@kidswong999 不太懂应该怎么写啊 关键是现在我这个代码就是那个网站都进不去水为什么呢

@kidswong999 那我需要怎么改才能像那个传输小球坐标一样一直传呢

@kidswong999 我感觉我这个模型训练和这个WiFi传输没有加好，还有一个问题就是我这个程序运行起来没几分钟摄像头就不动了，就像卡住了一样，但是不报错

@kidswong999
我加了那个可是现在的问题就是那个196.168.1.1都进不去

import sensor, image, time, os, tf, uos, gc, network, usocket, sys, json

SSID ='OPENMV_AP'    # Network SSID
KEY  ='1234567890'    # Network key (must be 10 chars)
HOST = ''           # Use first available interface
PORT = 8080         # Arbitrary non-privileged port



# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_contrast(1)
sensor.set_brightness(1)
sensor.set_saturation(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

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()


# Init wlan module in AP mode.
wlan = network.WINC(mode=network.WINC.MODE_AP)
wlan.start_ap(SSID, key=KEY, security=wlan.WEP, channel=2)

# You can block waiting for client to connect
#print(wlan.wait_for_sta(10000))

def response(s):
    print ('Waiting for connections..')
    client, addr = s.accept()
    # set client socket timeout to 2s
    client.settimeout(2.0)
    print ('Connected to ' + addr[0] + ':' + str(addr[1]))

    # Read request from client
    data = client.recv(1024)
    # Should parse client request here

    # Send multipart header
    client.send("HTTP/1.1 200 OK\r\n" \
                "Server: OpenMV\r\n" \
                "Content-Type: application/json\r\n" \
                "Cache-Control: no-cache\r\n" \
                "Pragma: no-cache\r\n\r\n")

    # FPS clock
    clock = time.clock()

    # Start streaming images
    # NOTE: Disable IDE preview to increase streaming FPS.

    clock.tick()

    img = sensor.snapshot()

    # 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]))
            a=obj.output()[0]
            b=obj.output()[1]
            c=obj.output()[2]
            d=obj.output()[3]
            f=obj.output()[4]
            g=obj.output()[5]
            if a>0.95:
                h=1
                print(h)
    client.send(json.dumps(h))
    client.close()

while (True):
    # Create server socket
    s = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM)
    try:
        # Bind and listen
        s.bind([HOST, PORT])
        s.listen(5)

        # Set server socket timeout
        # NOTE: Due to a WINC FW bug, the server socket must be closed and reopened if
        # the client disconnects. Use a timeout here to close and re-create the socket.
        s.settimeout(3)
        response(s)
    except OSError as e:
        s.close()
        print("socket error: ", e)
        #sys.print_exception(e)

open mv如何实现检测睁眼还是闭眼？

# Template Matching Example - Normalized Cross Correlation (NCC)
#
# 这个例子展示了如何使用OpenMV凸轮的NCC功能将小部分图像与图像的各个部分
# 进行匹配...期望获得极其可控的环境NCC并不是全部有用的。
#
# 警告：NCC支持需要重做！到目前为止，这个功能需要做大量的工作才能有用。
# 这个脚本将重新表明功能的存在，但在目前的状态是不足的。

import time, sensor, image
from image import SEARCH_EX, SEARCH_DS
#从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX, 
#SEARCH_DS两个需要的部分，而不把image模块全部引入。

# Reset sensor
sensor.reset()

# Set sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# Max resolution for template matching with SEARCH_EX is QQVGA
sensor.set_framesize(sensor.QQVGA)
# You can set windowing to reduce the search image.
#sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))
sensor.set_pixformat(sensor.GRAYSCALE)

# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
num_quantity=9
for t in range(0,num_quantity+1):#循环将根目录下F文件夹的数字图片载入
    templates = [image.Image('/F/'+str(t)+'.pgm')] #保存多个模板
#加载模板图片

clock = time.clock()

# Run template matching
while (True):
    clock.tick()
    img = sensor.snapshot()

    # find_template(template, threshold, [roi, step, search])
    # ROI: The region of interest tuple (x, y, w, h).
    # Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
    # Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
    #
    # Note1: ROI has to be smaller than the image and bigger than the template.
    # Note2: In diamond search, step and ROI are both ignored.
    for t in templates:
        template = image.Image(t)
        #对每个模板遍历进行模板匹配
        r = img.find_template(template, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    #find_template(template, threshold, [roi, step, search]),threshold中
    #的0.7是相似度阈值,roi是进行匹配的区域（左上顶点为（10，0），长80宽60的矩形），
    #注意roi的大小要比模板图片大，比frambuffer小。
    #把匹配到的图像标记出来
        if r:
            img.draw_rectangle(r)
            print(t) #打印模板名字

    #print(clock.fps())

threshold = 3500比较合适。如果视野中检测到的圆过多，请增大阈值；

import sensor, image, time
from pyb import UART
# 为了使色彩追踪效果真的很好，你应该在一个非常受控制的照明环境中。
BLUE_threshold   = (   0,   80,  -70,   -10,   -0,   30)
# 设置蓝色的阈值


sensor.reset() # 初始化sensor

sensor.set_pixformat(sensor.RGB565) # use RGB565.

sensor.set_framesize(sensor.QQVGA)
#设置图像像素大小

sensor.skip_frames(10) # 让新的设置生效。
sensor.set_auto_whitebal(False)
clock = time.clock()

uart = UART(3,115200)
uart.init(115200, bits=8, parity=None, stop=1)  #通信

K=5000#自取

while(True):
    clock.tick()
    img = sensor.snapshot() # 拍一张照片并返回图像。
    blobs = img.find_blobs([BLUE_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
        length = K/Lm
        print(length)
        c=int(length)

    if blobs:
    #如果找到了目标颜色
        for b in blobs:
        #迭代找到的目标颜色区域
            # Draw a rect around the blob.
            img.draw_rectangle(b[0:4]) # rect
            #用矩形标记出目标颜色区域
            img.draw_cross(b[5], b[6]) # cx, cy
            #在目标颜色区域的中心画十字形标记
            print(b[5], b[6])
            #输出目标物体中心坐标
        print("1")
        a=1
    else:
        print("2")
        b=2
    data = bytearray([0xb3,a,b,c,0x5b])