Implement face recognition training in UI (#15786)

* Rename debug to train

* Add api to train image as person

* Cleanup model running

* Formatting

* Fix

* Set face recognition page title

frigate/api/classification.py

@@ -2,6 +2,9 @@
 
 import logging
 import os
+import random
+import shutil
+import string
 
 from fastapi import APIRouter, Request, UploadFile
 from fastapi.responses import JSONResponse
@@ -22,7 +25,13 @@ def get_faces():
 
     for name in os.listdir(FACE_DIR):
         face_dict[name] = []
-        for file in os.listdir(os.path.join(FACE_DIR, name)):
+
+        face_dir = os.path.join(FACE_DIR, name)
+
+        if not os.path.isdir(face_dir):
+            continue
+
+        for file in os.listdir(face_dir):
             face_dict[name].append(file)
 
     return JSONResponse(status_code=200, content=face_dict)
@@ -38,6 +47,39 @@ async def register_face(request: Request, name: str, file: UploadFile):
     )
 
 
+@router.post("/faces/train/{name}/classify")
+def train_face(name: str, body: dict = None):
+    json: dict[str, any] = body or {}
+    training_file = os.path.join(
+        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
+    )
+
+    if not training_file or not os.path.isfile(training_file):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Invalid filename or no file exists: {training_file}",
+                }
+            ),
+            status_code=404,
+        )
+
+    rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+    new_name = f"{name}-{rand_id}.webp"
+    new_file = os.path.join(FACE_DIR, f"{name}/{new_name}")
+    shutil.move(training_file, new_file)
+    return JSONResponse(
+        content=(
+            {
+                "success": True,
+                "message": f"Successfully saved {training_file} as {new_name}.",
+            }
+        ),
+        status_code=200,
+    )
+
+
 @router.post("/faces/{name}/delete")
 def deregister_faces(request: Request, name: str, body: dict = None):
     json: dict[str, any] = body or {}

frigate/embeddings/maintainer.py

@@ -517,7 +517,7 @@ class EmbeddingMaintainer(threading.Thread):
 
         if self.config.face_recognition.save_attempts:
             # write face to library
-            folder = os.path.join(FACE_DIR, "debug")
+            folder = os.path.join(FACE_DIR, "train")
             file = os.path.join(folder, f"{id}-{sub_label}-{score}-{face_score}.webp")
             os.makedirs(folder, exist_ok=True)
             cv2.imwrite(file, face_frame)

frigate/util/model.py

@@ -163,7 +163,12 @@ class FaceClassificationModel:
         self.config = config
         self.db = db
         self.landmark_detector = cv2.face.createFacemarkLBF()
-        self.landmark_detector.loadModel("/config/model_cache/facedet/landmarkdet.yaml")
+
+        if os.path.isfile("/config/model_cache/facedet/landmarkdet.yaml"):
+            self.landmark_detector.loadModel(
+                "/config/model_cache/facedet/landmarkdet.yaml"
+            )
+
         self.recognizer: cv2.face.LBPHFaceRecognizer = (
             cv2.face.LBPHFaceRecognizer_create(
                 radius=2, threshold=(1 - config.min_score) * 1000
@@ -178,13 +183,21 @@ class FaceClassificationModel:
 
         dir = "/media/frigate/clips/faces"
         for idx, name in enumerate(os.listdir(dir)):
-            if name == "debug":
+            if name == "train":
+                continue
+
+            face_folder = os.path.join(dir, name)
+
+            if not os.path.isdir(face_folder):
                 continue
 
             self.label_map[idx] = name
-            face_folder = os.path.join(dir, name)
             for image in os.listdir(face_folder):
                 img = cv2.imread(os.path.join(face_folder, image))
+
+                if img is None:
+                    continue
+
                 img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                 img = self.__align_face(img, img.shape[1], img.shape[0])
                 faces.append(img)