change readmeW.md to readme.md

proj_deepface/readme.md

@@ -0,0 +1,54 @@
+ref: https://github.com/serengil/deepface
+
+# model store 
+model checkpoint 放在了 `C:\Users\User\.deepface\weights`
+
+# installation
+```
+$ pip install deepface
+``` 
+
+# Usage: 
+```
+(deepface_v2) PS path/to/workspace> python
+Python 3.7.16 (default, Jan 17 2023, 16:06:28) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
+Type "help", "copyright", "credits" or "license" for more information.
+>>> from deepface import DeepFace
+```
+
+# Model introduce
+```
+Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: 
+model_name	  Declared LFW Score
+VGG-Face	    98.9%
+Facenet	        99.2%
+Facenet512	    99.6%  drop 
+OpenFace	    92.9%
+DeepID	        97.4%
+Dlib	        99.3%
+SFace	        99.5%
+ArcFace	        99.5% 阈值调整为1.00  ** 
+GhostFaceNet	99.7%   **
+Human-beings	97.5%
+
+The default configuration uses VGG-Face model.
+```
+
+```
+backends = {
+    "opencv": OpenCv.OpenCvClient,
+    "mtcnn": MtCnn.MtCnnClient,
+    "ssd": Ssd.SsdClient,
+    "dlib": Dlib.DlibClient,
+    "retinaface": RetinaFace.RetinaFaceClient,
+    "mediapipe": MediaPipe.MediaPipeClient,
+    "yolov8": Yolo.YoloClient,
+    "yunet": YuNet.YuNetClient,
+    "fastmtcnn": FastMtCnn.FastMtCnnClient,
+}
+```
+
+```
+distance_metric = ['euclidean_l2','cosine','euclidean']
+author: Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.
+```

proj_deepface/readmeW.md

@@ -1,14 +0,0 @@
-ref: https://github.com/serengil/deepface
-
-# store
-model checkpoint 放在了 `C:\Users\JIALE\.deepface\weights`
-
-# installation
-
-
-# Usage: 
-(deepface_v2) PS C:\Users\JIALE\Desktop\bns_proj\proj_deepface> python
-Python 3.7.16 (default, Jan 17 2023, 16:06:28) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
-Type "help", "copyright", "credits" or "license" for more information.
->>> from deepface import DeepFace
-

proj_deepface/webmain.py

@@ -51,6 +51,7 @@ class Status(Enum):
     BASE64_EMPTY = "base64 is empty"
     NotImplementedError = "Not Implemented Error"
     ADD_HAS_EXISTED = "when adding, file has existed"
+    DB_NO_FACE = "face_db has no face"
 
 GLOBAL_DICT = {
     "ADD": Opt.ADD,
@@ -65,6 +66,7 @@ GLOBAL_DICT = {
 }
 
 class FaceHelper:
+
     def __init__(self, db_path: str, detector_backend: str, distance_metric: str, model_name: str):
         self.db_path = "C:/Users/JIALE/Desktop/bns_proj/proj_deepface/face"
         self.detector_backend = "opencv"
@@ -75,6 +77,10 @@ class FaceHelper:
         self.threshold = verification.find_threshold(model_name, distance_metric)
         self.pkl_name = f"ds_{self.model_name}_{self.detector_backend}_v2.pkl".replace("-", "").lower()
 
+        self.dirty = True 
+        self.db_representations = None
+        self.keepDBLive()
+
     def get_facial_areas(self, img: np.ndarray):
         """
         Find facial area coordinates in the given image
@@ -88,8 +94,6 @@ class FaceHelper:
                         detected_face 
                         descriptions 
         """
-        # print("get facial areas, img.shape: ", img.shape)
-        # beg = time.time()
         try:
             face_objs = detection.extract_faces(
                 img_path = img,
@@ -203,7 +207,6 @@ class FaceHelper:
         if retjson['status'] == 0: 
             return retjson
     
-        # db_imgs = os.listdir(self.db_path)
         db_imgs = [db_img.split(".")[0] for db_img in os.listdir(self.db_path) if db_img.endswith(".jpg")]   
         if   opt_mode == Opt.ADD: # add 
             if img_name in db_imgs:
@@ -215,34 +218,18 @@ class FaceHelper:
             return Status.NotImplementedError
 
         # update the face_db to keep latest
-        search_identity( # 保证database是最新的
+        self.dirty = True
-            detected_face=np.zeros([224, 224, 3]),
+        self.keepDBLive() 
-            db_path=self.db_path,
-            detector_backend=self.detector_backend,
-            distance_metric=self.distance_metric,
-            model_name=self.model_name,
-        )    
  
         return retjson 
 
-
     #interface 2: face recognition using embeddings on face_db 
     def faceRecog(self, img: Union[str,np.ndarray], img_mode: Ftype):
         # keep the database is the newest
-        search_identity( 
+        if self.dirty:
-            detected_face=np.zeros([224, 224, 3]),
+            self.keepDBLive()
-            db_path=self.db_path,
+        if len(self.db_representations) <= 0:
-            detector_backend=self.detector_backend,
+            return Status.DB_NO_FACE
-            distance_metric=self.distance_metric,
-            model_name=self.model_name,
-        )
-
-        # open and read face db 
-        datastore_path = os.path.join(self.db_path, self.pkl_name)
-        with open(datastore_path, "rb") as f:
-            db_representations = pickle.load(f)
-        assert len(db_representations) > 0, 'no face in database'
-        print(f"{len(db_representations)} faces representataion in {self.pkl_name}.")
 
         # process the img 
         ret = FaceHelper.process_img(img, img_mode) 
@@ -251,7 +238,7 @@ class FaceHelper:
         target_img = ret 
 
         # begin face recognition 
-        df = pd.DataFrame(db_representations)
+        df = pd.DataFrame(self.db_representations)
         labels = []
 
         face_coors, detected_faces, descriptions = self.get_facial_areas(target_img)
@@ -292,8 +279,27 @@ class FaceHelper:
         success, encoded_target_img = cv2.imencode('.png', target_img)
         if success:
             target_img_base64 = base64.b64encode(encoded_target_img).decode("utf-8")
+
         return labels, face_coors, target_img_base64 
 
+    def keepDBLive(self):
+        # keep the database is the newest
+        search_identity( 
+            detected_face=np.zeros([224, 224, 3]),
+            db_path=self.db_path,
+            detector_backend=self.detector_backend,
+            distance_metric=self.distance_metric,
+            model_name=self.model_name,
+        )
+        self.dirty = False 
+        
+        # load the representations from face_db 
+        datestore_path = os.path.join(self.db_path, self.pkl_name)
+        with open(datestore_path, "rb") as f: 
+            self.db_representations = pickle.load(f)
+
+        return len(self.db_representations) 
+
 
 facehelper = FaceHelper(
     db_path = "C:/Users/JIALE/Desktop/bns_proj/proj_deepface/face",
@@ -305,22 +311,47 @@ facehelper = FaceHelper(
 app = FastAPI()
 
 class item(BaseModel):
-    type: str 
+    TYPE: str 
     img: str
     img_name: str 
 
+class config(BaseModel):
+    db_path: str   # default: "C:/Users/JIALE/Desktop/bns_proj/proj_deepface/face"
+    detector_backend: str   #default: 
+    distance_metric: str 
+    model_name: str
 
-# @app.post("/master/")
+@app.post("/init/")
-# def master(input: item ):
+def init(input: config):
-#     return {"type": item["type"], "img":"fdasfsdf"}
+    facehelper = FaceHelper(
+        db_path = input.db_path,
+        detector_backend = input.detector_backend,
+        distance_metric = input.distance_metric,
+        model_name = input.model_name,
+)
+
+@app.get("/keepDBLastest")
+def keepDBLastest():
+    ret = facehelper.keepDBLive()
+    return {"status":"the faceDB is lastest", "description": f"db has {ret} faces"}
 
 
 @app.post("/interface/")
 def main(input: item):
-    interface_type, img_mode, opt_mode = input.type.split("_")
+    vars = input.TYPE.split("_")
+    if len(vars) != 3:
+        return {"status": 0, "description":"wrong TYPE"}
+    interface_type, img_mode, opt_mode = vars
+    try:
         img_mode = GLOBAL_DICT[img_mode]
-    if interface_type == "UPDATEDB":
+    except KeyError:
+        return {"status": 0, "description": "wrong img_mode"}
+    try:
         opt_mode = GLOBAL_DICT[opt_mode]
+    except KeyError:
+        return {"status": 0, "description": "wrong opt_mode"}
+    
+    if interface_type == "UPDATEDB":
         # call updateFaceDB for handling the picture 
         ret = facehelper.updateFaceDB(img = input.img, 
                     img_name = input.img_name,