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@kidswong999 可以帮忙看一下，这个代码的问题在哪吗？一直显示Waiting for connections..
socket error: -116

import sensor, image, time, network, usocket, sys
from pyb import UART
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.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(time=10)
sensor.set_auto_whitebal(False)
uart = UART(3, 115200)
# 在AP模式下启动wlan模块。
wlan = network.WINC(mode=network.WINC.MODE_AP)
wlan.start_ap(SSID, key=KEY, security=wlan.WEP, channel=2)

#您可以阻止等待客户端连接
#print(wlan.wait_for_sta(10000))

def start_streaming(s):
    print ('Waiting for connections..')
    client, addr = s.accept()
    # 将客户端套接字超时设置为2秒
    client.settimeout(2.0)
    print ('Connected to ' + addr[0] + ':' + str(addr[1]))

    # 从客户端读取请求
    data = client.recv(1024)
    # 应该在这里解析客户端请求

    # 发送多部分head
    client.send("HTTP/1.1 200 OK\r\n" \
                "Server: OpenMV\r\n" \
                "Content-Type: multipart/x-mixed-replace;boundary=openmv\r\n" \
                "Cache-Control: no-cache\r\n" \
                "Pragma: no-cache\r\n\r\n")

    # FPS clock
face_cascade = image.HaarCascade("frontalface", stages=25)
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
    clock = time.clock()

    # 开始流媒体图像
    #注：禁用IDE预览以增加流式FPS。
    while (True):
        clock.tick() # Track elapsed milliseconds between snapshots().
        frame = sensor.snapshot()
        cframe = frame.compressed(quality=35)
        header = "\r\n--openmv\r\n" \
                 "Content-Type: image/jpeg\r\n"\
                 "Content-Length:"+str(cframe.size())+"\r\n\r\n"
        client.send(header)
        client.send(cframe)
        blobs = frame.find_features(face_cascade, threshold=0.75, scale=1.35)
        if blobs:
            max_blob = find_max(blobs)
            frame.draw_rectangle(max_blob)
            frame.draw_cross(int(max_blob[0]+max_blob[2]/2), int(max_blob[1]+max_blob[3]/2))
            a=int(max_blob[0]+max_blob[2]/2)
            b=int(max_blob[1]+max_blob[3]/2)
            print("a:%d b:%d"%(a, b))
            output_str="[%d,%d]" % (a,b)
            print('you send:',output_str)
            uart.write(output_str+'\r\n')
        else:
            print('not found!')
while (True):
    # 创建服务器套接字
    s = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM)
    try:
        # Bind and listen
        s.bind([HOST, PORT])
        s.listen(5)

        # 设置服务器套接字超时
        # 注意：由于WINC FW bug，如果客户端断开连接，服务器套接字必须
        # 关闭并重新打开。在这里使用超时关闭并重新创建套接字。
        s.settimeout(3)
        start_streaming(s)
    except OSError as e:
        s.close()
        print("socket error: ", e)

回复: 怎么用WiFi传人脸识别的视频@kidswong999 这个链接失效了....还有吗？把人脸追踪和wifi传视频流的写到一个里怎么改呢？或者我直接在flash里放两个文件，在人脸追踪的程序(main.py)里用语句import uuu导入另一个文件(uuu.py)上电两个会都运行吗

串口显示结果

{"size":54, "threshold":10, "normalized":1} matched:18 dt:0
(46, 8, 182, 182)
{"size":50, "threshold":10, "normalized":1} matched:17 dt:0
(46, 8, 182, 182)
{"size":53, "threshold":10, "normalized":1} matched:18 dt:0
(46, 8, 182, 182)
{"size":52, "threshold":10, "normalized":1} matched:17 dt:0
(46, 8, 182, 182)
{"size":56, "threshold":10, "normalized":1} matched:18 dt:0
(46, 8, 182, 182)
{"size":62, "threshold":10, "normalized":1} matched:18 dt:0
(46, 8, 182, 182)
{"size":55, "threshold":10, "normalized":1} matched:15 dt:0
(46, 8, 182, 182)
{"size":70, "threshold":10, "normalized":1} matched:10 dt:0
(46, 8, 182, 182)
{"size":85, "threshold":10, "normalized":1} matched:11 dt:0
(46, 8, 182, 182)
{"size":61, "threshold":10, "normalized":1} matched:9 dt:0
(46, 8, 182, 182)
{"size":58, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":49, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":51, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":30, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":44, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)
{"size":45, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":45, "threshold":10, "normalized":1} matched:9 dt:0
(46, 8, 182, 182)
{"size":37, "threshold":10, "normalized":1} matched:6 dt:15
(46, 8, 182, 182)
{"size":41, "threshold":10, "normalized":1} matched:6 dt:15
(46, 8, 182, 182)
{"size":45, "threshold":10, "normalized":1} matched:6 dt:270
(46, 8, 182, 182)
{"size":38, "threshold":10, "normalized":1} matched:8 dt:15
(46, 8, 182, 182)
{"size":46, "threshold":10, "normalized":1} matched:8 dt:15
(46, 8, 182, 182)
{"size":43, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":40, "threshold":10, "normalized":1} matched:7 dt:15
(46, 8, 182, 182)
{"size":49, "threshold":10, "normalized":1} matched:9 dt:30
(46, 8, 182, 182)
{"size":38, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":63, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":53, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":49, "threshold":10, "normalized":1} matched:9 dt:0
(46, 8, 182, 182)
{"size":43, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)
{"size":47, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":42, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)
{"size":41, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":34, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":21, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":29, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)
{"size":24, "threshold":10, "normalized":1} matched:9 dt:0
(46, 8, 182, 182)
{"size":29, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)
{"size":23, "threshold":10, "normalized":1} matched:7 dt:0
(46, 8, 182, 182)
{"size":29, "threshold":10, "normalized":1} matched:8 dt:0
(46, 8, 182, 182)
{"size":29, "threshold":10, "normalized":1} matched:6 dt:0
(46, 8, 182, 182)

Traceback (most recent call last):
  File "<stdin>", line 56, in <module>
Exception: IDE interrupt
MicroPython v1.9.4-4553-gb4eccdfe3 on 2019-05-02; OPENMV4 with STM32H743

只是加了这一句

for face in objects:
print(face)

# Face Tracking Example
#
# This example shows off using the keypoints feature of your OpenMV Cam to track
# a face after it has been detected by a Haar Cascade. The first part of this
# script finds a face in the image using the frontalface Haar Cascade.
# After which the script uses the keypoints feature to automatically learn your
# face and track it. Keypoints can be used to automatically track anything.
import sensor, time, image

# Reset sensor
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
sensor.set_pixformat(sensor.GRAYSCALE)

# Skip a few frames to allow the sensor settle down
sensor.skip_frames(time = 2000)

# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)

# First set of keypoints
kpts1 = None

# Find a face!
while (kpts1 == None):
    img = sensor.snapshot()
    img.draw_string(0, 0, "Looking for a face...")
    # Find faces
    objects = img.find_features(face_cascade, threshold=0.5, scale=1.25)
    if objects:
        # Expand the ROI by 31 pixels in every direction
        face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
        # Extract keypoints using the detect face size as the ROI
        kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, roi=face)
        # Draw a rectangle around the first face
        img.draw_rectangle(objects[0])
       
# Draw keypoints
print(kpts1)
img.draw_keypoints(kpts1, size=24)
img = sensor.snapshot()
time.sleep(2000)

# FPS clock
clock = time.clock()

while (True):
    clock.tick()
    img = sensor.snapshot()
    # Extract keypoints from the whole frame
    kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True)

    if (kpts2):
        # Match the first set of keypoints with the second one
        c=image.match_descriptor(kpts1, kpts2, threshold=85)
        match = c[6] # C[6] contains the number of matches.
        if (match>5):
            img.draw_rectangle(c[2:6])
            img.draw_cross(c[0], c[1], size=10)
            t=img.draw_rectangle(c[2:6])
            print(kpts2, "matched:%d dt:%d"%(match, c[7]))
            for face in objects:
                print(face)

    # Draw FPS
    img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))

@kidswong999 我看了这个，可是我不太懂[0][0]和[0][1]都代表着什么，教学视频里是只有0是x，1是y，2是w，3是h，那这里的[0][0]是什么？