那我要怎么获取我发的那个数据，我用下标引不出来么

这个接受怎么输出的有额外字符，如发111，收到b'111\r\n'，我要怎么获取我发的字符呢

import time
from pyb import UART

uart = UART(3, 115200)
buf=0
while(True):
buf=uart.read()
print(buf)
time.sleep_ms(1000)

报错Corrupt JPEG data: premature end of data segment
图片非正常显示，用串口传输的uart.write(img.compressed(quality=50))
在软件段接收到后能显示，但是显示如图!，有分割，这是怎么回事

能实现吗，如果能效果卡不卡

怎么通过串口发出图像使软件接受显示

@kidswong999 已经解决了，谢谢

import sensor, image, time
from pyb import UART
from pyb import LED
from image import SEARCH_EX,SEARCH_DS


#pink_threshold = (65, 21, 7, 67, 55, -2)
#pink_threshold =(42, 62, 67, 14, -16, 65)
pink_threshold = (65, 21, 7, 67, 55, -2)
pink_color_code = 1 # code = 2^0 = 1
uart = UART(3, 115200)
uart.init(115200, bits=8, parity=None, stop=1)  #8位数据位，无校验位，1位停止位、

sensor.reset() # 初始化摄像头
sensor.set_contrast(1)#对比度
sensor.set_gainceiling(16)#图像增益上限
sensor.set_pixformat(sensor.RGB565) # 选择像素模式 RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(time = 2000) # Let new settings take affect.
#sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_auto_gain(False) # 颜色跟踪必须关闭自动增益
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。
templates =["111.pgm","221.pgm","333.pgm","334.pgm","335.pgm","443.pgm","444.pgm.",
"555.pgm","556.pgm","557.pgm","666.pgm","667.pgm","777.pgm","778.pgm","888.pgm"]
clock = time.clock() # Tracks FPS.

shj=0
while(shj == 0):
    clock.tick()
    img = sensor.snapshot()
    img = img.to_grayscale()
    for t in templates:
        template = image.Image(t)
        r = img.find_template(template, 0.70, step=4, search=SEARCH_EX)
        if r:
            shj=t[0]
            LED(2).on()
            time.sleep_ms(3000)
            LED(2).off()
x=[]
for i in range(len(templates)):
    if shj == templates[i][0]:
        x.append(templates[i])
flag = 1
while(True):
    clock.tick()
    img = sensor.snapshot()
    img = img.to_grayscale()
    if flag == 1:
        for t in x:
                h = image.Image(t)
                r = img.find_template(h, 0.70, step=4, search=SEARCH_EX)
                if r:
                    img.draw_rectangle(r,0)
                    if(r[0]+(r[2]/2) > sensor.width() /2):
                        uartData = bytearray([0xa5,0x2b,0x01])
                    elif(r[0]+(r[2]/2) < sensor.width()/2):
                        uartData = bytearray([0xa5,0x2b,0x02])
                    flag = 0
                    LED(1).on()
                    uart.write(uartData)
    
    clock.tick()
    img = sensor.snapshot()
    blobs = img.find_blobs([pink_threshold], area_threshold=100)
    if blobs:
    ##如果找到了目标颜色
        for blob in blobs:
        ##迭代找到的目标颜色区域
            x = blob[0]
            y = blob[1] #
            width = blob[2] # 色块矩形的宽度
            height = blob[3] # 色块矩形的高度

            center_x = blob[5] # 色块中心点x值
            center_y = blob[6] # 色块中心点y值

            color_code = blob[8] # 颜色代码

            ## 添加颜色说明
            if color_code == pink_color_code:
                img.draw_string(x, y - 10, "red", color = (0xFF, 0x00, 0x00))

            ##用矩形标记出目标颜色区域
            img.draw_rectangle([x, y, width, height])
            ##在目标颜色区域的中心画十字形标记
            img.draw_cross(center_x, center_y)

@g36h 为什么和教程一样的分辨率，但是有问题

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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

img = sensor.snapshot()

# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, 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]))

print(clock.fps(), "fps")

@kidswong999 您好，是很多库都删了吗，我现在好多程序运行不了了

@kidswong999 您好，我下载咱这个测试也有问题，会报那个错

@kidswong999 您好，是有的，格式化fat32之后再重放还是这样的

@kidswong999 您好，还是不行 一直是这个错误

@kidswong999 这个我插了个SD卡，因为空间不够，有影响吗

@kidswong999 您好，我试了还是会中途报错，图像出现了1秒后就会报那个错误

@kidswong999 我是拿这个生成的，library那个版本没了

@kidswong999 复制了，三个都有的

@kidswong999 # Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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

img = sensor.snapshot()

# default settings just do one detection... change them to search the image...
for obj in tf.classify(net, 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]))

print(clock.fps(), "fps")