模板匹配算法如何每隔三秒检测一次

@kidswong999 那岂不是除了板子上不就剩下一个P9能用了吗

@kidswong999 在 LCD为什么和模板匹配算法一起使用一照到东西就白屏 中说：

P0,P1,P2,P3,P6,P7,P8

那岂不是除了串口的引脚就剩P9了

from pyb import Pin
from image import SEARCH_EX, SEARCH_DS

# 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)
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template = image.Image("10.pgm")
lcd.init()
clock = time.clock()
uart = UART(3, 9600)
while(True):
    #uart.write("Hello World!\r")
    #p1_out.high()
    lcd.display(sensor.snapshot())
    clock.tick()
    img = sensor.snapshot()
    
    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 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)
        p3_out = Pin('P7', Pin.OUT_PP)
        p3_out.low()
        time.sleep_ms(1000)
        p3_out.high()
        uart.write("Hello World!\r")
        print(clock.fps())

    if uart.any():
        a=uart.readline().decode().strip()
        print(a)
        if a == '1':
            p1_out = Pin('P1', Pin.OUT_PP)
            p1_out.low()
            time.sleep_ms(1000)
            p1_out.high()
            uart.write("Hello World!\r")

        if a=='2':
            uart.write("Hello ")
            p2_out = Pin('P2', Pin.OUT_PP)
            p2_out.low()
            time.sleep_ms(1000)
            p2_out.high()
        if a=='4':
            uart.write("Hello")
            p4_out = Pin('P0', Pin.OUT_PP)
            p4_out.low()
            time.sleep_ms(1000)
            p4_out.high()

麻烦问下模板匹配算法除了数字意外 可以识别图案吗

如何在LCD屏幕上写字啊 用什么函数。。

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # grayscale is faster (160x120 max on OpenMV-M7)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

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

    # 下面的`threshold`应设置为足够高的值，以滤除在图像中检测到的具有
    # 低边缘幅度的噪声矩形。最适用与背景形成鲜明对比的矩形。

    for r in img.find_rects(threshold = 10000):
        img.draw_rectangle(r.rect(), color = (255, 0, 0))
        for p in r.corners(): img.draw_circle(p[0], p[1], 5, color = (0, 255, 0))
        print(r)
    img1 = sensor.snapshot().lens_corr(1.8)
    for c in img1.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10,
                r_min = 2, r_max = 100, r_step = 2):
            area = (c.x()-c.r(), c.y()-c.r(), 2*c.r(), 2*c.r())
            #area为识别到的圆的区域，即圆的外接矩形框
            statistics = img1.get_statistics(roi=area)#像素颜色统计
            print(statistics)
            #(0,100,0,120,0,120)是红色的阈值，所以当区域内的众数（也就是最多的颜色），范围在这个阈值内，就说明是红色的圆。
            #l_mode()，a_mode()，b_mode()是L通道，A通道，B通道的众数。
            if 0<statistics.l_mode()<100 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127:#if the circle is red
                img.draw_rectangle(area, color = (255, 255, 255))#识别到的红色圆形用红色的圆框出来
            elif 30<statistics.l_mode()<100 and -64<statistics.a_mode()<-8 and -32<statistics.b_mode()<32:#if the circle is red
                img.draw_rectangle(area, color = (255, 255, 255))#识别到的红色圆形用红色的圆框出来
            elif 0<statistics.l_mode()<15 and 0<statistics.a_mode()<-40 and -80<statistics.b_mode()<-20:#if the circle is red
                img.draw_rectangle(area, color = (255, 255, 255))#识别到的红色圆形用红色的圆框出来
    print("FPS %f" % clock.fps())

如何同时识别不同颜色的圆形和矩形