update from asus 106

backend/api/batches.py

@@ -1,13 +1,14 @@
-"""Batch, frame and auto-annotation routes (REQ-020…034)."""
-
+import json
 import os
+import shutil
+import tempfile
 from typing import Optional
 
-from fastapi import APIRouter, HTTPException, Response
+from fastapi import APIRouter, File, Form, HTTPException, Response, UploadFile
 from fastapi.responses import FileResponse
 from pydantic import BaseModel
 
-from backend import autolabel, dataset, library
+from backend import autolabel, dataset, library, projects
 from backend import batches as batch_store
 from backend import review as review_store
 from backend.api.common import project_or_404, thumbnail
@@ -27,10 +28,12 @@ class AutolabelRequest(BaseModel):
     engines: Optional[list[str]] = None
     class_ids: Optional[list[int]] = None
     engine_classes: Optional[dict[str, list[str]]] = None
+    target_class_names: Optional[list[str]] = None
     threshold: float = autolabel.DEFAULT_THRESHOLD
     iou_threshold: float = autolabel.DEFAULT_IOU
     min_box_frac: float = 0.0
     resume: bool = False
+    append: bool = False
 
 
 @router.post("/api/projects/{project_id}/batches")
@@ -90,11 +93,115 @@ def start_autolabel(batch_id: int, request: AutolabelRequest) -> dict:
     try:
         engine_list = request.engines if (request.engines and len(request.engines) > 0) else [request.engine]
         return autolabel.start(batch_id, request.threshold, request.iou_threshold,
-                               request.min_box_frac, resume=request.resume,
+                               request.min_box_frac, resume=request.resume, append=request.append,
                                engines=engine_list, class_ids=request.class_ids,
-                               engine_classes=request.engine_classes)
+                               engine_classes=request.engine_classes,
+                               target_class_names=request.target_class_names)
     except batch_store.BatchError as exc:
         raise HTTPException(400, str(exc))
+@router.post("/api/batches/inspect-model")
+async def inspect_model(file: UploadFile = File(...)) -> dict:
+    if not (file.filename or "").endswith(".pt"):
+        raise HTTPException(400, "Model must be a .pt file")
+    with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as staged:
+        shutil.copyfileobj(file.file, staged)
+        staged_path = staged.name
+    await file.close()
+    try:
+        classes = projects.read_model_classes(staged_path)
+        return {"filename": file.filename, "classes": classes, "staged_path": staged_path}
+    except Exception as exc:
+        if os.path.exists(staged_path):
+            os.unlink(staged_path)
+        raise HTTPException(400, f"Could not inspect model: {exc}")
+
+
+@router.post("/api/batches/{batch_id}/autolabel-with-model")
+async def autolabel_with_model(
+    batch_id: int,
+    file: UploadFile = File(...),
+    threshold: float = Form(0.35),
+    iou_threshold: float = Form(0.8),
+    selected_classes: str = Form("[]"),
+    append: bool = Form(True),
+) -> dict:
+    if not (file.filename or "").endswith(".pt"):
+        raise HTTPException(400, "Model must be a .pt file")
+    with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as staged:
+        shutil.copyfileobj(file.file, staged)
+        staged_path = staged.name
+    await file.close()
+
+    try:
+        target_classes = json.loads(selected_classes) if selected_classes else None
+        return autolabel.start(
+            batch_id,
+            threshold=threshold,
+            iou_threshold=iou_threshold,
+            append=append,
+            custom_model_path=staged_path,
+            target_class_names=target_classes,
+        )
+    except Exception as exc:
+        if os.path.exists(staged_path):
+            os.unlink(staged_path)
+        raise HTTPException(400, f"Auto-annotation failed to start: {exc}")
+
+
+@router.post("/api/sam3/playground-test")
+async def sam3_playground_test(
+    file: UploadFile = File(...),
+    prompts: str = Form(...),
+    threshold: float = Form(0.35),
+    iou_threshold: float = Form(0.8),
+) -> dict:
+    from PIL import Image
+    from backend import labeling
+    from backend.sam3_engine import get_engine
+
+    try:
+        image = Image.open(file.file).convert("RGB")
+    except Exception as exc:
+        raise HTTPException(400, f"Could not read image: {exc}")
+
+    width, height = image.size
+    prompt_list = [p.strip() for p in prompts.split(",") if p.strip()]
+    if not prompt_list:
+        raise HTTPException(400, "At least one text prompt is required")
+
+    try:
+        engine = get_engine()
+        raw_dets = engine.detect(image, prompt_list, threshold)
+        kept_dets = labeling.deduplicate(raw_dets, iou_threshold=iou_threshold)
+    except Exception as exc:
+        raise HTTPException(500, f"SAM3 inference failed: {exc}")
+
+    results = []
+    for det in kept_dets:
+        norm_box = [
+            det.box[0] / width,
+            det.box[1] / height,
+            det.box[2] / width,
+            det.box[3] / height,
+        ]
+        polys = []
+        if det.mask is not None:
+            raw_polys = review_store.mask_to_polygons(det.mask)
+            polys = [[[float(pt[0]), float(pt[1])] for pt in poly] for poly in raw_polys]
+
+        results.append({
+            "class_id": det.class_id,
+            "prompt": det.class_name,
+            "score": round(float(det.score), 4),
+            "box": [round(v, 5) for v in norm_box],
+            "polygons": polys,
+        })
+
+    return {
+        "width": width,
+        "height": height,
+        "detections": results,
+    }
 
 
 
@@ -150,3 +257,11 @@ def clear_batch_class_annotations(batch_id: int, class_id: int) -> dict:
     deleted = review_store.clear_batch_class_annotations(batch_id, class_id)
     return {"deleted": deleted}
 
+
+@router.post("/api/batches/{batch_id}/reset-auto-annotations")
+def reset_batch_auto_annotations(batch_id: int) -> dict:
+    if batch_store.get(batch_id) is None:
+        raise HTTPException(404, "No such batch")
+    deleted = review_store.clear_batch_auto_annotations(batch_id)
+    return {"deleted": deleted}
+