def facetrack_run():
global kpts1
global img
global x
global y
global x_pos
global y_pos
clock = time.clock()
clock.tick()
img = sensor.snapshot()
# Extract keypoints using the detect face size as the ROI
kpts2 = img.find_keypoints(max_keypoints=150, threshold=10, normalized=True)
if (kpts2):
# Match the first set of keypoints with the second one
c = image.match_descriptor(kpts1, kpts2, threshold=85)
# If more than 10% of the keypoints match draw the matching set
if (c[2]>25):
img.draw_cross(c[0], c[1], size=5)
img.draw_string(0, 10, "Match %d%%"%(c[2]))
x = c[0]
y = c[1]
utime.sleep_ms(300)
def unittest(data_path, temp_path):
import image
# Load image and find keypoints
img = image.Image(data_path+"/graffiti.pgm", copy_to_fb=True)
kpts1 = img.find_keypoints(max_keypoints=150, threshold=20, normalized=False)
# Load descriptor
kpts2 = image.load_descriptor(data_path+"/graffiti.orb")
# Match keypoints
match = image.match_descriptor(kpts1, kpts2, threshold=85)
return (match.cx() == 138 and match.cy() == 117 and \
match.x() == 36 and match.y() == 34 and \
match.w() == 251 and match.h() == 167 and \
match.count() == 150 and match.theta() == 0)
def unittest(data_path, temp_path):
import image
# Load image and find keypoints
img = image.Image(data_path + "/graffiti.pgm", copy_to_fb=True)
kpts1 = img.find_keypoints(max_keypoints=150,
threshold=20,
normalized=False)
# Load descriptor
kpts2 = image.load_descriptor(data_path + "/graffiti.orb")
# Match keypoints
match = image.match_descriptor(kpts1, kpts2, threshold=85)
return (match.cx() == 138 and match.cy() == 117 and \
match.x() == 36 and match.y() == 34 and \
match.w() == 251 and match.h() == 167 and \
match.count() == 150 and match.theta() == 0)
def find_person(img):
d0 = img.find_lbp((0, 0, img.width(), img.height()))
identical_pic = 0 #the pic that closet to the person from data
end_range = 100000
for s in range(1, NUM_SUBJECTS+1):
dist = 0
for i in range(1, NUM_SUBJECTS_IMGS):
img = image.Image("persons/s%d/%d.pgm" %(s, i)).mask_ellipse()
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist += image.match_descriptor(d0, d1)
distOfAll = dist/NUM_SUBJECTS_IMGS #distance of all images per person
print("Average dist for subject %d: %d"%(s, distOfAll))
if (distOfAll < end_range):
identical_pic = s
end_range = distOfAll
if (end_range > 12000):
identical_pic = -1 #-1 his undefined person
return identical_pic
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
# 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):
img = sensor.snapshot()
img.draw_string(0, 0, "Please wait...")
d0 = None
#d0 = image.load_descriptor("/desc.lbp")
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.25)
if objects:
face = objects[0]
d1 = img.find_lbp(face)
if (d0 == None):
d0 = d1
else:
dist = image.match_descriptor(d0, d1)
img.draw_string(0, 10, "Match %d%%"%(dist))
img.draw_rectangle(face)
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
def face_recog(calc_time):
pin = pyb.millis()
print(pin)
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_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.
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)
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).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).mask_ellipse()
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(d0, d1, 50)
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()
cc += 1
print(cc)
# Expand the ROI by 31 pixels in each direction (half the pattern scale)
img_face = (img_objects[0][0] - 31, img_objects[0][1] - 31,
img_objects[0][2] + 31 * 2, img_objects[0][3] + 31 * 2)
# Extract keypoints using the detect face size as the ROI
kpts = img.find_keypoints(threshold=KEYPOINTS_THRESH,
scale_factor=1.0,
max_keypoints=KEYPOINTS_MAX,
normalized=NORMALIZED,
roi=img_face)
if (kpts):
#img.draw_keypoints(kpts,size=2,color=0)
#print("keypoints found!")
# Match the first set of keypoints with the second one
c1 = image.match_descriptor(andy_kpts,
kpts,
threshold=MATCHING_THRESH)
c2 = image.match_descriptor(aya_kpts,
kpts,
threshold=MATCHING_THRESH)
c3 = image.match_descriptor(leo_kpts,
kpts,
threshold=MATCHING_THRESH)
c4 = image.match_descriptor(taiga_kpts,
kpts,
threshold=MATCHING_THRESH)
# If more than 10% of the keypoints match draw the matching set
# find the maximum in C1-4[6]
c[0] = 0
c[1] = c1[6]
c[2] = c2[6]
clock.tick()
img = sensor.snapshot()
if (kpts1 == None):
# NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
kpts1 = img.find_keypoints(max_keypoints=150,
threshold=20,
scale_factor=1.35)
draw_keypoints(img, kpts1)
else:
# NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract
# keypoints from the first scale only, which will match one of the scales in the first descriptor.
kpts2 = img.find_keypoints(max_keypoints=150,
threshold=10,
normalized=True)
if (kpts2):
match = image.match_descriptor(kpts1, kpts2, threshold=85)
if (match.count() > 10):
# If we have at least n "good matches"
# Draw bounding rectangle and cross.
img.draw_rectangle(match.rect())
img.draw_cross(match.cx(), match.cy(), size=10)
print(kpts2, "matched:%d dt:%d" % (match.count(), match.theta()))
coords = list(match.rect())
#print(coords)
#convert the xyz coordinates for uarm
delta_y = (coords[2] / 2 + coords[0] - 160) / 20
delta_x = (coords[3] / 2 + coords[1] - 120) / 20
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
# 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):
img = sensor.snapshot()
img.draw_string(0, 0, "Please wait...")
d0 = None
#d0 = image.load_descriptor(image.LBP, "/desc.lbp")
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
objects = img.find_features(face_cascade, threshold=0.5, scale=1.25)
if objects:
face = objects[0]
d1 = img.find_lbp(face)
if (d0 == None):
d0 = d1
else:
dist = image.match_descriptor(image.LBP, d0, d1)
img.draw_string(0, 10, "Match %d%%"%(dist))
img.draw_rectangle(face)
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
def face_recog(pic_name, vi_ip):
print("~~~~~~~~~~~~~~~~FACE_RECOG~~~~~~~~~~~~~~~~~~~~~~")
gc.collect() #garbage collection
#find LBP value for snapshot saved in face_detect
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()))
# turn on lights signaling facial recognition calculations starting
pyb.LED(2).on()
pyb.LED(3).on()
#find LBP values for each image received in server_recv
name_lbp_list = []
uos.chdir(
"/CamFaces"
) # change directory to where all the images from server_recv are stored
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) #allocate more space for images
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist = image.match_descriptor(
d0, d1,
50) #set threshold lower than 70 to tighten matching algo
sensor.dealloc_extra_fb()
# extracting the person's name from the file name
pname = filename
und_loc = pname.index('_')
pname = pname[0:(und_loc)]
# add the person's name and LBP value for the image to the list
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() #garbage collection
# finding average LBP values for each name
end = 0
name_avg = []
i = 0
start = 0
while i < len(name_lbp_list): # for names 1 thru n-1
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() #garbage collection
# special case: find average LBP value for last name in list (name n)
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() #garbage collection
# find minimum average LBP and associated person name
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)
#delete snapshot of person
uos.remove("/snapshot-person.pgm")
# turn off lights signaling facial recognition calculations done
pyb.LED(2).off()
pyb.LED(3).off()
#TCP client socket to send name of the person recognized to the visually impaired user's smartphone
chost = vi_ip
cport = 8080
client = usocket.socket(
usocket.AF_INET,
usocket.SOCK_STREAM) #TCP client socket with IPv4 addressing
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() #client closed
gc.collect() #garbage collection
return
# Face recognition with LBP descriptors.
# See Timo Ahonen's "Face Recognition with Local Binary Patterns".
#
# Before running the example:
# 1) Download the AT&T faces database http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
# 2) Exract and copy the orl_faces directory to the SD card root.
import sensor, time, image
SUB = "s2"
NUM_SUBJECTS = 5
NUM_SUBJECTS_IMGS = 10
img = image.Image("orl_faces/%s/1.pgm" % (SUB)).mask_ellipse()
d0 = img.find_lbp((0, 0, img.width(), img.height()))
img = None
print("")
for s in range(1, NUM_SUBJECTS + 1):
dist = 0
for i in range(2, NUM_SUBJECTS_IMGS + 1):
img = image.Image("orl_faces/s%d/%d.pgm" % (s, i)).mask_ellipse()
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist += image.match_descriptor(image.LBP, d0, d1)
print("Average dist for subject %d: %d" % (s, dist / NUM_SUBJECTS_IMGS))
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=12)
time.sleep(1000)
# FPS clock
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
# Extract keypoints using the detect face size as the ROI
kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100)
if (kpts2):
# Match the first set of keypoints with the second one
c = image.match_descriptor(kpts1, kpts2)
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)
print(kpts2, "matched:%d dt:%d" % (match, c[7]))
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f" % (clock.fps()))
def start_face_rendering(self):
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.B128X128) # or sensor.QQVGA (or others)
sensor.set_windowing((92,112))
sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 5000) #等待5s
s3 = Servo(3) # servo on position 1 (P7)
#将蓝灯赋值给变量led
led = pyb.LED(3) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
#SUB = "s1"
NUM_SUBJECTS = 4 #图像库中不同人数，一共6人
NUM_SUBJECTS_IMGS = 17 #每人有20张样本图片
# 拍摄当前人脸。
img = sensor.snapshot()
#img = image.Image("singtown/%s/1.pgm"%(SUB))
d0 = img.find_lbp((0, 0, img.width(), img.height()))
#d0为当前人脸的lbp特征
img = None
pmin = 999999
self.num=0
for s in range(1, NUM_SUBJECTS+1):
dist = 0
for i in range(2, NUM_SUBJECTS_IMGS+1):
img = image.Image("singtown/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 = self.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)
print(self.num) # num为当前最匹配的人的编号。
#TS=3 没戴口罩
if (pmin>5000) & (TS==3):
uart.write("-- NO People! --")
led.off()
if (pmin>5000) & (TS==1):
uart.write("-- NO People! --")
led.off()
if pmin<=5000:
if self.num==1: #匹配到了people_One
uart.write("People One ")
if self.num==2:
uart.write("People Two ")
if self.num==3:
uart.write("People Three ")
if self.num==4:
uart.write("People New ")
led.on() #亮灯
led1.off()
time.sleep(3500) #延时1500ms
led.off()
for i in range(1,460):
s3.speed(50) # for continuous rotation servos
time.sleep(15)
s3.speed(0)
time.sleep(1500)
for i in range(1,230):
s3.speed(-50)
time.sleep(15)
s3.speed(0)
img = sensor.snapshot()
if (kpts1 == None):
# NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
kpts1 = img.find_keypoints(max_keypoints=150,
threshold=10,
scale_factor=1.2)
draw_keypoints(img, kpts1)
else:
# NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract
# keypoints from the first scale only, which will match one of the scales in the first descriptor.
kpts100 = img.find_keypoints(max_keypoints=150,
threshold=10,
normalized=1)
if (kpts100):
if (imgtarget == '1'):
match = image.match_descriptor(kpts1, kpts100, threshold=85)
elif (imgtarget == '2'):
match = image.match_descriptor(kpts2, kpts100, threshold=85)
elif (imgtarget == '3'):
match = image.match_descriptor(kpts3, kpts100, threshold=85)
elif (imgtarget == '4'):
match = image.match_descriptor(kpts4, kpts100, threshold=85)
if (match.count() > 10):
# If we have at least n "good matches"
# Draw bounding rectangle and cross.
#img.draw_rectangle(match.rect())
img.draw_cross(match.cx(), match.cy(), size=10)
output_str = "[%d,%d]" % (match.cx(), match.cy())
uart.write(output_str + '\r\n')
print(kpts100,
"matched:%d dt:%d" % (match.count(), match.theta()))
img = sensor.snapshot()
img.draw_string(0, 0, "Please wait...")
kpts1 = None
# Uncomment to load keypoints from file
#kpts1 = image.load_descriptor(image.FREAK, "/desc.freak")
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH,
normalized=NORMALIZED)
if (kpts1 == None):
kpts1 = kpts2
print(kpts1)
elif kpts2:
c = image.match_descriptor(image.FREAK,
kpts1,
kpts2,
threshold=MATCHING_THRESH)
# C[3] contains the percentage of matching keypoints.
# If more than 25% of the keypoints match, draw stuff.
if (c[2] > 25):
img.draw_cross(c[0], c[1], size=15)
img.draw_string(0, 10, "Match %d%%" % (c[2]))
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f" % (clock.fps()))
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
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)
print(kpts2, "matched:%d dt:%d"%(match, c[7]))
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
pmin = 999999 # pmin为最小特征差异度
iden_out_num=0
def min(pmin, a, s): # 确保几次循环后pmin还是最小特征差异度，a代表dist/NUM_SUBJECTS_IMGS
global iden_out_num
if a<pmin:
pmin=a
iden_out_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("identify/i%s/%s.pgm"%(s, i))
d1 = img.find_lbp((11,14,70,84)) # d1为第s文件夹中的第i张图片的lbp特征，识别ROI区域往内缩小1/8
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("第%d个的特征差异度是: %d"%(s, dist/NUM_SUBJECTS_IMGS)) # 输出当前特征差异度，方便在串口终端显示数据
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)
# 按照循环次数，将每次识别后的结果存入，共3次
if iden == 0:
iden_out_num0 = iden_out_num
print("第%d次:%d"%(iden+1,iden_out_num0))
if iden == 1:
iden_out_num1 = iden_out_num
print("第%d次:%d"%(iden+1,iden_out_num1))
if iden == 2:
iden_out_num2 = iden_out_num
print("第%d次:%d"%(iden+1,iden_out_num2))
def facial_recog(pic_name, vi_ip):
cc = 0
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()
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()
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("file found that is not of type pgm")
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()
cc += 1
print(cc)
#client socket
chost = vi_ip
print(chost)
#chost = "10.132.30.198"
cport = 8080
client = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM)
client.connect((chost, cport))
print("connected to android")
to_send = id_name + "\n"
# Send HTTP request and recv response
client.send(to_send.encode())
# Close socket
client.close()
gc.collect()
# Face recognition with LBP descriptors.
# See Timo Ahonen's "Face Recognition with Local Binary Patterns".
#
# Before running the example:
# 1) Download the AT&T faces database http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
# 2) Exract and copy the orl_faces directory to the SD card root.
#
# NOTE: This is just a PoC implementation of the paper mentioned above, it does Not work well in real life conditions.
import sensor, time, image
SUB = "s2"
NUM_SUBJECTS = 5
NUM_SUBJECTS_IMGS = 10
img = image.Image("orl_faces/%s/1.pgm" % (SUB)).mask_ellipse()
d0 = img.find_lbp((0, 0, img.width(), img.height()))
img = None
print("")
for s in range(1, NUM_SUBJECTS + 1):
dist = 0
for i in range(2, NUM_SUBJECTS_IMGS + 1):
img = image.Image("orl_faces/s%d/%d.pgm" % (s, i)).mask_ellipse()
d1 = img.find_lbp((0, 0, img.width(), img.height()))
dist += image.match_descriptor(d0, d1)
print("Average dist for subject %d: %d" % (s, dist / NUM_SUBJECTS_IMGS))
face = (objects[0][0]-22, objects[0][1]-22,objects[0][2]+22*2, objects[0][3]+22*2)
# Extract keypoints using the detect face size as the ROI
kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=face)
# Draw a rectangle around the first face
img.draw_rectangle(objects[0])
# Draw keypoints
print(kpts1)
img.draw_keypoints(kpts1, size=12)
time.sleep(1000)
# FPS clock
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
# Extract keypoints using the detect face size as the ROI
kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
if (kpts2):
# Match the first set of keypoints with the second one
c=image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH)
# If more than 10% of the keypoints match draw the matching set
if (c[2]>25):
img.draw_cross(c[0], c[1], size=5)
img.draw_string(0, 10, "Match %d%%"%(c[2]))
# Draw FPS
img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
if img_objects:
print("a new face detected!")
# Draw a rectangle around the first face
#img.draw_rectangle(img_objects[0])
# Expand the ROI by 11 pixels in each direction (half the pattern scale)
img_face = (img_objects[0][0]-31, img_objects[0][1]-31,img_objects[0][2]+31*2, img_objects[0][3]+31*2)
# Extract keypoints using the detect face size as the ROI
kpts2 = img.find_keypoints(threshold=10, scale_factor=1.3, max_keypoints=100, normalized=True)
# Extract keypoints using the detect face size as the ROI
#kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
if (kpts2):
img.draw_rectangle(img_objects[0])
print("keypoints found!")
# Match the first set of keypoints with the second one
c1 = image.match_descriptor(hjf_kpts, kpts2,threshold=60)
c2 = image.match_descriptor(sf_kpts, kpts2,threshold=60)
c3 = image.match_descriptor(hzy_kpts, kpts2,threshold=60)
c4 = image.match_descriptor(hmy_kpts, kpts2,threshold=60)
# If more than 10% of the keypoints match draw the matching set
# find the maximum in C1-4[6]
c[0]= 0
c[1]=c1[6]
c[2]=c2[6]
c[3]=c3[6]
c[4]=c4[6]
print(c)
c[0]=c[1] #init c[0]
m=1
for j in range(2):
if (c[0]<c[j+2] ):
def recognition(timeout=500):
face = None
img = None
matchMin = 999999
matchUser = ''
matchArr = []
basePath = "photo"
#basePath = "desc"
users = os.listdir(basePath)
time_start = pyb.millis()
while not face:
if pyb.elapsed_millis(time_start) > timeout:
break
img = sensor.snapshot()
face = facsTest(img)
checkDisplay(img)
if not face:
return matchUser, face, img
if not len(users):
# pyb.delay(timeout)
return matchUser, face, img
nowDesc = img.find_lbp(face)
photoFpath = ""
try:
for user in users:
userDescArr = []
baseDpath = "%s/%s" % (basePath, user)
files = os.listdir(baseDpath)
for file_ in files:
# descFpath = baseDpath+"/"+file_
# oldDesc = image.load_descriptor(descFpath)
photoFpath = baseDpath + "/" + file_
oldImg = image.Image(photoFpath)
oldDesc = oldImg.find_lbp(
(0, 0, oldImg.width(), oldImg.height()))
match = image.match_descriptor(nowDesc, oldDesc,
DESC_THRESHOLD,
DESC_FILTER_OUTLIERS)
userDescArr.append(match)
userDescArr.sort()
sliceCnt = CHECK_MIN_CNT or len(userDescArr)
matchResult = sum(userDescArr[:sliceCnt]) / sliceCnt
matchArr.append(matchResult)
# print("sliceCnt,userDescArr: ",sliceCnt,userDescArr)
if matchResult < matchMin:
matchMin = matchResult
if matchResult < MATCH_THRESHOLD:
matchUser = user
except: # OSError,err:
print("recognition error:", photoFpath) # ,err)
print(matchMin, matchUser, matchArr,
len(matchArr) >= 2 and (matchArr[0] - matchArr[1]))
return matchUser, face, img
