2021年电赛openmv数字识别

  openmv是什么？：OpenMV摄像头是一款小巧，低功耗，低成本的电路板，它帮助你很轻松的完成机器视觉（machine vision）应用。你可以通过高级语言Python脚本（准确的说是 MicroPython ），而不是C/C++。Python的高级数据结构使你很容易在机器视觉算法中处理复杂的输出。但是，你仍然可以完全控制OpenMV。你可以很容易的使用外部终端触发拍摄或者或者执行算法，也可以把算法的结果用来控制IO引脚。
 

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2021数字识别也可直接使用openmv的官方库中的数字识别来进行修改，在官方例程中加入识别的图片和数字判断外加串口通讯就可简单的使用。也可使用k210官方库进行训练使用，博客里也有k210的使用方法。

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1.外加图片

template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
templates3 = ["/3.pgm","/3_1.pgm","/3_2.pgm","/3_3.pgm","/3_4.pgm","/3_5.pgm","/3_6.pgm","/3_7.pgm","/3_8.pgm"]
templates4 = ["/4.pgm","/4_1.pgm","/4_2.pgm","/4_3.pgm","/4_4.pgm","/4_5.pgm","/4_6.pgm","/4_7.pgm","/4_8.pgm"]
templates5 = ["/5.pgm","/5_1.pgm","/5_2.pgm","/5_3.pgm","/5_4.pgm","/5_5.pgm","/5_6.pgm","/5_7.pgm","/5_8.pgm"]
templates6 = ["/6.pgm","/6_1.pgm","/6_2.pgm","/6_3.pgm","/6_4.pgm","/6_5.pgm","/6_6.pgm","/6_7.pgm","/6_8.pgm"]
templates7 = ["/7.pgm","/7_1.pgm","/7_2.pgm","/7_3.pgm","/7_4.pgm","/7_5.pgm","/7_6.pgm","/7_7.pgm","/7_8.pgm"]
templates8 = ["/8.pgm","/8_1.pgm","/8_2.pgm","/8_3.pgm","/8_4.pgm","/8_5.pgm","/8_6.pgm","/8_7.pgm","/8_8.pgm"]
#加载模板图片

2.串口通讯常用方法

    data = ustruct.pack("

3.总数字识别代码

# 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,ustruct
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS
#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。

# Reset sensor
sensor.reset()

# Set sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# 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)


LED(1).on()
LED(2).on()
LED(3).on()
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
templates3 = ["/3.pgm","/3_1.pgm","/3_2.pgm","/3_3.pgm","/3_4.pgm","/3_5.pgm","/3_6.pgm","/3_7.pgm","/3_8.pgm"]
templates4 = ["/4.pgm","/4_1.pgm","/4_2.pgm","/4_3.pgm","/4_4.pgm","/4_5.pgm","/4_6.pgm","/4_7.pgm","/4_8.pgm"]
templates5 = ["/5.pgm","/5_1.pgm","/5_2.pgm","/5_3.pgm","/5_4.pgm","/5_5.pgm","/5_6.pgm","/5_7.pgm","/5_8.pgm"]
templates6 = ["/6.pgm","/6_1.pgm","/6_2.pgm","/6_3.pgm","/6_4.pgm","/6_5.pgm","/6_6.pgm","/6_7.pgm","/6_8.pgm"]
templates7 = ["/7.pgm","/7_1.pgm","/7_2.pgm","/7_3.pgm","/7_4.pgm","/7_5.pgm","/7_6.pgm","/7_7.pgm","/7_8.pgm"]
templates8 = ["/8.pgm","/8_1.pgm","/8_2.pgm","/8_3.pgm","/8_4.pgm","/8_5.pgm","/8_6.pgm","/8_7.pgm","/8_8.pgm"]
#加载模板图片

clock = time.clock()
uart = UART(3,115200)   #定义串口3变量
uart.init(115200, bits=8, parity=None, stop=1) # init with given parameters

def outuart(x,num):
    global uart
    #frame=[0x2C,18,cx%0xff,int(cx/0xff),cy%0xff,int(cy/0xff),0x5B];
    #data = bytearray(frame)
    data = ustruct.pack("

比较简单的利用官方的例程进行修改并配置数字识别的代码，串口通讯主控端可进行解析来判断数字的类型执行相应的功能。

代码链接：链接：https://pan.baidu.com/s/1GUwmVZsuqKCH2MytqedELA 
提取码：f4qb 
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