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V831——人脸识别通过串口向下位机发送指令

时间：2022-12-22 08:00:00 继电器jqx一62fjqx一64m继电器

V831

文章目录

人脸识别串口发送指令

前言

V831人脸识别通过XY坐标和Z的距离判断向下位机发送的数据，下位机处理后发送数据。

一、V831人脸识别

读取模型文件

class Face_recognize :     score_threshold = 70                            #识别分数阈值     input_size = (224, 224, 3)                      #输入图片尺寸     input_size_fe = (128, 128, 3)                   #输入人脸数据     feature_len = 256                               #人脸数据宽度     steps = [8, 16, 32]                             #     channel_num = 0                                 #通道数量     users = []                                      #初始化用户列表     threshold = 0.5                                         #人脸阈值     nms = 0.3                                                    max_face_num = 3                                        #输出图中人脸最大数量     names = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"] #人脸标签定义 model = { 
          "param": "/home/model/face_recognize/model_int8.param", "bin": "/home/model/face_recognize/model_int8.bin" } model_fe = { 
          "param": "/home/model/face_recognize/fe_res18_117.param", "bin": "/home/model/face_recognize/fe_res18_117.bin" } def __init__(self): from maix import nn, camera, image, display from maix.nn.app.face import FaceRecognize for i in range(len(self.steps)): self.channel_num += self.input_size[1] / self.steps[i] * (self.input_size[0] / self.steps[i]) * 2 self.channel_num = int(self.channel_num) #统计通道数量 self.options = { 
          #准备人脸输出参数 "model_type": "awnn", "inputs": { 
          "input0": self.input_size }, "outputs": { 
          "output0": (1, 4, self.channel_num) , "431": (1, 2, self.channel_num) , "output2": (1, 10, self.channel_num) }, "mean": [127.5, 127.5, 127.5], "norm": [0.0078125, 0.0078125, 0.0078125], } self.options_fe = { 
          #准备特征提取参数 "model_type": "awnn", "inputs": { 
          "inputs_blob": self.input_size_fe }, "outputs": { 
          "FC_blob": (1, 1, self.feature_len) }, "mean": [127.5, 127.5, 127.5], "norm": [0.0078125, 0.0078125, 0.0078125], } print("-- load model:", self.model) self.m = nn.load(self.model, opt=self.options) print("-- load ok") print("-- load model:", self.model_fe) self.m_fe = nn.load(self.model_fe, opt=self.options_fe) print("-- load ok") self.face_recognizer = FaceRecognize(self.m, self.m_fe, self.feature_len, self.input_size, self.threshold, self.nms, self.max_face_num) def map_face(self, box,points): #将224*224空间的位置转换到240*240空间内 def tran(x): return int(x/224*240) box = list(map(tran, box)) def tran_p(p): return list(map(tran, p)) points = list(map(tran_p, points)) return box,points def recognize(self, feature): #进行人脸匹配 def _compare(user): #定义映射函数 return self.face_recognizer.compare(user, feature) #推测匹配分数 score相关分数 face_score_l = list(map(_compare,self.users)) #映射特征数据在记录中的比对分数 return max(enumerate(face_score_l), key=lambda x: x[-1]) #提取出人脸分数最大值和最大值所在的位置 def __del__(self): del self.face_recognizer del self.m_fe del self.m global face_recognizer face_recognizer = Face_recognize()

寻找人脸

from maix import camera, image, display
while True:
    img = camera.capture()                       #获取224*224*3的图像数据
    AI_img = img.copy().resize(224, 224)
    faces = face_recognizer.face_recognizer.get_faces(AI_img.tobytes(),False)           #提取人脸特征信息
    
    if faces:
        for prob, box, landmarks, feature in faces:
            disp_str = "Unmarked face"
            bg_color = (255, 0, 0)
            font_color=(255, 255, 255)
            box,points = face_recognizer.map_face(box,landmarks)
            font_wh = img.get_string_size(disp_str)
            for p in points:
                img.draw_rectangle(p[0] - 1, p[1] -1, p[0] + 1, p[1] + 1, color=bg_color)
            img.draw_rectangle(box[0], box[1], box[0] + box[2], box[1] + box[3], color=bg_color, thickness=2)
            img.draw_rectangle(box[0], box[1] - font_wh[1], box[0] + font_wh[0], box[1], color=bg_color, thickness = -1)
            img.draw_string(box[0], box[1] - font_wh[1], disp_str, color=font_color)
    display.show(img)

二、代码实现

串口和测距相关的知识看之前的博客，这里直接附上源码

from maix import camera, image, display
import serial
ser = serial.Serial("/dev/ttyS1",115200)    # 连接串口 UART-1 TX （PG6）和 UART-1 RX (PG7)
K=116

x=0
y=0
class Face_recognize :
    score_threshold = 70                            #识别分数阈值
    input_size = (224, 224, 3)                      #输入图片尺寸
    input_size_fe = (128, 128, 3)                   #输入人脸数据
    feature_len = 256                               #人脸数据宽度
    steps = [8, 16, 32]                             #
    channel_num = 0                                 #通道数量
    users = []                                      #初始化用户列表
    threshold = 0.5                                         #人脸阈值
    nms = 0.3
    max_face_num = 3                                        #输出的画面中的人脸的最大个数

    model = { 
        
        "param": "/home/model/face_recognize/model_int8.param",
        "bin": "/home/model/face_recognize/model_int8.bin"
    }
    model_fe = { 
        
        "param": "/home/model/face_recognize/fe_res18_117.param",
        "bin": "/home/model/face_recognize/fe_res18_117.bin"
    }
    def __init__(self):
        from maix import nn, camera, image, display
        from maix.nn.app.face import FaceRecognize
        for i in range(len(self.steps)):
            self.channel_num += self.input_size[1] / self.steps[i] * (self.input_size[0] / self.steps[i]) * 2
        self.channel_num = int(self.channel_num)     #统计通道数量
        self.options = { 
                                     #准备人脸输出参数
            "model_type":  "awnn",
            "inputs": { 
        
                "input0": self.input_size
            },
            "outputs": { 
        
                "output0": (1, 4, self.channel_num) ,
                "431": (1, 2, self.channel_num) ,
                "output2": (1, 10, self.channel_num)
            },
            "mean": [127.5, 127.5, 127.5],
            "norm": [0.0078125, 0.0078125, 0.0078125],
        }
        self.options_fe = { 
                                     #准备特征提取参数
            "model_type":  "awnn",
            "inputs": { 
        
                "inputs_blob": self.input_size_fe
            },
            "outputs": { 
        
                "FC_blob": (1, 1, self.feature_len)
            },
            "mean": [127.5, 127.5, 127.5],
            "norm": [0.0078125, 0.0078125, 0.0078125],
        }
        print("-- load model:", self.model)
        self.m = nn.load(self.model, opt=self.options)
        print("-- load ok")
        print("-- load model:", self.model_fe)
        self.m_fe = nn.load(self.model_fe, opt=self.options_fe)
        print("-- load ok")
        self.face_recognizer = FaceRecognize(self.m, self.m_fe, self.feature_len, self.input_size, self.threshold, self.nms, self.max_face_num)

    def map_face(self, box,points):                           #将224*224空间的位置转换到240*240空间内
        def tran(x):
            return int(x/224*240)
        box = list(map(tran, box))
        def tran_p(p):
            return list(map(tran, p))
        points = list(map(tran_p, points))
        return box,points

    def recognize(self, feature):                                                                   #进行人脸匹配
        def _compare(user):                                                         #定义映射函数
            return self.face_recognizer.compare(user, feature)                      #推测匹配分数 score相关分数
        face_score_l = list(map(_compare,self.users))                               #映射特征数据在记录中的比对分数
        return max(enumerate(face_score_l), key=lambda x: x[-1])                #提取出人脸分数最大值和最大值所在的位置




    def __del__(self):
        del self.face_recognizer
        del self.m_fe
        del self.m

global face_recognizer
face_recognizer = Face_recognize()
while True:
    img = camera.capture()                       #获取224*224*3的图像数据
    AI_img = img.copy().resize(224, 224)
    faces = face_recognizer.face_recognizer.get_faces(AI_img.tobytes(),False)           #提取人脸特征信息
    if faces:
        for prob, box, landmarks, feature in faces:
            disp_str = "face"
            bg_color = (0, 255, 0)
            font_color=(255, 0, 0)
            box,points = face_recognizer.map_face(box,landmarks)
            font_wh = img.get_string_size(disp_str)
            for p in points:
                img.draw_rectangle(p[0] - 1, p[1] -1, p[0] + 1, p[1] + 1, color=bg_color)
            img.draw_rectangle(box[0], box[1], box[0] + box[2], box[1] + box[3], color=bg_color, thickness=2)
            img.draw_rectangle(box[0], box[1] - font_wh[1], box[0] + font_wh[0], box[1], color=bg_color, thickness = -1)
            img.draw_string(box[0], box[1] - font_wh[1], disp_str, color=font_color)
            img.draw_string(0,30, "x="+str(((box[0]+box[3])/2-28)), color= font_color)
            img.draw_string(70,30, "y="+str((box[1]+box[2])/2-20), color= font_color)
            x=(box[0]+box[3])/2-28  #x坐标
            y=(box[1]+box[2])/2-20  #y坐标
            Lm = (box[1]+box[3])/2

            length = K*13/Lm #距离

            img.draw_string(0,60 , "Z="+str(round(length)), color= font_color)

            if  1<x<33 and 1<y<33 and 100>length>50:
                ser.write(b"0x5a 0x01 0x02 0x03 0x04\r\n")
                tmp=ser.read(4) #串口读取数据
                ser.write(tmp)
            if  1<x<33 and 1<y<33 and 150>length>100:
                ser.write(b"0x5a 0x01 0x02 0x03 0x04\r\n")
                tmp=ser.read(4) #串口读取数据.
                ser.write(tmp)
            if  1<x<33 and 1<y<33 and 200>length>150:
                ser.write(b"0x5a 0x01 0x02 0x03 0x04\r\n")
                tmp=ser.read(4) #串口读取数据
                ser

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