去哪里建设自己的网站?百度推广搜索排名

接着前几天的继续。之前说到了添加人员，现在咱们继续后面剩余的部分。

一、人员的查删

除了增加以外，还有删改查部分的代码，不过修改其实在这里没啥多大的作用，我就没写，直接是查询和删除，修改应该是可以修改部门和重新更换照片，有兴趣的自己写，也是很简单。

    def get_staff(self, staff_id=None):cursor = self.conn.cursor()if staff_id:cursor.execute('SELECT * FROM staff WHERE id=? AND is_active=1', (staff_id,))return cursor.fetchone()cursor.execute('SELECT * FROM staff WHERE is_active=1')return cursor.fetchall()def delete_staff(self, staff_id):try:cursor = self.conn.cursor()cursor.execute('UPDATE staff SET is_active=0 WHERE id=?', (staff_id,))self.conn.commit()except sqlite3.Error as e:print(f"删除员工失败: {e}")raise

二、考勤规则

我这边考勤规则三次设置，8点30分前一次，中午11点30-14点一次，下午17点以后一次，第一次和第二次取符合时间区间的最早的记录，并且只记录一次，第三次则取时间区间内最晚的一次。也就是三个阶段

0：00-11：30第一阶段；  对应标识morning

11：30-14：00第二阶段   对应标识noon

14：00-23：59第三阶段   对应标识night

加标识非常方便后续的统计输出。

每个阶段都可打卡，这是考勤部分的可考勤时间规则不是最终统计符合考勤要求的规则。

    def record_attendance(self, staff_id, check_type, is_holiday=False):with self.lock:try:current_date = datetime.now().strftime('%Y-%m-%d')cursor = self.conn.cursor()# 检查该员工在当前日期、当前考勤时间段内是否已经有考勤记录cursor.execute('''SELECT id, check_time FROM attendance WHERE staff_id =? AND check_type =? AND date =?''', (staff_id, check_type, current_date))existing_record = cursor.fetchone()if existing_record:record_id, existing_check_time = existing_recordexisting_check_time = datetime.strptime(existing_check_time, "%Y-%m-%d %H:%M:%S.%f")current_time = datetime.now()if check_type in ('morning', 'noon'):# 第一次和第二次打卡取首次打卡时间，不更新passelif check_type == 'night':# 第三次打卡更新为最新时间cursor.execute('''UPDATE attendance SET check_time =? WHERE id =?''', (current_time, record_id))self.conn.commit()else:# 没有记录则插入新记录cursor.execute('''INSERT INTO attendance (staff_id, check_time, date, check_type, is_holiday)VALUES (?,?,?,?,?)''', (staff_id, datetime.now(), current_date, check_type, is_holiday))self.conn.commit()except sqlite3.Error as e:print(f"记录考勤失败: {e}")raise

代码中都做了注释了，不多解释了。

考勤时间区间的规则如此，则对应查询亦如此。

# 新增查询方法def query_attendance(self, staff_name=None, start_date=None, end_date=None):cursor = self.conn.cursor()conditions = []values = []if staff_name:conditions.append("s.name =?")values.append(staff_name)if start_date:conditions.append("a.date >=?")values.append(start_date)if end_date:conditions.append("a.date <=?")values.append(end_date)query = '''SELECT s.name, a.date, MIN(CASE WHEN a.check_type ='morning' THEN a.check_time END) AS morning_check_time,MIN(CASE WHEN a.check_type = 'noon' THEN a.check_time END) AS noon_check_time,MAX(CASE WHEN a.check_type = 'night' THEN a.check_time END) AS night_check_timeFROM attendance aJOIN staff s ON a.staff_id = s.id'''if conditions:query += " WHERE " + " AND ".join(conditions)query += " GROUP BY s.name, a.date"logging.info(f"执行查询: {query}, 参数: {values}")cursor.execute(query, tuple(values))return cursor.fetchall()

三、虹软SDK实现   重点内容

重点来了，虹软SDK实现，一个ArcSoftService类完整解决。一步步看。

很多代码都做了注释，直接看代码就很清晰了。

def __init__(self, db_service):self.db = db_serviceself.handle = Noneself.lib = Noneself.engine_mutex = QMutex()  # 添加互斥锁self.data_mutex = QMutex()  # 初始化data_mutextry:self._init_engine()self.known_ids, self.known_features = self._load_known_features()except Exception as e:print(f"ArcSoftService初始化失败: {e}")

引擎部分，包括加载SDK，激活检测等等，这里注意激活结果的代码输出，相关代码可以在虹软开发知识库上查到。

def _init_engine(self):self.appid = config.arcsoft_appidself.sdkkey = config.arcsoft_sdkkeytry:self.lib = cdll.LoadLibrary(config.arcsoft_lib_path)print("虹软SDK库文件加载成功")self.lib.ASFActivation.argtypes = [c_char_p, c_char_p]self.lib.ASFActivation.restype = c_intself.lib.ASFGetActiveFileInfo.argtypes = [POINTER(ASF_ActiveFileInfo)]self.lib.ASFGetActiveFileInfo.restype = c_intself.lib.ASFInitEngine.argtypes = [c_int, c_int, c_int, c_int, c_int, POINTER(c_void_p)]self.lib.ASFDetectFaces.restype = c_intself.lib.ASFFaceFeatureExtract.argtypes = [c_void_p, c_int, c_int, c_int, c_void_p,POINTER(ASF_SingleFaceInfo),POINTER(ASF_FaceFeature)]self.lib.ASFFaceFeatureExtract.restype = c_intself.lib.ASFFaceFeatureCompare.argtypes = [c_void_p, POINTER(ASF_FaceFeature),POINTER(ASF_FaceFeature),POINTER(c_float)]self.lib.ASFFaceFeatureCompare.restype = c_intself.lib.ASFUninitEngine.argtypes = [c_void_p]self.lib.ASFUninitEngine.restype = c_intexcept Exception as e:error_msg = f"虹软SDK库文件加载失败，请检查文件路径或重新安装SDK。具体错误信息: {e}"print(error_msg)QMessageBox.critical(None, "错误", error_msg)raise# 检查是否已经激活active_file_info = ASF_ActiveFileInfo()get_info_result = self.lib.ASFGetActiveFileInfo(ctypes.byref(active_file_info))if get_info_result == 0:print("虹软SDK已激活，跳过激活步骤")else:# 激活SDKactivation_result = self.lib.ASFActivation(c_char_p(self.appid),c_char_p(self.sdkkey))if activation_result != 0:print(f"虹软SDK激活失败，错误码: {activation_result}")self._handle_error(activation_result)raise Exception(f"虹软SDK激活失败，错误码: {activation_result}")else:print("虹软SDK激活成功")# 初始化引擎combinedMask = ASF_FACE_DETECT | ASF_FACERECOGNITION | ASF_AGE | ASF_GENDER | ASF_FACE3DANGLEdetectMode = ASF_DETECT_MODE_IMAGEdetectFaceOrientPriority = ASF_OP_0_ONLYminFaceSize = 20maxFaceNum = 50self.handle = c_void_p()init_result = self.lib.ASFInitEngine(detectMode,detectFaceOrientPriority,minFaceSize,maxFaceNum,combinedMask,ctypes.byref(self.handle))if init_result != 0:print(f"虹软引擎初始化失败，错误码: {init_result}")self._handle_error(init_result)raise Exception(f"虹软引擎初始化失败，错误码: {init_result}")else:print("虹软引擎初始化成功")

0就是激活成功了，其他错误代码如下：

def _handle_error(self, error_code):# 根据虹软SDK手册添加具体错误处理逻辑error_messages = {-1: "未知错误",-2: "参数错误",-3: "内存分配失败",-4: "引擎初始化失败",-5: "引擎释放失败",-6: "引擎未初始化",-7: "引擎已初始化",-8: "引擎激活失败",-9: "引擎激活已过期",-10: "引擎未激活",-11: "引擎激活次数超出限制",-12: "引擎激活失败，请检查激活码是否正确",# 添加其他错误码及其对应的错误信息}if error_code in error_messages:print(f"具体错误信息: {error_messages[error_code]}")else:print("未识别的错误码，请参考虹软SDK手册")

引擎释放

def _release_engine(self):if self.handle:self.engine_mutex.lock()  # 加锁try:uninit_result = self.lib.ASFUninitEngine(self.handle)if uninit_result != 0:print(f"虹软引擎释放失败，错误码: {uninit_result}")self._handle_error(uninit_result)self.handle = Noneprint("虹软引擎已释放")finally:self.engine_mutex.unlock()  # 解锁if self.lib:del self.libprint("虹软SDK库已卸载")

特征库提取

def refresh_data(self):self.data_mutex.lock()  # 加锁try:self.known_features.clear()self.known_ids.clear()staff_list = self.db.get_staff()# print(f"从数据库中获取到的人员数量: {len(staff_list)}")for staff in staff_list:self.known_ids.append(staff[0])# 虹软特征数据直接存储bytesfeature_bytes = staff[4]face_feature = ASF_FaceFeature()face_feature.feature = ctypes.cast(ctypes.create_string_buffer(feature_bytes), ctypes.c_void_p)face_feature.featureSize = len(feature_bytes)self.known_features.append(face_feature)# print(f"刷新后已知特征数量: {len(self.known_features)}")finally:self.data_mutex.unlock()  # 解锁def _load_known_features(self):known_ids = []known_features = []staff_list = self.db.get_staff()for staff in staff_list:known_ids.append(staff[0])feature_bytes = staff[4]face_feature = ASF_FaceFeature()face_feature.feature = ctypes.cast(ctypes.create_string_buffer(feature_bytes), ctypes.c_void_p)face_feature.featureSize = len(feature_bytes)known_features.append(face_feature)return known_ids, known_features

把OpenCV读取摄像头获取图像帧转换为虹软SDK所需的格式

def _convert_frame_to_arcsoft_format(self, frame):"""将OpenCV读取的图像帧转换为虹软SDK所需的格式:param frame: OpenCV读取的图像帧（numpy.ndarray）:return: 转换data, width, height, format"""height, width, channels = frame.shapeif channels == 3:format = ASF_PAF_RGB24_B8G8R8else:raise ValueError("不支持的图像通道数，仅支持3通道（RGB）图像")data = frame.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8))return data, width, height, format

人脸特征比对，支持多人识别

def recognize(self, frame):# print("进入ArcSoft识别方法")multi_face_info = ASF_MultiFaceInfo()data, width, height, format = self._convert_frame_to_arcsoft_format(frame)self.engine_mutex.lock()  # 加锁try:detect_result = self.lib.ASFDetectFaces(self.handle,width,height,format,data,ctypes.byref(multi_face_info))finally:self.engine_mutex.unlock()  # 解锁if detect_result != 0:print(f"虹软人脸检测失败，错误码：{detect_result}")self._handle_error(detect_result)return []print(f"检测到的人脸数量: {multi_face_info.faceNum}")recognized_ids = []for i in range(multi_face_info.faceNum):single_face_info = ASF_SingleFaceInfo()single_face_info.faceRect = multi_face_info.faceRect[i]single_face_info.faceOrient = multi_face_info.faceOrient[i]self.engine_mutex.lock()  # 加锁try:current_feature = ASF_FaceFeature()extract_result = self.lib.ASFFaceFeatureExtract(self.handle,width,height,format,data,ctypes.byref(single_face_info),ctypes.byref(current_feature))finally:self.engine_mutex.unlock()  # 解锁if extract_result != 0:print(f"虹软特征提取失败，错误码：{extract_result}")self._handle_error(extract_result)continueelse:print(f"虹软特征提取成功，特征大小: {current_feature.featureSize}")self.data_mutex.lock()  # 加锁try:for j, known_feature in enumerate(self.known_features):similarity = c_float()self.engine_mutex.lock()  # 加锁try:compare_result = self.lib.ASFFaceFeatureCompare(self.handle,ctypes.byref(current_feature),ctypes.byref(known_feature),ctypes.byref(similarity))finally:self.engine_mutex.unlock()  # 解锁if compare_result != 0:print(f"虹软特征比对失败，错误码：{compare_result}")self._handle_error(compare_result)continueprint(f"当前人脸与已知人脸{self.known_ids[j]}的相似度: {similarity.value}")print(f"比对阈值: {config.recognition_threshold}")  # 输出比对阈值if similarity.value > config.recognition_threshold:recognized_ids.append(self.known_ids[j])breakelse:print(f"当前人脸与已知人脸{self.known_ids[j]}相似度未达到阈值")finally:self.data_mutex.unlock()  # 解锁print(f"最终识别出的人员ID: {recognized_ids}")return recognized_ids

增加人员时，导入照片自动识别是否存在人脸并且提取特征

def get_face_feature(image_path):engine = Nonetry:engine = ArcSoftService(None)image = cv2.imread(image_path)data, width, height, format = engine._convert_frame_to_arcsoft_format(image)# 人脸检测multi_face_info = ASF_MultiFaceInfo()res = engine.lib.ASFDetectFaces(engine.handle,width,height,format,data,ctypes.byref(multi_face_info))if res == 0 and multi_face_info.faceNum > 0:single_face_info = ASF_SingleFaceInfo()single_face_info.faceRect = multi_face_info.faceRect[0]single_face_info.faceOrient = multi_face_info.faceOrient[0]# 特征提取face_feature = ASF_FaceFeature()res = engine.lib.ASFFaceFeatureExtract(engine.handle,width,height,format,data,ctypes.byref(single_face_info),ctypes.byref(face_feature))if res == 0:feature_bytes = ctypes.create_string_buffer(face_feature.featureSize)ctypes.memmove(feature_bytes, face_feature.feature, face_feature.featureSize)return feature_bytes.rawelse:print(f"虹软特征提取失败，错误码：{res}")engine._handle_error(res)else:print(f"虹软人脸检测失败，错误码：{res}或未检测到人脸")engine._handle_error(res)return Noneexcept Exception as e:print(f"虹软特征提取失败: {e}")return Nonefinally:if engine:engine._release_engine()

今天先到这里，后续还有。