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"cells": [
{
"cell_type": "markdown",
"id": "26283604",
"metadata": {},
"source": [
"# Export tập test MIMIC-CXR_resized → thư mục theo 14 nhãn bệnh lý\n",
"\n",
"Chỉ cần điền **CONFIG** bên dưới rồi **Run All**.\n",
"\n",
"- Tải `manifest_test.csv` + tar shards từ HF (`hieu3636/cxr-vlm-data/MIMIC-CXR_resized/`).\n",
"- Rút ảnh test, đổ vào `OUT/<Tên_bệnh>/`. Ảnh multi-label → copy vào nhiều thư mục.\n",
"- **Kèm report**: mỗi ảnh `<dicom>.jpg` có file `<dicom>.txt` (nội dung report của study đó) đặt ngay cạnh.\n",
"- Repo **private** → cần token HF (điền vào `HF_TOKEN`, hoặc đã `huggingface-cli login` thì để trống)."
]
},
{
"cell_type": "markdown",
"id": "c41dd18e",
"metadata": {},
"source": [
"## 1. CONFIG — chỉnh ở đây"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8fd81f44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CONFIG ok | split = test | out = D:\\USTH\\KLTN\\test_by_pathology | with_report = True\n"
]
}
],
"source": [
"# ==== CHỈNH CÁC BIẾN NÀY ====\n",
"HF_TOKEN = \"\" # token HF (Read là đủ). Để \"\" nếu đã huggingface-cli login.\n",
"REPO_ID = \"hieu3636/cxr-vlm-data\"\n",
"SPLIT = \"test\" # \"train\" | \"val\" | \"test\"\n",
"\n",
"OUT = r\"D:\\USTH\\KLTN\\test_by_pathology\" # thư mục output\n",
"WORK = r\"D:\\USTH\\KLTN\\_hf_resized_dl\" # nơi cache tải từ HF\n",
"\n",
"# Nếu ĐÃ có shards giải nén/tar sẵn ở máy thì trỏ vào đây để KHỎI tải lại,\n",
"# ví dụ r\"D:\\USTH\\KLTN\\_hf_resized_dl\\MIMIC-CXR_resized\". Để None = tải từ HF.\n",
"EXTRACTED_ROOT = None\n",
"\n",
"WITH_REPORT = True # True = ghi kèm <dicom>.txt (report) cạnh mỗi ảnh\n",
"UNCERTAIN = \"separate\" # \"separate\" (_uncertain/<P>) | \"merge\" | \"skip\"\n",
"LINK = \"copy\" # \"copy\" | \"hardlink\" | \"symlink\" (hardlink đỡ tốn ổ)\n",
"# ============================\n",
"print(\"CONFIG ok | split =\", SPLIT, \"| out =\", OUT, \"| with_report =\", WITH_REPORT)"
]
},
{
"cell_type": "markdown",
"id": "8085defe",
"metadata": {},
"source": [
"## 2. Cài thư viện (chạy 1 lần)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "11afbe51",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"huggingface_hub đã có: 1.11.0\n"
]
}
],
"source": [
"# Chỉ cần huggingface_hub; tarfile/csv là built-in.\n",
"try:\n",
" import huggingface_hub # noqa\n",
" print(\"huggingface_hub đã có:\", huggingface_hub.__version__)\n",
"except ImportError:\n",
" import sys, subprocess\n",
" subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"huggingface_hub\"])\n",
" print(\"đã cài huggingface_hub\")"
]
},
{
"cell_type": "markdown",
"id": "ac53032e",
"metadata": {},
"source": [
"## 3. Logic (không cần sửa)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ac60a59",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"logic loaded\n"
]
}
],
"source": [
"import os, csv, tarfile\n",
"from collections import defaultdict\n",
"from pathlib import Path\n",
"\n",
"# 14 nhãn CheXpert — đúng thứ tự dùng trong project.\n",
"PATHOLOGIES = [\n",
" \"No Finding\", \"Enlarged Cardiomediastinum\", \"Cardiomegaly\", \"Lung Opacity\",\n",
" \"Lung Lesion\", \"Edema\", \"Consolidation\", \"Pneumonia\", \"Atelectasis\",\n",
" \"Pneumothorax\", \"Pleural Effusion\", \"Pleural Other\", \"Fracture\",\n",
" \"Support Devices\",\n",
"]\n",
"_POS = {\"1\", \"1.0\"}\n",
"_UNC = {\"-1\", \"-1.0\"}\n",
"_MANIFEST = {\"train\": \"manifest_train.csv\", \"val\": \"manifest_val.csv\",\n",
" \"validate\": \"manifest_val.csv\", \"test\": \"manifest_test.csv\"}\n",
"\n",
"def _safe(n): return n.replace(\" \", \"_\")\n",
"def _norm(p): return p.replace(\"\\\\\", \"/\").lstrip(\"/\")\n",
"\n",
"def download_from_hf(repo_id, split, work):\n",
" from huggingface_hub import snapshot_download\n",
" mname = _MANIFEST[split]\n",
" print(f\"[download] {repo_id}:MIMIC-CXR_resized (manifest + shards) -> {work}\")\n",
" snapshot_download(\n",
" repo_id=repo_id, repo_type=\"dataset\", local_dir=str(work),\n",
" allow_patterns=[f\"MIMIC-CXR_resized/{mname}\", \"MIMIC-CXR_resized/shards/*.tar\"],\n",
" )\n",
" mr = Path(work) / \"MIMIC-CXR_resized\"\n",
" manifest = mr / mname\n",
" shards = sorted((mr / \"shards\").glob(\"*.tar\"))\n",
" assert manifest.is_file(), f\"không thấy manifest: {manifest}\"\n",
" assert shards, f\"không thấy tar shard dưới {mr/'shards'}\"\n",
" print(f\"[download] manifest={manifest.name} shards={len(shards)}\")\n",
" return manifest, shards\n",
"\n",
"def load_label_map(manifest):\n",
" label_map = {}\n",
" with open(manifest, encoding=\"utf-8\", newline=\"\") as f:\n",
" reader = csv.DictReader(f); cols = reader.fieldnames or []\n",
" chex_cols = {p: f\"chex_{p}\" for p in PATHOLOGIES if f\"chex_{p}\" in cols}\n",
" miss = [p for p in PATHOLOGIES if f\"chex_{p}\" not in cols]\n",
" assert \"image_relpath\" in cols, f\"manifest thiếu image_relpath. Có: {cols}\"\n",
" has_report = \"report_relpath\" in cols\n",
" for row in reader:\n",
" rel = _norm(str(row[\"image_relpath\"]).strip())\n",
" pos, unc = set(), set()\n",
" for p, c in chex_cols.items():\n",
" v = str(row.get(c, \"\")).strip()\n",
" if v in _POS: pos.add(p)\n",
" elif v in _UNC: unc.add(p)\n",
" rep = _norm(str(row[\"report_relpath\"]).strip()) if has_report else None\n",
" label_map[rel] = {\"pos\": pos, \"unc\": unc, \"report\": rep or None}\n",
" if miss: print(f\"[labels] CẢNH BÁO thiếu cột: {miss}\")\n",
" if not has_report: print(\"[labels] CẢNH BÁO: manifest không có report_relpath → bỏ qua report\")\n",
" print(f\"[labels] {len(label_map):,} ảnh trong manifest\")\n",
" return label_map\n",
"\n",
"def gather_reports(shards, report_set):\n",
" \"\"\"Gom text các report cần dùng (1 pass qua tar). Report nhỏ → giữ RAM.\"\"\"\n",
" reports = {}\n",
" if not report_set: return reports\n",
" for shard in shards:\n",
" with tarfile.open(shard, \"r\") as tf:\n",
" for m in tf:\n",
" if not m.isfile(): continue\n",
" name = _norm(m.name)\n",
" if name in report_set and name not in reports:\n",
" reports[name] = tf.extractfile(m).read()\n",
" print(f\"[reports] rút được {len(reports):,} / {len(report_set):,} report\")\n",
" return reports\n",
"\n",
"def _place(data, dicom, paths, base, counts, link, report=None):\n",
" txt = Path(dicom).stem + \".txt\"\n",
" first = None\n",
" for lab in paths:\n",
" d = base / _safe(lab); d.mkdir(parents=True, exist_ok=True)\n",
" dst = d / dicom; counts[lab] += 1\n",
" if report is not None: (d / txt).write_bytes(report)\n",
" if dst.exists(): continue\n",
" if link == \"copy\" or first is None:\n",
" dst.write_bytes(data); first = dst\n",
" else:\n",
" try:\n",
" os.link(first, dst) if link == \"hardlink\" else os.symlink(os.path.abspath(first), dst)\n",
" except OSError:\n",
" dst.write_bytes(data)\n",
"\n",
"def export(shards, label_map, out, uncertain, link, with_report=True):\n",
" out = Path(out); out.mkdir(parents=True, exist_ok=True)\n",
" unc_base = out / \"_uncertain\"\n",
" test_set = set(label_map)\n",
" reports = {}\n",
" if with_report:\n",
" rset = {label_map[k][\"report\"] for k in test_set if label_map[k].get(\"report\")}\n",
" reports = gather_reports(shards, rset)\n",
" cpos, cunc = defaultdict(int), defaultdict(int)\n",
" n_imgs = 0; n_no_rep = 0; seen = set()\n",
" for si, shard in enumerate(shards, 1):\n",
" print(f\"[extract] [{si}/{len(shards)}] {shard.name}\")\n",
" with tarfile.open(shard, \"r\") as tf:\n",
" for m in tf:\n",
" if not m.isfile(): continue\n",
" name = _norm(m.name)\n",
" if name not in test_set: continue\n",
" seen.add(name)\n",
" ent = label_map[name]; pos, unc = ent[\"pos\"], ent[\"unc\"]\n",
" if not pos and not (uncertain != \"skip\" and unc): continue\n",
" data = tf.extractfile(m).read(); dicom = Path(name).name; n_imgs += 1\n",
" rep = reports.get(ent.get(\"report\")) if with_report else None\n",
" if with_report and rep is None: n_no_rep += 1\n",
" if pos: _place(data, dicom, pos, out, cpos, link, rep)\n",
" if unc and uncertain != \"skip\":\n",
" _place(data, dicom, unc, (out if uncertain == \"merge\" else unc_base), cunc, link, rep)\n",
" if with_report and n_no_rep:\n",
" print(f\"[reports] CẢNH BÁO: {n_no_rep:,} ảnh không thấy report → chỉ có .jpg\")\n",
" missing = test_set - seen\n",
" print(f\"\\n[done] ảnh rút được: {n_imgs:,} / {len(test_set):,} trong manifest\")\n",
" if missing:\n",
" print(f\"[done] CẢNH BÁO: {len(missing):,} ảnh manifest không có trong shard (vd: {list(missing)[:2]})\")\n",
" with open(out / \"_summary.csv\", \"w\", encoding=\"utf-8\", newline=\"\") as f:\n",
" w = csv.writer(f); w.writerow([\"pathology\", \"positive_images\", \"uncertain_images\"])\n",
" for p in PATHOLOGIES: w.writerow([p, cpos.get(p, 0), cunc.get(p, 0)])\n",
" print(\"\\n Nhãn positive uncertain\")\n",
" for p in PATHOLOGIES:\n",
" print(f\" {p:28s} {cpos.get(p,0):8d} {cunc.get(p,0):8d}\")\n",
" return cpos, cunc\n",
"\n",
"print(\"logic loaded\")"
]
},
{
"cell_type": "markdown",
"id": "f23b1e30",
"metadata": {},
"source": [
"## 4. Run"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "df4a1338",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[download] hieu3636/cxr-vlm-data:MIMIC-CXR_resized (manifest + shards) -> D:\\USTH\\KLTN\\_hf_resized_dl\n"
]
},
{
"data": {
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"model_id": "4dfc7af1d4194177a7d67f83df163309",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1626e7949cf45a3b33fd2d1c7416aa9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching ... files: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[download] manifest=manifest_test.csv shards=2\n",
"[labels] 5,000 ảnh trong manifest\n",
"[reports] rút được 5,000 / 5,000 report\n",
"[extract] [1/2] cxr-0000.tar\n",
"[extract] [2/2] cxr-0001.tar\n",
"\n",
"[done] ảnh rút được: 4,883 / 5,000 trong manifest\n",
"\n",
" Nhãn positive uncertain\n",
" No Finding 2375 0\n",
" Enlarged Cardiomediastinum 29 13\n",
" Cardiomegaly 387 58\n",
" Lung Opacity 802 65\n",
" Lung Lesion 100 22\n",
" Edema 506 265\n",
" Consolidation 116 73\n",
" Pneumonia 313 465\n",
" Atelectasis 540 198\n",
" Pneumothorax 92 12\n",
" Pleural Effusion 670 93\n",
" Pleural Other 27 14\n",
" Fracture 71 16\n",
" Support Devices 449 4\n",
"\n",
"Xong! Output: D:\\USTH\\KLTN\\test_by_pathology\n"
]
}
],
"source": [
"# token\n",
"if HF_TOKEN.strip():\n",
" os.environ[\"HF_TOKEN\"] = HF_TOKEN.strip()\n",
" os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = HF_TOKEN.strip()\n",
"\n",
"# 1) manifest + shards\n",
"if EXTRACTED_ROOT:\n",
" mr = Path(EXTRACTED_ROOT)\n",
" shards = sorted((mr / \"shards\").glob(\"*.tar\")) or sorted(mr.glob(\"*.tar\"))\n",
" manifest = mr / _MANIFEST[SPLIT]\n",
" assert shards, f\"không thấy *.tar dưới {mr}\"\n",
" assert manifest.is_file(), f\"không thấy manifest: {manifest}\"\n",
" print(f\"[local] manifest={manifest} shards={len(shards)}\")\n",
"else:\n",
" manifest, shards = download_from_hf(REPO_ID, SPLIT, WORK)\n",
"\n",
"# 2) đọc nhãn 3) rút ảnh (+ report)\n",
"label_map = load_label_map(manifest)\n",
"cpos, cunc = export(shards, label_map, OUT, UNCERTAIN, LINK, with_report=WITH_REPORT)\n",
"print(f\"\\nXong! Output: {Path(OUT).resolve()}\")"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|