from fpioa_manager import fm
from machine import UART
from board import board_info
from Maix import FPIOA,GPIO
import utime
import sensor,image,lcd
import KPU as kpu
import time
import os
fm.register(9,fm.fpioa.UART2_TX,force=True)
fm.register(10,fm.fpioa.UART2_RX,force=True)
fm.register(11, fm.fpioa.GPIO0)
led_r = GPIO(GPIO.GPIO0,GPIO.OUT)
uart_A = UART(UART.UART2,9600,8,0,1,read_buf_len=4096)
lcd.init(invert=True)
lcd.rotation(2)
sensor.reset() #初始化摄像头
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(1) #镜像
sensor.set_vflip(1) #翻转
img = image.Image()
while True:
img.draw_string(10, 20, "A. Face recognition\n\n\nB. Mask recognition\n\n\nC. LCDShow temperature", scale=2)
lcd.display(img)
utime.sleep_ms(10)
img.clear()
mode_selection = uart_A.read()
if mode_selection != None:
mode_str = mode_selection.decode()
print(mode_str)
if mode_str[0:1] == 'a':
#运行人脸识别程序
task_fd = kpu.load("/sd/FD.smodel")
task_ld = kpu.load("/sd/KP.smodel")
task_fe = kpu.load("/sd/FE.smodel")
clock = time.clock() #初始化时钟
key_pin=16 #设置引脚
fpioa = FPIOA()
fpioa.set_function(key_pin,FPIOA.GPIO7)
key_gpio=GPIO(GPIO.GPIO7,GPIO.IN)
last_key_state=1
key_pressed=0 #初始化引脚
def check_key(): #是否按下
global last_key_state
global key_pressed
val=key_gpio.value()
if last_key_state == 1 and val == 0:
key_pressed=1
else:
key_pressed=0
last_key_state = val
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025) #anchor for face detect 用于人脸检测的Anchor
dst_point = [(44,59),(84,59),(64,82),(47,105),(81,105)] #standard face key point position 标准正脸的5关键点坐标 分别为 左眼 右眼 鼻子 左嘴角 右嘴角
kpu.init_yolo2(task_fd, 0.5, 0.3, 5, anchor) #初始化模型
img_face=image.Image(size=(128,128)) #设置128 * 128图片
img_face.pix_to_ai()
record_ftr=[]
record_ftrs=[]
fll = True
names = ['Number1', 'Number2', 'Number3', 'Number4', 'Number5', 'Number6', 'Number7', 'Number8', 'Number9' , 'Number10'] # 人名标签,与上面列表特征值一一对应。
while fll: # 主循环
check_key() #按键检测
img = sensor.snapshot() #获取一张图片
clock.tick() #计算帧率
code = kpu.run_yolo2(task_fd, img) #运行人脸检测模型,获取人脸坐标位置
if code: #如果检测到人脸
for i in code: # 迭代坐标框
img.draw_rectangle(i.rect(),color=(0,0,255),scale=3) # 在屏幕显示人脸方框
face_cut=img.cut(i.x(),i.y(),i.w(),i.h()) # 裁剪人脸部分图片到 face_cut
face_cut_128=face_cut.resize(128,128) # 将裁出的人脸图片 缩放到128 * 128像素
a=face_cut_128.pix_to_ai() # 将猜出图片转换为kpu接受的格式
# Landmark for face 5 points
fmap = kpu.forward(task_ld, face_cut_128) # 运行人脸5点关键点检测模型
plist=fmap[:] # 获取关键点预测结果
le=(i.x()+int(plist[0]*i.w() - 10), i.y()+int(plist[1]*i.h())) # 计算左眼位置, 这里在w方向-10 用来补偿模型转换带来的精度损失
re=(i.x()+int(plist[2]*i.w()), i.y()+int(plist[3]*i.h())) # 右眼位置
nose=(i.x()+int(plist[4]*i.w()), i.y()+int(plist[5]*i.h())) #鼻子位置
lm=(i.x()+int(plist[6]*i.w()), i.y()+int(plist[7]*i.h())) #左嘴角位置
rm=(i.x()+int(plist[8]*i.w()), i.y()+int(plist[9]*i.h())) #右嘴角位置
src_point = [le, re, nose, lm, rm] # 图片中 5 坐标的位置
T = image.get_affine_transform(src_point, dst_point) # 根据获得的5点坐标与标准正脸坐标获取仿射变换矩阵
image.warp_affine_ai(img, img_face, T) #对原始图片人脸图片进行仿射变换,变换为正脸图像
img_face.ai_to_pix() # 将正脸图像转为kpu格式
#a = img.draw_image(img_face, (128,0))
del(face_cut_128) # 释放裁剪人脸部分图片
# calculate face feature vector
fmap = kpu.forward(task_fe, img_face) # 计算正脸图片的196维特征值
#a = kpu.deinit(task_fe)
feature=kpu.face_encode(fmap[:]) #获取计算结果
reg_flag = False
scores = [] # 存储特征比对分数
for j in range(len(record_ftrs)): #迭代已存特征值
score = kpu.face_compare(record_ftrs[j], feature) #计算当前人脸特征值与已存特征值的分数
scores.append(score) #添加分数总表
max_score = 0
index = 0
for k in range(len(scores)): #迭代所有比对分数,找到最大分数和索引值
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > 65: # 如果最大分数大于65, 可以被认定为同一个人
img.draw_string(i.x(),i.y(), ("%s" %names[index]), color=(0,255,0),scale=2) # 显示人名 与 分数
else:
led_r.value(0)
utime.sleep_ms(30)
led_r.value(1)
utime.sleep_ms(30)
img.draw_string(i.x(),i.y(), ("Stranger! ! !"), color=(255,0,0),scale=2) #显示未知 与 分数
if key_pressed == 1: #如果检测到按键
key_pressed = 0 #重置按键状态
record_ftr = feature
record_ftrs.append(record_ftr) #将当前特征添加到已知特征列表
break
lcd.display(img) #刷屏显示
mode_selection = uart_A.read()
if mode_selection != None:
mode_str = mode_selection.decode('UTF-8')
print(mode_str)
if mode_str[0:1] == '*':
fll = False
lcd.clear()
img.clear()
#lcd.clear()
kpu.deinit(task_fd)
kpu.deinit(task_ld)
kpu.deinit(task_fe)
kpu.memtest()
elif mode_str[0:1] == 'c':
pass
#fff = 70
#while fff:
#img.draw_string(100,100, ("Temperature: %s.%s C" %(mode_str[1:3],mode_str[3:5])), color=(255,200,0),scale=2)
#lcd.display(img)
#utime.sleep_ms(10)
#img.clear()
#fff = fff-1
#img.clear()
#lcd.clear()
elif mode_str[0:1] == 'b':
color_R = (255, 0, 0)
color_G = (0, 255, 0)
color_B = (0, 0, 255)
class_IDs = ['no_mask', 'mask']
def drawConfidenceText(image, rol, classid, value):
text = ""
_confidence = int(value * 100)
if classid == 1:
text = 'OK' #+ str(_confidence) + '%'
image.draw_string(rol[0], rol[1], text, color=color_G, scale=2.5)
else:
text = 'WARNING'# + str(_confidence) + '%'
image.draw_string(rol[0], rol[1], text, color=color_R, scale=2.5)
sensor.run(1)
task = kpu.load("/sd/mask.kmodel")
anchor = (0.1606, 0.3562, 0.4712, 0.9568, 0.9877, 1.9108, 1.8761, 3.5310, 3.4423, 5.6823)
kpu.init_yolo2(task, 0.5, 0.3, 5, anchor)
#img = image.Image()
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
code = kpu.run_yolo2(task, img)
if code:
totalRes = len(code)
for item in code:
confidence = float(item.value())
itemROL = item.rect()
classID = int(item.classid())
#if confidence < 0.52:
# _ = img.draw_rectangle(itemROL, color=color_B, tickness=5)
#continue
if classID == 1 and confidence > 0.65:
img.draw_rectangle(itemROL, color_G, tickness=4)
if totalRes == 1:
drawConfidenceText(img, (0, 0), 1, confidence)
else:
img.draw_rectangle(itemROL, color=color_R, tickness=8)
if totalRes == 1:
drawConfidenceText(img, (0, 0), 0, confidence)
led_r.value(0)
utime.sleep_ms(30)
led_r.value(1)
utime.sleep_ms(30)
lcd.display(img)
print(clock.fps())
mode_selection = uart_A.read()
if mode_selection != None:
mode_str = mode_selection.decode('UTF-8')
#mode_selection = char(mode_selection
print(mode_str)
if mode_str[0:1] == '*':
kpu.deinit(task)
kpu.memtest()
img.clear()
lcd.clear()
break
uart_A.deinit()
del uart_A