def overlay_example():
detector_data_path = '../../data/shape_predictor_68_face_landmarks.dat'
landmark_detector = LandmarkDetector(detector_data_path)
base_component = LandmarkComponent(landmark_detector)
hat = AllFaces.wrap(SpriteDecorator(base_component,
'../sprites/Party_Hat'))
glasses = AllFaces.wrap(SpriteDecorator(hat, '../sprites/sunglasses'))
use_webcam = False
video_stream = cv2.VideoCapture("../../videos/trump.mp4")
if use_webcam:
video_stream = cv2.VideoCapture(0)
while True:
ret, frame = video_stream.read()
frame = cv2.resize(frame, (0, 0), fx=0.6, fy=0.6)
if use_webcam:
frame = cv2.flip(frame, 1)
base_component.set_image(frame)
output = glasses.get_image()
output = cv2.resize(output, (0, 0), fx=2.0, fy=2.0)
cv2.imshow("Frame", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
video_stream.release()
def predicting_example():
pygame.init()
# Instanciate a new landmark detector
detector_data_path = '../../data/shape_predictor_68_face_landmarks.dat'
landmark_detector = LandmarkDetector(predictor_file=detector_data_path)
# Create a frame component with landmarks
base_component = LandmarkComponent(landmark_detector)
# Instantiate and initialize the trained predictor
predictor = PredictorInterface('../examples/mouth.pkl')
predictor.initialize()
landmarks = LandmarkOverlayDecorator(base_component)
# Add decorator for the predictor
predictor_decorator = ClassDecorator(parent_component=landmarks,
classifier=predictor)
sound = SoundDecorator(landmarks, "../sounds/boring.wav")
predictor_decorator.set_decorator_for_class(sound, 0)
multifaces = AllFaces.wrap(predictor_decorator)
hat = AllFaces.wrap(SpriteDecorator(multifaces, '../sprites/Party_Hat'))
use_webcam = True
video_stream = cv2.VideoCapture("../../videos/trump.mp4")
if use_webcam:
video_stream = cv2.VideoCapture(0)
while True:
ret, frame = video_stream.read()
frame = cv2.resize(frame, (0, 0), fx=1.0, fy=1.0)
if use_webcam:
frame = cv2.flip(frame, 1)
base_component.set_image(frame)
output = hat.get_image()
output = cv2.resize(output, (0, 0), fx=1.0, fy=1.0)
cv2.imshow("Frame", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
video_stream.release()
def predicting_example():
pygame.init()
# Instanciate a new landmark detector
detector_data_path = '../../data/shape_predictor_68_face_landmarks.dat'
landmark_detector = LandmarkDetector(predictor_file=detector_data_path)
# Create a frame component with landmarks
base_component = LandmarkComponent(landmark_detector)
# Instantiate and initialize the trained predictor
predictor = PredictorInterface('../examples/mouth.pkl')
predictor.initialize()
landmarks = LandmarkOverlayDecorator(base_component)
# Add decorator for the predictor
predictor_decorator = ClassDecorator(parent_component=landmarks, classifier=predictor)
sound = SoundDecorator(landmarks, "../sounds/boring.wav")
predictor_decorator.set_decorator_for_class(sound, 0)
multifaces = AllFaces.wrap(predictor_decorator)
hat = AllFaces.wrap(SpriteDecorator(multifaces, '../sprites/Party_Hat'))
use_webcam = True
video_stream = cv2.VideoCapture("../../videos/trump.mp4")
if use_webcam:
video_stream = cv2.VideoCapture(0)
while True:
ret, frame = video_stream.read()
frame = cv2.resize(frame, (0, 0), fx=1.0, fy=1.0)
if use_webcam:
frame = cv2.flip(frame, 1)
base_component.set_image(frame)
output = hat.get_image()
output = cv2.resize(output, (0, 0), fx=1.0, fy=1.0)
cv2.imshow("Frame", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
video_stream.release()
def predicting_example():
# Instanciate a new landmark detector
detector_data_path = '../../data/shape_predictor_68_face_landmarks.dat'
landmark_detector = LandmarkDetector(predictor_file=detector_data_path)
# Create a frame component with landmarks
base_component = LandmarkComponent(landmark_detector)
# Instantiate and initialize the trained predictor
predictor = PredictorInterface('../examples/blink.pkl')
predictor.initialize()
# Add decorator for the predictor
predictor_decorator = ClassDecorator(parent_component=base_component,
classifier=predictor)
sunglasses = SpriteDecorator(base_file_name='../sprites/sunglasses')
eyes = SpriteDecorator(base_file_name='../sprites/eyes')
predictor_decorator.set_decorator_for_class(sunglasses, 0)
predictor_decorator.set_decorator_for_class(eyes, 1)
multifaces = AllFaces.wrap(predictor_decorator)
use_webcam = False
video_stream = cv2.VideoCapture("../../videos/trump.mp4")
if use_webcam:
video_stream = cv2.VideoCapture(0)
while True:
ret, frame = video_stream.read()
frame = cv2.resize(frame, (0, 0), fx=0.6, fy=0.6)
if use_webcam:
frame = cv2.flip(frame, 1)
base_component.set_image(frame)
output = multifaces.get_image()
output = cv2.resize(output, (0, 0), fx=2.0, fy=2.0)
cv2.imshow("Frame", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
video_stream.release()
def predicting_example():
# Instanciate a new landmark detector
detector_data_path = '../../data/shape_predictor_68_face_landmarks.dat'
landmark_detector = LandmarkDetector(predictor_file=detector_data_path)
# Create a frame component with landmarks
base_component = LandmarkComponent(landmark_detector)
# Instantiate and initialize the trained predictor
predictor = PredictorInterface('../examples/blink.pkl')
predictor.initialize()
# Add decorator for the predictor
predictor_decorator = ClassDecorator(parent_component=base_component, classifier=predictor)
sunglasses = SpriteDecorator(base_file_name='../sprites/sunglasses')
eyes = SpriteDecorator(base_file_name='../sprites/eyes')
predictor_decorator.set_decorator_for_class(sunglasses, 0)
predictor_decorator.set_decorator_for_class(eyes, 1)
multifaces = AllFaces.wrap(predictor_decorator)
use_webcam = False
video_stream = cv2.VideoCapture("../../videos/trump.mp4")
if use_webcam:
video_stream = cv2.VideoCapture(0)
while True:
ret, frame = video_stream.read()
frame = cv2.resize(frame, (0, 0), fx=0.6, fy=0.6)
if use_webcam:
frame = cv2.flip(frame, 1)
base_component.set_image(frame)
output = multifaces.get_image()
output = cv2.resize(output, (0, 0), fx=2.0, fy=2.0)
cv2.imshow("Frame", output)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
video_stream.release()
class TestDecorator(ImageTestCase):
@classmethod
def setUpClass(cls):
detector_data_path = '../data/shape_predictor_68_face_landmarks.dat'
cls.landmark_detector = LandmarkDetector(detector_data_path)
def setUp(self):
self.backdrop = cv2.imread('../tests/test_backdrop.jpg')
self.hat = AttachedOverlayDecorator.construct_from_graphic_path(
'../sprites/Party_Hat.png')
self.base_component = CachingLandmarkComponent(
TestDecorator.landmark_detector)
self.base_component.set_image(self.backdrop)
def test_overlay_landmarks(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
result = landmark_overlay.get_image()
self.assertUnalteredImage(result)
def test_move_sprite(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_angle_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.angle_offset = 90
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_perpendicular_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -50
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_parallel_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.parallel_offset = 30.5
self.expected_landmark_distance = 300.0
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_scale(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.expected_landmark_distance = 600.0
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_multifaces(self):
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -20
self.hat.expected_landmark_distance = 300.0
multifaces = MultiFacesLoopDecorator(self.base_component)
self.hat.parent = multifaces.get_start_of_loop_component()
multifaces.set_end_of_loop_component(self.hat)
result = multifaces.get_image()
self.assertUnalteredImage(result)
def test_multifaces_wrap(self):
self.hat.parent = self.base_component
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -20
self.hat.expected_landmark_distance = 300.0
multifaces = MultiFacesLoopDecorator.wrap(self.hat)
result = multifaces.get_image()
self.assertUnalteredImage(result)
def test_forgot_input(self):
base_component = CachingLandmarkComponent(
TestDecorator.landmark_detector)
base_component.get_landmarks()
def set_up_classification_decorator(self):
self.classification_decorator = ClassificationDecorator(
self.base_component)
self.classifier = ConstantClassifier()
first_class_decorator = self.hat
second_class_decorator = LandmarkOverlayDecorator()
self.classification_decorator.classifier = self.classifier
self.classification_decorator.set_decorator_for_class(
first_class_decorator, 0)
self.classification_decorator.set_decorator_for_class(
second_class_decorator, 1)
def test_classification_first_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = 0
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)
def test_classification_second_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = 1
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)
def test_classification_nonsense_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = "nonsense"
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)
class TestDecorator(ImageTestCase):
@classmethod
def setUpClass(cls):
detector_data_path = '../data/shape_predictor_68_face_landmarks.dat'
cls.landmark_detector = LandmarkDetector(detector_data_path)
def setUp(self):
self.backdrop = cv2.imread('../tests/test_backdrop.jpg')
self.hat = AttachedOverlayDecorator.construct_from_graphic_path('../sprites/Party_Hat.png')
self.base_component = CachingLandmarkComponent(TestDecorator.landmark_detector)
self.base_component.set_image(self.backdrop)
def test_overlay_landmarks(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
result = landmark_overlay.get_image()
self.assertUnalteredImage(result)
def test_move_sprite(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_angle_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.angle_offset = 90
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_perpendicular_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -50
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_parallel_offset(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.parallel_offset = 30.5
self.expected_landmark_distance = 300.0
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_scale(self):
landmark_overlay = LandmarkOverlayDecorator(self.base_component)
self.hat.parent = landmark_overlay
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.expected_landmark_distance = 600.0
result = self.hat.get_image()
self.assertUnalteredImage(result)
def test_multifaces(self):
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -20
self.hat.expected_landmark_distance = 300.0
multifaces = MultiFacesLoopDecorator(self.base_component)
self.hat.parent = multifaces.get_start_of_loop_component()
multifaces.set_end_of_loop_component(self.hat)
result = multifaces.get_image()
self.assertUnalteredImage(result)
def test_multifaces_wrap(self):
self.hat.parent = self.base_component
self.hat.sprite_anchors = ((25, 105), (60, 110))
self.hat.anchored_landmark_indexes = (21, 22)
self.hat.perpendicular_offset = -20
self.hat.expected_landmark_distance = 300.0
multifaces = MultiFacesLoopDecorator.wrap(self.hat)
result = multifaces.get_image()
self.assertUnalteredImage(result)
def test_forgot_input(self):
base_component = CachingLandmarkComponent(TestDecorator.landmark_detector)
base_component.get_landmarks()
def set_up_classification_decorator(self):
self.classification_decorator = ClassificationDecorator(self.base_component)
self.classifier = ConstantClassifier()
first_class_decorator = self.hat
second_class_decorator = LandmarkOverlayDecorator()
self.classification_decorator.classifier = self.classifier
self.classification_decorator.set_decorator_for_class(first_class_decorator, 0)
self.classification_decorator.set_decorator_for_class(second_class_decorator, 1)
def test_classification_first_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = 0
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)
def test_classification_second_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = 1
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)
def test_classification_nonsense_class(self):
self.set_up_classification_decorator()
self.classifier.prediction = "nonsense"
result = self.classification_decorator.get_image()
self.assertUnalteredImage(result)