import sensor, image, time, math
from pyb import UART
import lcd
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA2) # High Res!
sensor.set_windowing((640, 200)) # V Res of 80 == less work (40 for 2X the speed).
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False)  # 必须关闭此功能，以防止图像冲洗…
sensor.set_auto_whitebal(False)  # 必须关闭此功能，以防止图像冲洗…
clock = time.clock()
uart = UART(3, 9600)
# 条形码检测可以在OpenMV Cam的OV7725相机模块的640x480分辨率下运行。
# 条码检测也将在RGB565模式下工作，但分辨率较低。 也就是说，
# 条形码检测需要更高的分辨率才能正常工作，因此应始终以640x480的灰度运行。
lcd.init()
def barcode_name(code):
    if(code.type() == image.EAN2):
        return "EAN2"
    if(code.type() == image.EAN5):
        return "EAN5"
    if(code.type() == image.EAN8):
        return "EAN8"
    if(code.type() == image.UPCE):
        return "UPCE"
    if(code.type() == image.ISBN10):
        return "ISBN10"
    if(code.type() == image.UPCA):
        return "UPCA"
    if(code.type() == image.EAN13):
        return "EAN13"
    if(code.type() == image.ISBN13):
        return "ISBN13"
    if(code.type() == image.I25):
        return "I25"
    if(code.type() == image.DATABAR):
        return "DATABAR"
    if(code.type() == image.DATABAR_EXP):
        return "DATABAR_EXP"
    if(code.type() == image.CODABAR):
        return "CODABAR"
    if(code.type() == image.CODE39):
        return "CODE39"
    if(code.type() == image.PDF417):
        return "PDF417"
    if(code.type() == image.CODE93):
        return "CODE93"
    if(code.type() == image.CODE128):
        return "CODE128"

while(True):
    clock.tick()
    img = sensor.snapshot()
    codes = img.find_barcodes() 
    for code in codes:
        img.draw_rectangle(code[0:4])
        #img.draw_cross(code[5], code[6]) # cx, cy   
        x=code[5]
        print(x)
        print_args = (barcode_name(code), code.payload(), (180 * code.rotation()) / math.pi, code.quality(), clock.fps())
        print("Barcode %s, Payload \"%s\", rotation %f (degrees), quality %d, FPS %f" % print_args)
        tiaoxinma=code.payload()
        print(tiaoxinma)
        #FZ = bytearray([0x31,0x31])
        #uart.write(FZ)
        #date = bytearray([tiaoxinma])
        #uart.write(date)
        lcd.display(sensor.snapshot()) 
        img.draw_string(100,100,str(tiaoxinma),color=(0,0,0))
    if not codes:
        print("FPS %f" % clock.fps())

在pytorch上面训练好神经网络模型可以在openmv上面运行吗？效果是否和在网站上面搭建的模型一样

openmv4 h7 plus 500万 ov5640配置的openmv最多可以识别多远的条形码

openmv可以识别条形码和自动对焦吗？如果有能给链接吗

@kidswong999 无符号的！

在openmv里面把10进制数字转换成16进制数字再取高低位然后通过串口发出去，例如2000的十六进制为7d0然后 取高低位 高位在后，低位在前。0xd0，0x07.这样格式发出去如何实现。

# 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, pyb
from pyb import UART

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

#sensor.set_windowing((100, 100))

# 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   = (54, 95, -4, 30, 22, 70)
size_threshold = 1500
blue_led = pyb.LED(3)
green_led = pyb.LED(2)
red_led = pyb.LED(1)
uart = UART(3, 9600)
sensor.set_auto_exposure(True, exposure_us=5000) # make smaller to go faster

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([green_threshold])
    #FZ = bytearray([0x55,0x55,0x12,0x03,0x03,0xd0,0x07,0x01,0xa1,0x02,0x02,0x04,0x05,0x03,0x6c,0x06])
    #FZ1 = bytearray([0x55,0x55,0x14,0x03,0x05,0xd0,0x07,0x01,0x05,0x09,0x02,0x4c,0x05,0x03,0xe9,0x02,0x04,0xc1,0x07,0x05,0x24,0x02])
    #FZ1 = bytearray([0x55,0x55,0x08,0x03,0x01,0xd0,0x07,0x01,0x05,0x09])
    #uart.write(FZ1)
    
    #FZ2 = bytearray([0x55,0x55,0x08,0x03,0x02,0xd0,0x07,0x02,0x4c,0x05])
    #uart.write(FZ2)
    #time.sleep_ms(1000)
    #FZ3 = bytearray([0x55,0x55,0x08,0x03,0x03,0xd0,0x07,0x03,0xe9,0x02])
    #uart.write(FZ3)
    #time.sleep_ms(1000)
    #FZ4 = bytearray([0x55,0x55,0x08,0x03,0x04,0xd0,0x07,0x04,0xc1,0x07])
    #uart.write(FZ)
    time.sleep_ms(1000)
    xiangshu=0
    
    if blobs:
        max_blob = find_max(blobs)
        max_blob1=find_max1(blobs)
        xiangshu = max_blob.blob[4]
        img.draw_rectangle(max_blob[0:4]) # rect
        img.draw_cross(max_blob[5], max_blob[6]) # cx, cy
        if xiangshu > size_threshold:
                    #FZ = bytearray([0x55,0x55,0x05,0x06,0x64,0x01,0x00])
                    #art.write(FZ)
                    red_led.on()
                    time.sleep_ms(150)#延时150ms
                    red_led.off()
        else:
                    blue_led.on()
                    time.sleep_ms(150)     #延时150ms    
                    blue_led.off()
        
    else:
        green_led.on()
        time.sleep_ms(150)     #延时150ms    
        green_led.off()

如何做到，在运动中物体出现在镜头正对的方向时，开始识别物体颜色大小

如何提高openmv刷新帧数，让openmv识别颜色快速反应

如果可以需要怎么做！