原来采用RGB565识别红色再转换为灰度识别继续进行下面的循迹代码，
但是无法实现多次识别，只能在摄像头第一次初始化后可识别红色，
但转化为灰色后，进入了死循环，程序转换为在灰度识别下对黑线的识别，
无法运行下面的循迹代码。根据这一特点，我将初始化摄像头加入while循环，发现多次初始化摄像头图像会不断闪烁。
最终，重新编写代码，关于识别色块的程序，一但识别的红色则车速为0，一但识别到黑色向左加速，一但没有识别到黑色向右加速，这样就能实现在跑道内左转。欢迎大家评论指出不足之处
main.py
import sensor, image, time
import car
# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
red_threshold_01 = (61, 48, -128, 127, -128, 127)
black_threshold_02=(0, 22, -22, 127, -33, 18)
#设置红色的阈值，括号里面的数值分别是L A B 的最大值和最小值（minL, maxL, minA,
# maxA, minB, maxB），LAB的值在图像左侧三个坐标图中选取。如果是灰度图，则只需
#设置（min, max）两个数字即可。
# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False)
#关闭白平衡。白平衡是默认开启的，在颜色识别中，需要关闭白平衡。
clock = time.clock() # Tracks FPS.
while(True):
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
# pixels_threshold=100, area_threshold=100
blobs = img.find_blobs([red_threshold_01], area_threshold=150)
if blobs:
#如果找到了目标颜色
print(blobs)
for b in blobs:
#迭代找到的目标颜色区域
# Draw a rect around the blob.
img.draw_rectangle(b[0:4]) # rect
#用矩形标记出目标颜色区域
img.draw_cross(b[5], b[6]) # cx, cy
#在目标颜色区域的中心画十字形标记
car.run(0,0)
else:
clock.tick() # Track elapsed milliseconds between snapshots().
img = sensor.snapshot() # Take a picture and return the image.
# pixels_threshold=100, area_threshold=100
blobs = img.find_blobs([black_threshold_02], area_threshold=150)
if blobs:
#如果找到了目标颜色
print(blobs)
for b in blobs:
#迭代找到的目标颜色区域
# Draw a rect around the blob.
img.draw_rectangle(b[0:4]) # rect
#用矩形标记出目标颜色区域
img.draw_cross(b[5], b[6]) # cx, cy
#在目标颜色区域的中心画十字形标记
car.run(30,60)
else:
car.run(60,30)
#car.run(60,60)
print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
# connected to your computer. The FPS should increase once disconnected.
Car.py
from pyb import Pin, Timer
inverse_left=False #change it to True to inverse left wheel
inverse_right=False #change it to True to inverse right wheel
ain1 = Pin('P0', Pin.OUT_PP)
ain2 = Pin('P1', Pin.OUT_PP)
bin1 = Pin('P2', Pin.OUT_PP)
bin2 = Pin('P3', Pin.OUT_PP)
ain1.low()
ain2.low()
bin1.low()
bin2.low()
pwma = Pin('P7')
pwmb = Pin('P8')
tim = Timer(4, freq=1000)
ch1 = tim.channel(1, Timer.PWM, pin=pwma)
ch2 = tim.channel(2, Timer.PWM, pin=pwmb)
ch1.pulse_width_percent(0)
ch2.pulse_width_percent(0)
def run(left_speed, right_speed):
if inverse_left==True:
left_speed=(-left_speed)
if inverse_right==True:
right_speed=(-right_speed)
if left_speed < 0:
ain1.low()
ain2.high()
else:
ain1.high()
ain2.low()
ch1.pulse_width_percent(abs(left_speed))
if right_speed < 0:
bin1.low()
bin2.high()
else:
bin1.high()
bin2.low()
ch2.pulse_width_percent(abs(right_speed))
