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from arcsoft import lib_func
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from arcsoft.struct_info import *
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MOK = 0 # 函数调用成功状态码
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MERR_ASF_ALREADY_ACTIVATED = 90114 # 已激活状态返回码
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ASF_NONE = 0x00000000 # 无属性
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ASF_FACE_DETECT = 0x00000001 # 此处detect可以是tracking或者detection两个引擎之一，具体的选择由detect mode 确定
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ASF_FACERECOGNITION = 0x00000004 # 人脸特征
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ASF_AGE = 0x00000008 # 年龄
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ASF_GENDER = 0x00000010 # 性别
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ASF_FACE3DANGLE = 0x00000020 # 3D角度
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ASF_LIVENESS = 0x00000080 # RGB活体
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ASF_IR_LIVENESS = 0x00000400 # 红外活体
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ASF_DETECT_MODE_VIDEO = 0x00000000 # Video模式，一般用于多帧连续检测
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ASF_DETECT_MODE_IMAGE = 0xFFFFFFFF # Image模式，一般用于静态图的单次检测
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ASVL_PAF_RGB24_B8G8R8 = 0x201 # RGB24位图，bgr格式(opencv 读取图像的默认格式)
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ASVL_PAF_GRAY = 0x701 # 灰度图
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# 人脸检测方向
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ASF_OP_0_ONLY = 0x1 # 仅检测 0 度
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ASF_OP_90_ONLY = 0x2 # 仅检测 90 度
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ASF_OP_270_ONLY = 0x3 # 仅检测 270 度
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ASF_OP_180_ONLY = 0x4 # 仅检测 180 度
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ASF_OP_0_HIGHER_EXT = 0x5 # 全角度检测
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# 检测到的人脸角度
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ASF_OC_0 = 0x1 # 0 度
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ASF_OC_90 = 0x2 # 90 度
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ASF_OC_270 = 0x3 # 270 度
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ASF_OC_180 = 0x4 # 180 度
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ASF_OC_30 = 0x5 # 30 度
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ASF_OC_60 = 0x6 # 60 度
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ASF_OC_120 = 0x7 # 120 度
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ASF_OC_150 = 0x8 # 150 度
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ASF_OC_210 = 0x9 # 210 度
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ASF_OC_240 = 0xa # 240 度
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ASF_OC_300 = 0xb # 300 度
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ASF_OC_330 = 0xc # 330 度
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def ASFOnlineActivation(Appkey, SDKey):
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"""
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在线激活 SDK
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:param Appkey: 官网获取的 APPID
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:param SDKey: 官网获取的 SDKKEY
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:return: 状态码
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"""
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res = lib_func.ASFOnlineActivation(Appkey, SDKey)
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if res != 90114 and res != 0:
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print("激活失败!错误码:{}".format(res))
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return res
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def ASFGetActiveFileInfo():
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"""
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获取激活文件信息
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:return: 状态码， 激活文件信息
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"""
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activeFileInfo = ASF_ActiveFileInfo()
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return lib_func.ASFGetActiveFileInfo(byref(activeFileInfo)), activeFileInfo
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class ArcFace():
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def __init__(self):
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self.Handle = c_void_p() # 引擎指针对象
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def ASFInitEngine(self, detectMode: int, detectFaceOrientPriority: int, detectFaceScaleVal: int,
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detectFaceMaxNum: int, combinedMask: int):
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"""
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初始化引擎
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:param detectMode: VIDEO 模式/IMAGE 模式 ,VIDEO 模式:处理连续帧的图像数据 IMAGE 模式:处理单张的图像数据
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:param detectFaceOrientPriority: 人脸检测角度，推荐单一角度检测；IMAGE 模式下不支持全角度（ASF_OP_0_HIGHER_EXT）检测
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:param detectFaceScaleVal: 识别的最小人脸比例（图片长边与人脸框长边的比值）,VIDEO 模式取值范围[2,32]，推荐值为 16 ,IMAGE 模式取值范围[2,32]，推荐值为 30
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:param detectFaceMaxNum: 最大需要检测的人脸个数，取值范围[1,50]
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:param combinedMask: 需要启用的功能组合，可多选
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:return: 状态码
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"""
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return lib_func.ASFInitEngine(detectMode, detectFaceOrientPriority, detectFaceScaleVal, detectFaceMaxNum,
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combinedMask, byref(self.Handle))
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def ASFDetectFaces(self, frame):
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"""
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人脸检测
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:param frame: 原始图像：注意：图片宽度必须 为 4 的倍数
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:return: 状态码,人脸检测信息
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"""
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height, width = frame.shape[:2]
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detectedFaces = ASF_MultiFaceInfo()
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res = lib_func.ASFDetectFaces(self.Handle, int(width), int(height), ASVL_PAF_RGB24_B8G8R8,
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frame.ctypes.data_as(POINTER(c_ubyte)), byref(detectedFaces))
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return res, detectedFaces
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def ASFProcess_IR(self, frame, detectedFaces, processIRMask=ASF_IR_LIVENESS):
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"""
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:param frame: 原始图像：注意：图片宽度必须 为 4 的倍数
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:param detectedFaces: 多人脸检测信息对象
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:param processIRMask: 当前只有ASF_IR_LIVENESS 一种选择
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注：检测的属性须在引擎初始化接口的 combinedMask 参 数中启用
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:return:
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"""
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height, width = frame.shape[:2]
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res = lib_func.ASFProcess_IR(self.Handle, int(width), int(height), ASVL_PAF_GRAY,
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frame.ctypes.data_as(POINTER(c_ubyte)), byref(detectedFaces), processIRMask)
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return res
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def ASFFaceFeatureExtract(self, frame, singleFaceInfo: ASF_SingleFaceInfo):
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"""
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人脸特征提取
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:param frame: 原始图像：注意：图片宽度必须 为 4 的倍数
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:param singleFaceInfo: 单个人脸检测框信息
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:return: 状态码,人脸检测信息
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"""
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height, width = frame.shape[:2]
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face_feature = ASF_FaceFeature()
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res = lib_func.ASFFaceFeatureExtract(self.Handle, int(width), int(height), ASVL_PAF_RGB24_B8G8R8,
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frame.ctypes.data_as(POINTER(c_ubyte)), singleFaceInfo,
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byref(face_feature))
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copy_face_feature = ASF_FaceFeature()
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copy_face_feature.featureSize = face_feature.featureSize
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copy_face_feature.feature = lib_func.malloc(face_feature.featureSize)
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lib_func.memcpy(copy_face_feature.feature, face_feature.feature, face_feature.featureSize)
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return res, copy_face_feature
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def ASFFaceFeatureCompare(self, face_feature1: ASF_FaceFeature, face_feature2: ASF_FaceFeature):
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"""
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人脸特征比较
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:param face_feature1: 特征对象1
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:param face_feature2: 特征对象2
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:return: 状态码，人脸得分
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"""
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compare_score = c_float()
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ret = lib_func.ASFFaceFeatureCompare(self.Handle, face_feature1, face_feature2, byref(compare_score))
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return ret, compare_score.value
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def ASFProcess(self, frame, detectedFaces: ASF_MultiFaceInfo, combinedMask: int):
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"""
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人脸信息检测（年龄/性别/人脸 3D 角度/rgb活体），最多支持 4 张人脸信息检测，超过部分返回未知（活体仅支持单张人脸检测，超出返回未知）
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:param frame: 原始图像：注意：图片宽度必须 为 4 的倍数
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:param detectedFaces: 多人脸检测信息对象
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:param combinedMask: 检测的属性（ASF_AGE、ASF_GENDER、ASF_FACE3DANGLE、ASF_LIVENESS），支持多选
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注：检测的属性须在引擎初始化接口的 combinedMask 参 数中启用
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:return:
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"""
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height, width = frame.shape[:2]
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res = lib_func.ASFProcess(self.Handle, int(width), int(height), ASVL_PAF_RGB24_B8G8R8,
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frame.ctypes.data_as(POINTER(c_ubyte)),
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byref(detectedFaces), combinedMask)
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return res
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def ASFGetFace3DAngle(self):
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"""
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获取 3D 角度信息
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:return: 状态码，人脸3d角度信息
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"""
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angleInfo = ASF_Face3DAngle()
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res = lib_func.ASFGetFace3DAngle(self.Handle, angleInfo)
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return res, angleInfo
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def ASFGetLivenessScore(self):
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"""
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获取 RGB 活体信息
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:return:状态码，RGB 活体信息
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"""
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rgbLivenessInfo = ASF_LivenessInfo()
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res = lib_func.ASFGetLivenessScore(self.Handle, byref(rgbLivenessInfo))
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return res, rgbLivenessInfo
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def ASFGetAge(self):
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"""
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获取年龄信息
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:return: :状态码，年龄信息
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"""
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ageInfo = ASF_AgeInfo()
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res = lib_func.ASFGetAge(self.Handle, byref(ageInfo))
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return res, ageInfo
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def ASFGetGender(self):
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"""
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获取性别信息
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:return:状态码，性别信息
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"""
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genderInfo = ASF_GenderInfo()
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res = lib_func.ASFGetGender(self.Handle, byref(genderInfo))
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return res, genderInfo
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def ASFGetLivenessScore_IR(self):
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"""
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获取IR识别结果
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:return:状态码，IR识别结果
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"""
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irLivenessInfo = ASF_LivenessInfo()
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res = lib_func.ASFGetLivenessScore_IR(self.Handle, byref(irLivenessInfo))
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return res, irLivenessInfo
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def ASFSetLivenessParam(self, threshold: ASF_LivenessThreshold):
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"""
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设置活体检测置信度
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:return:
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"""
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return lib_func.ASFSetLivenessParam(self.Handle, threshold)
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def ASFUninitEngine(self):
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"""
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销毁引擎
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:return: 状态码
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"""
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return lib_func.ASFUninitEngine(self.Handle)
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# 从二进制数据获取特征值
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def ASFGetFeatureFromBinary(self, binaryValue):
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face_feature = ASF_FaceFeature()
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b = binaryValue
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face_feature.featureSize = b.__len__()
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face_feature.feature = lib_func.malloc(face_feature.featureSize)
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lib_func.memcpy(face_feature.feature, b, face_feature.featureSize)
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return face_feature
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