face_net = net.face_detection()
landmark_net = net.face_lanmark()
face_reid_net = net.face_reid()
body_net = myCV.Net("mo_mobilenet-ssd.xml", "mo_mobilenet-ssd.bin", (300, 300))
frame = cv2.imread('2.jpg')
faces_data = {}
img = frame.copy()
cv2.imshow('Input', frame)
cv2.waitKey(0)
n = 0
# 15 = person id in mobilessd list
bodies = myCV.detect(body_net, frame, 0.7, 15)
for bxmin, bymin, bxmax, bymax in bodies:
cv2.rectangle(img, (bxmin, bymin), (bxmax, bymax), (255, 255, 0), 2)
bchip = frame[bymin:bymax, bxmin:bxmax]
n += 1
cv2.imshow('Person{}'.format(n), bchip)
cv2.waitKey(0)
face = myCV.detect(face_net, bchip, 0.7)
for fxmin, fymin, fxmax, fymax in face:
fxmin += bxmin
fymin += bymin
fxmax += bxmin
fymax += bymin
BODY_NET = net.mobilenet_ssd()
# Dictionary for face descriptors
face_database = {}
frame = cv2.imread(args.img)
cv2.imshow('Input', frame)
cv2.waitKey(0)
# Copy the input image for continuing processing
img = frame.copy()
n = 0
# Detect bodies from the input image
# 15 = person id in mobilessd list
bodies = myCV.detect(BODY_NET, frame, 0.7, 15)
for bxmin, bymin, bxmax, bymax in bodies:
cv2.rectangle(img, (bxmin, bymin), (bxmax, bymax), (255, 255, 0), 2)
# Crop body area from frame
bcrop = frame[bymin:bymax, bxmin:bxmax]
n += 1
cv2.imshow('Person{}'.format(n), bcrop)
cv2.waitKey(0)
# Detect faces from the the body crop image
face = myCV.detect(FACE_NET, bcrop, 0.7)
for fxmin, fymin, fxmax, fymax in face:
fxmin += bxmin
fymin += bymin
# Dictionary for face descriptors
face_database = {}
while True:
counter.stop()
counter.start()
grab, frame = STREAM.read()
if not grab:
raise Exception('Image not found')
# Copy the input image for continuing processing
img = frame.copy()
# Detecting faces from the input image
faces = myCV.detect(FACE_NET, frame, 0.7)
for fxmin, fymin, fxmax, fymax in faces:
cv2.rectangle(img, (fxmin, fymin), (fxmax, fymax), (255, 255, 0), 2)
# Crop face area from frame
fcrop = frame[fymin:fymax, fxmin:fxmax]
# Search and draw facial landmarks
dots = myCV.get_landmarks(LANDMARK_NET, fcrop, (fxmin, fymin))
for x, y in dots:
cv2.circle(img, (x, y), 3, (255, 0, 255), -1)
# Transfrom the face to improve recognition
fcrop = myCV.face_transform(fcrop)
# Getting facial descriptor
bodies_database = {}
while True:
counter.stop()
counter.start()
grab, frame = STREAM.read()
if not grab:
raise Exception('Frame not found')
# Copy the input image for continuing processing
img = frame.copy()
# Detect bodies from the input image
# 15 = person id in mobilessd list
bodies = myCV.detect(BODY_NET, frame, 0.7, 15)
for bxmin, bymin, bxmax, bymax in bodies:
cv2.rectangle(img, (bxmin, bymin), (bxmax, bymax), (255, 255, 0), 2)
# Crop body area from frame
bcrop = frame[bymin:bymax, bxmin:bxmax]
# Getting body descriptor
body_features = myCV.get_descriptor(BODY_REID_NET, bcrop)
# Searching the body in body_database
ID, distance = myCV.object_in_database(body_features, bodies_database,
0.5)
# If our database dont contain the body add it
if not ID: