def face_detect():
print("Detecting face...")
sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
clock = time.clock()
LED.on()
for i in range(60):
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
# Draw objects
if objects:
for r in objects:
img.draw_rectangle(r)
cx=r[0]+r[2]//2
cy=r[1]+r[3]//2
img.draw_cross(cx,cy)
obj = find_max(objects)
cx_array.append(obj[0]+obj[2]//2)
cy_array.append(obj[1]+obj[3]//2)
h_array.append(obj[2])
h_threshold = obj[2]
if len(cx_array) == filter_stages:
LED.off()
print('Face detected.\nStart tracking...')
return True
LED.off()
print('No face detected.\nTrying color mode...')
return False
def run(argv):
mode = argv
if (mode == 0):
#check Arrow
sensor.reset()
'''sensor.set_auto_gain(False)
sensor.set_contrast(1)
sensor.set_gainceiling(16)
#sensor.set_windowing((200, 200)) # 240x240 center pixels of VGA
sensor.set_framesize(sensor.QQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_auto_whitebal(False)
'''
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.set_vflip(True)
sensor.set_hmirror(True)
sensor.skip_frames(time=2000)
findArrow()
else:
#check signal mode
sensor.reset()
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(True)
sensor.set_contrast(-3)
sensor.set_brightness(-3)
sensor.set_gainceiling(8)
sensor.set_pixformat(sensor.RGB565)
sensor.set_vflip(True)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((240, 240)) # 240x240 center pixels of VGA
#sensor.set_windowing((200, 200)) # 200x200 center pixels of VGA
sensor.skip_frames(time=800)
checkSignal()
def FaceTest():
sensor.reset()
# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# HQVGA and GRAYSCALE are the best for face tracking.
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
#sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
#sensor.set_framerate(2<<9|3<<11)
# 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)
clock = time.clock()
for i in range(250):
clock.tick()
img = sensor.snapshot()
objects = img.find_features(face_cascade,
threshold=0.75,
scale_factor=1.25)
fID = 0
for r in objects:
img.draw_rectangle(r, color=(0, 0, 0), thickness=3)
#img.draw_rectangle(r[0], r[1], 48, 10, fill=True, color=(0,0,0))
fID += 1
s = 'face %d' % (fID)
img.draw_string(r[0], r[1], s)
print(clock.fps())
def init(is_debug, pixformat, delay_time):
#关闭串口，防止初始化过程溢出
uart.deinit()
uart2.deinit()
sensor.reset()
sensor.set_pixformat(sensor.RGB565) #RGB565
sensor.set_framesize(sensor.QVGA) #320*240
sensor.set_gainceiling(128) #增益上限 2,4,8,16,32,64,128
sensor.set_contrast(3) #对比度 -3至3
sensor.set_brightness(0) #亮度。-3至+3
sensor.set_saturation(3) #饱和度。-3至+3
sensor.set_auto_exposure(True) #自动曝光
sensor.skip_frames(time=delay_time)
sensor.set_auto_gain(False) # 在进行颜色追踪时，必须关闭
sensor.set_auto_whitebal(False) # 在进行颜色追踪时，必须关闭
#重新打开串口
uart.init(115200, timeout_char=1000)
uart2.init(115200, timeout_char=1000)
#判断是否debug模式
global CompetitionScene
if is_debug == True:
CompetitionScene = 0
else:
CompetitionScene = 1
def CorrTest(loopCnt = 220, barLen=120):
sensor.reset()
# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565)
#sensor.set_windowing((480,272))
clock = time.clock()
avg = 0.0
startTick = time.ticks()
corr = 0.3
while (True):
if time.ticks() - startTick > loopCnt:
break
clock.tick()
img = sensor.snapshot()
for i in range(7):
img.draw_rectangle(160-i*15, 120-i*15, i*15*2, i*15*2)
corr += 0.05
if corr >= 4.0:
corr = 0.3
img.lens_corr(corr)
lnLen = (barLen * (loopCnt - (time.ticks() - startTick))) // loopCnt
DrawPgsBar(img, barLen, loopCnt, startTick)
img.draw_string(4,4,'Lens correction %.2f' % (corr), color=(0,0,0))
def facetrack_camInit():
print("INIT Facetrack")
# Reset sensor
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((200, 200))
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.skip_frames(time = 2000)
def face_recog(calc_time, vi_ip):
pin = pyb.millis()
print(pin)
print(calc_time)
cc = 0
#pyb.elapsed_millis(start)
while pyb.elapsed_millis(pin) < calc_time:
print("top of face recog function")
#snapshot on face detection
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
#sensor.alloc_extra_fb()
sensor.skip_frames(time=2000) # Let new settings take affect.
face_cascade = image.HaarCascade("frontalface", stages=25)
uos.chdir("/")
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(time=2000) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
try:
while (diff):
img = sensor.snapshot()
sensor.alloc_extra_fb(img.width(), img.height(),
sensor.GRAYSCALE)
faces = img.find_features(face_cascade,
threshold=0.5,
scale_factor=1.5)
sensor.dealloc_extra_fb()
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
elif (pyb.elapsed_millis(pin)) > calc_time:
raise Exception
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
pic_name = "snapshot-person.pgm"
sensor.snapshot().save(
pic_name) # Save Pic. to root of SD card -- uos.chdir("/")
pyb.delay(100)
facial_recog(pic_name, vi_ip)
gc.collect()
except Exception as go:
print("we are in exception")
pyb.LED(BLUE_LED_PIN).off()
gc.collect()
def face_detect(init_start, calc_time):
print("~~~~~~~~~~~~~~~~FACE_DETECT~~~~~~~~~~~~~~~~~~~~~~")
gc.collect() #garbage collection
while pyb.elapsed_millis(init_start) < calc_time: #while time not expired
#snapshot on face detection
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(3) #set to highest contrast setting
sensor.set_gainceiling(16)
sensor.set_pixformat(
sensor.GRAYSCALE) #grayscale for facial recognition
sensor.set_framesize(sensor.HQVGA)
sensor.skip_frames(time=2000) # Let new settings take affect.
face_cascade = image.HaarCascade(
"frontalface",
stages=25) #Using Frontal Face Haar Cascade Classifier
uos.chdir("/")
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(time=2000) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
try:
while (diff):
img = sensor.snapshot()
sensor.alloc_extra_fb(
img.width(), img.height(),
sensor.GRAYSCALE) #allocate more space for image
faces = img.find_features(
face_cascade, threshold=0.5,
scale_factor=1.5) #detecting face features
sensor.dealloc_extra_fb()
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
elif (pyb.elapsed_millis(init_start)
) > calc_time: #if time is expired, leave function
raise Exception
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
pic_name = "snapshot-person.pgm"
sensor.snapshot().save(pic_name) # Save Pic. to root of SD card
pyb.delay(100)
gc.collect() #garbage collection
return pic_name
except Exception as go:
print("exception - time expired")
pyb.LED(BLUE_LED_PIN).off()
gc.collect() #garbage collection
def test_color_bars():
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(3)
sensor.set_gainceiling(8)
sensor.set_contrast(2)
# Set sensor pixel format
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565)
# Enable colorbar test mode
sensor.set_colorbar(True)
# Skip a few camera to allow the sensor settle down
for i in range(0, 100):
image = sensor.snapshot()
#color bars thresholds
t = [
lambda r, g, b: r < 70 and g < 70 and b < 70, # Black
lambda r, g, b: r < 70 and g < 70 and b > 200, # Blue
lambda r, g, b: r > 200 and g < 70 and b < 70, # Red
lambda r, g, b: r > 200 and g < 70 and b > 200, # Purple
lambda r, g, b: r < 70 and g > 200 and b < 70, # Green
lambda r, g, b: r < 70 and g > 200 and b > 200, # Aqua
lambda r, g, b: r > 200 and g > 200 and b < 70, # Yellow
lambda r, g, b: r > 200 and g > 200 and b > 200
] # White
# color bars are inverted for OV7725
if (sensor.get_id() == sensor.OV7725):
t = t[::-1]
#320x240 image with 8 color bars each one is approx 40 pixels.
#we start from the center of the frame buffer, and average the
#values of 10 sample pixels from the center of each color bar.
for i in range(0, 8):
avg = (0, 0, 0)
idx = 40 * i + 20 #center of colorbars
for off in range(0, 10): #avg 10 pixels
rgb = image.get_pixel(idx + off, 120)
avg = tuple(map(sum, zip(avg, rgb)))
if not t[i](avg[0] / 10, avg[1] / 10, avg[2] / 10):
raise Exception('COLOR BARS TEST FAILED.'
'BAR#(%d): RGB(%d,%d,%d)' %
(i + 1, avg[0] / 10, avg[1] / 10, avg[2] / 10))
print('COLOR BARS TEST PASSED...')
def face_detect(init_start, calc_time):
print("~~~~~~~~~~~~~~~~FACE_DETECT~~~~~~~~~~~~~~~~~~~~~~")
gc.collect()
while pyb.elapsed_millis(init_start) < calc_time:
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA)
sensor.skip_frames(time=2000)
face_cascade = image.HaarCascade("frontalface", stages=25)
uos.chdir("/")
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(time=2000)
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10
try:
while (diff):
img = sensor.snapshot()
sensor.alloc_extra_fb(img.width(), img.height(),
sensor.GRAYSCALE)
faces = img.find_features(face_cascade,
threshold=0.5,
scale_factor=1.5)
sensor.dealloc_extra_fb()
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
elif (pyb.elapsed_millis(init_start)) > calc_time:
raise Exception
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
pic_name = "snapshot-person.pgm"
sensor.snapshot().save(pic_name)
pyb.delay(100)
gc.collect()
return pic_name
except Exception as go:
print("exception - time expired")
pyb.LED(BLUE_LED_PIN).off()
gc.collect()
def test_color_bars():
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(8)
sensor.set_contrast(2)
# Set sensor pixel format
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565)
# Enable colorbar test mode
sensor.set_colorbar(True)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 100):
image = sensor.snapshot()
# Color bars thresholds
t = [lambda r, g, b: r < 50 and g < 50 and b < 50, # Black
lambda r, g, b: r < 50 and g < 50 and b > 200, # Blue
lambda r, g, b: r > 200 and g < 50 and b < 50, # Red
lambda r, g, b: r > 200 and g < 50 and b > 200, # Purple
lambda r, g, b: r < 50 and g > 200 and b < 50, # Green
lambda r, g, b: r < 50 and g > 200 and b > 200, # Aqua
lambda r, g, b: r > 200 and g > 200 and b < 50, # Yellow
lambda r, g, b: r > 200 and g > 200 and b > 200] # White
# 320x240 image with 8 color bars each one is approx 40 pixels.
# we start from the center of the frame buffer, and average the
# values of 10 sample pixels from the center of each color bar.
for i in range(0, 8):
avg = (0, 0, 0)
idx = 40*i+20 # center of colorbars
for off in range(0, 10): # avg 10 pixels
rgb = image.get_pixel(idx+off, 120)
avg = tuple(map(sum, zip(avg, rgb)))
if not t[i](avg[0]/10, avg[1]/10, avg[2]/10):
raise Exception("COLOR BARS TEST FAILED. "
"BAR#(%d): RGB(%d,%d,%d)"%(i+1, avg[0]/10, avg[1]/10, avg[2]/10))
print("COLOR BARS TEST PASSED...")
def init(is_debug, pixformat, delay_time):
uart.deinit()
sensor.reset()
if pixformat == "GRAY":
sensor.set_pixformat(sensor.GRAYSCALE)
elif pixformat == "RGB":
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.set_gainceiling(128)
sensor.set_contrast(3)
sensor.set_brightness(0)
sensor.set_saturation(3)
sensor.set_auto_exposure(True)
sensor.skip_frames(time=delay_time)
sensor.set_auto_gain(False)
sensor.set_auto_whitebal(False)
uart.init(115200, timeout_char=1000)
global CompetitionScene
if is_debug == True:
CompetitionScene = 0
else:
CompetitionScene = 1
def setConfig():
while not uart.uart.any() > 0:
pass
uart.checking = True
data = uart.uart.readline()
data = data.decode('ascii')
data = list(map(int, data.split('_')))
Camera.frame = RES[data[0]]
Camera.mode = CLR[data[1]]
Camera.filter1 = data[2]
Camera.filter2 = data[3]
Camera.postproc = data[4]
Camera.contrast = data[5] / 10
Camera.brightness = data[6] / 10
Camera.saturation = data[7]
sensor.set_gainceiling(2**(data[8] if data[8] > 0 and data[8] < 8 else 3))
Camera.flash = data[9] if data[9] <= 255 and data[9] >= 0 else 0
LED.set_led(0, (Camera.flash, Camera.flash, Camera.flash))
LED.display()
sensor.set_pixformat(Camera.mode)
sensor.set_framesize(Camera.frame)
sensor.set_saturation(Camera.saturation)
uart.checking = False
def face_recog(calc_time):
pin = pyb.millis()
print(pin)
print(calc_time)
cc = 0
#print(pyb.elapsed_millis(pin))
while (pyb.elapsed_millis(pin)) < calc_time:
print("top of face recog function")
#snapshot on face detection
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time=2000) # Let new settings take affect.
face_cascade = image.HaarCascade("frontalface", stages=25)
uos.chdir("/")
pyb.LED(RED_LED_PIN).on()
print("About to start detecting faces...")
sensor.skip_frames(time=2000) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
print("Now detecting faces!")
pyb.LED(BLUE_LED_PIN).on()
diff = 10 # We'll say we detected a face after 10 frames.
#if (pyb.elapsed_millis(pin)) > calc_time:
#continue
try:
while (diff):
img = sensor.snapshot()
faces = img.find_features(face_cascade,
threshold=0.5,
scale_factor=1.5)
if faces:
diff -= 1
for r in faces:
img.draw_rectangle(r)
elif (pyb.elapsed_millis(pin)) > calc_time:
raise Exception
except Exception as e:
print("Get Out Exception called")
pyb.LED(BLUE_LED_PIN).off()
print("Face detected! Saving image...")
pic_name = "snapshot-person.pgm"
sensor.snapshot().save(
pic_name) # Save Pic. to root of SD card -- uos.chdir("/")
pyb.delay(100)
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()))
# face recognition
pyb.LED(2).on()
name_lbp_list = []
uos.chdir(
"/Faces"
) # change directory to where all the webex photos from tcp are stored
for filename in uos.listdir("/Faces"):
if filename.endswith(".pgm"):
try:
img = None
img = image.Image(filename, copy_to_fb=True).mask_ellipse()
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(d0, d1, 50)
#print("weve matched")
word = filename
#print(filename)
und_loc = word.index('_')
word = word[0:(und_loc)]
name_lbp_list.append(word)
name_lbp_list.append(dist)
continue
except Exception as e:
print(e)
print("error reading file")
else:
print("ERROR")
print(name_lbp_list)
#print(len(name_lbp_list))
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
#print(start)
#print(end)
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)
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)
#print(len(lbps))
min_lbp = min(lbps)
print(min_lbp)
ind = lbps.index(min(lbps))
#print(ind)
ind += 1
found_person = name_avg[2 * ind - 2]
id_name = "The person you are looking at is: " + found_person
print(id_name)
#delete snapshot of person
uos.remove("/snapshot-person.pgm")
pyb.LED(2).off()
LED(2).on()
LED(3).on()
pin1 = Pin('P1', Pin.IN, Pin.PULL_UP)
pin2 = Pin('P2', Pin.IN, Pin.PULL_UP)
pin3 = Pin('P3', Pin.IN, Pin.PULL_UP)
old_L = 0
old_R = 0
clock = time.clock()
sensor.reset()
#sensor.set_vflip(True)
#sensor.set_hmirror(True)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(
sensor.QQQVGA) # 80x60 (4,800 pixels) - O(N^2) max = 2,3040,000.
sensor.set_gainceiling(2)
sensor.set_auto_exposure(False, exposure_us=2500) #baoguanglv
sensor.set_contrast(+3)
sensor.set_saturation(-1)
sensor.set_brightness(-3)
while (True):
if pin1.value():
clock.tick()
img = sensor.snapshot().binary([THRESHOLD])
#img = sensor.snapshot()
line = img.get_regression([(-100, -100, 0, 0, 0, 0)], robust=True)
if (line):
rho_err = abs(line.rho()) - img.width() / 2
if line.theta() > 90:
theta_err = line.theta() - 180
import sensor, time
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(8)
sensor.set_contrast(2)
# Set sensor pixel format
sensor.set_framesize(sensor.QVGA)
sensor.set_pixformat(sensor.RGB565)
# Enable colorbar test mode
sensor.set_colorbar(True)
# Skip a few frames to allow the sensor settle down
for i in range(0, 30):
image = sensor.snapshot()
#color bars thresholds
t = [lambda r, g, b: r < 50 and g < 50 and b < 50, # Black
lambda r, g, b: r < 50 and g < 50 and b > 200, # Blue
lambda r, g, b: r > 200 and g < 50 and b < 50, # Red
lambda r, g, b: r > 200 and g < 50 and b > 200, # Purple
lambda r, g, b: r < 50 and g > 200 and b < 50, # Green
lambda r, g, b: r < 50 and g > 200 and b > 200, # Aqua
lambda r, g, b: r > 200 and g > 200 and b < 50, # Yellow
lambda r, g, b: r > 200 and g > 200 and b > 200] # White
#320x240 image with 8 color bars each one is approx 40 pixels.
# 使用Canny算法做边缘检测:
#
# 这个例子演示了Canny边缘检测器.
#
#翻译：01Studio
import sensor, image, time
#初始化摄像头
sensor.reset() # 初始化摄像头模块.
sensor.set_pixformat(sensor.GRAYSCALE) # 或者使用 sensor.RGB565 彩色
sensor.set_framesize(sensor.QQVGA) # 或者使用 sensor.QVGA (or others)
sensor.skip_frames(time=2000) #延时让摄像头文稳定.
sensor.set_gainceiling(8) #设置增益，这是官方推荐的参数
clock = time.clock() # Tracks FPS.
while (True):
clock.tick() # 用于计算FPS（每秒帧数）.
img = sensor.snapshot() # 拍摄并返回图像.
#使用 Canny 边缘检测器
img.find_edges(image.EDGE_CANNY, threshold=(50, 80))
# 也可以使用简单快速边缘检测，效果一般，配置如下
#img.find_edges(image.EDGE_SIMPLE, threshold=(100, 255))
print(clock.fps()) #显示FPS（每秒帧数）
radius = 40
windowX = 240
windowY = 240
buttColor = 4
headColor = 1
fps = 120
#SETUP
sensor.reset()
sensor.set_hmirror(True)
sensor.set_vflip(True)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((windowX, windowY)) # 240x240 center pixels of VGA
sensor.set_auto_gain(False, gain_db = 20) # must be turned off for color tracking
sensor.set_gainceiling(128)
sensor.set_auto_whitebal(False, rgb_gain_db = (-6.0, -3.0, 2)) # must be turned off for color tracking
#sensor.set_brightness(-1)
sensor.set_saturation(3)
#sensor.set_quality(100)
#sensor.set_auto_exposure(False, 1000)
#sensor.set_contrast(3)
sensor.skip_frames(time = 2000)
clock = time.clock()
kernel_size = 1 # 3x3==1, 5x5==2, 7x7==3, etc.
kernel = [-2, -1, 0, \
-1, 6, -1, \
0, -1, -2]
while(True):
import sensor, pyb, time
# Reset sensor
sensor.reset()
# Set sensor settings
sensor.set_brightness(0)
sensor.set_saturation(0)
sensor.set_gainceiling(16)
sensor.set_contrast(1)
sensor.set_framesize(sensor.QVGA)
# Enable JPEG and set quality
sensor.set_pixformat(sensor.JPEG)
sensor.set_quality(98)
# Red LED
led = pyb.LED(1)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
for i in range(0, 30):
sensor.snapshot()
# Turn on red LED and wait for a second
led.on()
time.sleep(1000)
# Write JPEG image to file
with open("/test.jpeg", "w") as f:
f.write(sensor.snapshot())
#************************************ (C) COPYRIGHT 2019 ANO ***********************************#
import sensor, image, time, math, struct, lcd
import json
from pyb import LED, Timer
from struct import pack, unpack
#初始化镜头
sensor.reset()
sensor.set_pixformat(sensor.RGB565) #设置相机模块的像素模式
sensor.set_framesize(sensor.QVGA) #设置相机分辨率160*120
sensor.skip_frames(time=3000) #时钟
sensor.set_auto_whitebal(False) #若想追踪颜色则关闭白平衡
clock = time.clock() #初始化时钟
sensor.set_contrast(1) #设置相机图像对比度。-3至+3
sensor.set_gainceiling(16) #设置相机图像增益上限。2, 4, 8, 16, 32, 64, 128。
#lcd.init()
#主循环
class Recognition(object):
flag = 0
color = 0
cx = 0
cy = 0
Recognition = Recognition()
# 红色阈值
red_threshold = (40, 91, 34, 127, -60, 96)
# 绿色阈值
green_threshold = (42, 100, -84, -26, -2, 108)
# the threshold the higher the number of keypoints extracted.
KEYPOINTS_THRESH=30
# Keypoint-level threshold, range from 0 to 100.
# This threshold is used when matching two keypoint descriptors, it's the
# percentage of the distance between two descriptors to the max distance.
# In other words, the minimum matching percentage between 2 keypoints.
MATCHING_THRESH=70
#greenled = pyb.LED(1)
#blueled = pyb.LED(2)
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# Skip a few frames to allow the sensor settle down
# Note: This takes more time when exec from the IDE.
#for i in range(0, 10):
# img = sensor.snapshot()
# img.draw_string(0, 0, "Please wait...")
# 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
# 人脸检测是通过在图像上使用Haar Cascade特征检测器来实现的。
# 一个 Haar Cascade 是一系列简单区域的对比检查.
# 人脸识别有25个阶段，每个阶段有几百次检测。Haar Cascades 运行很快因为是逐个阶段
# 递进检测的。 此外，OpenMV Cam使用一种称为积分图像的数据结构来在恒定时间内快速
#执行每个区域的对比度检查(特征检测需要用灰度图像的原因是因为图像积分需要更多空间)。
#
#翻译和注释：01Studio
import sensor, time, image
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_contrast(1) #设置相机图像对比度为1
sensor.set_gainceiling(16) #设置相机图像增益上限为16
# HQVGA 和 GRAYSCALE 是人脸检测最佳配置.
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
# 加载 Haar Cascade 模型
# 默认使用25个步骤，减少步骤会加快速度但会影响识别成功率.
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
# FPS clock
clock = time.clock()
while (True):
clock.tick()
