Add event_id support to classification categorization API

Co-authored-by: Teagan42 <2989925+Teagan42@users.noreply.github.com>

frigate/api/classification.py

@@ -890,7 +890,8 @@ def rename_classification_category(
     dependencies=[Depends(require_role(["admin"]))],
     summary="Categorize a classification image",
     description="""Categorizes a specific classification image for a given classification model and category.
-    The image must exist in the specified category. Returns a success message or an error if the name or category is invalid.""",
+    Accepts either a training file from the train directory or an event_id to extract
+    the object crop from. Returns a success message or an error if the name or category is invalid.""",
 )
 def categorize_classification_image(request: Request, name: str, body: dict = None):
     config: FrigateConfig = request.app.frigate_config
@@ -909,19 +910,17 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
     json: dict[str, Any] = body or {}
     category = sanitize_filename(json.get("category", ""))
     training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(
+    event_id = json.get("event_id")
-        CLIPS_DIR, sanitize_filename(name), "train", training_file_name
-    )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if not training_file_name and not event_id:
         return JSONResponse(
             content=(
                 {
                     "success": False,
-                    "message": f"Invalid filename or no file exists: {training_file_name}",
+                    "message": "A training file or event_id must be passed.",
                 }
             ),
-            status_code=404,
+            status_code=400,
         )
 
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
@@ -933,10 +932,117 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
 
     os.makedirs(new_file_folder, exist_ok=True)
 
+    if training_file_name:
+        # Use existing training file
+        training_file = os.path.join(
+            CLIPS_DIR, sanitize_filename(name), "train", training_file_name
+        )
+
+        if not os.path.isfile(training_file):
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": f"Invalid filename or no file exists: {training_file_name}",
+                    }
+                ),
+                status_code=404,
+            )
+
         # use opencv because webp images can not be used to train
         img = cv2.imread(training_file)
         cv2.imwrite(os.path.join(new_file_folder, new_name), img)
         os.unlink(training_file)
+    else:
+        # Extract from event
+        try:
+            event: Event = Event.get(Event.id == event_id)
+        except DoesNotExist:
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": f"Invalid event_id or no event exists: {event_id}",
+                    }
+                ),
+                status_code=404,
+            )
+
+        snapshot = get_event_snapshot(event)
+
+        # Get object bounding box for the first detection
+        if not event.data.get("attributes") or len(event.data["attributes"]) == 0:
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": f"Event {event_id} has no detection attributes.",
+                    }
+                ),
+                status_code=400,
+            )
+
+        # Use the first attribute's box
+        box = event.data["attributes"][0]["box"]
+
+        try:
+            # Extract the crop from the snapshot
+            detect_config: DetectConfig = config.cameras[event.camera].detect
+            frame = cv2.imread(snapshot)
+            
+            if frame is None:
+                return JSONResponse(
+                    content=(
+                        {
+                            "success": False,
+                            "message": f"Failed to read snapshot for event {event_id}.",
+                        }
+                    ),
+                    status_code=500,
+                )
+
+            height, width = frame.shape[:2]
+
+            # Convert relative coordinates to absolute
+            x1 = int(box[0] * width)
+            y1 = int(box[1] * height)
+            x2 = int(box[2] * width)
+            y2 = int(box[3] * height)
+
+            # Ensure coordinates are within frame boundaries
+            x1 = max(0, x1)
+            y1 = max(0, y1)
+            x2 = min(width, x2)
+            y2 = min(height, y2)
+
+            # Extract the crop
+            crop = frame[y1:y2, x1:x2]
+
+            if crop.size == 0:
+                return JSONResponse(
+                    content=(
+                        {
+                            "success": False,
+                            "message": f"Failed to extract crop from event {event_id}.",
+                        }
+                    ),
+                    status_code=500,
+                )
+
+            # Save the crop
+            cv2.imwrite(os.path.join(new_file_folder, new_name), crop)
+
+        except Exception as e:
+            logger.error(f"Failed to extract classification crop: {e}")
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": f"Failed to process event {event_id}: {str(e)}",
+                    }
+                ),
+                status_code=500,
+            )
 
     return JSONResponse(
         content=({"success": True, "message": "Successfully categorized image."}),