Openmv realizes color tracking

One 、 Driver code

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

# The four pins respectively control the direction of motor movement of the two pins 
ain1 =  Pin('P0', Pin.OUT_PP)
ain2 =  Pin('P1', Pin.OUT_PP)
bin1 =  Pin('P2', Pin.OUT_PP)
bin2 =  Pin('P3', Pin.OUT_PP)
# First set the four pins to low level 
ain1.low()
ain2.low()
bin1.low()
bin2.low()

pwma = Pin('P7')
pwmb = Pin('P8')
tim = Timer(4, freq=1000)   # Set the timer 4
ch1 = tim.channel(1, Timer.PWM, pin=pwma)   # Set up channels 1
ch2 = tim.channel(2, Timer.PWM, pin=pwmb)   # Set up channels 2
ch1.pulse_width_percent(0)  # Set the duty cycle of the channel 0
ch2.pulse_width_percent(0)  # Set the duty cycle of the channel 0

def run(left_speed, right_speed):       # Define a RUN function 
    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(int(abs(left_speed)))# Set the left wheel duty cycle 

    if right_speed < 0:
        bin1.low()
        bin2.high()
    else:
        bin1.high()
        bin2.low()
    ch2.pulse_width_percent(int(abs(right_speed)))# Set the duty cycle of the right wheel 

Two 、PID Adjustment code

from pyb import millis
from math import pi, isnan

class PID:
    _kp = _ki = _kd = _integrator = _imax = 0
    _last_error = _last_derivative = _last_t = 0
    _RC = 1/(2 * pi * 20)
    def __init__(self, p=0, i=0, d=0, imax=0):
        self._kp = float(p)
        self._ki = float(i)
        self._kd = float(d)
        self._imax = abs(imax)
        self._last_derivative = float('nan')

    def get_pid(self, error, scaler):
        tnow = millis()
        dt = tnow - self._last_t
        output = 0
        if self._last_t == 0 or dt > 1000:
            dt = 0
            self.reset_I()
        self._last_t = tnow
        delta_time = float(dt) / float(1000)
        output += error * self._kp
        if abs(self._kd) > 0 and dt > 0:
            if isnan(self._last_derivative):
                derivative = 0
                self._last_derivative = 0
            else:
                derivative = (error - self._last_error) / delta_time
            derivative = self._last_derivative + \
                                     ((delta_time / (self._RC + delta_time)) * \
                                        (derivative - self._last_derivative))
            self._last_error = error
            self._last_derivative = derivative
            output += self._kd * derivative
        output *= scaler
        if abs(self._ki) > 0 and dt > 0:
            self._integrator += (error * self._ki) * scaler * delta_time
            if self._integrator < -self._imax: self._integrator = -self._imax
            elif self._integrator > self._imax: self._integrator = self._imax
            output += self._integrator
        return output
    def reset_I(self):
        self._integrator = 0
        self._last_derivative = float('nan')

3、 ... and 、 Main function code ：

# Blob Detection Example
#
# This example shows off how to use the find_blobs function to find color
# blobs in the image. This example in particular looks for dark green objects.

import sensor, image, time
import car
from pid import PID

# 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) # turn this off. In color recognition , Be sure to turn off white balance , Otherwise, the effect of color tracking will be affected 
clock = time.clock() # Tracks FPS.

# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
green_threshold   = (76, 96, -110, -30, 8, 66)
red_threshold   = (55 , 80 , 20 , 70 , 0 , 25)
size_threshold = 2000       # Pixel point . You can adjust pixels , Adjust the range of color tracking 
x_pid = PID(p=0.5, i=1, imax=100)   # Control the direction of the motor , If the angle of the corner is very large , Adjust the p value 
h_pid = PID(p=0.05, i=0.1, imax=50) # Control the speed of the trolley , If the speed is too fast , Can be adjusted P value 

def find_max(blobs):        # This function is to find the field of view , The biggest color block 
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() #  Take an image from the field of view 

    blobs = img.find_blobs([green_threshold])
    if blobs:
        max_blob = find_max(blobs)
        x_error = max_blob[5]-img.width()   # Setting adjustment PID Parameters of 
        h_error = max_blob[2]*max_blob[3]-size_threshold    
        print("x error: ", x_error)
        ''' for b in blobs: # Draw a rect around the blob. img.draw_rectangle(b[0:4]) # rect img.draw_cross(b[5], b[6]) # cx, cy '''
        img.draw_rectangle(max_blob[0:4]) # rect
        img.draw_cross(max_blob[5], max_blob[6]) # cx, cy
        x_output=x_pid.get_pid(x_error,1)   # obtain PID The final result 
        h_output=h_pid.get_pid(h_error,1)
        print("h_output",h_output)
        car.run(-h_output-x_output,-h_output+x_output)# control PID The duty cycle from the rear to the trolley 
    else:
        car.run(18,-18)

In this paper openMV You can find it on the official website of （ It is only convenient for me to find ）:openMV Tracking car