bzvo 发布的帖子

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.
    #image.bilateral(size[, color_sigma=0.1[, space_sigma=1[, threshold=False[, offset=0[, invert=False[, mask=None]]]]]])
    img.replace(img[0, hmirror=False[1, vflip=True[1, mask=None]]])
    blobs = img.find_blobs([red_threshold])
    if blobs:

# 单色灰度色块跟踪示例
#
# 这个例子展示了使用OpenMV Cam的单色灰度跟踪。

import sensor, image, time

# Color Tracking Thresholds (Grayscale Min, Grayscale Max)
# 下面的灰度阈值设置为只能找到非常明亮的白色区域。
thresholds = (61, 255)

sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
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()

# 只有比“pixel_threshold”多的像素和多于“area_threshold”的区域才被
# 下面的“find_blobs”返回。 如果更改相机分辨率，
# 请更改“pixels_threshold”和“area_threshold”。 “merge = True”合并图像中所有重叠的色块。

while(True):
    clock.tick()
    img = sensor.snapshot()
    for blob in img.find_blobs([thresholds], pixels_threshold=50, area_threshold=50, merge=False):
        img.draw_rectangle(blob.rect())
        img.draw_cross(blob.cx(), blob.cy())
    print(clock.fps())
![0_1563181342955_test.bmp](正在上传 100%) 

# Measure the distance
#
# This example shows off how to measure the distance through the size in imgage
# This example in particular looks for yellow pingpong ball.

import sensor, image, time

# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...+
yellow_threshold   =(0, 30, 127, -128, -128, 127)
# 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(time = 2000) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

K=325#the value should be measured   #设定一个距离值，根据该距离值的像素值得到K值 K=20*25
K2=0.16 #K2值等于物体的高度（长度）/像素值 如K2=4/25
while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    #img = sensor.snapshot() # Take a picture and return the image.
    img = sensor.snapshot()
    blobs = img.find_blobs([yellow_threshold])
    if len(blobs) == 1:
        # Draw a rect around the blob.
        b = blobs[0]
        img.draw_rectangle(b[0:4]) # rect
        img.draw_cross(b[5], b[6]) # cx, cy
        Lm = (b[2]+b[3])/2   #小球的长加宽/2得到LM的值
        length = K/Lm
        print(length)
        size=K2*Lm
        #print(size)

    #print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
    # connected to your computer. The FPS should increase once disconnected.

![0_1562035915783_5415.bmp](正在上传 100%) 


请在这里粘贴代码

好的，谢谢啦。在提出这个问题的时候我又仔细思考了下，是自己想复杂啦

if 90<statistics.l_mode()<110 and -35<statistics.a_mode()<16 and -28<statistics.b_mode()<20:#if the circle is white
                img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))#识别到的白色圆形用红色的圆形框出来
                print(circle.x(),circle.y())
            else:
                img.draw_rectangle(area, color = (255, 255, 255))#将非黑、白色的圆用白色的矩形框出来
                wai = img.find_rects(threshold = 10000)
                x_error = wai[0]+wai[2]/2-img.width()/2        #左右偏移量
                y_error = wai[1]+wai[3]/2-img.height()/2       #上下偏移量
                print(x_error,y_error)![0_1561693260419_13-09-04-002.jpg](https://fcdn.singtown.com/7193a081-5396-4b27-8adb-aeab91271772.jpg) 

对的，是缩进没对，谢谢啦

import sensor, image, time

from pid import PID
from pyb import Servo

from pyb import UART
import json

pan_servo=Servo(1)   #下
tilt_servo=Servo(2)   #上

yellow_threshold  = (100, 0, -128, 53, 39, 127)

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.skip_frames(time =2800)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

K=500#the value should be measured   #设定一个距离值，根据该距离值的像素值得到K值 K=20*25
uart = UART(3, 115200)
def find_max(blobs):          #寻找最大色块函数
    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.
    img = sensor.snapshot().lens_corr(1.8)
    blobs = img.find_blobs([yellow_threshold])    #颜色识别
    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
    if 90<statistics.l_mode()<110 and -35<statistics.a_mode()<16 and -28<statistics.b_mode()<20:#if the circle is white
        img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))#识别到的白色圆形用红色的矩形框出来
    else:
        img.draw_rectangle(area, color = (255, 255, 255))#将非黑、白色的圆用白色的矩形框出来
        
    if blobs:
        max_blob = find_max(blobs)           #判断最大的色块是哪个
        pan_error = max_blob.cx()-img.width()/2        #下面舵机的左右偏移量
        tilt_error = max_blob.cy()-img.height()/2       #上面舵机的上下偏移量

        #print("pan_error: ", pan_error)

        img.draw_rectangle(max_blob.rect()) # rect
        img.draw_cross(max_blob.cx(), max_blob.cy()) # 色块中心点cx, cy      #在寻找到的最大色块周围画矩形框

        print(pan_error,tilt_error)

        pan_output=pan_pid.get_pid(pan_error,1)/2
        tilt_output=tilt_pid.get_pid(tilt_error,1)     #PID参数
        #print("pan_output",pan_output)
        pan_servo.angle(pan_servo.angle()+pan_output)
        tilt_servo.angle(tilt_servo.angle()-tilt_output)    #舵机旋转的角度（控制舵机跟踪色块的中心坐标）

    if len(blobs) == 1:# Draw a rect around the blob.
            b = blobs[0]
            img.draw_rectangle(b[0:4]) # rect
            img.draw_cross(b[5], b[6]) # cx, cy
            Lm = (b[2]+b[3])/2   #小球的长加宽/2得到LM的值
            length = K/Lm
            print(length)

            head="00"
            output_str="[%d,%d,%.2f]" % (pan_error,tilt_error,length) #方式1
            print('you send:',output_str)
            uart.write(head+'\r\n')
            uart.write(output_str+'\r\n')
            uart.write(head+'\r\n')
    else:
        print('not found!')
        
:folded_hands: :folded_hands: :folded_hands: 

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)
    if objects:
        max_blob = find_max(objects)
        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

        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)
![0_1561518235957_}`8~7DP7X6_]2FW`IA%J3{M.png](https://fcdn.singtown.com/4654577d-b6fc-4356-8548-328ea224ffa7.png)