V831——人脸识别开锁
V831
文章目录
- V831
- 前言
- 一、读取模型文件
- 二、识别人脸
-
- 1.找人脸
- 2.添加人脸
- 3.识别人脸
- 三、代码实现
- 总结
人脸开锁
前言
前面做了关于人脸识别的项目，后续会发出来，下午顺便做了一个人脸识别开锁，通过录入人脸，然后进行识别，识别到正确的人脸进行开锁，错误人脸不开锁。不会驱动舵机的去看上一篇博客，这一篇博客只有人脸识别。
一、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 #输出的画面中的人脸的最大个数
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()
二、识别人脸
1.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)
2.添加人脸
from maix import camera, image, display
face_flage = 1
while face_flage:
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:
if len(face_recognizer.users) < len(face_recognizer.names):
face_recognizer.users.append(feature)
face_flage = 0
else:
print("user full")
disp_str = "add face"
bg_color = (0, 255, 0)
font_color=(0, 0, 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)
3.识别人脸
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:
if len(face_recognizer.users): #判断是否记录人脸
maxIndex = face_recognizer.recognize(feature)
if maxIndex[1] > face_recognizer.score_threshold: #判断人脸识别阈值,当分数大于阈值时认为是同一张脸,当分数小于阈值时认为是相似脸
disp_str = "{}".format(face_recognizer.names[maxIndex[0]])
bg_color = (0, 255, 0)
font_color=(0, 0, 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)
else:
disp_str = "error 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)
else: #没有记录脸
disp_str = "error 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)
三、代码实现
通过右键录入人脸，左键删除人脸。
from maix import nn, camera, image, display
from maix.nn.app.face import FaceRecognize
import time
from evdev import InputDevice
from select import select
import pickle
from maix import pwm
import time
pwm6 = pwm.PWM(6) #选择通道 这里接PH6
pwm6.export() #设置出口
pwm6.period = 20000000 # 表示 pwm 的周期，单位 ns
pwm6.duty_cycle = 10000000 # 表示占空比，单位 ns
pwm6.enable = True # 表示是否使能 pwm
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
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"
}
for i in range(len(steps)):
channel_num += input_size[1] / steps[i] * (input_size[0] / steps[i]) * 2
channel_num = int(channel_num) #统计通道数量
options = { #准备人脸输出参数
"model_type": "awnn",
"inputs": {
"input0": input_size
},
"outputs": {
"output0": (1, 4, channel_num) ,
"431": (1, 2, channel_num) ,
"output2": (1, 10, channel_num)
},
"mean": [127.5, 127.5, 127.5],
"norm": [0.0078125, 0.0078125, 0.0078125],
}
options_fe = { #准备特征提取参数
"model_type": "awnn",
"inputs": {
"inputs_blob": input_size_fe
},
"outputs": {
"FC_blob": (1, 1, feature_len)
},
"mean": [127.5, 127.5, 127.5],
"norm": [0.0078125, 0.0078125, 0.0078125],
}
keys = InputDevice('/dev/input/event0')
threshold = 0.5 #人脸阈值
nms = 0.3
max_face_num = 1 #输出的画面中的人脸的最大个数
print("-- load model:", model)
m = nn.load(model, opt=options)
print("-- load ok")
print("-- load model:", model_fe)
m_fe = nn.load(model_fe, opt=options_fe)
print("-- load ok")
face_recognizer = FaceRecognize(m, m_fe, feature_len, input_size, threshold, nms, max_face_num)
def get_key(): #按键检测函数
r,w,x = select([keys], [], [],0)
if r:
for event in keys.read():
if event.value == 1 and event.code == 0x02: # 右键
return 1
elif event.value == 1 and event.code == 0x03: # 左键
return 2
elif event.value == 2 and event.code == 0x03: # 左键连按
return 3
return 0
def map_face(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 darw_info(draw, box, points, disp_str, bg_color=(255, 0, 0), font_color=(255, 255, 255)): #画框函数
box,points = map_face(box,points)
font_wh = draw.get_string_size(disp_str)
for p in points:
draw.draw_rectangle(p[0] - 1, p[1] -1, p[0] + 1, p[1] + 1, color=bg_color)
draw.draw_rectangle(box[0], box[1], box[0] + box[2], box[1] + box[3], color=bg_color, thickness=2)
draw.draw_rectangle(box[0], box[1] - font_wh[1], box[0] + font_wh[0], box[1], color=bg_color, thickness = -1)
draw.draw_string(box[0], box[1] - font_wh[1], disp_str, color=font_color)
def recognize(feature): #进行人脸匹配
def _compare(user): #定义映射函数
return face_recognizer.compare(user, feature) #推测匹配分数 score相关分数
face_score_l = list(map(_compare,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 run():
img = camera.capture() #获取224*224*3的图像数据
AI_img = img.copy().resize(224, 224)
if not img:
time.sleep(0.02)
return
faces = 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:
key_val = get_key()
if key_val == 1: # 右键添加人脸记录
if len(users) < len(names):
print("add user:", len(users))
users.append(feature)
else:
print("user full")
elif key_val == 2: # 左键删除人脸记录
if len(users) > 0:
print("remove user:", names[len(users) - 1])
users.pop()
else:
print("user empty")
if len(users): #判断是否记录人脸
maxIndex = recognize(feature)
if maxIndex[1] > score_threshold: #判断人脸识别阈值,当分数大于阈值时认为是同一张脸,当分数小于阈值时认为是相似脸
pwm6.duty_cycle = 15000000
darw_info(img, box, landmarks, "{}:{:.2f}".format(names[maxIndex[0]], maxIndex[1]), font_color=(0, 0, 255, 255), bg_color=(0, 255, 0, 255))
print("user: {}, score: {:.2f}".format(names[maxIndex[0]], maxIndex[1]))
else:
pwm6.duty_cycle = 10000000
darw_info(img, box, landmarks, "{}:{:.2f}".format(names[maxIndex[0]], maxIndex[1]), font_color=(255, 255, 255, 255), bg_color=(255, 0, 0, 255))
print("maybe user: {}, score: {:.2f}".format(names[maxIndex[0]], maxIndex[1]))
else: #没有记录脸
darw_info(img, box, landmarks, "error face", font_color=(255, 255, 255, 255), bg_color=(255, 0, 0, 255))
display.show(img)
if __name__ == "__main__":
import signal
def handle_signal_z(signum,frame):
print("APP OVER")
exit(0)
signal.signal(signal.SIGINT,handle_signal_z)
while True:
run()
总结
虽然可以做到识别指定人脸进行开锁，但是复位之后还需要重新读取，并不完善，后续会进行完善，做到录入之后可以一直使用。