예제 #1
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    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)
예제 #2
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파일: testsuite.py 프로젝트: bolme/pyvision
    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 )
예제 #3
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    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)
예제 #4
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    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 )

        
    
    
        
예제 #5
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    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 )

        
    
    
        
예제 #6
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    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)