代码如下，A4纸上有一串数字，现在需要识别出来数字，固定摄像头和A4纸的距离时，总是有个别数字很难识别出来。这时候如果把距离增大或者减小，移动的过程中就能识别出来。但是加一个移动机构有点复杂，有没有固定状态下快速识别的好方法。
import time, sensor, image
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。

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.QVGA)

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)

Load template.

Template should be a small (eg. 32x32 pixels) grayscale image.

templates = ["/N.pgm","/0.pgm","/1.pgm","/2.pgm","/3.pgm","/4.pgm","/5.pgm"]

clock = time.clock()
uart1 = UART(1, 115200,timeout_char=1000)
uart1.init(115200, bits=8, parity=None, stop=1, timeout_char=1000) # 使用给定参数初始化

Run template matching

while (True):
clock.tick()
img = sensor.snapshot()

# find_template(template, threshold, [roi, step, search])
# ROI: The region of interest tuple (x, y, w, h).
# Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
# Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
#
# Note1: ROI has to be smaller than the image and bigger than the template.
# Note2: In diamond search, step and ROI are both ignored.
for t in templates:
    template = image.Image(t)
    #对每个模板遍历进行模板匹配
    r = img.find_template(template, 0.65, step=2, search=SEARCH_EX)#, roi=(40, 100, 240, 60))
#find_template(template, threshold, [roi, step, search]),threshold中
#的0.7是相似度阈值,roi是进行匹配的区域（左上顶点为（10，0），长80宽60的矩形），
#注意roi的大小要比模板图片大，比frambuffer小。
#把匹配到的图像标记出来
    if r:

        img.draw_rectangle(r)
        print(t) #打印模板名字

        #print(r.cx(),r.cy())

        img.draw_rectangle(r)
        x = r[0]
        y = r[1]
        w = r[2]
        h = r[3]

        print(x,y,w,h) #打印获取到的坐标和大小

        uart1.write(t)

#print(clock.fps())