feat: sync live-count, exemplar annotation modules, and update .gitignore

backend/autolabel.py

@@ -259,125 +259,3 @@ def _reset_reviewed(batch_id: int) -> None:
             "AND review_status = 'approved'",
             (batch_id,),
         )
-
-
-def preview_frame(
-    batch_id: int,
-    frame_id: int,
-    engine: str,
-    threshold: float = DEFAULT_THRESHOLD,
-    iou_threshold: float = DEFAULT_IOU,
-    min_box_frac: float = 0.0,
-    target_class_names: Optional[List[str]] = None,
-    custom_model_path: Optional[str] = None
-) -> List[dict]:
-    batch = batches.get(batch_id)
-    if not batch:
-        raise ValueError("No such batch")
-    project = projects.get(batch["project_id"])
-    
-    frame = next((f for f in batches.frames(batch_id) if f["id"] == frame_id), None)
-    if not frame:
-        raise ValueError("Frame not found")
-        
-    directory = batches.frames_dir(batch["project_slug"], batch_id)
-    frame_file = os.path.join(directory, frame["filename"])
-    fw = max(1, frame.get("width") or 1)
-    fh = max(1, frame.get("height") or 1)
-
-    yolo_model = None
-    sam3_target_classes = []
-    
-    if engine == "sam3" and not custom_model_path:
-        allowed_classes_set = {c.strip().lower() for c in target_class_names} if target_class_names else None
-        if allowed_classes_set:
-            sam3_target_classes = [c for c in project["classes"] if c["name"].strip().lower() in allowed_classes_set or c["prompt"].strip().lower() in allowed_classes_set]
-        else:
-            sam3_target_classes = [c for c in project["classes"]]
-        
-        prompts = [c["prompt"] for c in sam3_target_classes]
-        if prompts:
-            from backend.sam3_engine import get_engine
-            get_engine()
-    else:
-        from ultralytics import YOLO
-        if custom_model_path and os.path.isfile(custom_model_path):
-            m_path = custom_model_path
-        else:
-            m_path = projects.training_start_point(project)
-            with db.cursor() as cur:
-                cur.execute("SELECT weights_path FROM model_versions WHERE project_id = ? ORDER BY version DESC LIMIT 1", (project["id"],))
-                row = cur.fetchone()
-                if row and os.path.isfile(row[0]):
-                    m_path = row[0]
-        yolo_model = YOLO(m_path)
-
-    name_to_class_id = {item["name"].strip().lower(): item["class_id"] for item in project["classes"]}
-    allowed_classes_set = {c.strip().lower() for c in target_class_names} if target_class_names else None
-
-    all_raw_detections = []
-
-    if yolo_model is not None:
-        results = yolo_model.predict(frame_file, conf=threshold, verbose=False)
-        if results and len(results) > 0:
-            model_names = results[0].names
-            for box in results[0].boxes:
-                cls_idx = int(box.cls[0].item())
-                raw_cls_name = str(model_names.get(cls_idx, cls_idx)).strip().lower()
-
-                target_class_id = name_to_class_id.get(raw_cls_name)
-                if target_class_id is None:
-                    for item in project["classes"]:
-                        if item["class_id"] == cls_idx:
-                            target_class_id = item["class_id"]
-                            break
-                if target_class_id is None and 0 <= cls_idx < len(project["classes"]):
-                    target_class_id = project["classes"][cls_idx]["class_id"]
-
-                if target_class_id is None:
-                    continue
-
-                target_cls_obj = next((c for c in project["classes"] if c["class_id"] == target_class_id), None)
-                proj_cls_name = target_cls_obj["name"].strip().lower() if target_cls_obj else ""
-
-                if allowed_classes_set is not None:
-                    if (raw_cls_name not in allowed_classes_set and 
-                        proj_cls_name not in allowed_classes_set and 
-                        str(target_class_id) not in allowed_classes_set):
-                        continue
-
-                score = float(box.conf[0].item())
-                xyxyn = box.xyxyn[0].tolist()
-                all_raw_detections.append(labeling.Detection(
-                    class_id=target_class_id,
-                    class_name=proj_cls_name or raw_cls_name,
-                    box=[xyxyn[0]*fw, xyxyn[1]*fh, xyxyn[2]*fw, xyxyn[3]*fh],
-                    score=score,
-                    mask=None
-                ))
-
-    if engine == "sam3" and sam3_target_classes:
-        prompts = [(c.get("prompt") or c["name"]).strip() for c in sam3_target_classes]
-        res = labeling.label_image(
-            frame_file, frame["filename"], prompts, threshold,
-            iou_threshold=iou_threshold, min_box_frac=min_box_frac
-        )
-        if not res.error and res.detections:
-            for det in res.detections:
-                if 0 <= det.class_id < len(sam3_target_classes):
-                    real_cls = sam3_target_classes[det.class_id]
-                    det.class_id = real_cls["class_id"]
-                    det.class_name = real_cls["name"]
-                    all_raw_detections.append(det)
-
-    kept = labeling.deduplicate(all_raw_detections, iou_threshold=iou_threshold)
-    items = []
-    for det in kept:
-        if project["label_type"] == "bbox" or det.mask is None:
-            geom = review.bbox(det.box[0]/fw, det.box[1]/fh, det.box[2]/fw, det.box[3]/fh)
-            items.append({"class_id": det.class_id, "geometry": geom, "score": det.score})
-        else:
-            for geometry in _geometries(det, fw, fh, project["label_type"]):
-                items.append({"class_id": det.class_id, "geometry": geometry, "score": det.score})
-                
-    return items