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)
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 test_ASEFEyeLocalization(self):
'''
This trains the FaceFinder on the scraps database.
'''
# Load a face database
ssdb = ScrapShotsDatabase()
# Create a face detector
face_detector = cd.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():
face = ssdb[face_id]
im = face.image
# Detect the faces
faces = face_detector.detect(im)
# Detect the eyes
pred_eyes = eye_locator(im,faces)
truth_eyes = [[face.left_eye,face.right_eye]]
pred_eyes = [ [leye,reye] for _,leye,reye in pred_eyes]
# Add to eye detection test
edt.addSample(truth_eyes, pred_eyes, im=im, annotate=False)
edt.createSummary()
self.assertAlmostEqual( edt.face_rate , 0.97109826589595372, places = 3 ) # Updated numbers for OpenCV 2.0
self.assertAlmostEqual( edt.both25_rate , 0.82658959537572252, places = 3 )
self.assertAlmostEqual( edt.both10_rate , 0.47976878612716761, places = 3 )
self.assertAlmostEqual( edt.both05_rate , 0.30635838150289019, places = 3 )
def test_ASEFEyeLocalization(self):
'''
This trains the FaceFinder on the scraps database.
'''
# Load a face database
ssdb = ScrapShotsDatabase()
# Create a face detector
face_detector = cd.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():
face = ssdb[face_id]
im = face.image
# Detect the faces
faces = face_detector.detect(im)
# Detect the eyes
pred_eyes = eye_locator(im,faces)
truth_eyes = [[face.left_eye,face.right_eye]]
pred_eyes = [ [leye,reye] for _,leye,reye in pred_eyes]
# Add to eye detection test
edt.addSample(truth_eyes, pred_eyes, im=im, annotate=False)
edt.createSummary()
self.assertAlmostEqual( edt.face_rate , 0.97109826589595372, delta = 0.01 ) # Updated numbers for OpenCV 2.0
self.assertAlmostEqual( edt.both25_rate , 0.82658959537572252, delta = 0.01 )
self.assertAlmostEqual( edt.both10_rate , 0.47976878612716761, delta = 0.01 )
self.assertAlmostEqual( edt.both05_rate , 0.30635838150289019, delta = 0.01 )
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)