def test_3_EigenFaceCovariance(self):
'''Eigenfaces: Stddev of non whitened training vectors..............'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps(whiten=False)
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
matrix = []
for face_id in keys:
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
fr = ef.getFaceRecord(face.image, face.face_rect, face.left_eye,
face.right_eye)
matrix.append(fr.eigenfaces_feature)
matrix = np.array(matrix)
vals = matrix.std(axis=0).flatten()
self.assertEquals(len(vals), 53)
self.assert_(np.abs(vals - 1.0).max() > 0.0001)
for i in range(len(vals)):
# Make sure that the eigenvalues are an estimate of the variance
self.assertAlmostEquals(vals[i],
np.sqrt(ef.eigenvalues[i]),
places=4)
def test_2_EigenFaceWhiten(self):
'''Eigenfaces: Stddev of whitened training vectors == 1.0...........'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps()
# TODO: Double check. Remove these later
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
matrix = []
for face_id in keys:
#print "Processing:",face_id
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
fr = ef.getFaceRecord(face.image, face.face_rect, face.left_eye,
face.right_eye)
matrix.append(fr.eigenfaces_feature)
#print matrix
matrix = np.array(matrix)
vals = matrix.std(axis=0)
self.assertEquals(len(vals), 53)
self.assert_(np.abs(vals - 1.0).max() < 0.0001)
def test_3_EigenFaceCovariance(self):
'''Eigenfaces: Stddev of non whitened training vectors..............'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps(whiten=False)
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
matrix = []
for face_id in keys:
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
fr = ef.getFaceRecord(face.image,face.face_rect,face.left_eye,face.right_eye)
matrix.append(fr.eigenfaces_feature)
matrix = np.array(matrix)
vals = matrix.std(axis=0).flatten()
self.assertEquals(len(vals),53)
self.assert_(np.abs(vals-1.0).max()>0.0001)
for i in range(len(vals)):
# Make sure that the eigenvalues are an estimate of the variance
self.assertAlmostEquals(vals[i],np.sqrt(ef.eigenvalues[i]),places=4)
def test_2_EigenFaceWhiten(self):
'''Eigenfaces: Stddev of whitened training vectors == 1.0...........'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps()
# TODO: Double check. Remove these later
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
matrix = []
for face_id in keys:
#print "Processing:",face_id
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
fr = ef.getFaceRecord(face.image,face.face_rect,face.left_eye,face.right_eye)
matrix.append(fr.eigenfaces_feature)
#print matrix
matrix = np.array(matrix)
vals = matrix.std(axis=0)
self.assertEquals(len(vals),53)
self.assert_(np.abs(vals-1.0).max()<0.0001)
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 trainScraps(self,whiten=True,ilog=None):
# Load a face database
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
# Create an eigenfaces instance
ef = Eigenfaces(whiten=whiten)
for face_id in keys:
#print "Processing:",face_id
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
ef.addTraining(face.image,face.face_rect,face.left_eye,face.right_eye,ilog=ilog)
ef.train(ilog=ilog)
return ef
def test_5_EigenfacesScrapShotsIdentNonWhitened(self):
'''Eigenfaces: Scrapshot nonwhitened rate == 0.46 at FAR == 1/10...'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps(whiten=False)
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
face_ids = []
face_records = []
for face_id in keys:
face = ssdb[face_id]
if face.person_id not in self.scraps_testing:
continue
fr = ef.getFaceRecord(face.image, face.face_rect, face.left_eye,
face.right_eye)
face_records.append(fr)
face_ids.append(face_id)
sim_matrix = ef.similarityMatrix(face_records, face_records)
match_scores = []
nonmatch_scores = []
for i in range(1, len(face_ids)):
for j in range(i + 1, len(face_ids)):
i_id = face_ids[i]
j_id = face_ids[j]
if i_id[:5] == j_id[:5]:
match_scores.append(sim_matrix[i, j])
else:
nonmatch_scores.append(sim_matrix[i, j])
roc = ROC(match_scores, nonmatch_scores, is_distance=False)
roc_point = roc.getFAR(1.0 / 10.0)
self.assertAlmostEqual(roc_point.frr, 0.53714285714285714, places=2)
def test_5_EigenfacesScrapShotsIdentNonWhitened(self):
'''Eigenfaces: Scrapshot nonwhitened rate == 0.46 at FAR == 1/10...'''
ilog = None
if 'ilog' in globals().keys():
ilog = globals()['ilog']
ef = self.trainScraps(whiten=False)
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
face_ids = []
face_records = []
for face_id in keys:
face = ssdb[face_id]
if face.person_id not in self.scraps_testing:
continue
fr = ef.getFaceRecord(face.image,face.face_rect,face.left_eye,face.right_eye)
face_records.append(fr)
face_ids.append(face_id)
sim_matrix = ef.similarityMatrix(face_records,face_records)
match_scores = []
nonmatch_scores = []
for i in range(1,len(face_ids)):
for j in range(i+1,len(face_ids)):
i_id = face_ids[i]
j_id = face_ids[j]
if i_id[:5] == j_id[:5]:
match_scores.append(sim_matrix[i,j])
else:
nonmatch_scores.append(sim_matrix[i,j])
roc = ROC(match_scores,nonmatch_scores,is_distance=False)
roc_point = roc.getFAR(1.0/10.0)
self.assertAlmostEqual(roc_point.frr,0.53714285714285714,places=2)
def trainScraps(self, whiten=True, ilog=None):
# Load a face database
ssdb = ScrapShotsDatabase()
keys = list(ssdb.keys())
keys.sort()
# Create an eigenfaces instance
ef = Eigenfaces(whiten=whiten)
for face_id in keys:
#print "Processing:",face_id
face = ssdb[face_id]
if face.person_id not in self.scraps_training:
continue
ef.addTraining(face.image,
face.face_rect,
face.left_eye,
face.right_eye,
ilog=ilog)
ef.train(ilog=ilog)
return ef