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分享摄像头数字识别：arduino和openmv
先放代码段
视频演示：
arduino端代码：
#include <U8g2lib.h>
int Aim;
int inData;
void setup() {
Serial.begin(9600);
Serial1.begin(9600);//连接蓝牙
Serial2.begin(9600);//连接openmv
Serial3.begin(9600);
}
void OpenmvRead(void)
{
while(Serial2.read()>=0);//清除缓存
while(Serial2.available()==0);//等待信号传输
if(Serial2.available())//接收到openmv发来的病房号数字
{
inData=Serial2.read();//读取接收的字符
if(inData=='1')Aim=1;
if(inData=='2')Aim=2;
if(inData=='3')Aim=3;
if(inData=='4')Aim=4;
if(inData=='5')Aim=5;
if(inData=='6')Aim=6;
if(inData=='7')Aim=7;
if(inData=='8')Aim=8;
inData=' ';
while(Serial2.read()>=0);//清除缓存区数据
Serial.println(Aim);
delay(1000);
Serial.println("ok");
}
}
void loop() { //调试
Serial.println("loop");
OpenmvRead();//获取任务码
}
只是用来把识别的数字打印到串口显示用，目前openmv只是调试阶段，都是最基本的代码
openmv端代码：
# Template Matching Example - Normalized Cross Correlation (NCC)
#
# This example shows off how to use the NCC feature of your OpenMV Cam to match
# image patches to parts of an image... expect for extremely controlled enviorments
# NCC is not all to useful.
#
# WARNING: NCC supports needs to be reworked! As of right now this feature needs
# a lot of work to be made into somethin useful. This script will reamin to show
# that the functionality exists, but, in its current state is inadequate.
import time, sensor, image,os,tf
from image import SEARCH_EX, SEARCH_DS
from pyb import UART
#从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX,
#SEARCH_DS两个需要的部分，而不把image模块全部引入。
uart = UART(3, 9600)
# Reset sensor
sensor.reset()
# Template Matching Example - Normalized Cross Correlation (NCC)
#
# This example shows off how to use the NCC feature of your OpenMV Cam to match
# image patches to parts of an image... expect for extremely controlled enviorments
# NCC is not all to useful.
#
# WARNING: NCC supports needs to be reworked! As of right now this feature needs
# a lot of work to be made into somethin useful. This script will reamin to show
# that the functionality exists, but, in its current state is inadequate.
import time, sensor, image,os,tf
from image import SEARCH_EX, SEARCH_DS
from pyb import UART
#从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX,
#SEARCH_DS两个需要的部分，而不把image模块全部引入。
uart = UART(3, 9600)
# Reset sensor
sensor.reset()
# Set sensor settings
# Max resolution for template matching with SEARCH_EX is QQVGA
sensor.set_framesize(sensor.QQVGA)
# You can set windowing to reduce the search image.
#sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))
sensor.set_pixformat(sensor.GRAYSCALE)
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template1 = "/1.pgm"
template2 = "/2.pgm"
template3 = "/3.pgm"
template4 = "/4.pgm"
template5 = "/5.pgm"
template6 = "/6.pgm"
template7 = "/7.pgm"
template8 = "/8.pgm"
A0=1
A9=1
B0=1
A1=0
A2=0
A3=0
A4=0
A5=0
A6=0
A7=0
A8=0
B0=1
C0=1
C1=0
C2=1
D0=1
D1=1
clock = time.clock()
# Run template matching
while (True):
uart = UART(3, 9600)
sensor.reset()
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
template1 = "/1.pgm"
template2 = "/2.pgm"
template3 = "/3.pgm"
template4 = "/4.pgm"
template5 = "/5.pgm"
template6 = "/6.pgm"
template7 = "/7.pgm"
template8 = "/8.pgm"
A0=1
A9=1
B0=1
A1=0
A2=0
A3=0
A4=0
A5=0
A6=0
A7=0
A8=0
B0=1
C0=1
C1=0
C2=1
D0=1
D1=1
clock.tick()
img = sensor.snapshot()
A0=A9=1
while(A0):
clock.tick()
img = sensor.snapshot()
t1 = image.Image(template1)
r1 = img.find_template(t1, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r1:
img.draw_rectangle(r1)
A1=1
A0=0
t2 = image.Image(template2)
r2 = img.find_template(t2, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r2:
img.draw_rectangle(r2)
A2=1
A0=0
t3 = image.Image(template3)
r3 = img.find_template(t3, 0.85, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r3:
img.draw_rectangle(r3)
print('3') #打印模板名字
A3=1
A0=0
t4 = image.Image(template4)
r4 = img.find_template(t4, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r4:
img.draw_rectangle(r4)
print('4') #打印模板名字
A4=1
A0=0
t5 = image.Image(template5)
r5 = img.find_template(t5, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r5:
img.draw_rectangle(r5)
print('5') #打印模板名字
A5=1
A0=0
t6 = image.Image(template6)
r6 = img.find_template(t6, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r6:
img.draw_rectangle(r6)
print('6') #打印模板名字
A6=1
A0=0
t7 = image.Image(template7)
r7 = img.find_template(t7, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r7:
img.draw_rectangle(r7)
print('7') #打印模板名字
A7=1
A0=0
t8 = image.Image(template8)
r8 = img.find_template(t8, 0.85, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
if r8:
img.draw_rectangle(r8)
print('8') #打印模板名字
A8=1
A0=0
while(A9):
if A1==1:
uart.write('1')
print('1') #打印模板名字
num=1
A9=0
if A2==1:
uart.write('2')
print('2') #打印模板名字
num=2
A9=0
if A3==1:
uart.write('3')
print('3')
num=3
A9=0
if A4==1:
uart.write('4')
print('4')
num=4
A9=0
if A5==1:
uart.write('5')
print('5')
num=5
A9=0
if A6==1:
uart.write('6')
print('6')
num=6
A9=0
if A7==1:
uart.write('7')
print('7')
num=7
A9=0
if A8==1:
uart.write('8')
print('8')
num=8
A9=0
具体怎么用请查阅资料，我的文字表达能力实在不好
训练集：
用的edge impluse网站训练生成三个文件，具体操作见别的老师的blog
具体的
openmv IDE软件用法
以及edge impluse网站神经网络算法训练用法
详见其他老师blog
OK，我只负责代码
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