用哪些部分可以建立呢

已经解决了解决了

我运行也没有错误，但是和其他单片机通信，一样的程序，openmv4可以成功通信，plus不行，不清楚原因

import sensor, image, time
from pyb import UART,LED
#from pid import PID

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_whitebal(False)
clock = time.clock()
LED(1).on()
LED(2).on()
LED(3).on()
uart = UART(3,9600,timeout_char=1000)
yellow_threshold   = [(100, 58, -128, 127, 31, 127),
                     (33, 77, -41, -69, 11, 33)]
size_threshold = 2000

def find_max(blobs):
    max_size=0
    max_blob=None
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

#flag = 0

while(True):
    clock.tick()
    img = sensor.snapshot()
    blobs = img.find_blobs(yellow_threshold)
    max_blob = None
    if blobs:
        max_blob = find_max(blobs)
        img.draw_rectangle(max_blob[0:4])
        img.draw_cross(max_blob[5], max_blob[6])
    if max_blob:
        pan_error = max_blob.cx()-img.width()/2
        if pan_error <0:
            print("左移")
            uart.write("$LY!")
            time.sleep_ms(100)
        if pan_error >0:
            print("右移")
            uart.write("$RY!")
            time.sleep_ms(100)
        if pan_error ==0:
            print("抓取")
            if max_blob.code()==1:
                uart.write("$ZC!")
                time.sleep_ms(400)
              #  flag=1

# Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None
min_confidence = 0.5

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects

    for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):     此句
        if (i == 0): continue # background class
        if (len(detection_list) == 0): continue # no detections for this class?

        print("********** %s **********" % labels[i])
        for d in detection_list:
            [x, y, w, h] = d.rect()
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print('x %d\ty %d' % (center_x, center_y))
            img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

    print(clock.fps(), "fps", end="\n\n")

# Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None
min_confidence = 0.5

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
    (255,   0,   0),
    (  0, 255,   0),
    (255, 255,   0),
    (  0,   0, 255),
    (255,   0, 255),
    (  0, 255, 255),
    (255, 255, 255),
]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # detect() returns all objects found in the image (splitted out per class already)
    # we skip class index 0, as that is the background, and then draw circles of the center
    # of our objects

    for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
        if (i == 0): continue # background class
        if (len(detection_list) == 0): continue # no detections for this class?

        print("********** %s **********" % labels[i])
        for d in detection_list:
            [x, y, w, h] = d.rect()
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print('x %d\ty %d' % (center_x, center_y))
            img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

    print(clock.fps(), "fps", end="\n\n")

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc, pyb
from pyb import UART,LED

sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((128, 128)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
LED(1).on()
LED(2).on()
LED(3).on()
uart = UART(3,9600,timeout_char=1000)
net = None
labels = None

try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
print(e)
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()

obj = net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5)

# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
    print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
    img.draw_rectangle(obj.rect())
    # This combines the labels and confidence values into a list of tuples
    predictions_list = list(zip(labels, obj.output()))

    for i in range(len(predictions_list)):
        print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

程序运行都是摄像头整幅画面

import sensor, image, time
from pyb import UART
from pid import PID


sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.set_vflip(True)#垂直方向的翻转
sensor.set_hmirror(True)#水平方向的翻转
sensor.skip_frames(10)
sensor.set_auto_whitebal(False)
clock = time.clock()
uart = UART(3,115200,timeout_char=1000)

yellow_threshold   = [(17, 96, -17, 40, 88, 19),#黄色
                    (37, 100, 3, 47, -31, 2)] #红色)
size_threshold = 2000

def find_max(blobs):
    max_size=0
    max_blob=None
    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()
    img = sensor.snapshot()
    blobs = img.find_blobs(yellow_threshold)
    max_blob = None
    if blobs:
        max_blob = find_max(blobs)
        img.draw_rectangle(max_blob[0:4])
        img.draw_cross(max_blob[5], max_blob[6])
    if max_blob:
        pan_error = max_blob.cx()-img.width()/2
    if max_blob == None:
        print("旋转")
        uart.write("$ZY!")
        time.sleep_ms(100)
    if pan_error >0:     #此处显示没有定义
        print("左移")
        uart.write("$ZY!")
        time.sleep_ms(100)