train", "group", group_name, "masks")
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dest_val_img = os.path.join(pasta_destino, "val", "group", group_name, "images")
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dest_val_msk = os.path.join(pasta_destino, "val", "group", group_name, "masks")
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dest_test_img = os.path.join(pasta_destino, "test", "group", group_name, "images")
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dest_test_msk = os.path.join(pasta_destino, "test", "group", group_name, "masks")
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dest_train_msk2 = os.path.join(pasta_destino, "train", "group", group_name, "masks2") if use_msk2 else None
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dest_val_msk2 = os.path.join(pasta_destino, "val", "group", group_name, "masks2") if use_msk2 else None
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dest_test_msk2 = os.path.join(pasta_destino, "test", "group", group_name, "masks2") if use_msk2 else None
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dest_train_label = os.path.join(pasta_destino, "train", "group", group_name, "labels") if use_labels else None
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dest_val_label = os.path.join(pasta_destino, "val", "group", group_name, "labels") if use_labels else None
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dest_test_label = os.path.join(pasta_destino, "test", "group", group_name, "labels") if use_labels else None
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m_train = copiar(
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nomes_train,
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src_img_dir,
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src_msk_dir,
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dest_train_img,
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dest_train_msk,
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src_msk2_dir,
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dest_train_msk2,
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src_label_dir,
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dest_train_label,
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strict_label=strict_label,
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)
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m_val = copiar(
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nomes_val,
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src_img_dir,
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src_msk_dir,
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dest_val_img,
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dest_val_msk,
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src_msk2_dir,
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dest_val_msk2,
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src_label_dir,
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dest_val_label,
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strict_label=strict_label,
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)
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m_test = copiar(
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nomes_test,
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src_img_dir,
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src_msk_dir,
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dest_test_img,
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dest_test_msk,
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src_msk2_dir,
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dest_test_msk2,
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src_label_dir,
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dest_test_label,
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strict_label=strict_label,
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)
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print(
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f"[{group_name}] famílias={total_familias} → "
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f"train(imgs)={m_train}, val(imgs)={m_val}, test(imgs)={m_test}"
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)
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return {"train": m_train, "val": m_val, "test": m_test, "familias": total_familias}
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# ===================== MAIN =====================
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def parse_cap_map(s):
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caps = {}
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if not s:
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return caps
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for item in s.split(","):
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if not item.strip():
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continue
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k, v = item.strip().split(":")
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caps[k.strip()] = int(v)
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return caps
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def main():
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ap = argparse.ArgumentParser(description="Split estratificado por grupo sem vazamento, com suporte a labels.")
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ap.add_argument("--train", type=float, default=0.70, help="Proporção de treino.")
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|
ap.add_argument("--val", type=float, default=0.29, help="Proporção de validação.")
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|
ap.add_argument("--test", type=float, default=0.01, help="Proporção de teste.")
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|
ap.add_argument("--seed", type=int, default=42, help="Seed do embaralhamento.")
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|
|
|
ap.add_argument("--min-train", type=int, default=1, help="Mínimo de famílias por grupo em train.")
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|
ap.add_argument("--min-val", type=int, default=1, help="Mínimo de famílias por grupo em val.")
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|
ap.add_argument("--min-test", type=int, default=0, help="Mínimo de famílias por grupo em test.")
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|
|
|
ap.add_argument("--modelo", type=str, default=None, help="Sobrescreve MODELO do config.json.")
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|
ap.add_argument("--resolucao", type=str, default=None, help="Sobrescreve resolução no formato WxH.")
|
|
|
|
ap.add_argument(
|
|
"--cap-train-families",
|
|
type=str,
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|
default="",
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|
help="Mapa grupo:cap para limitar famílias no TRAIN. Ex: 'navegavel:350'",
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|
)
|
|
ap.add_argument(
|
|
"--strict-label",
|
|
action="store_true",
|
|
help="Se dual_head_label=true e faltar label, pula item/grupo.",
|
|
)
|
|
|
|
args = ap.parse_args()
|
|
|
|
modelo = args.modelo or MODELO
|
|
if args.resolucao:
|
|
try:
|
|
w, h = args.resolucao.lower().split("x")
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|
resolucao = (int(w), int(h))
|
|
except Exception:
|
|
resolucao = RESOLUCAO
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|
else:
|
|
resolucao = RESOLUCAO
|
|
|
|
caps_map = parse_cap_map(args.cap_train_families)
|
|
|
|
global pasta_origem, pasta_destino
|
|
pasta_origem = os.path.join(modelo, "dataset", f"{resolucao[0]}x{resolucao[1]}", "group")
|
|
pasta_destino = os.path.join(modelo, "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"[INFO] Origem: {pasta_origem}")
|
|
print(f"[INFO] Destino: {pasta_destino}")
|
|
print(f"[INFO] dual_head_mask/masks2: {USE_MASKS2}")
|
|
print(f"[INFO] dual_head_label/labels: {USE_LABELS}")
|
|
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: 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, strict_label=args.strict_label)
|
|
for k in total_global.keys():
|
|
total_global[k] += res.get(k, 0)
|
|
|
|
print("\nResumo global, imagens copiadas:")
|
|
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()
|