rb35 发布的帖子

rb35

这个是人脸追踪代码

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)
red_threshold = (13, 49, 18, 61, 6, 47)
pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID

#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE) # 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.
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

while(True):

clock.tick() # Track elapsed milliseconds between snapshots().

img = sensor.snapshot() # Take a picture and return the image.



blobs = img.find_features(face_cascade, threshold=0.75, scale=1.35)

if blobs:

    max_blob = find_max(blobs)

    pan_error = max_blob[0]+max_blob[2]/2-img.width()/2

    tilt_error = max_blob[1]+max_blob[3]/2-img.height()/2



    print("pan_error: ", pan_error)



    img.draw_rectangle(max_blob) # rect

    img.draw_cross (int(max_blob[0]+max_blob[2]/2), int(max_blob[1]+max_blob[3]/2)) # cx, cy



    pan_output=pan_pid.get_pid(pan_error,1)/2

    tilt_output=tilt_pid.get_pid(tilt_error,1)

    print("pan_output",pan_output)

    pan_servo.angle(pan_servo.angle()+pan_output)

    tilt_servo.angle(tilt_servo.angle()-tilt_output)

这个是人脸识别代码

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)
red_threshold = (13, 49, 18, 61, 6, 47)
pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID

#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.

sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 500)

sensor.set_auto_whitebal(False) # turn this off.

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

NUM_SUBJECT = 6
NUM_SUBJECT_IMGS = 10

imgs = sensor.snapshot()
d0 = imgs.find_lbp((0,0,imgs.width(),imgs.height()))

def min(pmin,a,s):
global num
if a<pmin:
pmin=a
num=s
return pmin

imgs = None
pmin = 999999
num=0

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():

clock.tick() # Track elapsed milliseconds between snapshots().

img = sensor.snapshot() # Take a picture and return the image.



blobs = img.find_features(face_cascade, threshold=0.75, scale=1.35)

if blobs:

    max_blob = find_max(blobs)

    pan_error = max_blob[0]+max_blob[2]/2-img.width()/2

    tilt_error = max_blob[1]+max_blob[3]/2-img.height()/2



    print("pan_error: ", pan_error)



    img.draw_rectangle(max_blob) # rect

    img.draw_cross (int(max_blob[0]+max_blob[2]/2), int(max_blob[1]+max_blob[3]/2)) # cx, cy



    pan_output=pan_pid.get_pid(pan_error,1)/2

    tilt_output=tilt_pid.get_pid(tilt_error,1)

    print("pan_output",pan_output)

    pan_servo.angle(pan_servo.angle()+pan_output)

    tilt_servo.angle(tilt_servo.angle()-tilt_output)




    for s in range(2,NUM_SUBJECT+1):
            dist = 0
            for i in range(2,NUM_SUBJECT+1):
                imgs = image.Image("singtown/s%d/%d.pmg"%(s,i))
                d1 = imgs.find_lbp((0,0,imgs.width(),imgs.height()))
                dist +=image.match_descriptor(d0,d1)
            pmin = min(pmin.dist/NUM_SUBJECTS_IMGS，s)
    if pmin<7000:
        print("wellcome!")
    else：
    print("检测到陌生人")
else:
    print("未检测到人脸")

rb35

是在两段代码后面直接加上去吗

rb35

这个是人脸追踪的代码：


import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)
red_threshold  = (13, 49, 18, 61, 6, 47)
pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID

#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE) # 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.
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





while(True):

    clock.tick() # Track elapsed milliseconds between snapshots().

    img = sensor.snapshot() # Take a picture and return the image.



    blobs = img.find_features(face_cascade, threshold=0.75, scale=1.35)

    if blobs:

        max_blob = find_max(blobs)

        pan_error = max_blob[0]+max_blob[2]/2-img.width()/2

        tilt_error = max_blob[1]+max_blob[3]/2-img.height()/2



        print("pan_error: ", pan_error)



        img.draw_rectangle(max_blob) # rect

        img.draw_cross (int(max_blob[0]+max_blob[2]/2), int(max_blob[1]+max_blob[3]/2)) # cx, cy



        pan_output=pan_pid.get_pid(pan_error,1)/2

        tilt_output=tilt_pid.get_pid(tilt_error,1)

        print("pan_output",pan_output)

        pan_servo.angle(pan_servo.angle()+pan_output)

        tilt_servo.angle(tilt_servo.angle()-tilt_output)

这个是wifi模块的代码：

MJPEG Streaming AP.

这个例子展示了如何在AccessPoint模式下进行MJPEG流式传输。

Android上的Chrome，Firefox和MJpegViewer App已经过测试。

连接到OPENMV_AP并使用此URL：http://192.168.1.1:8080查看流。

import sensor, image, time, network, usocket, sys

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.GRAYSCALE)

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
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)
    print(clock.fps())

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)
    #sys.print_exception(e)