26-7-31-1

2026-07-31 12:27:41 +08:00
commit 9b8b8b57b3
142 changed files with 22408 additions and 0 deletions
@@ -0,0 +1,89 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+from fastapi import APIRouter, File, Form, HTTPException, UploadFile
+
+from app.schemas.models import ImagingAnalyzeResponse
+from app.services.monai_preprocess import load_image_bgr, preprocess
+from app.services.report_generator import build_imaging_texts, generate_imaging_report, make_full_report
+from app.schemas.models import ImagingReportRequest
+from app.services.yolo_detector import get_detector
+from app.services.yolo_manager import get_yolo_manager
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/imaging", tags=["imaging"])
+
+
+@router.post("/analyze", response_model=ImagingAnalyzeResponse)
+async def analyze_imaging(
+    file: UploadFile | None = File(default=None),
+    image_path: str | None = Form(default=None),
+    study_type: str = Form(default="CT"),
+    body_part: str = Form(default=""),
+    patient_summary: str = Form(default=""),
+) -> ImagingAnalyzeResponse:
+    raw = await _read_bytes(file, image_path)
+    try:
+        image_bgr = load_image_bgr(raw)
+        prep = preprocess(image_bgr)
+        detector = get_detector()
+        detections = detector.detect(image_bgr, study_type=study_type)
+        annotated = detector.annotate(image_bgr, detections)
+        findings, diagnosis, recommendations, confidence = build_imaging_texts(
+            study_type, body_part, detections, detector.mode
+        )
+        report = generate_imaging_report(
+            ImagingReportRequest(
+                study_type=study_type,
+                body_part=body_part,
+                patient_summary=patient_summary,
+                preliminary_diagnosis=diagnosis,
+                findings=findings,
+                detections=detections,
+                confidence=confidence,
+            )
+        )
+        backend = prep.get("backend", "opencv")
+        model_version = f"yolo-{detector.mode}+{backend}+{report.model_version}"
+        try:
+            get_yolo_manager().record_inference(
+                mode=detector.mode,
+                detections=detections,
+                study_type=study_type,
+                model_version=model_version,
+            )
+        except Exception as e:
+            logger.warning("记录 YOLO 统计失败: %s", e)
+        return ImagingAnalyzeResponse(
+            detections=detections,
+            annotated_image_base64=annotated,
+            preliminary_diagnosis=report.impression or diagnosis,
+            findings=report.findings or findings,
+            recommendations=report.recommendations or recommendations,
+            confidence=confidence,
+            model_version=model_version,
+            mode=detector.mode,  # type: ignore[arg-type]
+            full_report=report.full_report
+            or make_full_report(study_type, body_part, findings, diagnosis, recommendations, patient_summary),
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+    except Exception as e:
+        logger.exception("影像分析失败")
+        raise HTTPException(status_code=500, detail=f"影像分析失败: {e}") from e
+
+
+async def _read_bytes(file: UploadFile | None, image_path: str | None) -> bytes:
+    if file is not None:
+        data = await file.read()
+        if not data:
+            raise HTTPException(status_code=400, detail="上传文件为空")
+        return data
+    if image_path:
+        path = Path(image_path)
+        if not path.is_file():
+            raise HTTPException(status_code=400, detail=f"影像路径不存在: {image_path}")
+        return path.read_bytes()
+    raise HTTPException(status_code=400, detail="请提供 file 或 image_path")