# Save_Image - By: ZZY - 周四 5月 3 2018
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time=2000)
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
cut = img.save("/t.pgm", roi=(5, 5, 10, 10), quality=50)
time.sleep(100)
temp = image.Image("/t.pgm")
print(temp.size())
#注册音频使能IO
if AUDIO_PA_EN_PIN:
fm.register(AUDIO_PA_EN_PIN, fm.fpioa.GPIO1, force=True)
audio_en = GPIO(GPIO.GPIO1, GPIO.OUT, value=1)
#注册音频控制IO
fm.register(34, fm.fpioa.I2S0_OUT_D1, force=True)
fm.register(35, fm.fpioa.I2S0_SCLK, force=True)
fm.register(33, fm.fpioa.I2S0_WS, force=True)
wav_dev = I2S(I2S.DEVICE_0)
lcd.init()
#lcd.clear()
#lcd.rotation(1) #由于图像默认是240*320，因此顺时钟旋转90°。
lcd.display(image.Image("neu.jpg"))
#speak('0')
player = audio.Audio(path="/sd/service-yuyin/{}.wav".format(0))
wav_info = player.play_process(wav_dev)
wav_dev.channel_config(wav_dev.CHANNEL_1,
I2S.TRANSMITTER,
resolution=I2S.RESOLUTION_16_BIT,
cycles=I2S.SCLK_CYCLES_32,
align_mode=I2S.RIGHT_JUSTIFYING_MODE)
wav_dev.set_sample_rate(wav_info[1])
wav_dev.set_sample_rate(44100)
player.volume(100)
while True:
ret = player.play()
if ret == None:
def unittest(data_path, temp_path):
import image
img = image.Image("unittest/data/shapes.ppm", copy_to_fb=True)
circles = img.find_circles(threshold = 5000, x_margin = 30, y_margin = 30, r_margin = 30)
return len(circles) >= 1
from fpioa_manager import *
import KPU as kpu
lcd.init()
lcd.rotation(2)
try:
from pmu import axp192
pmu = axp192()
pmu.enablePMICSleepMode(True)
except:
pass
try:
img = image.Image("/sd/startup.jpg")
lcd.display(img)
except:
lcd.draw_string(lcd.width() // 2 - 100,
lcd.height() // 2 - 4, "Error: Cannot find start.jpg",
lcd.WHITE, lcd.RED)
task = kpu.load("/sd/e7148e972b5aaf9d_mbnet10_quant.kmodel")
labels = ["1", "2"] #You can check the numbers here to real names.
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((224, 224))
sensor.run(1)
# Exercise 8.13.7
# Write a function to remove all the red from an image:
# http://interactivepython.org/runestone/static/thinkcspy/_static/LutherBellPic.jpg
# For this and the following exercises, use the luther.jpg photo.
import image
img = image.Image('luther.jpg')
win = image.ImageWin(img.getWidth(), img.getHeight())
img.draw(win)
img.setDelay(1, 15)
for row in range(img.getHeight()):
for col in range(img.getWidth()):
p = img.getPixel(col, row)
g = p.getGreen()
b = p.getBlue()
newp = image.Pixel(0, g, b)
img.setPixel(col, row, newp)
img.draw(win)
def create(cls,
repoPath,
sdUUID,
imgUUID,
size,
volFormat,
preallocate,
diskType,
volUUID,
desc,
srcImgUUID,
srcVolUUID,
initialSize=None):
"""
Create a new volume with given size or snapshot
'size' - in sectors
'volFormat' - volume format COW / RAW
'preallocate' - Preallocate / Sparse
'diskType' - enum (API.Image.DiskTypes)
'srcImgUUID' - source image UUID
'srcVolUUID' - source volume UUID
'initialSize' - initial volume size in sectors,
in case of thin provisioning
"""
dom = sdCache.produce(sdUUID)
dom.validateCreateVolumeParams(volFormat,
srcVolUUID,
preallocate=preallocate)
imgPath = image.Image(repoPath).create(sdUUID, imgUUID)
volPath = os.path.join(imgPath, volUUID)
volParent = None
volType = sc.type2name(sc.LEAF_VOL)
# Get the specific class name and class module to be used in the
# Recovery tasks.
clsModule, clsName = cls._getModuleAndClass()
try:
if srcVolUUID != sc.BLANK_UUID:
# When the srcImgUUID isn't specified we assume it's the same
# as the imgUUID
if srcImgUUID == sc.BLANK_UUID:
srcImgUUID = imgUUID
volParent = cls(repoPath, sdUUID, srcImgUUID, srcVolUUID)
if not volParent.isLegal():
raise se.createIllegalVolumeSnapshotError(
volParent.volUUID)
if imgUUID != srcImgUUID:
volParent.share(imgPath)
volParent = cls(repoPath, sdUUID, imgUUID, srcVolUUID)
# Override the size with the size of the parent
size = volParent.getSize()
except se.StorageException:
cls.log.error("Unexpected error", exc_info=True)
raise
except Exception as e:
cls.log.error("Unexpected error", exc_info=True)
raise se.VolumeCannotGetParent(
"Couldn't get parent %s for volume %s: %s" %
(srcVolUUID, volUUID, e))
try:
cls.log.info("Creating volume %s", volUUID)
# Rollback sentinel to mark the start of the task
vars.task.pushRecovery(
task.Recovery(task.ROLLBACK_SENTINEL, clsModule, clsName,
"startCreateVolumeRollback",
[sdUUID, imgUUID, volUUID]))
# Create volume rollback
vars.task.pushRecovery(
task.Recovery("Halfbaked volume rollback", clsModule, clsName,
"halfbakedVolumeRollback",
[sdUUID, volUUID, volPath]))
# Specific volume creation (block, file, etc...)
try:
metaId = cls._create(dom,
imgUUID,
volUUID,
size,
volFormat,
preallocate,
volParent,
srcImgUUID,
srcVolUUID,
volPath,
initialSize=initialSize)
except (se.VolumeAlreadyExists, se.CannotCreateLogicalVolume,
se.VolumeCreationError, se.InvalidParameterException) as e:
cls.log.error("Failed to create volume %s: %s", volPath, e)
vars.task.popRecovery()
raise
# When the volume format is raw what the guest sees is the apparent
# size of the file/device therefore if the requested size doesn't
# match the apparent size (eg: physical extent granularity in LVM)
# we need to update the size value so that the metadata reflects
# the correct state.
if volFormat == sc.RAW_FORMAT:
apparentSize = int(
dom.getVSize(imgUUID, volUUID) / sc.BLOCK_SIZE)
if apparentSize < size:
cls.log.error(
"The volume %s apparent size %s is smaller "
"than the requested size %s", volUUID, apparentSize,
size)
raise se.VolumeCreationError()
if apparentSize > size:
cls.log.info(
"The requested size for volume %s doesn't "
"match the granularity on domain %s, "
"updating the volume size from %s to %s", volUUID,
sdUUID, size, apparentSize)
size = apparentSize
vars.task.pushRecovery(
task.Recovery("Create volume metadata rollback", clsModule,
clsName, "createVolumeMetadataRollback",
map(str, metaId)))
cls.newMetadata(metaId, sdUUID, imgUUID, srcVolUUID, size,
sc.type2name(volFormat), sc.type2name(preallocate),
volType, diskType, desc, sc.LEGAL_VOL)
if dom.hasVolumeLeases():
cls.newVolumeLease(metaId, sdUUID, volUUID)
except se.StorageException:
cls.log.error("Unexpected error", exc_info=True)
raise
except Exception as e:
cls.log.error("Unexpected error", exc_info=True)
raise se.VolumeCreationError("Volume creation %s failed: %s" %
(volUUID, e))
# Remove the rollback for the halfbaked volume
vars.task.replaceRecoveries(
task.Recovery("Create volume rollback", clsModule, clsName,
"createVolumeRollback",
[repoPath, sdUUID, imgUUID, volUUID, imgPath]))
return volUUID
# CorgiDude GPIO | From AiDude.io, aiiotshop.com/p/58
import image, lcd, time
from Dude import dude,PORT
lcd.init()
lcd.rotation(1)
dude.BeginADC(PORT.INPUT2)
dude.BeginAHT(PORT.INPUT2)
while(True):
img = image.Image(size=(240,240))
#read sensor
adc = dude.AnalogRead(PORT.INPUT2,2)
temp,humid = dude.ReadAHT(PORT.INPUT2)
img.draw_rectangle(0,0,240,240, fill=True, color=(int(adc/3.5*255),0,0))
img.draw_string(5,5, "T=%2.2f" % temp,color=(0,255,0),scale=3)
img.draw_string(5,35, "H=%2.2f" % humid,color=(0,255,0),scale=3)
img.draw_string(5,65, "ADC=%2.2f" % adc,color=(0,255,0),scale=3)
'''
if dude.DigitalRead(PORT.INPUT1,4) == 1:
img.draw_rectangle(0,0,240,240, fill=True, color=(0,0,255))
'''
lcd.display(img)
time.sleep(0.1)
key_gpio.irq(set_key_state, GPIO.IRQ_RISING, GPIO.WAKEUP_NOT_SUPPORT)
lcd.init()
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(1)
sensor.set_vflip(1)
sensor.run(1)
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025) # anchor for face detect
dst_point = [(44, 59), (84, 59), (64, 82), (47, 105),
(81, 105)] # standard face key point position
a = kpu.init_yolo2(task_fd, 0.5, 0.3, 5, anchor)
img_lcd = image.Image()
img_face = image.Image(size=(128, 128))
a = img_face.pix_to_ai()
record_ftr = []
record_ftrs = []
names = [
'Mr.1', 'Mr.2', 'Mr.3', 'Mr.4', 'Mr.5', 'Mr.6', 'Mr.7', 'Mr.8', 'Mr.9',
'Mr.10'
]
ACCURACY = 85
while (1):
img = sensor.snapshot()
clock.tick()
code = kpu.run_yolo2(task_fd, img)
def blank_draw():
if ui.enable:
ui.canvas = image.Image(size=(ui.height, ui.weight)
) # 10ms # 168.75kb (112kb)
def unittest(data_path, temp_path):
import image
img = image.Image("unittest/data/datamatrix.pgm", copy_to_fb=True)
matrices = img.find_datamatrices()
return len(matrices) == 1 and matrices[0][0:] == (34, 15, 90, 89, 'https://openmv.io/', 0.0, 18, 18, 18, 0)
def face_recog(pic_name, vi_ip):
print("~~~~~~~~~~~~~~~~FACE_RECOG~~~~~~~~~~~~~~~~~~~~~~")
gc.collect()
snap_img = image.Image(pic_name, copy_to_fb=True).mask_ellipse()
d0 = snap_img.find_lbp((0, 0, snap_img.width(), snap_img.height()))
pyb.LED(2).on()
pyb.LED(3).on()
name_lbp_list = []
uos.chdir("/CamFaces")
for filename in uos.listdir("/CamFaces"):
if filename.endswith(".pgm"):
try:
img = None
img = image.Image(filename, copy_to_fb=True).mask_ellipse()
sensor.alloc_extra_fb(img.width(), img.height(),
sensor.GRAYSCALE)
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(d0, d1, 50)
sensor.dealloc_extra_fb()
pname = filename
und_loc = pname.index('_')
pname = pname[0:(und_loc)]
name_lbp_list.append(pname)
name_lbp_list.append(dist)
continue
except Exception as e:
print(e)
print("error producing LBP value")
else:
print("file found that is not of type pgm")
print(name_lbp_list)
gc.collect()
end = 0
name_avg = []
i = 0
start = 0
while i < len(name_lbp_list):
if ((i + 2) < len(name_lbp_list)) and (name_lbp_list[i] !=
name_lbp_list[i + 2]):
end = i + 2
face = []
face = name_lbp_list[start:end]
print(face)
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
start = i + 2
i += 2
face = []
face = name_lbp_list[(end):(len(name_lbp_list))]
print(face)
gc.collect()
j = 1
sum_lbp = 0
while j < len(face):
sum_lbp += face[j]
j += 2
name_avg.append(face[0])
name_avg.append(sum_lbp / (len(face) / 2))
print(name_avg)
lbps = []
k = 1
while k < len(name_avg):
lbps.append(name_avg[k])
k += 2
print(lbps)
gc.collect()
min_lbp = min(lbps)
print(min_lbp)
ind = lbps.index(min(lbps))
ind += 1
found_person = name_avg[2 * ind - 2]
id_name = "The person you are looking at is: " + found_person
print(id_name)
uos.remove("/snapshot-person.pgm")
pyb.LED(2).off()
pyb.LED(3).off()
chost = vi_ip
cport = 8080
client = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM)
client.connect((chost, cport))
print("connected to visually impaired user's smartphone")
to_send = id_name + "\n"
client.send(to_send.encode())
print("sent name to phone")
client.close()
gc.collect()
return
#-------------------------------------------------------------------------------
# main body
#-------------------------------------------------------------------------------
print('Listing all templates...')
tpl_dname = '../../results/template_trans/'
tpl_fnames = [f for f in listdir(tpl_dname) if isfile(join(tpl_dname, f))]
tpl_fnames.sort()
print('Creating a template object array...')
tpls = [template.Template(tpl_dname + f) for f in tpl_fnames]
print('Reading the image...')
img_fname = '../../outputs/4_flipped/EUD-visit2-r1.jpg'
img = image.Image(img_fname)
print('Performing template matching...')
rets = [img.find_match(t) for t in tpls]
nccs = [ret['ncc'] for ret in rets]
locs = [ret['loc'] for ret in rets]
max_score = max(nccs)
index = [i for i, s in enumerate(nccs) if s == max_score]
index = index[0]
print('Max matching score (ncc) is %0.4f' % max_score)
print('Showing best matching...')
tpl = tpls[index]
ncc = nccs[index]
loc = locs[index]
lcd.init() # 初始化lcd
lcd.rotation(2)
sensor.reset() #初始化sensor 摄像头
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_hmirror(1) #设置摄像头镜像
sensor.set_vflip(1) #设置摄像头翻转
sensor.run(1) #使能摄像头
anchor = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275,
6.718375, 9.01025) #anchor for face detect 用于人脸检测的Anchor
dst_point = [
(44, 59), (84, 59), (64, 82), (47, 105), (81, 105)
] #standard face key point position 标准正脸的5关键点坐标 分别为 左眼 右眼 鼻子 左嘴角 右嘴角
a = kpu.init_yolo2(task_fd, 0.5, 0.3, 5, anchor) #初始化人脸检测模型
img_lcd = image.Image() # 设置显示buf
img_face = image.Image(size=(128, 128)) #设置 128 * 128 人脸图片buf
a = img_face.pix_to_ai() # 将图片转为kpu接受的格式
record_ftr = [] #空列表 用于存储当前196维特征
record_ftrs = [] #空列表 用于存储按键记录下人脸特征，可以将特征以txt等文件形式保存到sd卡后，读取到此列表，即可实现人脸断电存储。
record_names = [] #空列表 用于存储按键记录下人脸名字，顺序与record_ftrs相一致。
#names = ['Mr.1', 'Mr.2', 'Mr.3', 'Mr.4', 'Mr.5', 'Mr.6', 'Mr.7', 'Mr.8', 'Mr.9' , 'Mr.10'] # 人名标签，与上面列表特征值一一对应。
if "names.txt" in os.listdir():
with open("names.txt", 'r') as f:
record_names = f.read().splitlines()
print(record_names)
if "ftrs.txt" in os.listdir():
with open("ftrs.txt", 'r') as f:
record_ftrs = f.read().split('\n|||||\n')
import time, sensor, image
from image import SEARCH_EX, SEARCH_DS
from pyb import UART
sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
template1 = image.Image("LorR1left.pgm")
template2 = image.Image("LorR2right.pgm")#camera rotate 90 degrees clockwise
uart = UART(3, 9600)
clock = time.clock()
#Run template matching
while (True):
clock.tick()
inp = 1
#inp = uart.read()
if inp:
import sys
import random
from fpioa_manager import fm
from machine import UART
fm.register(35, fm.fpioa.UART2_TX, force=True)
fm.register(34, fm.fpioa.UART2_RX, force=True)
uart_Port = UART(UART.UART2, 115200, 8, 0, 0, timeout=1000, read_buf_len=4096)
data_packet = bytearray([0x00, 0x00, 0x00, 0x00, 0x00])
lcd.init()
lcd.rotation(2) #Rotate the lcd 180deg
try:
img = image.Image("/flash/startup.jpg")
lcd.display(img)
except:
lcd.draw_string(lcd.width() // 2 - 100,
lcd.height() // 2 - 4, "Error: Cannot find start.jpg",
lcd.WHITE, lcd.RED)
from Maix import I2S, GPIO
import audio
from Maix import GPIO
from fpioa_manager import *
fm.register(board_info.SPK_SD, fm.fpioa.GPIO0)
spk_sd = GPIO(GPIO.GPIO0, GPIO.OUT)
spk_sd.value(1) #Enable the SPK output
def bg_draw():
if ui.enable:
if ui.bak == None:
ui.bak = image.Image(ui.bg_path) # 90ms
ui.canvas.draw_image(ui.bak, 0, 0) # 20ms
'''
实验名称：LCD
版本： v1.0
日期： 2019.12
作者： 01Studio
说明：编程实现LCD显示信息。需要将01Studio.bmp文件发送到开发板。
'''
import lcd, image, utime
lcd.init() #初始化LCD
lcd.clear(lcd.WHITE) #清屏白色
#显示字符
lcd.draw_string(110, 120, "Hello 01Studio!", lcd.BLACK, lcd.WHITE) #显示字符
utime.sleep(2) #延时2秒
#显示图像，记得先将01Studio.bmp文件发送到开发板
lcd.rotation(1) #由于图像默认是240*320，因此顺时钟旋转90°。
lcd.display(image.Image("01Studio.bmp"))
# This example shows off how to draw images in the frame buffer with a custom generated color palette.
import sensor, image, time, pyb
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE) # or GRAYSCALE...
sensor.set_framesize(sensor.QQVGA) # or QQVGA...
sensor.skip_frames(time=2000)
clock = time.clock()
# the color palette is actually an image, this allows you to use image ops to create palettes
# the image must have 256 entries i.e. 256x1, 64x4, 16x16 and have the format rgb565
# Initialise palette source colors into an image
palette_source_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 0, 255)]
palette_source_color_image = image.Image(len(palette_source_colors), 1,
sensor.RGB565)
for i, color in enumerate(palette_source_colors):
palette_source_color_image[i] = color
# Scale the image to palette width and smooth them
palette = image.Image(256, 1, sensor.RGB565)
palette.draw_image(palette_source_color_image,
0,
0,
x_scale=palette.width() /
palette_source_color_image.width())
palette.mean(int(palette.width() / palette_source_color_image.width() / 2))
while (True):
clock.tick()
def speaker(pin, melody, noteDurations):
if melody == 0:
tim = Timer(Timer.TIMER2, Timer.CHANNEL0, mode=Timer.MODE_PWM)
tim.stop()
noteDuration = int(noteDurations * 1000)
time.sleep_ms(noteDuration)
return
listmelody = [
0,
131,
147,
165,
175,
196,
220,
247, #3, 1 - 7
262,
294,
330,
349,
392,
440,
494, #4, 8 - 14
523,
587,
659,
698,
784,
880,
988, #5, 15 - 21
139, # C#3, 22
156, # Eb3, 23
185, # F#3, 24
208, # G#3, 25
233, # Bb3, 26
277, # C#4, 27
311, # Eb4, 28
370, # F#4, 29
415, # G#4, 30
466, # Bb4, 31
555, # C#5, 32
622, # Eb5, 33
740, # F#5, 34
831, # G#5, 35
932, # Bb5, 36
1047,
1175,
1319,
1397,
1568,
1760,
1976, #6, 37 - 43
2093,
2349,
2637,
2794,
3136,
3520,
3951, #7, 44 - 50
1109, # C#6, 52
1245, # Eb6, 53
1480, # F#6, 54
1661, # G#6, 55
1865, # Bb6, 56
2217, # C#7, 57
2489, # Eb6, 58
2960, # F#6, 59
3322, # G#6, 60
3729 # Bb6, 61
]
listnoteDurations = noteDurations
# to calculate the note duration, take one second
# divided by the note type.
#e.g. quarter note = 1000 / 4, eighth note = 1000/8, etc.
noteDuration = int(listnoteDurations * 1000)
lcd.display(image.Image('music.jpg'))
#lcd.draw_string(5, 15, 'Note is playing', lcd.RED, lcd.WHITE)
tone(pin, listmelody[melody], noteDuration)
lcd.display(image.Image('logo.jpg'))
# Copy image to framebuffer.
#
# This example shows how to load and copy an image to framebuffer for testing.
import sensor, image
# Still need to init sensor
sensor.reset()
# Set sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# Set sensor pixel format
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# Load image
img = image.Image("/image.pgm")
# Copy image to framebuffer
img.copy_to_fb()
# Update drawing
sensor.snapshot()
buff = (data[i * 2] << 8 | data[i * 2 + 1])
if buff > 32768:
buff -= 65536
if i < 3:
MPU6886_DATA[i] = buff * 8.0 / 32768.0
elif i == 3:
MPU6886_DATA[i] = buff / 326.8 + 25.0
else:
MPU6886_DATA[i] = buff * 1000.0 / 32768.0
MPU6886_init()
# Init graph
graph = image.Image()
graph.draw_rectangle(20, 15, 200, 100, (150, 150, 150), 1, False)
graph.draw_line(10, 65, 230, 65, (100, 100, 100))
def write_graph(graph,
data,
graph_x_origin=20,
graph_y_origin=115,
x_offset=0,
y_offset=0,
color=(30, 111, 150)):
for t in range(len(data)):
graph.set_pixel(t + graph_x_origin + x_offset,
graph_y_origin - data[t] - y_offset, color)
import network, socket, time, utime, sensor, image, lcd, os
from machine import UART
from Maix import GPIO
from maix_motor import Maix_motor
from fpioa_manager import fm
import ujson
lcd.display(image.Image('logo.jpg'))
msg = 'Open Code&Robots APP, enter your WiFi credentials and scan the resulting QR code with MARK camera'
num_rows = len(msg) // 28
for i in range(num_rows + 3):
lcd.draw_string(5, i * 15, msg[i * 28:i * 28 + 28], lcd.RED, lcd.WHITE)
time.sleep(2)
########## config ################
WIFI_SSID = 0
WIFI_PASSWD = 0
server_ip = 0
server_port = 3456
pan_angle = 90
tilt_angle = 90
bullet = 90
fm.register(25, fm.fpioa.GPIOHS25) #cs
fm.register(8, fm.fpioa.GPIOHS8) #rst
fm.register(9, fm.fpioa.GPIOHS9) #rdy
print("Use hardware SPI for other maixduino")
fm.register(28, fm.fpioa.SPI1_D0, force=True) #mosi
fm.register(26, fm.fpioa.SPI1_D1, force=True) #miso
fm.register(27, fm.fpioa.SPI1_SCLK, force=True) #sclk
##################################
# 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.
pin = image.Image("/template_pin.pgm")
hole = image.Image("/template_hole.pgm")
clock = time.clock()
#initialisation de la variable qui dit vaut 1 pour un trou, 2 pour une épingle et 0 si on ne sait pas
voir = 0
# 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
oldpixel1 = oldimage.getPixel(col, row)
oldpixel2 = oldimage.getPixel(col + 1, row)
oldpixel3 = oldimage.getPixel(col, row + 1)
oldpixel4 = oldimage.getPixel(col + 1, row + 1)
newred = int((oldpixel1.getRed() + oldpixel2.getRed() +
oldpixel3.getRed() + oldpixel4.getRed()) / 4)
newgreen = int((oldpixel1.getGreen() + oldpixel2.getGreen() +
oldpixel3.getGreen() + oldpixel4.getGreen()) / 4)
newblue = int((oldpixel1.getBlue() + oldpixel2.getBlue() +
oldpixel3.getBlue() + oldpixel4.getBlue()) / 4)
newpixel = image.Pixel(newred, newgreen, newblue)
oldimage.setPixel(col, row, newpixel)
oldimage.setPixel(col + 1, row, newpixel)
oldimage.setPixel(col, row + 1, newpixel)
oldimage.setPixel(col + 1, row + 1, newpixel)
print("here")
return oldimage
img = image.Image("tiger.jpg")
smoothed = smooth(img)
print(smoothed)
win = image.ImageWin(smoothed.getWidth(), smoothed.getHeight())
smoothed.draw(win)
win.exitonclick()
# img.setDelay(1,15) # setDelay(0) turns off animation
sensor.run(1)
# Init button
fpioa = FPIOA()
fpioa.set_function(board_info.PIN15, fm.fpioa.GPIOHS0)
pin = fpioa.get_Pin_num(fm.fpioa.GPIOHS0)
pin = GPIO(GPIO.GPIOHS0, GPIO.IN)
# Init touchscreen
i2c = I2C(I2C.I2C0, freq=400000, scl=30, sda=31)
ts.init(i2c)
# Load model
task = kpu.load(kmodel_path)
img = image.Image()
img_icon = image.Image()
# ===== Configure trash classification specific parameters =====
# Class label vector
# Last entry is the label for values below the threshold -> unknown
class_labels = [
'Pappe', 'Glas', 'Metall', 'Papier', 'Kunststoffflasche', 'Kunststoff',
'Restmuell'
]
# ===== Main program loop =====
first_cycle = True
pause = False
img = None
pmin = 999999
num = 0
def min(pmin, a, s):
global num
if a < pmin:
pmin = a
num = s
return pmin
for s in range(1, NUM_SUBJECTS + 1):
dist = 0
for i in range(2, NUM_SUBJECTS_IMGS + 1):
img = image.Image("face/s%d/%d.pgm" % (s, i))
d1 = img.find_lbp((0, 0, img.width(),
img.height())) #d1为第s文件夹中的第i张图片的lbp特征
dist += image.match_descriptor(
d0, d1) #计算d0 d1即样本图像与被检测人脸的特征差异度。
print("Average dist for subject %d: %d" %
(s, dist / NUM_SUBJECTS_IMGS))
pmin = min(pmin, dist / NUM_SUBJECTS_IMGS,
s) #特征差异度越小，被检测人脸与此样本更相似更匹配。
print(pmin)
i = 0
s = 0
if pmin > 100000:
print(0)
else:
import image
img=image.Image("luther.jpg")
newimg=image.EmptyImage(im.getWidth(),img.getHeight())
win=image.ImageWin()
for column in range(img.getWidth()):
for row in range(img.getHeight()):
p=img.getPixel(column,row)
newpixel=image.Pixel(0,p.getGreen(),p.getBlue())
img.setPixel(column,row,newpixel)
newimg.draw(win)
win.exitonclick()
sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) hint = image.BICUBIC # image.BILINEAR image.BICUBIC # RGB channel extraction is done after scaling normally, this # may produce false colors. Set this flag to do it before. # hint |= 0 # image.EXTRACT_RGB_CHANNEL_FIRST # Color table application is done after scaling normally, this # may produce false colors. Set this flag to do it before. # hint |= 0 # image.APPLY_COLOR_PALETTE_FIRST small_img = image.Image(4, 4, sensor.RGB565) small_img.set_pixel(0, 0, (0, 0, 127)) small_img.set_pixel(1, 0, (47, 255, 199)) small_img.set_pixel(2, 0, (0, 188, 255)) small_img.set_pixel(3, 0, (0, 0, 127)) small_img.set_pixel(0, 1, (0, 176, 255)) small_img.set_pixel(1, 1, (222, 0, 0)) small_img.set_pixel(2, 1, (50, 255, 195)) small_img.set_pixel(3, 1, (86, 255, 160)) small_img.set_pixel(0, 2, (255, 211, 0)) small_img.set_pixel(1, 2, (83, 255, 163)) small_img.set_pixel(2, 2, (255, 211, 0)) small_img.set_pixel(3, 2, (0, 80, 255)) small_img.set_pixel(0, 3, (255, 118, 0)) small_img.set_pixel(1, 3, (127, 0, 0)) small_img.set_pixel(2, 3, (0, 144, 255))
from Maix import FFT
from fpioa_manager import *
import image
import lcd
lcd.init()
fm.register(8, fm.fpioa.GPIO0)
wifi_en = GPIO(GPIO.GPIO0, GPIO.OUT)
wifi_en.value(0)
fm.register(20, fm.fpioa.I2S0_IN_D0)
fm.register(19, fm.fpioa.I2S0_WS)
fm.register(18, fm.fpioa.I2S0_SCLK)
rx = I2S(I2S.DEVICE_0)
rx.channel_config(rx.CHANNEL_0, rx.RECEIVER, align_mode=I2S.STANDARD_MODE)
sample_rate = 2048
rx.set_sample_rate(sample_rate)
img = image.Image()
sample_points = 512
FFT_points = 256
lcd_width = 320
lcd_height = 240
#hist_num = FFT_points #changeable
hist_num = 32 #changeable
if hist_num > 320:
hist_num = 320
hist_width = int(320 / hist_num) #changeable
x_shift = 0
while True:
audio = rx.record(sample_points)
FFT_res = FFT.run(audio.to_bytes(), FFT_points)
FFT_amp = FFT.amplitude(FFT_res)
img = img.clear()
# Signal Detect RED/GREEN and Arrow Detection.
# Using Template maching.
# Template Matching Example - Normalized Cross Correlation (NCC)
#
import sys
import time, sensor, image
import utime
from pyb import Pin, Timer, LED
from image import SEARCH_EX, SEARCH_DS
# template match
#tL = image.Image("/LA_64_64.pgm")
#tR = image.Image("/RA_64_64.pgm")
tL = image.Image("/LA_32_32_1.pgm")
tR = image.Image("/RA_32_32_1.pgm")
tU = image.Image("/UA_32_32.pgm")
#template = image.Image("/H.pgm")
# Color
# PWM OUT
tim = Timer(4, freq=1000) # Frequency in Hz
tim.channel(1, Timer.PWM, pin=Pin("P7"), pulse_width_percent=5)
thresholds = [
(30, 100, 15, 127, 15, 127), # generic_red_thresholds
(30, 100, -64, -8, -32, 32)
] # generic_green_thresholds
loopTime = 800
clock = time.clock()
#timled = Timer(2)
#timled.init(freq=2) # trigger at 2Hz
