chore: sync annotation UI, shortcuts, and misc updates

backend/api/review.py

@@ -41,9 +41,11 @@ class AssistRequest(BaseModel):
 
 
 class PoolExemplar(BaseModel):
-    # Normalized xyxy against the frame, as drawn on the review canvas.
-    box: List[float]
+    # Normalized xyxy box or [x, y] point against the frame.
+    box: Optional[List[float]] = None
+    point: Optional[List[float]] = None
     positive: bool = True
+    kind: Optional[str] = None
 
 
 class ExemplarLabelRequest(BaseModel):

backend/exemplar.py

@@ -143,8 +143,17 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
 
     positives, negatives = [], []
     for item in exemplars:
-        box = review.validate({"type": "bbox", "points": item["box"]}, "bbox")["points"]
-        (positives if item.get("positive", True) else negatives).append(box)
+        is_pos = bool(item.get("positive", True))
+        target_list = positives if is_pos else negatives
+        if item.get("point") is not None:
+            px, py = float(item["point"][0]), float(item["point"][1])
+            target_list.append({"kind": "point", "cxcywh": [px, py, 0.03, 0.03], "point": [px, py]})
+        elif item.get("box") is not None:
+            box = review.validate({"type": "bbox", "points": item["box"]}, "bbox")["points"]
+            target_list.append({"kind": "box", "cxcywh": _cxcywh(box), "box": box})
+        elif "points" in item and len(item["points"]) == 2:
+            px, py = float(item["points"][0]), float(item["points"][1])
+            target_list.append({"kind": "point", "cxcywh": [px, py, 0.03, 0.03], "point": [px, py]})
     if not positives and not negatives:
         raise review.ReviewError("No exemplars to run")
 
@@ -154,15 +163,11 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
     if not jobs.gpu_lock.acquire(timeout=5):
         busy = jobs.running_types()
         kind = busy[0] if busy else "background"
-        drawn = [{"geometry": _rect(box, label_type), "score": 1.0, "source": "manual"}
-                 for box in positives]
+        drawn = [{"geometry": _rect(p["box"], label_type), "score": 1.0, "source": "manual"}
+                 for p in positives if p["kind"] == "box"]
         if apply:
-            # Never the replace path here: with no detections to put back, it
-            # would wipe the class and leave only the drawings. Applying a
-            # detection-less run just files the drawings and honours the
-            # negatives.
             _append_drawn(frame_id, class_id, drawn)
-            _drop_negative_overlaps(frame_id, class_id, negatives)
+            _drop_negative_overlaps(frame_id, class_id, [n["box"] for n in negatives if n["kind"] == "box"])
         return _result(frame_id, drawn, apply, redetected=False,
                        message=f"The GPU is busy with a {kind} job — this is your drawing "
                                "only, nothing was detected")
@@ -178,8 +183,8 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
             state = engine.open_state(image)
             found = engine.apply_prompts(
                 state, threshold=threshold, text=prompt,
-                exemplars=[{"box": _cxcywh(box), "positive": True} for box in positives]
-                          + [{"box": _cxcywh(box), "positive": False} for box in negatives],
+                exemplars=[{"box": p["cxcywh"], "positive": True} for p in positives]
+                          + [{"box": n["cxcywh"], "positive": False} for n in negatives],
             )
     finally:
         jobs.gpu_lock.release()
@@ -198,9 +203,11 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
     detections = [(_norm_box(d.box, width, height), d) for d in found]
     items: List[dict] = []
 
-    # The user's own boxes first, so the duplicate check below measures against
-    # what they drew rather than the other way round.
-    for box in positives:
+    # Manual drawn boxes first:
+    for item in positives:
+        if item["kind"] != "box":
+            continue
+        box = item["box"]
         geometry = _rect(box, label_type)
         if label_type != "bbox":
             snapped = _snap(box, detections, width, height)
@@ -208,10 +215,16 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
                 geometry = snapped
         items.append({"geometry": geometry, "score": 1.0, "source": "manual"})
 
+    manual_boxes = [p["box"] for p in positives if p["kind"] == "box"]
+    negative_boxes = [n["box"] for n in negatives if n["kind"] == "box"]
+    negative_points = [n["point"] for n in negatives if n["kind"] == "point"]
+
     for norm, detection in detections:
-        if any(_iou(norm, box) >= NEGATIVE_IOU for box in negatives):
+        if any(_iou(norm, box) >= NEGATIVE_IOU for box in negative_boxes):
             continue
-        if any(_iou(norm, box) >= DUPLICATE_IOU for box in positives):
+        if any(norm[0] <= pt[0] <= norm[2] and norm[1] <= pt[1] <= norm[3] for pt in negative_points):
+            continue
+        if any(_iou(norm, box) >= DUPLICATE_IOU for box in manual_boxes):
             continue
         for geometry in _detection_shapes(detection, norm, width, height, label_type):
             items.append({"geometry": geometry, "score": detection.score, "source": "auto"})