oswy 发布的帖子

#include <ArduinoJson.h>

volatile int cx;
volatile int cy;
volatile char c;
String json;

void setup(){
  cx = 0;
  cy = 0;
  c = 0;
  json = "";
  Serial.begin(9600);
}

void loop(){
  if (Serial.available() > 0) {
    c = char(Serial.read());
    json = String(json) + String(c);
    if (c == '}') {
      StaticJsonBuffer<200> jsonBuffer;//出错
      JsonObject& root = jsonBuffer.parseObject(json);//出错
      
      int cx = root["cx"];
      int cy = root["cy"];
      Serial.print("cx = ");
      Serial.print(cx);
      Serial.print('\t');
      Serial.print("cy = ");
      Serial.println(cy);
      json = "";
    }
  }
}

exit status 1
StaticJsonBuffer is a class from ArduinoJson 5. Please see arduinojson.org/upgrade to learn how to upgrade your program to ArduinoJson version 6

#include <ArduinoJson.h>

exit status 1
ArduinoJson.h: No such file or directory

还会停在这一步
self.i2c.writeto_mem(self.address, 0x06 + 4 * index, data)
main.py和servo.py都是只改动了舵机相关的参数。

import utime
import ustruct

class PCA9685:
    def __init__(self, i2c, address=0x40):
        self.i2c = i2c
        self.address = address
        self.reset()

    def _write(self, address, value):
        self.i2c.writeto_mem(self.address, address, bytearray([value]))//程序停在这一步报错？

    def _read(self, address):
        return self.i2c.readfrom_mem(self.address, address, 1)[0]

    def reset(self):
        self._write(0x00, 0x00) # Mode1

    def freq(self, freq=None):
        if freq is None:
            return int(25000000.0 / 4096 / (self._read(0xfe) - 0.5))
        prescale = int(25000000.0 / 4096.0 / freq + 0.5)
        old_mode = self._read(0x00) # Mode 1
        self._write(0x00, (old_mode & 0x7F) | 0x10) # Mode 1, sleep
        self._write(0xfe, prescale) # Prescale
        self._write(0x00, old_mode) # Mode 1
        utime.sleep_us(5)
        self._write(0x00, old_mode | 0xa1) # Mode 1, autoincrement on

    def pwm(self, index, on=None, off=None):
        if on is None or off is None:
            data = self.i2c.readfrom_mem(self.address, 0x06 + 4 * index, 4)
            return ustruct.unpack('<HH', data)
        data = ustruct.pack('<HH', on, off)
        self.i2c.writeto_mem(self.address, 0x06 + 4 * index,  data)

    def duty(self, index, value=None, invert=False):
        if value is None:
            pwm = self.pwm(index)
            if pwm == (0, 4096):
                value = 0
            elif pwm == (4096, 0):
                value = 4095
            value = pwm[1]
            if invert:
                value = 4095 - value
            return value
        if not 0 <= value <= 4095:
            raise ValueError("Out of range")
        if invert:
            value = 4095 - value
        if value == 0:
            self.pwm(index, 0, 4096)
        elif value == 4095:
            self.pwm(index, 4096, 0)
        else:
            self.pwm(index, 0, value)

相机和标定板，计算相机的外参

findChessboardCorners
检测标定板内角点或亚像素点

识别出中间是黄色圆的图案，不识别左下的那个

![0_1564038826855_QQ图片20190719141214.jpg](正在上传 0

blob.x()
返回色块的边界框的x坐标(int)。

您也可以通过索引 [0] 取得这个值。

blob.y()
返回色块的边界框的y坐标(int)。

您也可以通过索引 [1] 取得这个值。

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)

red_threshold  = (13, 49, 18, 61, 6, 47)

pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

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.
clock = time.clock() # Tracks FPS.

def find_max(blobs):
    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 a picture and return the image.

    blobs = img.find_blobs([red_threshold])
    if blobs:
        max_blob = find_max(blobs)
        pan_error = max_blob.cx()-img.width()/2
        tilt_error = max_blob.cy()-img.height()/2

        print("pan_error: ", pan_error)

        img.draw_rectangle(max_blob.rect()) # rect
        img.draw_cross(max_blob.cx(), max_blob.cy()) # cx, cy

        pan_output=pan_pid.get_pid(pan_error,1)/2
        tilt_output=tilt_pid.get_pid(tilt_error,1)
        print("pan_output",pan_output)
        pan_servo.angle(pan_servo.angle()+pan_output)
        tilt_servo.angle(tilt_servo.angle()-tilt_output)

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot().lens_corr(1.8)
    for c in img.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 = img.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，就说明是黄色的圆。
        #l_mode()，a_mode()，b_mode()是L通道，A通道，B通道的众数。
        
        if (r*r)>1
            if 0<statistics.l_mode()<88 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127:#if the circle is red
                img.draw_circle(c.x(), c.y(), c.r(), color = (255, 255, 0))#识别到的黄色圆形用黄色的圆框出来
                img.draw_cross(c.x(), c.y())
        else:
            img.draw_rectangle(area, color = (255, 255, 255))
            #将非红色的圆用白色的矩形框出来
    print("FPS %f" % clock.fps())

QQVGA已经是允许的最大分辨率吗？如果不是，最大是多少呢

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot().lens_corr(1.8)
    for c in img.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 = img.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_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))#识别到的红色圆形用红色的圆框出来
        else:
            img.draw_rectangle(area, color = (255, 255, 255))
            #将非红色的圆用白色的矩形框出来
    print("FPS %f" % clock.fps())