代码如下：

THRESHOLD = (5, 70, -23, 15, -57, 0) # Grayscale threshold for dark things...
import sensor, image, time
from pyb import LED
from pyb import UART
from pid import PID
rho_pid = PID(p=0.4, i=0)
theta_pid = PID(p=0.001, i=0)

LED(1).on()
LED(2).on()
LED(3).on()
    
sensor.reset()
#sensor.set_vflip(True)
#sensor.set_hmirror(True)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQQVGA) # 80x60 (4,800 pixels) - O(N^2) max = 2,3040,000.
#sensor.set_windowing([0,20,80,40])
sensor.skip_frames(time = 2000)     # WARNING: If you use QQVGA it may take seconds
clock = time.clock()  # to process a frame sometimes.
uart = UART(3, 115200)

while(True):
    clock.tick()
    img = sensor.snapshot().binary([THRESHOLD])
    line = img.get_regression([(100,100,0,0,0,0)], robust = True)
    if (line):
        rho_err = abs(line.rho())-img.width()/2
        if line.theta()>90:
            theta_err = line.theta()-180
        else:
            theta_err = line.theta()
        img.draw_line(line.line(), color = 127)
        print(rho_err,line.magnitude(),rho_err)
        if line.magnitude()>8:
            #if -40<b_err<40 and -30<t_err<30:
            rho_output = rho_pid.get_pid(rho_err,1)
            theta_output = theta_pid.get_pid(theta_err,1)
            output = rho_output+theta_output
           # output_str="[%d,%d]" % (50+output,50-outpu)
            uart.write(output)
            #car.run(50+output, 50-output)
        else:
            #output_str="[%d,%d]" % (0,0)
            uart.write("100")
            #car.run(0,0)
    else:
        #output_str="[%d,%d]" % (50,-50)
        uart.write("99")
        #car.run(50,-50)
        pass
    #print(clock.fps())

运行hello world 图像总是偏红怎么办？

openmv要接受2这个信号，怎么写代码？

@kidswong999 怎么还是不能把彩图转变成灰度图啊

import time, sensor, image
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.

template0 = image.Image("/0.pgm")
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
template4 = image.Image("/4.pgm")
template5 = image.Image("/5.pgm")
template6 = image.Image("/6.pgm")
template7 = image.Image("/7.pgm")
template8 = image.Image("/8.pgm")
template9 = image.Image("/9.pgm")

clock = time.clock()

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.
r0 = img.find_template(template0, 0.70, step=4, search=SEARCH_EX)
if r0:
    print('0')
r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) 
if r1:
    print('1')
r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX)
if r2:
    print('2')
r3 = img.find_template(template3, 0.70, step=4, search=SEARCH_EX) 
if r3:
    print('3')
r4 = img.find_template(template4, 0.70, step=4, search=SEARCH_EX)
if r4:
    print('4')
r5 = img.find_template(template5, 0.70, step=4, search=SEARCH_EX) 
if r5:
    print('5')
r6 = img.find_template(template6, 0.70, step=4, search=SEARCH_EX)
if r6:
    print('6')
r7 = img.find_template(template7, 0.70, step=4, search=SEARCH_EX) 
if r7:
    print('7')
r8 = img.find_template(template8, 0.70, step=4, search=SEARCH_EX) 
if r8:
    print('8')
r9 = img.find_template(template9, 0.70, step=4, search=SEARCH_EX) 
if r9:
    print('9')

import time, sensor, image
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.

template0 = image.Image("/0.pgm")
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
template4 = image.Image("/4.pgm")
template5 = image.Image("/5.pgm")
template6 = image.Image("/6.pgm")
template7 = image.Image("/7.pgm")
template8 = image.Image("/8.pgm")
template9 = image.Image("/9.pgm")

clock = time.clock()

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.
r0 = img.find_template(0, 0.70, step=4, search=SEARCH_EX)
if r0:
    print('0')
r1 = img.find_template(1, 0.70, step=4, search=SEARCH_EX) 
if r1:
    print('1')
r2 = img.find_template(2, 0.70, step=4, search=SEARCH_EX)
if r2:
    print('2')
r3 = img.find_template(3, 0.70, step=4, search=SEARCH_EX) 
if r3:
    print('3')
r4 = img.find_template(4, 0.70, step=4, search=SEARCH_EX)
if r4:
    print('4')
r5 = img.find_template(5, 0.70, step=4, search=SEARCH_EX) 
if r5:
    print('5')
r6 = img.find_template(6, 0.70, step=4, search=SEARCH_EX)
if r6:
    print('6')
r7 = img.find_template(7, 0.70, step=4, search=SEARCH_EX) 
if r7:
    print('7')
r8 = img.find_template(8, 0.70, step=4, search=SEARCH_EX) 
if r8:
    print('8')
r9 = img.find_template(9, 0.70, step=4, search=SEARCH_EX) 
if r9:
    print('9')

import sensor, image, math,pyb

sensor.reset()
sensor.set_framesize(sensor.QVGA)
while(True):
sensor.set_pixformat(sensor.GRAYSCALE)
for i in range(10):
img=sensor.snapshot()
for j in range(100):
x=(pyb.rng()%(2img.width()))-(img.width()//2)
y=(pyb.rng()%(2img.height()))-(img.height()//2)
img.set_pixel(x,y,255)
sensor.set_pixformat(sensor.RGB565)
for i in range(10):
img=sensor.snapshot()
for j in range(100):
x=(pyb.rng()%(2img.width()))-(img.width()//2)
y=(pyb.rng()%(2img.height()))-(img.height()//2)
img.set_pixel(255,255,255)

另外想问一下，模板识别想识别0到9，但是它会报错，说是请降低像素，减少在运行的数量，降低像素还是不能解决。最后减少了3个数字它才能正常的运行，这怎么解决？

这怎么解决

刚识别完红色，继续识别其他东西，结果整个画面偏红，这怎么办？（运行的是示例中的single_color_rgb565_blob_tracking.py)

想用openmv识别手写数字，有大佬有相关代码吗？