Базовый коммит

service/reports.py

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+import os
+import importlib
+import base64
+from zipfile import ZipFile
+from datetime import datetime, timezone, timedelta
+from pydicom.dataset import Dataset, validate_file_meta
+from pydicom.uid import UID, generate_uid
+import numpy as np
+from service import sr_tags, preprocessor, structs
+
+MODEL_VERSION = "1.0.0"
+
+
+def _gen_seriesIUID(orig_seriesIUID, model, sr=False):
+    if len(orig_seriesIUID) > 56:
+        orig_seriesIUID = orig_seriesIUID[:56]
+    match model:
+        case "sinus":
+            model_id = 1208
+        case "wrist":
+            model_id = 1249
+        case "shoulder":
+            model_id = 1250
+    return f"{orig_seriesIUID}.{model_id}.{'2' if sr else '1'}"
+
+
+def _to_sr_datetime(datetime):
+    return datetime.strftime("%d.%m.%Y %H:%M")
+
+
+def to_iso8601(datetime):
+    msk_tz = timezone(timedelta(hours=3), name="MSK")
+    date = datetime.astimezone(msk_tz).isoformat("T", "milliseconds")
+    rindex_colon = date.rindex(':')
+    return date[:rindex_colon] + date[rindex_colon+1:]
+
+
+def _create_ds_base(meta_tags: structs.MetaTags, sop_class_uid: UID):
+    meta_info = Dataset()
+    sop_instance_uid = generate_uid()
+    meta_info.MediaStorageSOPClassUID = sop_class_uid
+    meta_info.MediaStorageSOPInstanceUID = sop_instance_uid
+    meta_info.TransferSyntaxUID = UID('1.2.840.10008.1.2')
+
+    ds = Dataset()
+    ds.file_meta = meta_info
+    validate_file_meta(ds.file_meta, enforce_standard=True)
+
+    ds.SOPClassUID = sop_class_uid
+    ds.InstitutionName = "LORKT"
+    ds.StudyInstanceUID = meta_tags.study_iuid
+    ds.SOPInstanceUID = sop_instance_uid
+    if meta_tags.patient_id:
+        ds.PatientID = meta_tags.patient_id
+    if meta_tags.accession_number:
+        ds.AccessionNumber = meta_tags.accession_number
+    if meta_tags.issuer_of_patient_id:
+        ds.IssuerOfPatientID = meta_tags.issuer_of_patient_id
+    if meta_tags.filler_number:
+        ds.FillerOrderNumberImagingServiceRequest = meta_tags.filler_number
+    return ds
+
+
+def _create_sr(meta_tags: structs.MetaTags, report: str, conclusion: str,
+               process_end: datetime, model: str, username: str):
+    sop_class_uid = UID('1.2.840.10008.5.1.4.1.1.88.33')
+    ds = _create_ds_base(meta_tags, sop_class_uid)
+    ds.SeriesInstanceUID = _gen_seriesIUID(meta_tags.series_iuid, model,
+                                           sr=True)
+    ds.Modality = "SR"
+    ds.InstanceNumber = 1
+
+    process_end = _to_sr_datetime(process_end)
+    tags = sr_tags.tags_for_models[model]
+    tags_and_texts = (
+        ("Модальность", "РГ"),
+        ("Область исследования", tags["Область исследования"]),
+        ("Идентификатор исследования", meta_tags.study_iuid),
+        ("Дата и время формирования заключения ИИ-сервисом", process_end),
+        ("Предупреждение", "Заключение подготовлено программным обеспечением "\
+                           "с применением технологий искусственного "\
+                           "интеллекта"),
+        ("Предупреждение", "В исследовательских целях"),
+        ("Наименование сервиса", "ЛОР КТ"),
+        ("Версия сервиса", MODEL_VERSION),
+        ("Назначение сервиса", tags["Назначение сервиса"]),
+        ("Технические данные", tags["Технические данные"]),
+        ("Описание", report),
+        ("Заключение", conclusion),
+        ("Руководство пользователя", tags["Руководство пользователя"])
+    )
+
+    ds.SpecificCharacterSet = "ISO_IR 192"
+    ds.add_new((0x0040, 0xa730), 'SQ', 
+               [Dataset() for _ in range(len(tags_and_texts))])
+    seq = ds.ContentSequence
+
+    for i, (tag, text) in enumerate(tags_and_texts):
+        seq[i].RelationshipType = "CONTAINS"
+        seq[i].ValueType = "TEXT"
+        seq[i].TextValue = text
+        seq[i].add_new((0x0040, 0xa043), 'SQ', [Dataset()])
+        name_seq = seq[i].ConceptNameCodeSequence[0]
+        name_seq.CodeValue = "209001"
+        name_seq.CodingSchemeDesignator = "99PMP"
+        name_seq.CodeMeaning = tag
+
+    save_path = f"data/{username}/sr/{meta_tags.study_iuid}.dcm"
+    ds.save_as(save_path, implicit_vr=True, little_endian=True)
+    return save_path
+
+
+def _create_a_series(meta_tags: structs.MetaTags, img: np.ndarray, 
+                     model: str, username: str):
+    acquisition_date = datetime.now().strftime("%Y%m%d")
+    acquisition_time = datetime.now().strftime("%H%M%S")
+    series_uid = _gen_seriesIUID(meta_tags.series_iuid, model)
+    sop_class_uid = UID('1.2.840.10008.5.1.4.1.1.7')
+    ds = _create_ds_base(meta_tags, sop_class_uid)
+    ds.SeriesInstanceUID = series_uid
+    ds.InstanceNumber = 1
+    ds.Modality = "DX"
+    ds.SeriesDescription = "LORKT"
+    ds.InstitutionName = "LORKT"
+    ds.InstitutionalDepartmentName = MODEL_VERSION
+    ds.AcquisitionDate = acquisition_date
+    ds.AcquisitionTime = acquisition_time
+    ds.OperatorsName = "AI"
+    ds.PixelData = bytes(img)
+    ds.Rows, ds.Columns = img.shape[:2]
+    ds.BitsAllocated = 8
+    ds.BitsStored = 8
+    ds.HighBit = 7
+    ds.SamplesPerPixel = 3
+    ds.PhotometricInterpretation = "RGB"
+    ds.PixelRepresentation = 0
+    ds.PlanarConfiguration = 0
+    save_path = f"data/{username}/additional_series/{meta_tags.study_iuid}.dcm"
+    ds.save_as(save_path, implicit_vr=True, little_endian=True)
+    return save_path
+
+
+def _zip_reports(paths: list[str], username: str):
+    study_uid = os.path.split(paths[0])[1]
+    with ZipFile(f"data/{username}/reports/{study_uid.replace('.dcm', '.zip')}", 'w') as z:
+        for path in paths:
+            dir, id = os.path.split(path)
+            dir = os.path.split(dir)[1]
+            arcname = os.path.join(dir, id)
+            z.write(path, arcname)
+
+
+def make_reports(pathology: str, study_path: str, 
+                 username: str) -> structs.Prediction:
+    meta_tags, pred_input = preprocessor.prep_imgs(pathology, study_path)
+    module = importlib.import_module("service.predictors." + pathology)
+    predict_func = getattr(module, "predict")
+    prediction = predict_func(pred_input)
+    process_end = datetime.now()
+    sr_path = _create_sr(meta_tags, prediction.report, prediction.conclusion, 
+                         process_end, pathology, username)
+    a_series_path = _create_a_series(meta_tags, prediction.image, pathology,
+                                     username)
+    _zip_reports([sr_path, a_series_path], username)
+    prediction = prediction._replace(image=f"{meta_tags.study_iuid}.png")
+    return prediction