second commit the RaceRegWeb

.gitignore

@@ -6,3 +6,4 @@
 !/readme.md
 !/componentdevelopment/
 !/FaceRegWeb/
+!/FaceRegWeb5.2/

FaceRegWeb5.2/FaceRegWeb/process.py

@@ -47,27 +47,30 @@ for i in range(NUM_WORKERS):
 rw_lock = RedisReadWriteLock(redis_client, max_readers=max_readers)
 
 ErrorMsg = {
-    "101":"no face in the database", 
-    "102":"invalid file path",
-    "103":"invaild file suffix",
     
-    "201":"input file type is not support, only support: base64, url, local-path", 
+    "30003": "no face found in the database",
-    "202":"this updateDB type is not support",
+    "30004": "invalid file path",
+    "30005": "invalid file suffix",
 
-    "300":"deal the image successfully.",
+    "30006": "unsupported input file type; only supports: base64, URL, local path",
-    "301":"no face in the image.", 
+    "30007": "unsupported type for database update",
-    "302":"too many face in the image.",
-    "303":"bad bright of face in the image.",
-    "3041":"face shifted left/right, partially not captured.",
-    "3042":"face shifted top/bottom, partially not captured.",
-    "3051":"rightFace in the image.",
-    "3052":"leftFace in the image.",
-    "3053":"upFace in the image.",
-    "3054":"downFace in the image.",
-    "306":"too small resolution of face in the image.",
-    "307":"bad clarity of face in the image.",
 
-    "401":"identity has exist. to pretect the db, reject opt of this time ",
+    "30000": "facial recognition of a known individual",
+    "30001": "facial recognition of an unknown individual",
+    "30002": "successful image processing",
+    "30018": "no face detected in the image",
+    "30008": "multiple faces found in the image",
+    "30009": "poor bright conditions affecting facial quality in the image",
+    "30010": "face partially out of frame, shifted left or right",
+    "30011": "face partially out of frame, shifted top or bottom",
+    "30012": "face rotated to the right in the image",
+    "30013": "face rotated to the left in the image",
+    "30014": "face rotated upwards in the image",
+    "30015": "face rotated downwards in the image",
+    "30016": "low resolution of a face in the image",
+    "30017": "poor clarity of a face in the image",
+    
+    "30019": "identity already exists; for database protection, operation rejected at this time"
 }
 
 class FileError(Exception):
@@ -158,9 +161,9 @@ def load_img(img_path:str):
             image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
         elif img_path.startswith(("./","/","C:","D:","E:",".\\")) or os.path.isfile(img_path):  # local-path  
             if not os.path.isfile(img_path):
-                raise FileError("102")  # push: invalid file path 
+                raise FileError("30004")  # push: invalid file path 
             elif not img_path.lower().endswith((".jpg",'.jpeg','.png')):
-                raise FileError("103") # push: invaild file suffix
+                raise FileError("30005") # push: invaild file suffix
             else:
                 image = cv2.imread(img_path)
         elif img_path.startswith("data:") and "base64" in img_path: # base64 
@@ -172,7 +175,7 @@ def load_img(img_path:str):
             nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
             image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
         else:
-            raise NotImpltError("201")  # push: input file type is not support, only support: base64, url, local-path
+            raise NotImpltError("30006")  # push: input file type is not support, only support: base64, url, local-path
     except Exception as e:
         logger.info(f"{e}")
         raise e 
@@ -248,7 +251,7 @@ class FaceHelper:
 
         try: 
             if self.db_embeddings is None: 
-                raise FileError("101")  # push: no face in the database
+                raise FileError("30003")  # push: no face in the database
             
             image = load_img(img_path) # get bgr numpy image 
 
@@ -257,14 +260,10 @@ class FaceHelper:
             image = self.rotadjust(image) # 调整角度
             detect_result = self.fd.detect(image)
             detect_result = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result]
-            rett = {'code':" ", 'msg':" ", 'data':" "}
             if len(detect_result) == 0:
                 logger.info(f"{img_path[:200]}: no face in the image")
                 print(f"{img_path[:200]}: no face in the image")
-                raise FaceError("301") # push: no face in the image 
+                raise FaceError("30018") # push: no face in the image 
-            elif len(detect_result) > 1: 
-                rett['code'] = '302'
-                rett['data'] = {'code':" ", 'msg':" ", 'data':" "}
             for facebox in detect_result:
                 landmarks5 = self.ld5er.inference(image, facebox)  #  return: [(),(),(),(),()]  左眼 右眼 鼻子 左嘴角 右嘴角
                 # print("5点关键点：",landmarks5)
@@ -291,12 +290,15 @@ class FaceHelper:
             ret_data = []
             for i, (facebox, name) in enumerate(zip(detect_result, names)):
                 if name != 'unknown':
-                    ret_data.append({'code':"300", 'msg': ErrorMsg["300"], 'data':name})
+                    ret_data.append({'code':"30000", 'msg': ErrorMsg["30000"], 'data':[name,facebox]})
                 else:
                     code = self.check_face("None", image, facebox, prefix='facereg')
-                    ret_data.append({'code':code,  'msg': ErrorMsg[code],  'data':name})
+                    if code == "30002":
+                        ret_data.append({'code':"30001",  'msg': ErrorMsg["30001"],  'data':[name,facebox]}) 
+                    else:
+                        ret_data.append({'code':code,  'msg': ErrorMsg[code],  'data':[name,facebox]})
             if len(ret_data) != 1:
-                ret_data = {'code':"302", 'msg': ErrorMsg["302"], 'data': ret_data}
+                ret_data = {'code':"30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
             else:
                 ret_data = ret_data[0]
 
@@ -311,6 +313,49 @@ class FaceHelper:
             return ret_data
             # return names, [ encoder_img2base64(det) for det in cropped_images]
 
+    # 只是提取人脸特征，不要对别的有任何影响
+    def featureDetect(self, img_path:str):
+        rw_lock.acquire_read()
+
+        try:             
+            image = load_img(img_path) # get bgr numpy image 
+
+            unknown_embeddings = []
+            image = self.rotadjust(image) # 调整角度
+            detect_result = self.fd.detect(image)
+            detect_result = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result]
+            if len(detect_result) == 0:
+                logger.info(f"{img_path[:200]}: no face in the image")
+                print(f"{img_path[:200]}: no face in the image")
+                raise FaceError("30018") # push: no face in the image 
+            for facebox in detect_result:
+                landmarks5 = self.ld5er.inference(image, facebox)  #  return: [(),(),(),(),()]  左眼 右眼 鼻子 左嘴角 右嘴角
+                # print("5点关键点：",landmarks5)
+                # 输入image 和5点特征点位置(基于原图image的位置) , return all cropped aligned face （裁剪后的对齐后的人脸部分图像, 简写为aligned_faces）
+                landmarks5 = [ [ld5[0],ld5[1]] for ld5 in landmarks5]
+                cropped_face = self.fa.align(image, landmarks5=landmarks5)
+                # 输入aligned_faces ，return all features of aligned_faces 
+                feature = self.fr.inference(cropped_face)
+                unknown_embeddings.append(feature)
+
+            ret_data = []
+            for _, (facebox, feature) in enumerate(zip(detect_result, unknown_embeddings)):
+                code = self.check_face("None", image, facebox, prefix='featuredetect')
+                ret_data.append({'code':code,  'msg': ErrorMsg[code],  'data': [f"{feature.tobytes()},{str(feature.dtype)}", facebox]})
+                 
+            if len(ret_data) != 1:
+                ret_data = {'code':"30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
+            else:
+                ret_data = ret_data[0]
+
+        except Exception as e:
+            logger.info(f"{e}")
+            rw_lock.release_read()
+            raise e
+        else:
+            rw_lock.release_read()
+            return ret_data
+
     # opt in ['add','delete','replace'] identity作为检索的标识符，img_path只是提供文件路径
     # 写操作
     def updateDB(self, img_path :str, opt :str, identity :str, Onlyrefresh=False):
@@ -333,14 +378,14 @@ class FaceHelper:
                 img = self.rotadjust(img) # 调整角度
                 if opt in ["add","replace"]: 
                     if opt == "add" and self.db_identities is not None and identity in self.db_identities: 
-                        raise UpdatedbError("401") # push: identity has exist. to pretect the db, reject opt of this time 
+                        raise UpdatedbError("30019") # push: identity has exist. to pretect the db, reject opt of this time 
                     else:
                         
                         detect_result = self.fd.detect(img)
                         if len(detect_result) == 0: # no face
                             logger.info(f"{img_path[:200]}: when update, no face in the image")
                             print(f"{img_path[:200]}: when update, no face in the image")
-                            raise FaceError("301") # push: no face in the image 
+                            raise FaceError("30018") # push: no face in the image 
                         else: # 获取最大的face,然后进行check 
                             # H, W = img.shape[:2]
                             areas = [ box.area() for box in detect_result]
@@ -348,7 +393,7 @@ class FaceHelper:
                             facebox = detect_result[max_idx]
                             facebox = (facebox.x1, facebox.y1, facebox.x2, facebox.y2) # top_left point,  bottom_right point
                             FaceError_number = self.check_face(img_path=img_path[:200], img=img, facebox=facebox, prefix='update')
-                            if FaceError_number != "300":
+                            if FaceError_number != "30002":
                                 raise FaceError(FaceError_number)
 
                         cv2.imwrite(os.path.join(self.db_dir, identity+'.jpg'),img,[cv2.IMWRITE_JPEG_QUALITY, 100])  # 如果file已经存在，则会替换它
@@ -359,7 +404,7 @@ class FaceHelper:
                     except FileNotFoundError:
                         pass 
                 else:
-                    raise NotImpltError("202")  # push: this updateDB type is not support
+                    raise NotImpltError("30007")  # push: this updateDB type is not support
                 
                 print("end the updateDB")
                 logger.info(f"end the updateDB")
@@ -448,7 +493,7 @@ class FaceHelper:
                     
                     if check:
                         FaceError_number = self.check_face(img_path=new_image[:200], img=image, facebox=facebox, prefix='refreshdb')
-                        if FaceError_number != "300":
+                        if FaceError_number != "30002":
                             continue 
 
                     landmarks5 = self.ld5er.inference(image, facebox)  #  return: [(),(),(),(),()]  左眼 右眼 鼻子 左嘴角 右嘴角
@@ -489,14 +534,14 @@ class FaceHelper:
             if len(representations) <= 0:
                 self.db_embeddings = None
                 empty = True
-                # raise FileError("101")  # push: no face in db 
+                # raise FileError("30003")  # push: no face in db 
             else:
                 self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
                 self.db_identities = [os.path.splitext(os.path.basename(rep["identity"]))[0] for rep in representations]
             redis_client.set(f"worker_{self.pid}", 1)  # 当前进程已更新
             if empty: 
                 logger.info("no face in the database")
-                raise FileError("101")  # push: no face in db
+                raise FileError("30003")  # push: no face in db
 
     def rotadjust(self, img: np.ndarray):
         image = img.copy()
@@ -560,29 +605,29 @@ class FaceHelper:
         if facebox[0] < 0 or facebox[2] >= W:
             logger.info(f"{img_path}: when {prefix}, face shifted left/right")
             print(f"{img_path}: when {prefix}, face shifted left/right")
-            return "3041"  # face shifted left/right, partially not captured.
+            return "30010"  # face shifted left/right, partially not captured.
         if facebox[1] < 0 or facebox[3] >= H:
             logger.info(f"{img_path}: when {prefix}, face shifted top/bottom")
             print(f"{img_path}: when {prefix}, face shifted top/bottom")
-            return "3042" # face shifted top/bottom, partially not captured.
+            return "30011" # face shifted top/bottom, partially not captured.
         face_img = img[ max(0,int(facebox[1])):int(facebox[3]), max(0,int(facebox[0])):int(facebox[2]) ]
         if not self.qc.check_bright(face_img):
             logger.info(f"{img_path}: when {prefix}, bad bright face in the image")
             print(f"{img_path}: when {prefix}, bad bright face in the image")
-            return "303" #  bad bright face in the image
+            return "30009" #  bad bright face in the image
         if not self.qc.check_resolution(face_img):
             logger.info(f"{img_path}: when {prefix}, too small resolution of face in the image")
             print(f"{img_path}: when {prefix}, too small resolution of face in the image")
-            return "306"  #  small face in the image
+            return "30016"  #  small face in the image
         pose = self.qpose.check(face_img)
         if pose != "frontFace":
             logger.info(f"{img_path}: when {prefix}, {pose} in the image")
             print(f"{img_path}: when {prefix}, {pose} in the image")
-            dictt = {"rightFace": "3051", "leftFace": "3052", "upFace": "3053", "downFace": "3054"}
+            dictt = {"rightFace": "30012", "leftFace": "30013", "upFace": "30014", "downFace": "30015"}
             return dictt[pose] # pose of face in the image
         if not self.qclarity.check(face_img):
             logger.info(f"{img_path}: when {prefix}, bad clarity of face in the image")
             print(f"{img_path}: when {prefix}, bad clarity of face in the image")
-            return "307" #  bad clarity of face in the image
+            return "30017" #  poor clarity of face in the image
 
-        return "300"
+        return "30002"

FaceRegWeb5.2/FaceRegWeb/readme.md

@@ -3,6 +3,7 @@
 项目细节详见docx文档.  
 
 # requirements   
+```  
 fastapi                      0.103.2  
 uvicorn[standard]            0.22.0     
 opencv-python                4.9.0.80      
@@ -10,10 +11,11 @@ requests                     2.31.0
 Pillow                       9.4.0     
 redis                        5.0.6   
 onnxruntime                  1.18.1  
+```  
 推荐使用 `pip install` 安装上述库，可以不遵循其版本号（这里写出具体版本是为了复现原运行环境）.  
 
 # file struct  
-
+```
 ./             
     checkpoints     储存模型的目录  
     models          方法函数目录  
@@ -24,8 +26,9 @@ onnxruntime                  1.18.1
     redis.yaml      redis的配置文件  
     config.yaml     项目的配置文件  
     setup.sh        启动项目的脚本(linux版本)
+    reflushdb.sh    手动刷新数据库(usage: ./reflushdb.sh ip:port)    
     readme.md       本文件  
-
+```
 # setup 
 `bash setup.sh [ip] [port] [进程数] [并发工作线程数]`   
 e.g.: `bash setup.sh 0.0.0.0 8017 1 60`  

FaceRegWeb5.2/FaceRegWeb/reflushdb.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# keep input valid
+if [ "$#" -ne 1 ]; then
+    echo "Usage: $0 <server_url>"
+    exit 1
+fi
+
+# ip:host  
+SERVER=$1
+ENDPOINT="/refreshdb"
+
+# 使用 curl 调用接口
+response=$(curl -s -X POST "$SERVER$ENDPOINT" -H "Content-Type: application/json")
+
+# 打印响应
+echo "Response from $ENDPOINT: $response"
+

FaceRegWeb5.2/FaceRegWeb/webmain.py

@@ -18,9 +18,10 @@ class face(BaseModel):
 class dbface(BaseModel):
     img:str
     optMode:str
-    imgName:str 
+    uniqueKey:str 
 
 @app.post("/refreshdb")
+@app.post("/refreshdb/")
 def refresh():
     global facehelper
     try: 
@@ -30,9 +31,9 @@ def refresh():
         # return {"status":e.code, "detail":f"{e}"}
         return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
     else: 
-        return {'code': "300", 'msg': ErrorMsg['300'], 'data': 'None'}
+        return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': 'None'}
-
 
+@app.post("/facerecognition")
 @app.post("/facerecognition/")
 def faceRecognition(input:face):
     start = time.time()
@@ -49,19 +50,34 @@ def faceRecognition(input:face):
         return ret_data
         return {"status":1, "name":identity, "resImg":res_img_base64}
 
-
+@app.post("/featuredetect")
-@app.post("/updatedb/")
+@app.post("/featuredetect/")
-def updateDB(input:dbface):
+def featureDetect(input:face):
+    start = time.time()
     global facehelper
-    # 在这儿加一句，只取imgName中第一个`.`前面的部分str
-    input.imgName = os.path.splitext(os.path.basename(input.imgName))[0]  
-
     try: 
-        with lock:
+        ret_data = facehelper.featureDetect(input.img)
-            facehelper.updateDB(input.img, input.optMode, input.imgName)
+        print("finished featuredetect...")
+        end = time.time()
+        print("runtime: ", end-start)
     except Exception as e:
         return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
         # return {"status":f"{e.code}", "detail":f"{e}"} 
     else: 
-        return {'code': "300", 'msg': ErrorMsg['300'], 'data': 'None'}
+        return ret_data
+
+@app.post("/updatedb")
+@app.post("/updatedb/")
+def updateDB(input:dbface):
+    global facehelper
+    # input.uniqueKey = os.path.splitext(os.path.basename(input.uniqueKey))[0]  
+
+    try: 
+        with lock:
+            facehelper.updateDB(input.img, input.optMode, input.uniqueKey)
+    except Exception as e:
+        return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
+        # return {"status":f"{e.code}", "detail":f"{e}"}
+    else: 
+        return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': 'None'}