| """HEMATO v1.0 builder. Reads raw sources (raw/), writes dataset/<config>/<split>/<class>/<file> and manifest.csv. |
| Configs: cls (bone marrow + peripheral blood cell types), all (C-NMC ALL vs normal), malaria (NIH), det (BCCD, YOLO). |
| Images are copied byte-for-byte when already JPEG/PNG; BMP (C-NMC) is converted losslessly to PNG.""" |
| import csv, glob, io, os, random, re, shutil, zipfile |
| from collections import defaultdict |
| import pyarrow.parquet as pq |
| from PIL import Image |
|
|
| RAW, OUT = 'raw', 'dataset' |
| SEED = 42 |
| RATIOS = (('train', .70), ('val', .15), ('test', .15)) |
|
|
| |
| |
| CLASSES = { |
| 'Blast': ('Blast', 'BLA', None), |
| 'Promyelocyte': ('Promiyelosit', 'PMO', 'PMY'), |
| 'Myelocyte': ('Miyelosit', 'MYB', 'MY'), |
| 'Metamyelocyte': ('Metamiyelosit', 'MMZ', 'MMY'), |
| 'Band_neutrophil': ('Çomak nötrofil', 'NGB', 'BNE'), |
| 'Segmented_neutrophil': ('Segmentli nötrofil', 'NGS', 'SNE'), |
| 'Eosinophil': ('Eozinofil', 'EOS', 'EO'), |
| 'Basophil': ('Bazofil', 'BAS', 'BA'), |
| 'Lymphocyte': ('Lenfosit', 'LYT', 'LY'), |
| 'Monocyte': ('Monosit', 'MON', 'MO'), |
| 'Proerythroblast': ('Proeritroblast', 'PEB', None), |
| 'Erythroblast': ('Eritroblast', 'EBO', 'ERB'), |
| 'Plasma_cell': ('Plazma hücresi', 'PLM', None), |
| 'Hairy_cell': ('Tüylü hücre (hairy cell)', 'HAC', None), |
| 'Platelet': ('Trombosit', None, 'PLATELET'), |
| 'Artefact': ('Artefakt', 'ART', None), |
| 'Not_identifiable': ('Tanımlanamayan hücre', 'NIF', None), |
| } |
| BM_NAMES = ['ABE', 'ART', 'BAS', 'BLA', 'EBO', 'EOS', 'FGC', 'HAC', 'KSC', 'LYI', 'LYT', 'MMZ', 'MON', 'MYB', 'NGB', 'NGS', 'NIF', 'OTH', 'PEB', 'PLM', 'PMO'] |
| BM2CLS = {v[1]: k for k, v in CLASSES.items() if v[1]} |
| PBC2CLS = {v[2]: k for k, v in CLASSES.items() if v[2]} |
|
|
| rows, excluded = [], [] |
| def add(config, split, cls, fname, data, **meta): |
| d = os.path.join(OUT, config, split, cls); os.makedirs(d, exist_ok=True) |
| with open(os.path.join(d, fname), 'wb') as f: f.write(data) |
| rows.append(dict(config=config, split=split, label=cls, file=f'{config}/{split}/{cls}/{fname}', **meta)) |
|
|
| def split_items(items, key=lambda x: x): |
| """stratified random split of a list (per class) -> dict split->list""" |
| rnd = random.Random(SEED); items = sorted(items, key=key); rnd.shuffle(items) |
| n = len(items); a = round(n * .70); b = a + round(n * .15) |
| return {'train': items[:a], 'val': items[a:b], 'test': items[b:]} |
|
|
| def split_groups(groups_by_label): |
| """patient-level split: groups (patients) are shuffled per label and assigned 70/15/15""" |
| out = {} |
| for lab, groups in groups_by_label.items(): |
| for s, gs in split_items(sorted(groups)).items(): |
| for g in gs: out[g] = s |
| return out |
|
|
| |
| BM_FILES = sorted(glob.glob(f'{RAW}/bm/data/*.parquet')) |
| bm = defaultdict(list) |
| for f in BM_FILES: |
| pf = pq.ParquetFile(f) |
| for b in pf.iter_batches(batch_size=2000, columns=['image', 'label']): |
| d = b.to_pydict() |
| for im, lab in zip(d['image'], d['label']): |
| code = BM_NAMES[lab] |
| if code not in BM2CLS: |
| excluded.append(('bone_marrow', im['path'], code, 'class too small / heterogeneous')); continue |
| bm[BM2CLS[code]].append(im['path']) |
| bm_split = {} |
| for cls, paths in bm.items(): |
| for split, ps in split_items(paths).items(): |
| for p in ps: bm_split[p] = (split, cls) |
| for f in BM_FILES: |
| for b in pq.ParquetFile(f).iter_batches(batch_size=500, columns=['image', 'label']): |
| d = b.to_pydict() |
| for im, lab in zip(d['image'], d['label']): |
| if im['path'] not in bm_split: continue |
| split, cls = bm_split[im['path']] |
| add('cls', split, cls, f'BM_{im["path"]}', im['bytes'], source='bone_marrow_mll', original_label=BM_NAMES[lab], original_file=im['path'], patient_id='') |
| print('bone marrow done', len(bm_split)) |
|
|
| |
| z = zipfile.ZipFile(f'{RAW}/pbc/PBC_dataset_normal_DIB.zip') |
| pbc = defaultdict(list) |
| for n in z.namelist(): |
| if n.startswith('__MACOSX') or not n.lower().endswith('.jpg'): continue |
| fn = n.split('/')[-1]; pre = fn.split('_')[0] |
| if pre not in PBC2CLS: |
| excluded.append(('pbc', n, pre, 'subtype not specified (IG / NEUTROPHIL)')); continue |
| pbc[PBC2CLS[pre]].append((n, fn, pre)) |
| for cls, items in pbc.items(): |
| for split, its in split_items(items, key=lambda x: x[0]).items(): |
| for n, fn, pre in its: |
| add('cls', split, cls, f'PBC_{fn}', z.read(n), source='pbc_acevedo', original_label=pre, original_file=n, patient_id='') |
| print('pbc done', sum(len(v) for v in pbc.values())) |
|
|
| |
| CN_FILES = sorted(glob.glob(f'{RAW}/cnmc/data/*.parquet')) |
| g = defaultdict(set) |
| t = pq.read_table(CN_FILES, columns=['subject_id', 'label']).to_pydict() |
| for sid, lab in zip(t['subject_id'], t['label']): g['ALL' if lab == 'cancer' else 'Normal'].add(sid) |
| sp = split_groups(g) |
| n_cn = 0 |
| for f in CN_FILES: |
| for b in pq.ParquetFile(f).iter_batches(batch_size=300, columns=['subject_id', 'label', 'image', 'original_image_name']): |
| d = b.to_pydict() |
| for sid, lab, im, name in zip(d['subject_id'], d['label'], d['image'], d['original_image_name']): |
| lab = 'ALL' if lab == 'cancer' else 'Normal' |
| try: |
| buf = io.BytesIO(); Image.open(io.BytesIO(im['bytes'])).save(buf, 'PNG', optimize=True) |
| except Exception as e: |
| excluded.append(('c_nmc_2019', name, lab, f'corrupt image ({type(e).__name__})')); continue |
| add('all', sp[sid], lab, f'CNMC_{os.path.splitext(name)[0]}.png', buf.getvalue(), source='c_nmc_2019', original_label=lab, original_file=name, patient_id=f'CNMC_{sid}') |
| n_cn += 1 |
| print('cnmc done', n_cn) |
|
|
| |
| z = zipfile.ZipFile(f'{RAW}/malaria/cell_images.zip') |
| mal = [] |
| for n in z.namelist(): |
| if not n.endswith('.png'): continue |
| lab = 'Parasitized' if '/Parasitized/' in n else 'Uninfected' |
| fn = n.split('/')[-1]; pid = re.split(r'[Tt]hin', fn)[0].rstrip('_') |
| mal.append((pid, lab, n, fn)) |
| pat_lab = defaultdict(set) |
| for pid, lab, _, _ in mal: pat_lab[pid].add(lab) |
| g = defaultdict(set) |
| for pid, labs in pat_lab.items(): g['infected_patient' if 'Parasitized' in labs else 'healthy_patient'].add(pid) |
| sp = split_groups(g) |
| for pid, lab, n, fn in mal: |
| add('malaria', sp[pid], lab, f'NIH_{fn}', z.read(n), source='nih_malaria', original_label=lab, original_file=n, patient_id=f'NIH_{pid}') |
| print('malaria done', len(mal)) |
|
|
| |
| for s_in, s_out in (('train', 'train'), ('valid', 'val'), ('test', 'test')): |
| for img in sorted(glob.glob(f'{RAW}/bccd/{s_in}/images/*')): |
| base = os.path.splitext(os.path.basename(img))[0] |
| lbl = f'{RAW}/bccd/{s_in}/labels/{base}.txt' |
| for sub, src in (('images', img), ('labels', lbl)): |
| d = os.path.join(OUT, 'det', sub, s_out); os.makedirs(d, exist_ok=True) |
| shutil.copy(src, d) |
| rows.append(dict(config='det', split=s_out, label='', file=f'det/images/{s_out}/{os.path.basename(img)}', source='bccd', |
| original_label='', original_file=img.split('/', 2)[-1], patient_id='')) |
| with open(os.path.join(OUT, 'det', 'data.yaml'), 'w') as f: |
| f.write('path: .\ntrain: images/train\nval: images/val\ntest: images/test\nnc: 3\nnames: [Platelets, RBC, WBC]\n') |
| print('bccd done') |
|
|
| with open('manifest.csv', 'w', newline='') as f: |
| w = csv.DictWriter(f, fieldnames=list(rows[0])); w.writeheader(); w.writerows(rows) |
| with open('excluded.csv', 'w', newline='') as f: |
| w = csv.writer(f); w.writerow(['source', 'file', 'original_label', 'reason']); w.writerows(excluded) |
| print('rows', len(rows), 'excluded', len(excluded)) |
|
|
