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 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 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
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
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()
exit(1)
else:
print("Could not configure the remote cam!")
exit(1)
# skip frames as this is exactly what the remote is doing on its side
sensor.skip_frames(time=2000)
# save the ref image used for the diff
print("About to save background image...")
ref_img = sensor.alloc_extra_fb(img_width, img_height, sensor_format)
data_fb = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
ref_img.replace(sensor.snapshot().remap(data_fb, right=False,
upside_down=True))
sensor.dealloc_extra_fb()
print("Saved background image - Now frame differencing!")
# now add an additional part that will convey the mask info
sensor.set_windowing((int((sensor.width() - img_width) / 2) - 2,
int((sensor.height() - img_height) / 2), img_width,
img_height + mask_height))
time.sleep(500)
clock = time.clock()
idx = 0
data_fb = sensor.alloc_extra_fb(img_width, img_height, sensor.RGB565)
right_image = image.Image(img_width,
img_height + mask_height,
sensor_format,
copy_to_fb=False)