TranslateV831的人脸识别
2022-03-15
在文档中看到 V831 可以用来实现人脸识别，于是就将按键也添加到人脸识别中。
实现一个可以通过按键进行控制的人脸识别，进行人脸信息的添加和删除控制
源码
from maix import nn, camera, image, display
from maix.nn.app.face import FaceRecognize
import time
from evdev import InputDevice
from select import select
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或320*240空间内
# print(box,points)
if display.width() == display.height():
def tran(x):
return int(x/224*display.width())
box = list(map(tran, box))
def tran_p(p):
return list(map(tran, p))
points = list(map(tran_p, points))
else:
# 168x224(320x240) > 224x224(240x240) > 320x240
s = (224*display.height()/display.width()) # 168x224
w, h, c = display.width()/224, display.height()/224, 224/s
t, d = c*h, (224 - s) // 2 # d = 224 - s // 2 == 28
box[0], box[1], box[2], box[3] = int(box[0]*w), int((box[1]-28)*t), int(box[2]*w), int((box[3])*t)
def tran_p(p):
return [int(p[0]*w), int((p[1]-d)*t)] # 224 - 168 / 2 = 28 so 168 / (old_h - 28) = 240 / new_h
points = list(map(tran_p, points))
# print(box,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 = image.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
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:
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()
