def test_ASEFEyeLocalization(self):
'''FilterEyeLocator: Scrapshots Both10 rate == 0.4800...............'''
ilog = None
if 'ilog' in list(globals().keys()):
ilog = globals()['ilog']
# Load a face database
ssdb = ScrapShotsDatabase()
# Create a face detector
face_detector = CascadeDetector()
# Create an eye locator
eye_locator = FilterEyeLocator()
# Create an eye detection test
edt = EyeDetectionTest(name='asef_scraps')
#print "Testing..."
for face_id in list(ssdb.keys())[:25]:
face = ssdb[face_id]
im = face.image
dist = face.left_eye.l2(face.right_eye)
dist = np.ceil(0.1*dist)
im.annotateCircle(face.left_eye,radius=dist,color='white')
im.annotateCircle(face.right_eye,radius=dist,color='white')
# Detect the faces
faces = face_detector.detect(im)
# Detect the eyes
pred_eyes = eye_locator(im,faces)
for rect,leye,reye in pred_eyes:
im.annotateRect(rect)
im.annotateCircle(leye,radius=1,color='red')
im.annotateCircle(reye,radius=1,color='red')
truth_eyes = [[face.left_eye,face.right_eye]]
pred_eyes = [ [leye,reye] for rect,leye,reye in pred_eyes]
# Add to eye detection test
edt.addSample(truth_eyes, pred_eyes, im=im, annotate=True)
if ilog != None:
ilog.log(im,label='test_ASEFEyeLocalization')
edt.createSummary()
# Very poor accuracy on the scrapshots database
self.assertAlmostEqual( edt.face_rate , 1.0000, places = 3 )
self.assertAlmostEqual( edt.both25_rate , 0.8800, places = 3 )
self.assertAlmostEqual( edt.both10_rate , 0.5200, places = 3 )
self.assertAlmostEqual( edt.both05_rate , 0.2800, places = 3 )
def test_ASEFEyeLocalization(self):
'''FilterEyeLocator: Scrapshots Both10 rate == 0.4800...............'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
# Load a face database
ssdb = ScrapShotsDatabase()
# Create a face detector
face_detector = CascadeDetector()
# Create an eye locator
eye_locator = FilterEyeLocator()
# Create an eye detection test
edt = EyeDetectionTest(name='asef_scraps')
#print "Testing..."
for face_id in ssdb.keys()[:25]:
face = ssdb[face_id]
im = face.image
dist = face.left_eye.l2(face.right_eye)
dist = np.ceil(0.1 * dist)
im.annotateCircle(face.left_eye, radius=dist, color='white')
im.annotateCircle(face.right_eye, radius=dist, color='white')
# Detect the faces
faces = face_detector.detect(im)
# Detect the eyes
pred_eyes = eye_locator(im, faces)
for rect, leye, reye in pred_eyes:
im.annotateRect(rect)
im.annotateCircle(leye, radius=1, color='red')
im.annotateCircle(reye, radius=1, color='red')
truth_eyes = [[face.left_eye, face.right_eye]]
pred_eyes = [[leye, reye] for rect, leye, reye in pred_eyes]
# Add to eye detection test
edt.addSample(truth_eyes, pred_eyes, im=im, annotate=True)
if ilog != None:
ilog.log(im, label='test_ASEFEyeLocalization')
edt.createSummary()
# Very poor accuracy on the scrapshots database
self.assertAlmostEqual(edt.face_rate, 1.0000, places=3)
self.assertAlmostEqual(edt.both25_rate, 0.8800, places=3)
self.assertAlmostEqual(edt.both10_rate, 0.5200, places=3)
self.assertAlmostEqual(edt.both05_rate, 0.2800, places=3)
def __init__(self,
face_detector=CascadeDetector(),
tile_size=(128, 128),
subtile_size=(32, 32),
left_center=pv.Point(39.325481787836871, 50.756936769089975),
right_center=pv.Point(91.461135538006289, 50.845357457309881),
validate=None,
n_iter=1,
annotate=False,
**kwargs):
'''
Create an eye locator. This default implentation uses a
cascade classifier for face detection and then SVR for eye
location.
'''
#TODO: Learn the mean eye locations durring training.
self.face_detector = face_detector
self.left_center = left_center
self.right_center = right_center
self.tile_size = tile_size
self.subtile_size = subtile_size
self.validate = validate
self.n_iter = n_iter
self.annotate = annotate
self.perturbations = True
# Number of training images where the face detection did not work.
self.detection_failures = 0
# point locators that learn to find the eyes.
self.createLocators(**kwargs)
def execute(self, frame, detector=None):
if detector == None:
print "Initializing Face Detector."
detector = CascadeDetector(min_size=(128, 128))
faces = detector(frame)
for rect in faces:
frame.annotateRect(rect)
return [('FACES', self.getFrameId(), faces),
("_FACE_DETECTOR", self.getFrameId(), detector)]
def __init__(self, parent, id, name, demos=DEMO_DEFAULTS, size=(800, 550)):
wx.Frame.__init__(self, parent, id, name, size=size)
# ---------------- Basic Data -------------------
self.webcam = Webcam()
self.harris = DetectorHarris()
self.dog = DetectorDOG(n=100, selector='best')
self.face = CascadeDetector()
self.demos = demos
# ------------- Other Components ----------------
self.CreateStatusBar()
# ------------------- Menu ----------------------
# Creating the menubar.
# ----------------- Image List ------------------
# --------------- Image Display -----------------
self.static_bitmap = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.EmptyBitmap(640, 480))
self.radios = wx.RadioBox(self,
wx.NewId(),
'Demos',
choices=['None'] + self.demos.keys(),
style=wx.RA_SPECIFY_ROWS)
self.mirror = wx.CheckBox(self, wx.NewId(), 'Mirror')
self.mirror.SetValue(True)
# --------------- Window Layout -----------------
grid = wx.FlexGridSizer(2, 2)
grid.Add(self.static_bitmap)
grid.Add(self.radios)
grid.Add(self.mirror)
self.SetAutoLayout(True)
self.SetSizer(grid)
self.Layout()
# -----------------------------------------------
self.timer = FrameTimer(self)
self.timer.Start(200)
# -------------- Event Handleing ----------------
wx.EVT_SIZE(self.static_bitmap, self.onBitmapResize)
wx.EVT_LEFT_DOWN(self.static_bitmap, self.onClick)
wx.EVT_TIMER(self, -1, self.onTmp)
def test_detect_scraps_opencv(self):
fd = CascadeDetector(OPENCV_CASCADE)
fdt = FaceDetectionTest(name='scraps')
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA, "coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth, rects, im=img)
self.assertAlmostEqual(
fdt.pos_rate, 0.98265895953757221,
places=2) # TODO: Version 2 performance is better
def __init__(self, videoDevice_index, comm = None):
""" initiate variables"""
threading.Thread.__init__(self)
self.comm = comm
self.current_colour = None
self.face_detector = CascadeDetector(cascade_name=config.haar_casc,min_size=(50,50), image_scale=0.5)
self.webcam = Webcam(videoDevice_index)
if config.use_gui: # create windows
cv.NamedWindow('Camera', cv.CV_WINDOW_AUTOSIZE)
cv.CreateTrackbar ('edge threshold', 'Camera', 50, 100, self.change_value1)
cv.CreateTrackbar ('circle threshold', 'Camera', 90, 100, self.change_value2)
cv.CreateTrackbar ('gaussian blur', 'Camera', 11, 50, self.change_value3)
cv.CreateTrackbar ('hue', 'Camera', 0, 100, self.change_value4)
def __init__(self):
rospy.init_node("fasel")
self.enrollService = rospy.Service("enroll", Enroll, self.handleEnrollFace)
self.trainService = rospy.Service("train", Train, self.handleTrain)
self.resetService = rospy.Service("reset", Reset, self.handleReset)
self.namesPub = rospy.Publisher("names", String)
self.personEventPub = rospy.Publisher("person_event", PersonEvent)
self.imagePub = rospy.Publisher("images", Image)
self.imageSub = rospy.Subscriber("image", Image, self.handleImage)
self.bridge = CvBridge()
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME, image_scale=0.5)
self.fel = loadFilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 64
self.enroll_list = []
self.enrollCondition = Condition()
self.isTrained = False
self.nameList = []
self.faceNames = []
self.faceCount = 0
self.faceTrack = dict()
self.face_processing = True
self.eye_processing = True
self.image_flip = True
self.reqQueueLock = Lock()
self.faceTrackLock = Lock()
self.is_running = True
self.eventUpdateDelay = 2.0 # seconds
self.eventUpdateTimer = Thread(target=self.eventUpdate)
self.eventUpdateTimer.start()
def test_training(self):
'''
This trains the FaceFinder on the scraps database.
'''
#import cProfile
# Load an eyes file
eyes_filename = join(pv.__path__[0], 'data', 'csuScrapShots',
'coords.txt')
#print "Creating eyes File."
eyes_file = EyesFile(eyes_filename)
# Create a face detector
cascade_file = join(pv.__path__[0], 'config',
'facedetector_celebdb2.xml')
#print "Creating a face detector from:",cascade_file
face_detector = CascadeDetector(cascade_file)
image_dir = join(pv.__path__[0], 'data', 'csuScrapShots')
ed = SVMEyeDetectorFromDatabase(eyes_file,
image_dir,
image_ext=".pgm",
face_detector=face_detector,
random_seed=0)
edt = EyeDetectionTest(name='scraps')
#print "Testing..."
for img in self.images:
#print img.filename
faces = ed.detect(img)
#faces = ed.detect(img)
pred_eyes = []
for _, _, pleye, preye in faces:
#detections.append(rect)
pred_eyes.append((pleye, preye))
truth_eyes = self.eyes.getEyes(img.filename)
edt.addSample(truth_eyes, pred_eyes, im=img, annotate=False)
#print edt.createSummary()
self.assertAlmostEqual(edt.face_rate, 0.924855491329, places=3)
def __init__(self,
face_detector=CascadeDetector(),
tile_size=(128, 128),
validate=None,
n_iter=1,
annotate=False,
**kwargs):
'''
Create an eye locator. This default implentation uses a
cascade classifier for face detection and then SVR for eye
location.
'''
self.face_detector = face_detector
self.left_eye = None
self.right_eye = None
self.tile_size = tile_size
self.validate = validate
self.n_iter = n_iter
self.annotate = annotate
self.perturbations = True
# this object handles pca normalization
self.normalize = VectorClassifier.VectorClassifier(
VectorClassifier.TYPE_REGRESSION,
reg_norm=VectorClassifier.REG_NORM_NONE)
# point locators that learn to find the eyes.
self.left_locator = SVMLocator(
svm_type=SVM.TYPE_NU_SVR, normalization=VectorClassifier.NORM_NONE)
self.right_locator = SVMLocator(
svm_type=SVM.TYPE_NU_SVR, normalization=VectorClassifier.NORM_NONE)
# Number of training images where the face detection did not work.
self.detection_failures = 0
self.training_labels = []
def __init__(self):
self.face = CascadeDetector()
class CaptureVideo(threading.Thread):
""" captures video stream from camera and performs various detections (face, edge, circle)
"""
def __init__(self, videoDevice_index, comm = None):
""" initiate variables"""
threading.Thread.__init__(self)
self.comm = comm
self.current_colour = None
self.face_detector = CascadeDetector(cascade_name=config.haar_casc,min_size=(50,50), image_scale=0.5)
self.webcam = Webcam(videoDevice_index)
if config.use_gui: # create windows
cv.NamedWindow('Camera', cv.CV_WINDOW_AUTOSIZE)
cv.CreateTrackbar ('edge threshold', 'Camera', 50, 100, self.change_value1)
cv.CreateTrackbar ('circle threshold', 'Camera', 90, 100, self.change_value2)
cv.CreateTrackbar ('gaussian blur', 'Camera', 11, 50, self.change_value3)
cv.CreateTrackbar ('hue', 'Camera', 0, 100, self.change_value4)
def run(self):
self.main_loop()
def detect_face(self, img):
""" detect faces in the given video stream
"""
faces = self.findFaces(img)
if faces:
close_face_rect = None
close_face_w = 0.0
for rect, leye, reye in faces:
img.annotateRect(rect, color='blue') # draw square around face
if rect.w > close_face_w: # get closest face coordinates
close_face_w = rect.w
close_face_rect = rect
if config.eye_d: # draw point on eyes
img.annotatePoint(leye,color='blue')
img.annotatePoint(reye,color='blue')
if config.follow_face_gaze:
relative_x = (320 - (close_face_rect.x + (close_face_rect.w/2.0)))
relative_y = (240 - (close_face_rect.y + (close_face_rect.h/2.0)))
gaze = self.follow_face_with_gaze(relative_x, relative_y, close_face_rect.w)
neck = self.follow_face_with_neck(relative_x, relative_y, gaze[1])
if self.comm:
if self.comm.last_ack != "wait" and gaze:
self.comm.set_neck_gaze(gaze, neck)
self.comm.last_ack = "wait"
def findFaces(self, im):
""" run the face detection algorithm
"""
rects = self.face_detector.detect(im)
faces = []
for rect in rects:
affine = pv.AffineFromRect(rect,(1,1))
leye = affine.invertPoint(AVE_LEFT_EYE)
reye = affine.invertPoint(AVE_RIGHT_EYE)
faces.append([rect,leye,reye])
self.current_faces = faces
return faces
def follow_face_with_gaze(self, x, y, width):
"""adjust coordinates of detected faces to mask
"""
#TODO: change coordinates that are kept in config into something local
if config.slow_adjust and (config.face_x is not None and config.face_y is not None):
config.face_x += (x - config.face_x) * config.gain
config.face_y += (y - config.face_y) * config.gain
else:
config.face_x = x
config.face_y = y
face_distance = ((-88.4832801364568 * math.log(width)) + 538.378262966656)
x_dist = ((config.face_x/1400.6666)*face_distance)/100
y_dist = ((config.face_y/700.6666)*face_distance)/100
if config.camera_on_projector:
return (x_dist, (face_distance/100.0), y_dist) # x is inverted for compatibility
else:
return (-x_dist, (face_distance/100.0), y_dist)
def follow_face_with_neck(self, x, y, face_distance):
"""adjust coordinates of detected faces to neck movement
"""
move = False
if x > 95 or x < -95: # threshold
distance_x = (x/-640.0) * 0.2 * math.pi
move = True
else:
distance_x = 0.0
if y > 60 or y < -60: # threshold
distance_y = (y/-480.0) * 0.1 * math.pi
move = True
else:
distance_y = 0.0
if face_distance > 1.0: # threshold for moving forward when perceived face is far
config.getting_closer_to_face = 1.0
if config.getting_closer_to_face > 0.05:
distance_z = 0.1
config.getting_closer_to_face += -0.1
move = True
if face_distance < 0.2: # threshold for moving back when face is too close
distance_z = -0.3 + face_distance
move = True
else:
distance_z = 0
if move:
return ((distance_y, .0, -distance_x), (.0,distance_z,.0))
def detect_edge(self, image):
grayscale = cv.CreateImage(cv.GetSize(image), 8, 1)
cv.CvtColor(image, grayscale, cv.CV_BGR2GRAY)
cv.Canny(grayscale, grayscale, edge_threshold1, edge_threshold1 * 3, 3)
return grayscale
def detect_circle(self, image, image_org):
grayscale = cv.CreateImage(cv.GetSize(image), 8, 1)
grayscale_smooth = cv.CreateImage(cv.GetSize(image), 8, 1)
cv.CvtColor(image, grayscale, cv.CV_BGR2GRAY)
if config.edge_d_non_vision:
cv.Canny(grayscale, grayscale, edge_threshold1, edge_threshold1 * 3, 3)
cv.Smooth(grayscale, grayscale_smooth, cv.CV_GAUSSIAN, edge_threshold3)
mat = cv.CreateMat(100, 1, cv.CV_32FC3 )
cv.SetZero(mat)
cv.HoughCircles(grayscale_smooth, mat, cv.CV_HOUGH_GRADIENT, 2, 50, 200, (edge_threshold2 + 150) )
circles_simple = []
gazing = None
if mat.rows != 0:
for i in xrange(0, mat.rows):
c = mat[i,0]
point = (int(c[0]), int(c[1]))
radius = int(c[2])
cv.Circle(image, point, radius, (0, 0, 255))
if config.detect_colour:
self.get_colour(image, image_org, [int(c[0]), int(c[1])], radius)
config.detect_colour = False
colour = self.record_colour(image, image_org, [int(c[0]), int(c[1])], radius)
circles_simple.append([point, radius, colour])
if config.follow_ball_gaze and circles_simple:
x_adjust = 320 - circles_simple[0][0].x
y_adjust = 240 - circles_simple[0][0].y
gazing = self.follow_ball_with_gaze(x_adjust, y_adjust)
if config.follow_ball_neck and circles_simple:
#self.comm.send_msg("recognizing;*;1;;;;tag_SPEECH")
x_adjust = 320 - circles_simple[0][0].x
y_adjust = 240 - circles_simple[0][0].y
if x_adjust < 315 or x_adjust > 325:
distance_x = (x_adjust/-640.0) * 0.2 * math.pi
if y_adjust < 235 or y_adjust > 245:
distance_y = (y_adjust/-480.0) * 0.2 * math.pi
if self.comm.last_ack != "wait":
if gazing:
# self.comm.set_neck_gaze(gazing, "(" + str(config.neck_pos[0] + distance_y) + ",0," + str(config.neck_pos[2] + distance_x) + ")", "TRACK_GAZE")
pass
else:
self.comm.set_neck_orientation( "(" + str(config.neck_pos[0] + distance_y) + ",0," + str(config.neck_pos[2] + distance_x) + ")", "TRACKING")
config.neck_pos[2] += distance_x
config.neck_pos[0] += distance_y
self.comm.last_ack = "wait"
if config.colour_to_find and circles_simple:
dist = []
for i in circles_simple:
if i[2]:
#dist.append(auks.calculate_distance_hsv(params.colour_to_find, i[2]))
dist.append(auks.calculate_distance(config.colour_to_find, i[2]))
else:
dist.append(999999)
index = auks.posMin(dist)
#print dist
if dist[index] < config.detect_threshold:
#self.comm.send_msg("recognizing;*;1;;;;tag_SPEECH")
cv.Circle(image, circles_simple[index][0], 2, cvScalar(0, 100, 255), 2)
x_adjust = 320 - circles_simple[index][0].x
y_adjust = 240 - circles_simple[index][0].y
if x_adjust < 315 or x_adjust > 325:
distance_x = (x_adjust/-640.0) * 0.2 * math.pi
if y_adjust < 235 or y_adjust > 245:
distance_y = (y_adjust/-480.0) * 0.2 * math.pi
if self.comm.last_ack != "wait":
# print "x_dist:", distance_x, " y_dist:", distance_y
# print "x_neck:", str(config.neck_pos[2]), " y_neck:", str(config.neck_pos[0])
# print "x:", str(config.neck_pos[2] + distance_x), " y:", str(config.neck_pos[0] + distance_y)
if gazing:
# self.comm.set_neck_gaze(gazing, "(" + str(config.neck_pos[0] + distance_y) + ",0," + str(config.neck_pos[2] + distance_x) + ")", "TRACK_GAZE")
pass
else:
self.comm.set_neck_orientation( "(" + str(config.neck_pos[0] + distance_y) + ",0," + str(config.neck_pos[2] + distance_x) + ")", "TRACKING")
config.neck_pos[2] += distance_x
config.neck_pos[0] += distance_y
self.comm.last_ack = "wait"
return circles_simple
def follow_ball_with_gaze(self, x, y):
"""adjust coordinates of detected faces to mask
"""
#face_distance = ((-88.4832801364568 * math.log(width)) + 538.378262966656)
face_distance = 50.0
x_dist = ((x/1400.6666)*face_distance)/-100
y_dist = ((y/1400.6666)*face_distance)/100
# if self.comm:
# if self.comm.last_ack != "wait":
# self.comm.set_gaze(str(x_dist) + "," + str(face_distance/100) + "," + str(y_dist))
# self.comm.last_ack = "wait"
return str(x_dist) + "," + str(face_distance/100) + "," + str(y_dist)
def get_colour(self, image, image_org, pos, radius):
radius = int(radius*0.7)
rect = cv.Rect(pos[0]-radius,pos[1]-radius, radius*2, radius*2)
try:
subimage = cv.GetSubRect(image_org, rect)
cv.SaveImage("subimage.png", subimage)
#cvCvtColor(subimage, subimage, CV_BGR2HSV) # create hsv version
scalar = cv.Avg(subimage)
#self.current_colour = [int((scalar[0]*2)), int(scalar[1]/255.0*100), int(scalar[2]/255.0*100)]
#print "Average colour value: H:"+ str(int((scalar[0]*2))) + " S:"+ str(int(scalar[1]/255.0*100)) + " V:"+ str(int(scalar[2]/255.0*100))
self.current_colour = [int((scalar[2])), int(scalar[1]), int(scalar[0])]
print "Average colour value: R:"+ str(int((scalar[2]))) + " G:"+ str(int(scalar[1])) + " B:"+ str(int(scalar[0]))
except RuntimeError:
print "error"
cv.Rectangle(image, cv.Point( pos[0]-radius, pos[1]-radius), cv.Point(pos[0]+ radius, pos[1]+radius),cv.CV_RGB(0, 255, 0), 2, 8, 0)
def record_colour(self, image, image_org, pos, radius):
radius = int(radius*0.7)
if pos[1] > radius: # only record a square when it is in full camera view
try:
rect = cv.Rect(pos[0]-radius, pos[1]-radius, radius*2, radius*2)
subimage = cv.GetSubRect(image_org, rect)
#cvCvtColor(subimage, subimage, CV_BGR2HSV) # create hsv version
scalar = cv.Avg(subimage)
#return scalar1, [['c', [['h', int((scalar[0]*2))], ['s', int(scalar[1]/255.0*100)], ['v', int(scalar[2]/255.0*100)]]]]
return [['c', [['r', int((scalar[2]))], ['g', int(scalar[1])], ['b', int(scalar[0])]]]]
except RuntimeError:
print "error", "radius:", radius, "position:", pos
return None
else:
return None
def find_colour(self, image, colour):
""" searches for the given colour in the image
colour is in hsv
"""
# Create a 8-bit 1-channel image with same size as the frame
color_mask = cv.CreateImage(cv.GetSize(image), 8, 1)
image_h = cv.CreateImage(cv.GetSize(image), 8, 1)
cv.CvtColor(image, image, cv.CV_BGR2HSV) # convert to hsv
cv.Split(image, image_h, None, None, None)
# Find the pixels within the color-range, and put the output in the color_mask
cv.InRangeS(image_h, cv.Scalar((edge_threshold4*2)-5), cv.Scalar((edge_threshold4*2)+5), color_mask)
cv.CvtColor(image, image, cv.CV_HSV2BGR) # convert to bgr
cv.Set(image, cv.CV_RGB(0, 255, 0), color_mask)
def return_colour(self):
""" returns current colour
"""
return self.current_colour
def change_value1(self, new_value):
global edge_threshold1
edge_threshold1 = new_value
def change_value2(self, new_value):
global edge_threshold2
edge_threshold2 = new_value
def change_value3(self, new_value):
global edge_threshold3
if new_value % 2:
edge_threshold3 = new_value
else:
edge_threshold3 = new_value+1
def change_value4(self, new_value):
global edge_threshold4
edge_threshold4 = new_value
def main_loop(self):
#writer = cv.CreateVideoWriter("out.avi", cv.CV_FOURCC('P','I','M','1'), 30, (640,480),1)
while 1:
im = self.webcam.query()
# handle events
key = cv.WaitKey(10)
if key != -1 and key < 256:
key = chr(key)
if key == '1' or config.command == '1':
if config.face_d == False:
config.face_d = True
if key == '2' or config.command == 'edge':
if config.edge_d == False:
config.edge_d = True
print "detecting edges"
if key == '3' or config.command == '3':
if config.save_video == False:
config.save_video = True
print "saving video"
if key == '4' or config.command == '4':
if config.circle_d == False:
config.circle_d = True
print "detecting circles"
if key == '5' or config.command == '5':
if config.edge_d_non_vision == False:
config.edge_d_non_vision = True
print "detecting circles using edge detection"
else:
config.edge_d_non_vision = False
if key == 's' or config.command == 's':
if config.show == False:
config.show = True
print "showing video"
if key == 'b' or config.command == 'b':
if config.game_coors == "10.0, 50.0, 0.0":
self.commr.set_gaze("10.0, 50.0, 0.0")
config.game_coors = "0.0, 50.0, 0.0"
else:
self.commr.set_gaze("0.0, 50.0, 0.0")
config.game_coors = "10.0, 50.0, 0.0"
if key == 'e' or config.command == 'e':
config.face_d = False
config.edge_d = False
config.circle_d = False
config.save_video = False
config.colour_s = False
print "stop tracking"
if key == 'q' or config.command == 'q':
config.quit = True
config.command = '0'
if config.face_d: # face detection
if config.face_d_optimised:
if self.comm.last_ack != "wait":
self.detect_face(im)
else:
self.detect_face(im)
if config.colour_s:
self.find_colour(frame, 10)
if config.save_video: # save
cv.WriteFrame(writer,frame)
if config.quit: # quit
print 'Camera closed'
break
pil = im.asAnnotated() # get image as PIL
rgb = cv.CreateImageHeader(pil.size, cv.IPL_DEPTH_8U, 3) # create IPL image
cv.SetData(rgb, pil.tostring())
frame = cv.CreateImage(cv.GetSize(rgb), cv.IPL_DEPTH_8U,3) # convert to bgr
cv.CvtColor(rgb, frame, cv.CV_RGB2BGR)
cv.Flip(frame, None, 1) # mirror
if config.circle_d: # circle detection
frame_org = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U,3) # convert to bgr
cv.Copy(frame, frame_org)
self.detect_circle(frame, frame_org)
if config.edge_d: # edge detection
frame_org = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U,3) # convert to bgr
cv.Copy(frame, frame_org)
frame = self.detect_edge(frame)
if frame is None:
print "error capturing frame"
break
if config.use_gui:
if config.show:
cv.ShowImage('Camera', frame) # display webcam image
else:
cv.ShowImage('Camera', empty)
class Facel:
def __init__(self):
rospy.init_node("fasel")
self.enrollService = rospy.Service("enroll", Enroll, self.handleEnrollFace)
self.trainService = rospy.Service("train", Train, self.handleTrain)
self.resetService = rospy.Service("reset", Reset, self.handleReset)
self.namesPub = rospy.Publisher("names", String)
self.personEventPub = rospy.Publisher("person_event", PersonEvent)
self.imagePub = rospy.Publisher("images", Image)
self.imageSub = rospy.Subscriber("image", Image, self.handleImage)
self.bridge = CvBridge()
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME, image_scale=0.5)
self.fel = loadFilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 64
self.enroll_list = []
self.enrollCondition = Condition()
self.isTrained = False
self.nameList = []
self.faceNames = []
self.faceCount = 0
self.faceTrack = dict()
self.face_processing = True
self.eye_processing = True
self.image_flip = True
self.reqQueueLock = Lock()
self.faceTrackLock = Lock()
self.is_running = True
self.eventUpdateDelay = 2.0 # seconds
self.eventUpdateTimer = Thread(target=self.eventUpdate)
self.eventUpdateTimer.start()
# Convert opencv2 image to pyvision image
def opencv_to_pyvision(self, cv_image):
pil_img = PIL.Image.fromstring("RGB", (cv_image.width, cv_image.height), cv_image.tostring())
pyimg = pv.Image(pil_img)
return pyimg
# Convert pyvision image to opencv2 image
def pyvision_to_opencv(self, pyimg):
pil_img = pyimg.asPIL()
cv_img = cv.CreateImageHeader(pil_img.size, cv.IPL_DEPTH_8U, 3) # RGB image
cv.SetData(cv_img, pil_img.tostring(), pil_img.size[0] * 3)
return cv_img
def PIL_to_opencv(self, pil_img):
cv_img = cv.CreateImageHeader(pil_img.size, cv.IPL_DEPTH_8U, 3) # RGB image
cv.SetData(cv_img, pil_img.tostring(), pil_img.size[0] * 3)
return cv_img
def onFrame(self, img):
"""
Process a video frame.
"""
self.eye_time = 0.0
names = []
nFaces = 0
if self.face_processing:
faces = self.findFaces(img)
nFaces = len(faces)
if self.enrolling != None:
success = None
for rect, leye, reye in faces:
img.annotateRect(self.enrolling, color="yellow")
if (success == None) and is_success(self.enrolling, rect):
success = rect
img.annotateRect(rect, color="blue")
if self.eye_processing:
img.annotatePoint(leye, color="blue")
img.annotatePoint(reye, color="blue")
self.enroll_list.append([img, rect, leye, reye])
else:
img.annotateRect(rect, color="red")
if self.eye_processing:
img.annotatePoint(leye, color="red")
img.annotatePoint(reye, color="red")
img.annotateLine(
pv.Point(rect.x, rect.y), pv.Point(rect.x + rect.w, rect.y + rect.h), color="red"
)
img.annotateLine(
pv.Point(rect.x + rect.w, rect.y), pv.Point(rect.x, rect.y + rect.h), color="red"
)
if success == None:
rect = self.enrolling
img.annotateLine(
pv.Point(rect.x, rect.y), pv.Point(rect.x + rect.w, rect.y + rect.h), color="yellow"
)
img.annotateLine(
pv.Point(rect.x + rect.w, rect.y), pv.Point(rect.x, rect.y + rect.h), color="yellow"
)
else:
# enroll in the identification algorithm
pass
else:
for rect, leye, reye in faces:
img.annotateRect(rect, color="blue")
if self.eye_processing:
img.annotatePoint(leye, color="blue")
img.annotatePoint(reye, color="blue")
for rect, leye, reye in faces:
img.annotateRect(rect, color="blue")
img.annotatePoint(leye, color="blue")
img.annotatePoint(reye, color="blue")
if self.isTrained:
self.label_time = time.time()
for rect, leye, reye in faces:
if self.face_rec.isTrained():
label = self.face_rec.predict(img, leye, reye)
names.append([0.5 * (leye + reye), label])
self.label_time = time.time() - self.label_time
im = img.asAnnotated()
# Flip to mirror image
if self.image_flip:
im = im.transpose(FLIP_LEFT_RIGHT)
if self.enrolling != None:
self.enrollCondition.acquire()
self.enroll_count += 1
self.enrollCondition.notify()
self.enrollCondition.release()
# Draw on the image
draw = PIL.ImageDraw.Draw(im)
x, y = self.enrolling.x, self.enrolling.y
if self.image_flip:
xsize, ysize = im.size
x = xsize - (x + self.enrolling.w)
draw.text(
(x + 10, y + 10),
"Enrolling: %2d of %2d" % (self.enroll_count, self.enroll_max),
fill="yellow",
font=self.arialblack24,
)
del draw
facesEntered = []
if len(names) > 0:
draw = PIL.ImageDraw.Draw(im)
for pt, name in names:
x, y = pt.X(), pt.Y()
# Draw on the image
x, y = pt.X(), pt.Y()
w, h = draw.textsize(name, font=self.arialblack24)
if self.image_flip:
xsize, ysize = im.size
x = xsize - x - 0.5 * w
else:
x = x - 0.5 * w
draw.text((x, y - 20 - h), name, fill="green", font=self.arialblack24)
facesEntered.append(name)
# Publish only new names
if name not in self.faceNames:
str = "seeing %s" % name
rospy.loginfo(str)
self.namesPub.publish(String(name))
self.publishPersonEvent(name, "entered")
del draw
# Find all of the faces that are no longer detected
for name in self.faceNames:
if name not in facesEntered:
self.publishPersonEvent(name, "exited")
# print "{0} {1} {2}".format(nFaces, self.faceCount, len(facesEntered))
nFaces = nFaces - len(facesEntered)
# For unidentified faces
# figure out how many entered/exited
if (nFaces - self.faceCount) > 0:
self.publishPersonEvent("unknown", "entered")
if (nFaces - self.faceCount) < 0:
self.publishPersonEvent("unknown", "exited")
# Update all for the next round
self.faceNames = facesEntered
self.faceCount = nFaces
# Publish the image
cv_img = self.PIL_to_opencv(im)
# cv_img = self.pyvision_to_opencv(img)
msg = self.bridge.cv_to_imgmsg(cv_img, encoding="rgb8")
self.imagePub.publish(msg)
def publishPersonEvent(self, name, event):
"""
Updates the dictionary
"""
now = time.time()
# In case of non-existing records, force the update
if name in self.faceTrack:
self.faceTrackLock.acquire()
pastEvent, lastEvent, recTime = self.faceTrack[name]
self.faceTrackLock.release()
else:
recTime = now - self.eventUpdateDelay
lastEvent = "undefined"
pastEvent = lastEvent
self.faceTrackLock.acquire()
self.faceTrack[name] = (pastEvent, event, now)
self.faceTrackLock.release()
# print "publishPersonEvent for {0} {1} {2} {3} ".format(name,pastEvent,event,now)
def eventUpdate(self):
"""
Publishes events and updates the dictionary
"""
while self.is_running:
now = time.time()
# Publish evey n seconds to avoid hysterisis,
# where n = self.eventUpdateDelay
self.faceTrackLock.acquire()
for name in self.faceTrack:
lastEvent, event, eventTime = self.faceTrack[name]
if ((now - eventTime) >= self.eventUpdateDelay) and (event <> lastEvent):
# print "eventUpdate for {0} {1} {2} {3} ".format(name,lastEvent,event,now)
self.personEventPub.publish(event, name)
self.faceTrack[name] = (event, event, now)
# print "eventUpdate to {0} ".format(event)
self.faceTrackLock.release()
time.sleep(self.eventUpdateDelay)
def enrollFace(self, name):
with self.reqQueueLock:
"""
Starts the enrollment process.
"""
faces = self.current_faces
if len(faces) > 1:
return "too many faces to enroll"
for rect, leye, reye in faces:
print "Start Enrolling %s" % (name)
self.enrolling = rect
self.enroll_count = 0
self.nameList.append(name)
self.enrollCondition.acquire()
while self.enroll_count < self.enroll_max:
self.enrollCondition.wait()
self.enrollCondition.release()
self.enrolling = None
for data, rect, leye, reye in self.enroll_list:
self.face_rec.addTraining(data, leye, reye, name)
self.enroll_count = 0
self.enroll_list = []
return "ok"
def trainFaces(self):
with self.reqQueueLock:
"""
Start the SVM training process.
"""
result = "ok"
# Prevent face prediction while training
self.isTrained = False
if self.svm_mode == SVM_AUTOMATIC:
# Train with automatic tuning.
print "Training mode: auto tuning"
self.face_rec.train() # callback=progress.Update)
self.svm_C = self.face_rec.svm.C
self.svm_Gamma = self.face_rec.svm.gamma
else:
# Train with manual tuning.
print "Training mode: manual tuning"
self.face_rec.train(C=[self.svm_C], Gamma=[self.svm_Gamma]) # ,callback=progress.Update)
if self.face_rec.isTrained():
result = "ok"
self.isTrained = True
else:
result = "Error"
return result
def resetFaces(self):
with self.reqQueueLock:
"""
Clear the enrollment data.
"""
result = "ok"
self.isTrained = False
nameList = []
# Clear the enrollment data for the SVM
self.face_rec.reset()
return result
def findFaces(self, img):
faces = []
self.detect_time = time.time()
rects = self.face_detector.detect(img)
self.detect_time = time.time() - self.detect_time
cvtile = opencv.cvCreateMat(128, 128, opencv.CV_8UC3)
bwtile = opencv.cvCreateMat(128, 128, opencv.CV_8U)
cvimg = img.asOpenCV()
self.eye_time = time.time()
for rect in rects:
faceim = opencv.cvGetSubRect(cvimg, rect.asOpenCV())
opencv.cvResize(faceim, cvtile)
affine = pv.AffineFromRect(rect, (128, 128))
opencv.cvCvtColor(cvtile, bwtile, cv.CV_BGR2GRAY)
leye, reye, lcp, rcp = self.fel.locateEyes(bwtile)
leye = pv.Point(leye)
reye = pv.Point(reye)
leye = affine.invertPoint(leye)
reye = affine.invertPoint(reye)
faces.append([rect, leye, reye])
self.eye_time = time.time() - self.eye_time
self.current_faces = faces
return faces
def handleImage(self, image_message):
cv_image = self.bridge.imgmsg_to_cv(image_message, desired_encoding="rgb8")
pyimg = self.opencv_to_pyvision(cv_image)
self.onFrame(pyimg)
# msg = self.bridge.cv_to_imgmsg(cv_image, encoding='rgb8')
# self.imagePub.publish(msg)
def handleEnrollFace(self, msg):
result = "ok"
print "Enrolling new name:" + msg.name
result = self.enrollFace(msg.name)
return EnrollResponse(result)
def handleTrain(self, msg):
result = "ok"
print "Training..."
if len(self.nameList) >= 2:
result = self.trainFaces()
else:
result = "Error: need at least 2 faces to train"
return TrainResponse(result)
def handleReset(self, msg):
result = "ok"
print "Resetting..."
result = self.resetFaces()
return ResetResponse(result)
from pyvision.face.FilterEyeLocator import FilterEyeLocator
if __name__ == "__main__":
ilog = pv.ImageLog()
# Load the face image file
fname = os.path.join(pv.__path__[0], 'data', 'misc', 'FaceSample.jpg')
# Create the annotation image in black and white so that color
# annotations show up better.
im = pv.Image(fname, bw_annotate=True)
ilog(pv.Image(fname), "Original")
# Create a OpenCV cascade face detector object
cd = CascadeDetector()
# Create an eye detector object
el = FilterEyeLocator()
# Call the face detector like a function to get a list of face rectangles
rects = cd(im)
# print the list of rectangles
print("Face Detection Output:", rects)
# Also call the eye detector like a function with the original image and
# the list of face detections to locate the eyes.
eyes = el(im, rects)
# print the list of eyes. Format [ [ face_rect, left_eye, right_eye], ...]
class VideoWindow(wx.Frame):
'''
This is the main FaceL window which includes the webcam video and enrollment and training controls.
'''
def __init__(self, parent, id, name, size=(640, 672)):
'''
Create all the windows and controls used for the window and
'''
wx.Frame.__init__(self, parent, id, name, size=size)
self.CenterOnScreen(wx.HORIZONTAL)
self.timing_window = None # Initialize timing window
# ------------- Face Processing -----------------
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME,
image_scale=0.5)
self.fel = FilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e+00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 32
self.enroll_list = []
self.previous_time = time.time()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
# ---------------- Basic Data -------------------
try:
self.webcam = Webcam()
except SystemExit:
raise
except:
trace = traceback.format_exc()
message = TraceBackDialog(None, "Camera Error", CAMERA_ERROR,
trace)
message.ShowModal()
sys.stderr.write(
"FaceL Error: an error occurred while trying to connect to the camera. Details follow.\n\n"
)
sys.stderr.write(trace)
sys.exit(CAMERA_ERROR_CODE)
# ------------- Other Components ----------------
self.CreateStatusBar()
# ------------------- Menu ----------------------
# Creating the menubar.
# Menu IDs
license_id = wx.NewId()
mirror_id = wx.NewId()
face_id = wx.NewId()
svm_tune_id = wx.NewId()
performance_id = wx.NewId()
# Menu Items
self.file_menu = wx.Menu()
self.file_menu.Append(wx.ID_ABOUT, "&About...")
self.file_menu.Append(license_id, "FaceL License...")
self.file_menu.AppendSeparator()
self.file_menu.Append(wx.ID_EXIT, "E&xit")
self.options_menu = wx.Menu()
self.face_menuitem = self.options_menu.AppendCheckItem(
face_id, "Face Processing")
self.eye_menuitem = self.options_menu.AppendCheckItem(
face_id, "Eye Detection")
self.mirror_menuitem = self.options_menu.AppendCheckItem(
mirror_id, "Mirror Video")
self.options_menu.AppendSeparator()
self.options_menu.Append(svm_tune_id, "SVM Tuning...")
self.options_menu.Append(performance_id, "Performance...")
# Create Menu Bar
self.menu_bar = wx.MenuBar()
self.menu_bar.Append(self.file_menu, "&File")
self.menu_bar.Append(self.options_menu, "&Options")
self.SetMenuBar(self.menu_bar)
# Menu Events
wx.EVT_MENU(self, wx.ID_ABOUT, self.onAbout)
wx.EVT_MENU(self, license_id, self.onLicense)
wx.EVT_MENU(self, mirror_id, self.onNull)
wx.EVT_MENU(self, face_id, self.onNull)
wx.EVT_MENU(self, svm_tune_id, self.onSVMTune)
wx.EVT_MENU(self, performance_id, self.onTiming)
# Set up menu checks
self.face_menuitem.Check(True)
self.eye_menuitem.Check(True)
self.mirror_menuitem.Check(True)
# ----------------- Image List ------------------
# --------------- Image Display -----------------
self.static_bitmap = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.EmptyBitmap(640, 480))
self.controls_box = wx.StaticBox(self, wx.NewId(), "Controls")
self.facel_logo = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.Bitmap(FACEL_LOGO))
self.csu_logo = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.Bitmap(CSU_LOGO))
# self.performance_box = wx.StaticBox(self, wx.NewId(), "Performance")
self.enroll_chioce_label = wx.StaticText(self,
wx.NewId(),
"Enrollment Count:",
style=wx.ALIGN_LEFT)
self.enroll_choice = wx.Choice(self, wx.NewId(), wx.Point(0, 0),
wx.Size(-1, -1),
['16', '32', '48', '64', '128', '256'])
self.enroll_choice.Select(3)
self.train_button = wx.Button(self, wx.NewId(), 'Train Labeler')
self.reset_button = wx.Button(self, wx.NewId(), 'Clear Labels')
# --------------- Instrumentation ---------------
self.enroll_label = wx.StaticText(
self,
wx.NewId(),
"Click a face in the video to enroll.",
style=wx.ALIGN_LEFT)
self.ids_label = wx.StaticText(self,
wx.NewId(),
"Labels:",
size=wx.Size(-1, 16),
style=wx.ALIGN_LEFT)
self.ids_text = wx.StaticText(self,
wx.NewId(),
size=wx.Size(30, 16),
style=wx.ALIGN_RIGHT)
self.faces_label = wx.StaticText(self,
wx.NewId(),
"Faces:",
size=wx.Size(-1, 16),
style=wx.ALIGN_LEFT)
self.faces_text = wx.StaticText(self,
wx.NewId(),
size=wx.Size(30, 16),
style=wx.ALIGN_RIGHT)
# --------------- Window Layout -----------------
enroll_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_sizer.Add(self.ids_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.Add(self.ids_text,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.AddSpacer(20)
enroll_sizer.Add(self.faces_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.Add(self.faces_text,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
training_sizer = wx.BoxSizer(wx.HORIZONTAL)
training_sizer.Add(self.train_button,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
training_sizer.Add(self.reset_button,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_choice_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_choice_sizer.Add(self.enroll_chioce_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
enroll_choice_sizer.Add(self.enroll_choice,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
controls_sizer = wx.StaticBoxSizer(
self.controls_box, wx.VERTICAL) #wx.BoxSizer(wx.VERTICAL)
controls_sizer.Add(self.enroll_label,
flag=wx.ALIGN_LEFT | wx.ALL,
border=0)
controls_sizer.Add(enroll_sizer, flag=wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_choice_sizer,
flag=wx.ALIGN_LEFT | wx.ALL,
border=4)
controls_sizer.Add(training_sizer,
flag=wx.ALIGN_LEFT | wx.ALL,
border=0)
bottom_sizer = wx.BoxSizer(wx.HORIZONTAL)
bottom_sizer.Add(self.facel_logo,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
bottom_sizer.Add(controls_sizer, flag=wx.ALIGN_TOP | wx.ALL, border=4)
bottom_sizer.Add(self.csu_logo,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
main_sizer = wx.BoxSizer(wx.VERTICAL)
main_sizer.Add(self.static_bitmap,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
main_sizer.Add(bottom_sizer, flag=wx.ALIGN_CENTER | wx.ALL, border=4)
self.SetAutoLayout(True)
self.SetSizer(main_sizer)
self.Layout()
# -----------------------------------------------
self.timer = FrameTimer(self)
self.timer.Start(200)
# -------------- Event Handleing ----------------
wx.EVT_SIZE(self.static_bitmap, self.onBitmapResize)
wx.EVT_LEFT_DOWN(self.static_bitmap, self.onClick)
self.Bind(wx.EVT_BUTTON, self.onTrain, id=self.train_button.GetId())
self.Bind(wx.EVT_BUTTON, self.onReset, id=self.reset_button.GetId())
# --------------- Setup State -------------------
self.setupState()
def onTrain(self, event=None):
'''
Start the SVM training process.
'''
print "Train"
#progress = wx.ProgressDialog(title="SVM Training", message="Training the Face Recognition Algorithm. Please Wait...")
if self.svm_mode == SVM_AUTOMATIC:
# Train with automatic tuning.
self.face_rec.train() #callback=progress.Update)
self.svm_C = self.face_rec.svm.C
self.svm_Gamma = self.face_rec.svm.gamma
else:
# Train with manual tuning.
self.face_rec.train(C=[self.svm_C],
Gamma=[self.svm_Gamma
]) # ,callback=progress.Update)
#progress.Destroy()
def onReset(self, event=None):
'''
Clear the enrollment data for the SVM.
'''
self.face_rec.reset()
self.setupState()
def onFrame(self, event=None):
'''
Retrieve and process a video frame.
'''
self.timer.Stop()
starttime = time.time()
self.detect_time = 0.0
self.eye_time = 0.0
self.label_time = 0.0
img = self.webcam.query()
face_processing = self.face_menuitem.IsChecked()
eye_processing = self.eye_menuitem.IsChecked()
names = []
if face_processing:
faces = self.findFaces(img)
if self.enrolling != None:
success = None
for rect, leye, reye in faces:
img.annotateRect(self.enrolling, color='yellow')
if (success == None) and is_success(self.enrolling, rect):
success = rect
img.annotateRect(rect, color='blue')
if eye_processing:
img.annotatePoint(leye, color='blue')
img.annotatePoint(reye, color='blue')
self.enroll_list.append([img, rect, leye, reye])
else:
img.annotateRect(rect, color='red')
if eye_processing:
img.annotatePoint(leye, color='red')
img.annotatePoint(reye, color='red')
img.annotateLine(pv.Point(rect.x, rect.y),
pv.Point(rect.x + rect.w,
rect.y + rect.h),
color='red')
img.annotateLine(pv.Point(rect.x + rect.w, rect.y),
pv.Point(rect.x, rect.y + rect.h),
color='red')
if success == None:
rect = self.enrolling
img.annotateLine(pv.Point(rect.x, rect.y),
pv.Point(rect.x + rect.w,
rect.y + rect.h),
color='yellow')
img.annotateLine(pv.Point(rect.x + rect.w, rect.y),
pv.Point(rect.x, rect.y + rect.h),
color='yellow')
else:
#enroll in the identification algorithm
pass
else:
for rect, leye, reye in faces:
img.annotateRect(rect, color='blue')
if eye_processing:
img.annotatePoint(leye, color='blue')
img.annotatePoint(reye, color='blue')
if self.face_rec.isTrained():
self.label_time = time.time()
for rect, leye, reye in faces:
label = self.face_rec.predict(img, leye, reye)
names.append([0.5 * (leye + reye), label])
self.label_time = time.time() - self.label_time
# Displaying Annotated Frame
im = img.asAnnotated()
if self.mirror_menuitem.IsChecked():
im = im.transpose(FLIP_LEFT_RIGHT)
if self.enrolling != None:
draw = PIL.ImageDraw.Draw(im)
x, y = self.enrolling.x, self.enrolling.y
if self.mirror_menuitem.IsChecked():
x = 640 - (x + self.enrolling.w)
self.enroll_count += 1
draw.text(
(x + 10, y + 10),
"Enrolling: %2d of %2d" % (self.enroll_count, self.enroll_max),
fill='yellow',
font=self.arialblack24)
del draw
if self.enroll_count >= self.enroll_max:
print "Count:", self.enroll_count
if len(self.enroll_list) == 0:
warning_dialog = wx.MessageDialog(
self,
"No faces were detected during the enrollment process. Please face towards the camera and keep your face in the yellow rectangle during enrollment.",
style=wx.ICON_EXCLAMATION | wx.OK,
caption="Enrollment Error")
warning_dialog.ShowModal()
else:
name_dialog = wx.TextEntryDialog(
self,
"Please enter a name to associate with the face. (%d faces captured)"
% len(self.enroll_list),
caption="Enrollment ID")
result = name_dialog.ShowModal()
sub_id = name_dialog.GetValue()
if result == wx.ID_OK:
if sub_id == "":
print "Warning: Empty Subject ID"
warning_dialog = wx.MessageDialog(
self,
"A name was entered in the previous dialog so this face will not be enrolled in the database. Please repeat the enrollment process for this person.",
style=wx.ICON_EXCLAMATION | wx.OK,
caption="Enrollment Error")
warning_dialog.ShowModal()
else:
for data, rect, leye, reye in self.enroll_list:
self.face_rec.addTraining(
data, leye, reye, sub_id)
self.setupState()
self.enroll_count = 0
self.enrolling = None
self.enroll_list = []
if len(names) > 0:
draw = PIL.ImageDraw.Draw(im)
for pt, name in names:
x, y = pt.X(), pt.Y()
w, h = draw.textsize(name, font=self.arialblack24)
if self.mirror_menuitem.IsChecked():
x = 640 - x - 0.5 * w
else:
x = x - 0.5 * w
draw.text((x, y - 20 - h),
name,
fill='green',
font=self.arialblack24)
del draw
wxImg = wx.EmptyImage(im.size[0], im.size[1])
wxImg.SetData(im.tostring())
bm = wxImg.ConvertToBitmap()
self.static_bitmap.SetBitmap(bm)
# Update timing gauges
full_time = time.time() - starttime
if self.timing_window != None:
self.timing_window.update(self.detect_time, self.eye_time,
self.label_time, full_time)
self.ids_text.SetLabel("%d" % (self.face_rec.n_labels, ))
self.faces_text.SetLabel("%d" % (self.face_rec.n_faces, ))
sleep_time = 1
if sys.platform.startswith("linux"):
sleep_time = 10
# TODO: For macosx milliseconds should be 1
# TODO: For linux milliseconds may need to be set to a higher value 10
self.timer.Start(milliseconds=sleep_time, oneShot=1)
def setupState(self):
#print "state",self.face_rec.n_labels,self.IsEnabled()
if self.face_rec.n_labels >= 2:
self.train_button.Enable()
else:
self.train_button.Disable()
def onBitmapResize(self, event):
w = event.GetSize().GetWidth()
h = event.GetSize().GetHeight()
self.static_bitmap.SetSize(event.GetSize())
def onClick(self, event):
'''
Process a click in the Video window which starts the enrollment process.
'''
x = event.GetX()
y = event.GetY()
if self.mirror_menuitem.IsChecked():
x = 640 - x
for rect, leye, reye in self.current_faces:
if rect.containsPoint(pv.Point(x, y)):
self.enrolling = rect
self.enroll_count = 0
self.enroll_max = int(self.enroll_choice.GetStringSelection())
def findFaces(self, im):
eye_processing = self.eye_menuitem.IsChecked()
self.detect_time = time.time()
rects = self.face_detector.detect(im)
self.detect_time = time.time() - self.detect_time
self.eye_time = time.time()
if eye_processing:
faces = self.fel.locateEyes(im, rects)
else:
faces = []
for rect in rects:
affine = pv.AffineFromRect(rect, (1, 1))
leye = affine.invertPoint(AVE_LEFT_EYE)
reye = affine.invertPoint(AVE_RIGHT_EYE)
faces.append([rect, leye, reye])
self.eye_time = time.time() - self.eye_time
self.current_faces = faces
return faces
def onAbout(self, event):
wx.MessageBox(ABOUT_MESSAGE, "About FaceL",
wx.OK | wx.ICON_INFORMATION)
def onLicense(self, event):
wx.MessageBox(LICENSE_MESSAGE, "FaceL License",
wx.OK | wx.ICON_INFORMATION)
def onNull(self, *args, **kwargs):
pass
def onSVMTune(self, event):
dialog = SVMTuningDialog(self, self.svm_mode, self.svm_C,
self.svm_Gamma)
dialog.CenterOnParent()
result = dialog.ShowModal()
if result == wx.ID_OK:
self.svm_mode = dialog.mode
self.svm_C = dialog.C
self.svm_Gamma = dialog.Gamma
print "SVM Tuning Info <MODE:%s; C:%0.2e; Gamma:%0.2e>" % (
self.svm_mode, self.svm_C, self.svm_Gamma)
dialog.Destroy()
def onTiming(self, event):
if self.timing_window == None:
self.timing_window = TimingWindow(self, wx.NewId(), "Performance")
self.timing_window.CenterOnParent()
self.timing_window.Show(True)
self.timing_window.Bind(wx.EVT_CLOSE,
self.onCloseTiming,
id=self.timing_window.GetId())
else:
self.timing_window.Show(True)
self.timing_window.Raise()
def onCloseTiming(self, event):
self.timing_window.Destroy()
self.timing_window = None
def __init__(self, parent, id, name, size=(640, 672)):
'''
Create all the windows and controls used for the window and
'''
wx.Frame.__init__(self, parent, id, name, size=size)
self.CenterOnScreen(wx.HORIZONTAL)
self.timing_window = None # Initialize timing window
# ------------- Face Processing -----------------
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME,
image_scale=0.5)
self.fel = FilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e+00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 32
self.enroll_list = []
self.previous_time = time.time()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
# ---------------- Basic Data -------------------
try:
self.webcam = Webcam()
except SystemExit:
raise
except:
trace = traceback.format_exc()
message = TraceBackDialog(None, "Camera Error", CAMERA_ERROR,
trace)
message.ShowModal()
sys.stderr.write(
"FaceL Error: an error occurred while trying to connect to the camera. Details follow.\n\n"
)
sys.stderr.write(trace)
sys.exit(CAMERA_ERROR_CODE)
# ------------- Other Components ----------------
self.CreateStatusBar()
# ------------------- Menu ----------------------
# Creating the menubar.
# Menu IDs
license_id = wx.NewId()
mirror_id = wx.NewId()
face_id = wx.NewId()
svm_tune_id = wx.NewId()
performance_id = wx.NewId()
# Menu Items
self.file_menu = wx.Menu()
self.file_menu.Append(wx.ID_ABOUT, "&About...")
self.file_menu.Append(license_id, "FaceL License...")
self.file_menu.AppendSeparator()
self.file_menu.Append(wx.ID_EXIT, "E&xit")
self.options_menu = wx.Menu()
self.face_menuitem = self.options_menu.AppendCheckItem(
face_id, "Face Processing")
self.eye_menuitem = self.options_menu.AppendCheckItem(
face_id, "Eye Detection")
self.mirror_menuitem = self.options_menu.AppendCheckItem(
mirror_id, "Mirror Video")
self.options_menu.AppendSeparator()
self.options_menu.Append(svm_tune_id, "SVM Tuning...")
self.options_menu.Append(performance_id, "Performance...")
# Create Menu Bar
self.menu_bar = wx.MenuBar()
self.menu_bar.Append(self.file_menu, "&File")
self.menu_bar.Append(self.options_menu, "&Options")
self.SetMenuBar(self.menu_bar)
# Menu Events
wx.EVT_MENU(self, wx.ID_ABOUT, self.onAbout)
wx.EVT_MENU(self, license_id, self.onLicense)
wx.EVT_MENU(self, mirror_id, self.onNull)
wx.EVT_MENU(self, face_id, self.onNull)
wx.EVT_MENU(self, svm_tune_id, self.onSVMTune)
wx.EVT_MENU(self, performance_id, self.onTiming)
# Set up menu checks
self.face_menuitem.Check(True)
self.eye_menuitem.Check(True)
self.mirror_menuitem.Check(True)
# ----------------- Image List ------------------
# --------------- Image Display -----------------
self.static_bitmap = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.EmptyBitmap(640, 480))
self.controls_box = wx.StaticBox(self, wx.NewId(), "Controls")
self.facel_logo = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.Bitmap(FACEL_LOGO))
self.csu_logo = wx.StaticBitmap(self,
wx.NewId(),
bitmap=wx.Bitmap(CSU_LOGO))
# self.performance_box = wx.StaticBox(self, wx.NewId(), "Performance")
self.enroll_chioce_label = wx.StaticText(self,
wx.NewId(),
"Enrollment Count:",
style=wx.ALIGN_LEFT)
self.enroll_choice = wx.Choice(self, wx.NewId(), wx.Point(0, 0),
wx.Size(-1, -1),
['16', '32', '48', '64', '128', '256'])
self.enroll_choice.Select(3)
self.train_button = wx.Button(self, wx.NewId(), 'Train Labeler')
self.reset_button = wx.Button(self, wx.NewId(), 'Clear Labels')
# --------------- Instrumentation ---------------
self.enroll_label = wx.StaticText(
self,
wx.NewId(),
"Click a face in the video to enroll.",
style=wx.ALIGN_LEFT)
self.ids_label = wx.StaticText(self,
wx.NewId(),
"Labels:",
size=wx.Size(-1, 16),
style=wx.ALIGN_LEFT)
self.ids_text = wx.StaticText(self,
wx.NewId(),
size=wx.Size(30, 16),
style=wx.ALIGN_RIGHT)
self.faces_label = wx.StaticText(self,
wx.NewId(),
"Faces:",
size=wx.Size(-1, 16),
style=wx.ALIGN_LEFT)
self.faces_text = wx.StaticText(self,
wx.NewId(),
size=wx.Size(30, 16),
style=wx.ALIGN_RIGHT)
# --------------- Window Layout -----------------
enroll_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_sizer.Add(self.ids_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.Add(self.ids_text,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.AddSpacer(20)
enroll_sizer.Add(self.faces_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_sizer.Add(self.faces_text,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
training_sizer = wx.BoxSizer(wx.HORIZONTAL)
training_sizer.Add(self.train_button,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
training_sizer.Add(self.reset_button,
flag=wx.ALIGN_CENTER | wx.ALL,
border=4)
enroll_choice_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_choice_sizer.Add(self.enroll_chioce_label,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
enroll_choice_sizer.Add(self.enroll_choice,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
controls_sizer = wx.StaticBoxSizer(
self.controls_box, wx.VERTICAL) #wx.BoxSizer(wx.VERTICAL)
controls_sizer.Add(self.enroll_label,
flag=wx.ALIGN_LEFT | wx.ALL,
border=0)
controls_sizer.Add(enroll_sizer, flag=wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_choice_sizer,
flag=wx.ALIGN_LEFT | wx.ALL,
border=4)
controls_sizer.Add(training_sizer,
flag=wx.ALIGN_LEFT | wx.ALL,
border=0)
bottom_sizer = wx.BoxSizer(wx.HORIZONTAL)
bottom_sizer.Add(self.facel_logo,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
bottom_sizer.Add(controls_sizer, flag=wx.ALIGN_TOP | wx.ALL, border=4)
bottom_sizer.Add(self.csu_logo,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
main_sizer = wx.BoxSizer(wx.VERTICAL)
main_sizer.Add(self.static_bitmap,
flag=wx.ALIGN_CENTER | wx.ALL,
border=0)
main_sizer.Add(bottom_sizer, flag=wx.ALIGN_CENTER | wx.ALL, border=4)
self.SetAutoLayout(True)
self.SetSizer(main_sizer)
self.Layout()
# -----------------------------------------------
self.timer = FrameTimer(self)
self.timer.Start(200)
# -------------- Event Handleing ----------------
wx.EVT_SIZE(self.static_bitmap, self.onBitmapResize)
wx.EVT_LEFT_DOWN(self.static_bitmap, self.onClick)
self.Bind(wx.EVT_BUTTON, self.onTrain, id=self.train_button.GetId())
self.Bind(wx.EVT_BUTTON, self.onReset, id=self.reset_button.GetId())
# --------------- Setup State -------------------
self.setupState()
def __init__(self):
self.face = CascadeDetector(image_scale=0.4)
class VideoWindow(wx.Frame):
'''
This is the main FaceL window which includes the webcam video and enrollment and training controls.
'''
def __init__(self,parent,id,name,size=(640,672)):
'''
Create all the windows and controls used for the window and
'''
wx.Frame.__init__(self,parent,id,name,size=size)
self.CenterOnScreen(wx.HORIZONTAL)
self.timing_window = None # Initialize timing window
# ------------- Face Processing -----------------
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME,image_scale=0.5)
self.fel = FilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e+00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 32
self.enroll_list = []
self.previous_time = time.time()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
# ---------------- Basic Data -------------------
try:
self.webcam = Webcam()
except SystemExit:
raise
except:
trace = traceback.format_exc()
message = TraceBackDialog(None, "Camera Error", CAMERA_ERROR, trace)
message.ShowModal()
sys.stderr.write("FaceL Error: an error occurred while trying to connect to the camera. Details follow.\n\n")
sys.stderr.write(trace)
sys.exit(CAMERA_ERROR_CODE)
# ------------- Other Components ----------------
self.CreateStatusBar()
# ------------------- Menu ----------------------
# Creating the menubar.
# Menu IDs
license_id = wx.NewId()
mirror_id = wx.NewId()
face_id = wx.NewId()
svm_tune_id = wx.NewId()
performance_id = wx.NewId()
# Menu Items
self.file_menu = wx.Menu();
self.file_menu.Append( wx.ID_ABOUT, "&About..." )
self.file_menu.Append( license_id, "FaceL License..." )
self.file_menu.AppendSeparator();
self.file_menu.Append( wx.ID_EXIT, "E&xit" )
self.options_menu = wx.Menu();
self.face_menuitem = self.options_menu.AppendCheckItem( face_id, "Face Processing" )
self.eye_menuitem = self.options_menu.AppendCheckItem( face_id, "Eye Detection" )
self.mirror_menuitem = self.options_menu.AppendCheckItem( mirror_id, "Mirror Video" )
self.options_menu.AppendSeparator()
self.options_menu.Append( svm_tune_id, "SVM Tuning..." )
self.options_menu.Append( performance_id, "Performance..." )
# Create Menu Bar
self.menu_bar = wx.MenuBar();
self.menu_bar.Append( self.file_menu, "&File" )
self.menu_bar.Append( self.options_menu, "&Options" )
self.SetMenuBar( self.menu_bar )
# Menu Events
wx.EVT_MENU(self, wx.ID_ABOUT, self.onAbout )
wx.EVT_MENU(self, license_id, self.onLicense )
wx.EVT_MENU(self, mirror_id, self.onNull )
wx.EVT_MENU(self, face_id, self.onNull )
wx.EVT_MENU(self, svm_tune_id, self.onSVMTune )
wx.EVT_MENU(self, performance_id, self.onTiming )
# Set up menu checks
self.face_menuitem.Check(True)
self.eye_menuitem.Check(True)
self.mirror_menuitem.Check(True)
# ----------------- Image List ------------------
# --------------- Image Display -----------------
self.static_bitmap = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.EmptyBitmap(640, 480))
self.controls_box = wx.StaticBox(self, wx.NewId(), "Controls")
self.facel_logo = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.Bitmap(FACEL_LOGO))
self.csu_logo = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.Bitmap(CSU_LOGO))
# self.performance_box = wx.StaticBox(self, wx.NewId(), "Performance")
self.enroll_chioce_label = wx.StaticText(self, wx.NewId(), "Enrollment Count:", style=wx.ALIGN_LEFT)
self.enroll_choice = wx.Choice(self,wx.NewId(),wx.Point(0,0),wx.Size(-1,-1),['16','32','48','64','128','256'])
self.enroll_choice.Select(3)
self.train_button = wx.Button(self,wx.NewId(),'Train Labeler')
self.reset_button = wx.Button(self,wx.NewId(),'Clear Labels')
# --------------- Instrumentation ---------------
self.enroll_label = wx.StaticText(self, wx.NewId(), "Click a face in the video to enroll.", style=wx.ALIGN_LEFT)
self.ids_label = wx.StaticText(self, wx.NewId(), "Labels:", size=wx.Size(-1,16), style=wx.ALIGN_LEFT)
self.ids_text = wx.StaticText(self, wx.NewId(), size = wx.Size(30,16), style= wx.ALIGN_RIGHT )
self.faces_label = wx.StaticText(self, wx.NewId(), "Faces:", size=wx.Size(-1,16), style=wx.ALIGN_LEFT)
self.faces_text = wx.StaticText(self, wx.NewId(), size = wx.Size(30,16), style= wx.ALIGN_RIGHT )
# --------------- Window Layout -----------------
enroll_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_sizer.Add(self.ids_label, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.Add(self.ids_text, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.AddSpacer(20)
enroll_sizer.Add(self.faces_label, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.Add(self.faces_text, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
training_sizer = wx.BoxSizer(wx.HORIZONTAL)
training_sizer.Add(self.train_button, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
training_sizer.Add(self.reset_button, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_choice_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_choice_sizer.Add(self.enroll_chioce_label, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
enroll_choice_sizer.Add(self.enroll_choice, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
controls_sizer = wx.StaticBoxSizer(self.controls_box,wx.VERTICAL) #wx.BoxSizer(wx.VERTICAL)
controls_sizer.Add(self.enroll_label, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_choice_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=4)
controls_sizer.Add(training_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
bottom_sizer = wx.BoxSizer(wx.HORIZONTAL)
bottom_sizer.Add(self.facel_logo, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
bottom_sizer.Add(controls_sizer, flag = wx.ALIGN_TOP | wx.ALL, border=4)
bottom_sizer.Add(self.csu_logo, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
main_sizer = wx.BoxSizer(wx.VERTICAL)
main_sizer.Add(self.static_bitmap, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
main_sizer.Add(bottom_sizer, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
self.SetAutoLayout(True)
self.SetSizer(main_sizer)
self.Layout()
# -----------------------------------------------
self.timer = FrameTimer(self)
self.timer.Start(200)
# -------------- Event Handleing ----------------
wx.EVT_SIZE(self.static_bitmap, self.onBitmapResize)
wx.EVT_LEFT_DOWN(self.static_bitmap, self.onClick)
self.Bind(wx.EVT_BUTTON, self.onTrain, id=self.train_button.GetId())
self.Bind(wx.EVT_BUTTON, self.onReset, id=self.reset_button.GetId())
# --------------- Setup State -------------------
self.setupState()
def onTrain(self,event=None):
'''
Start the SVM training process.
'''
print "Train"
#progress = wx.ProgressDialog(title="SVM Training", message="Training the Face Recognition Algorithm. Please Wait...")
if self.svm_mode == SVM_AUTOMATIC:
# Train with automatic tuning.
self.face_rec.train() #callback=progress.Update)
self.svm_C = self.face_rec.svm.C
self.svm_Gamma = self.face_rec.svm.gamma
else:
# Train with manual tuning.
self.face_rec.train( C=[self.svm_C] , Gamma=[self.svm_Gamma])# ,callback=progress.Update)
#progress.Destroy()
def onReset(self,event=None):
'''
Clear the enrollment data for the SVM.
'''
self.face_rec.reset()
self.setupState()
def onFrame(self,event=None):
'''
Retrieve and process a video frame.
'''
self.timer.Stop()
starttime = time.time()
self.detect_time = 0.0
self.eye_time = 0.0
self.label_time = 0.0
img = self.webcam.query()
face_processing = self.face_menuitem.IsChecked()
eye_processing = self.eye_menuitem.IsChecked()
names = []
if face_processing:
faces = self.findFaces(img)
if self.enrolling != None:
success = None
for rect,leye,reye in faces:
img.annotateRect(self.enrolling,color='yellow')
if (success == None) and is_success(self.enrolling,rect):
success = rect
img.annotateRect(rect,color='blue')
if eye_processing:
img.annotatePoint(leye,color='blue')
img.annotatePoint(reye,color='blue')
self.enroll_list.append([img,rect,leye,reye])
else:
img.annotateRect(rect,color='red')
if eye_processing:
img.annotatePoint(leye,color='red')
img.annotatePoint(reye,color='red')
img.annotateLine(pv.Point(rect.x,rect.y),pv.Point(rect.x+rect.w,rect.y+rect.h), color='red')
img.annotateLine(pv.Point(rect.x+rect.w,rect.y),pv.Point(rect.x,rect.y+rect.h), color='red')
if success == None:
rect = self.enrolling
img.annotateLine(pv.Point(rect.x,rect.y),pv.Point(rect.x+rect.w,rect.y+rect.h), color='yellow')
img.annotateLine(pv.Point(rect.x+rect.w,rect.y),pv.Point(rect.x,rect.y+rect.h), color='yellow')
else:
#enroll in the identification algorithm
pass
else:
for rect,leye,reye in faces:
img.annotateRect(rect,color='blue')
if eye_processing:
img.annotatePoint(leye,color='blue')
img.annotatePoint(reye,color='blue')
if self.face_rec.isTrained():
self.label_time = time.time()
for rect,leye,reye in faces:
label = self.face_rec.predict(img,leye,reye)
names.append([0.5*(leye+reye),label])
self.label_time = time.time() - self.label_time
# Displaying Annotated Frame
im = img.asAnnotated()
if self.mirror_menuitem.IsChecked():
im = im.transpose(FLIP_LEFT_RIGHT)
if self.enrolling != None:
draw = PIL.ImageDraw.Draw(im)
x,y = self.enrolling.x,self.enrolling.y
if self.mirror_menuitem.IsChecked():
x = 640 - (x + self.enrolling.w)
self.enroll_count += 1
draw.text((x+10,y+10), "Enrolling: %2d of %2d"%(self.enroll_count,self.enroll_max), fill='yellow', font=self.arialblack24)
del draw
if self.enroll_count >= self.enroll_max:
print "Count:",self.enroll_count
if len(self.enroll_list) == 0:
warning_dialog = wx.MessageDialog(self,
"No faces were detected during the enrollment process. Please face towards the camera and keep your face in the yellow rectangle during enrollment.",
style=wx.ICON_EXCLAMATION | wx.OK,
caption="Enrollment Error")
warning_dialog.ShowModal()
else:
name_dialog = wx.TextEntryDialog(self, "Please enter a name to associate with the face. (%d faces captured)"%len(self.enroll_list), caption = "Enrollment ID")
result = name_dialog.ShowModal()
sub_id = name_dialog.GetValue()
if result == wx.ID_OK:
if sub_id == "":
print "Warning: Empty Subject ID"
warning_dialog = wx.MessageDialog(self,
"A name was entered in the previous dialog so this face will not be enrolled in the database. Please repeat the enrollment process for this person.",
style=wx.ICON_EXCLAMATION | wx.OK,
caption="Enrollment Error")
warning_dialog.ShowModal()
else:
for data,rect,leye,reye in self.enroll_list:
self.face_rec.addTraining(data,leye,reye,sub_id)
self.setupState()
self.enroll_count = 0
self.enrolling = None
self.enroll_list = []
if len(names) > 0:
draw = PIL.ImageDraw.Draw(im)
for pt,name in names:
x,y = pt.X(),pt.Y()
w,h = draw.textsize(name,font=self.arialblack24)
if self.mirror_menuitem.IsChecked():
x = 640 - x - 0.5*w
else:
x = x - 0.5*w
draw.text((x,y-20-h), name, fill='green', font=self.arialblack24)
del draw
wxImg = wx.EmptyImage(im.size[0], im.size[1])
wxImg.SetData(im.tostring())
bm = wxImg.ConvertToBitmap()
self.static_bitmap.SetBitmap(bm)
# Update timing gauges
full_time = time.time() - starttime
if self.timing_window != None:
self.timing_window.update(self.detect_time,self.eye_time,self.label_time,full_time)
self.ids_text.SetLabel("%d"%(self.face_rec.n_labels,))
self.faces_text.SetLabel("%d"%(self.face_rec.n_faces,))
sleep_time = 1
if sys.platform.startswith("linux"):
sleep_time = 10
# TODO: For macosx milliseconds should be 1
# TODO: For linux milliseconds may need to be set to a higher value 10
self.timer.Start(milliseconds = sleep_time, oneShot = 1)
def setupState(self):
#print "state",self.face_rec.n_labels,self.IsEnabled()
if self.face_rec.n_labels >= 2:
self.train_button.Enable()
else:
self.train_button.Disable()
def onBitmapResize(self,event):
w = event.GetSize().GetWidth()
h = event.GetSize().GetHeight()
self.static_bitmap.SetSize(event.GetSize())
def onClick(self,event):
'''
Process a click in the Video window which starts the enrollment process.
'''
x = event.GetX()
y = event.GetY()
if self.mirror_menuitem.IsChecked():
x = 640-x
for rect,leye,reye in self.current_faces:
if rect.containsPoint(pv.Point(x,y)):
self.enrolling = rect
self.enroll_count = 0
self.enroll_max = int(self.enroll_choice.GetStringSelection())
def findFaces(self,im):
eye_processing = self.eye_menuitem.IsChecked()
self.detect_time = time.time()
rects = self.face_detector.detect(im)
self.detect_time = time.time() - self.detect_time
self.eye_time = time.time()
if eye_processing:
faces = self.fel.locateEyes(im, rects)
else:
faces = []
for rect in rects:
affine = pv.AffineFromRect(rect,(1,1))
leye = affine.invertPoint(AVE_LEFT_EYE)
reye = affine.invertPoint(AVE_RIGHT_EYE)
faces.append([rect,leye,reye])
self.eye_time = time.time() - self.eye_time
self.current_faces = faces
return faces
def onAbout(self,event):
wx.MessageBox( ABOUT_MESSAGE,
"About FaceL", wx.OK | wx.ICON_INFORMATION )
def onLicense(self,event):
wx.MessageBox( LICENSE_MESSAGE,
"FaceL License", wx.OK | wx.ICON_INFORMATION )
def onNull(self,*args,**kwargs):
pass
def onSVMTune(self,event):
dialog = SVMTuningDialog(self, self.svm_mode, self.svm_C, self.svm_Gamma)
dialog.CenterOnParent()
result = dialog.ShowModal()
if result == wx.ID_OK:
self.svm_mode = dialog.mode
self.svm_C = dialog.C
self.svm_Gamma = dialog.Gamma
print "SVM Tuning Info <MODE:%s; C:%0.2e; Gamma:%0.2e>"%(self.svm_mode,self.svm_C,self.svm_Gamma)
dialog.Destroy()
def onTiming(self,event):
if self.timing_window == None:
self.timing_window = TimingWindow(self, wx.NewId(),"Performance")
self.timing_window.CenterOnParent()
self.timing_window.Show(True)
self.timing_window.Bind(wx.EVT_CLOSE, self.onCloseTiming, id=self.timing_window.GetId())
else:
self.timing_window.Show(True)
self.timing_window.Raise()
def onCloseTiming(self,event):
self.timing_window.Destroy()
self.timing_window = None
def __init__(self,parent,id,name,size=(640,672)):
'''
Create all the windows and controls used for the window and
'''
wx.Frame.__init__(self,parent,id,name,size=size)
self.CenterOnScreen(wx.HORIZONTAL)
self.timing_window = None # Initialize timing window
# ------------- Face Processing -----------------
self.face_detector = CascadeDetector(cascade_name=CASCADE_NAME,image_scale=0.5)
self.fel = FilterEyeLocator(FEL_NAME)
self.face_rec = SVMFaceRec()
self.svm_mode = SVM_AUTOMATIC
self.svm_C = 4.000e+00
self.svm_Gamma = 9.766e-04
self.current_faces = []
self.enrolling = None
self.enroll_count = 0
self.enroll_max = 32
self.enroll_list = []
self.previous_time = time.time()
self.arialblack24 = PIL.ImageFont.truetype(ARIAL_BLACK_NAME, 24)
# ---------------- Basic Data -------------------
try:
self.webcam = Webcam()
except SystemExit:
raise
except:
trace = traceback.format_exc()
message = TraceBackDialog(None, "Camera Error", CAMERA_ERROR, trace)
message.ShowModal()
sys.stderr.write("FaceL Error: an error occurred while trying to connect to the camera. Details follow.\n\n")
sys.stderr.write(trace)
sys.exit(CAMERA_ERROR_CODE)
# ------------- Other Components ----------------
self.CreateStatusBar()
# ------------------- Menu ----------------------
# Creating the menubar.
# Menu IDs
license_id = wx.NewId()
mirror_id = wx.NewId()
face_id = wx.NewId()
svm_tune_id = wx.NewId()
performance_id = wx.NewId()
# Menu Items
self.file_menu = wx.Menu();
self.file_menu.Append( wx.ID_ABOUT, "&About..." )
self.file_menu.Append( license_id, "FaceL License..." )
self.file_menu.AppendSeparator();
self.file_menu.Append( wx.ID_EXIT, "E&xit" )
self.options_menu = wx.Menu();
self.face_menuitem = self.options_menu.AppendCheckItem( face_id, "Face Processing" )
self.eye_menuitem = self.options_menu.AppendCheckItem( face_id, "Eye Detection" )
self.mirror_menuitem = self.options_menu.AppendCheckItem( mirror_id, "Mirror Video" )
self.options_menu.AppendSeparator()
self.options_menu.Append( svm_tune_id, "SVM Tuning..." )
self.options_menu.Append( performance_id, "Performance..." )
# Create Menu Bar
self.menu_bar = wx.MenuBar();
self.menu_bar.Append( self.file_menu, "&File" )
self.menu_bar.Append( self.options_menu, "&Options" )
self.SetMenuBar( self.menu_bar )
# Menu Events
wx.EVT_MENU(self, wx.ID_ABOUT, self.onAbout )
wx.EVT_MENU(self, license_id, self.onLicense )
wx.EVT_MENU(self, mirror_id, self.onNull )
wx.EVT_MENU(self, face_id, self.onNull )
wx.EVT_MENU(self, svm_tune_id, self.onSVMTune )
wx.EVT_MENU(self, performance_id, self.onTiming )
# Set up menu checks
self.face_menuitem.Check(True)
self.eye_menuitem.Check(True)
self.mirror_menuitem.Check(True)
# ----------------- Image List ------------------
# --------------- Image Display -----------------
self.static_bitmap = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.EmptyBitmap(640, 480))
self.controls_box = wx.StaticBox(self, wx.NewId(), "Controls")
self.facel_logo = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.Bitmap(FACEL_LOGO))
self.csu_logo = wx.StaticBitmap(self,wx.NewId(), bitmap=wx.Bitmap(CSU_LOGO))
# self.performance_box = wx.StaticBox(self, wx.NewId(), "Performance")
self.enroll_chioce_label = wx.StaticText(self, wx.NewId(), "Enrollment Count:", style=wx.ALIGN_LEFT)
self.enroll_choice = wx.Choice(self,wx.NewId(),wx.Point(0,0),wx.Size(-1,-1),['16','32','48','64','128','256'])
self.enroll_choice.Select(3)
self.train_button = wx.Button(self,wx.NewId(),'Train Labeler')
self.reset_button = wx.Button(self,wx.NewId(),'Clear Labels')
# --------------- Instrumentation ---------------
self.enroll_label = wx.StaticText(self, wx.NewId(), "Click a face in the video to enroll.", style=wx.ALIGN_LEFT)
self.ids_label = wx.StaticText(self, wx.NewId(), "Labels:", size=wx.Size(-1,16), style=wx.ALIGN_LEFT)
self.ids_text = wx.StaticText(self, wx.NewId(), size = wx.Size(30,16), style= wx.ALIGN_RIGHT )
self.faces_label = wx.StaticText(self, wx.NewId(), "Faces:", size=wx.Size(-1,16), style=wx.ALIGN_LEFT)
self.faces_text = wx.StaticText(self, wx.NewId(), size = wx.Size(30,16), style= wx.ALIGN_RIGHT )
# --------------- Window Layout -----------------
enroll_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_sizer.Add(self.ids_label, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.Add(self.ids_text, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.AddSpacer(20)
enroll_sizer.Add(self.faces_label, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_sizer.Add(self.faces_text, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
training_sizer = wx.BoxSizer(wx.HORIZONTAL)
training_sizer.Add(self.train_button, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
training_sizer.Add(self.reset_button, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
enroll_choice_sizer = wx.BoxSizer(wx.HORIZONTAL)
enroll_choice_sizer.Add(self.enroll_chioce_label, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
enroll_choice_sizer.Add(self.enroll_choice, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
controls_sizer = wx.StaticBoxSizer(self.controls_box,wx.VERTICAL) #wx.BoxSizer(wx.VERTICAL)
controls_sizer.Add(self.enroll_label, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
controls_sizer.Add(enroll_choice_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=4)
controls_sizer.Add(training_sizer, flag = wx.ALIGN_LEFT | wx.ALL, border=0)
bottom_sizer = wx.BoxSizer(wx.HORIZONTAL)
bottom_sizer.Add(self.facel_logo, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
bottom_sizer.Add(controls_sizer, flag = wx.ALIGN_TOP | wx.ALL, border=4)
bottom_sizer.Add(self.csu_logo, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
main_sizer = wx.BoxSizer(wx.VERTICAL)
main_sizer.Add(self.static_bitmap, flag = wx.ALIGN_CENTER | wx.ALL, border=0)
main_sizer.Add(bottom_sizer, flag = wx.ALIGN_CENTER | wx.ALL, border=4)
self.SetAutoLayout(True)
self.SetSizer(main_sizer)
self.Layout()
# -----------------------------------------------
self.timer = FrameTimer(self)
self.timer.Start(200)
# -------------- Event Handleing ----------------
wx.EVT_SIZE(self.static_bitmap, self.onBitmapResize)
wx.EVT_LEFT_DOWN(self.static_bitmap, self.onClick)
self.Bind(wx.EVT_BUTTON, self.onTrain, id=self.train_button.GetId())
self.Bind(wx.EVT_BUTTON, self.onReset, id=self.reset_button.GetId())
# --------------- Setup State -------------------
self.setupState()
import pyvision as pv
import cv2
from pyvision.face.CascadeDetector import CascadeDetector
from pyvision.face.FilterEyeLocator import FilterEyeLocator
def mouseCallback(event, x, y, flags, param):
if event in [cv2.EVENT_LBUTTONDOWN,cv2.EVENT_LBUTTONUP]:
print("Mouse Event:",event,x,y)
if __name__ == '__main__':
# Setup the webcam
webcam = pv.Webcam(size=(640,360))
# Setup the face and eye detectors
cd = CascadeDetector(min_size=(100,100))
el = FilterEyeLocator()
# Setup the mouse callback to handle mause events (optional)
cv2.namedWindow("PyVision Live Demo")
cv2.setMouseCallback("PyVision Live Demo", mouseCallback)
while True:
# Grab a frame from the webcam
frame = webcam.query()
# Run Face and Eye Detection
rects = cd(frame)
eyes = el(frame,rects)
# Annotate the result
# Open the file to use as output.
f = open(args[1],'wb')
csv_file = csv.writer(f)
headers = ['image_name','detect_number','detect_x','detect_y','detect_width','detect_height','eye1_x','eye1_y','eye2_x','eye2_y']
csv_file.writerow(headers)
# Create an image log if this is being saved to a file.
ilog = None
if options.log_dir != None:
print("Creating Image Log...")
ilog = pv.ImageLog(options.log_dir)
# For each image run face and eye detection
face_detect = CascadeDetector(image_scale=1.3*options.scale)
locate_eyes = FilterEyeLocator()#locator_filename)
c = 0
for pathname in image_names:
c += 1
im = pv.Image(pathname)
scale = options.log_scale
log_im = pv.AffineScale(scale,(int(scale*im.width),int(scale*im.height))).transformImage(im)
results = processFaces(im,face_detect,locate_eyes)
