def fourier_draw(self):
arr = image_to_array(self.draw_buffer.toImage())
values = arr[arr == self.color.red()] * (self.scale_factor / 255)
self.raw_fourier[arr == self.color.red()] = np.abs(2**values)
self.emit_fourier()
def add_noise(self):
dialog = NoiseDialog()
if not dialog.exec_():
return
function = dialog.painter_function
image = self.image_label.pixmap().toImage()
painter = QPainter(image)
function(painter, QColor(0, 0, 0))
self.update(image_to_array(image))
del painter
def add_noise(self):
dialog = NoiseDialog()
if not dialog.exec_():
return
function = dialog.painter_function
image = self.image_label.pixmap().toImage()
painter = QPainter(image)
function(painter, QColor(0, 0, 0))
self.update(image_to_array(image))
del painter
def get_face_landmarks(self, image):
"""Compute 68 facial landmarks"""
landmarks = []
image_array = image_to_array(image)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(self.predictor_path)
face_rectangles = detector(image_array)
if len(face_rectangles) < 1:
return None
dlib_shape = predictor(image_array, face_rectangles[0])
for i in range(0, dlib_shape.num_parts):
landmarks.append([dlib_shape.part(i).x, dlib_shape.part(i).y])
return landmarks
def get_keypoints(image: Image) -> Optional[Dict]:
# run inference to detect faces (on CPU only)
with tf.device("/cpu:0"):
detection = detector.detect_faces(image_to_array(image))
# run detection and keep results with certain confidence only
results = [
item for item in detection
if item['confidence'] > minimal_confidence
]
# nothing found
if len(results) == 0:
return None
# return result with highest confidence and size
return max(results,
key=lambda item: item['confidence'] * item['box'][2] * item[
'box'][3])
def load_file(self, file_name):
array = image_to_array(QImage(file_name), to_gray=True)
if array is None:
return
self.update(array.copy())
self.setWindowTitle(os.path.basename(file_name))
def load_file(self, file_name):
array = image_to_array(QImage(file_name), to_gray=True)
if array is None:
return
self.update(array.copy())
self.setWindowTitle(os.path.basename(file_name))
def load_file(self, file_name):
array = image_to_array(QImage(file_name), to_gray=True)
self.update(array)
self.setWindowTitle(os.path.basename(file_name))
def load_file(self, file_name):
array = image_to_array(QImage(file_name), to_gray=True)
self.update(array)
self.setWindowTitle(os.path.basename(file_name))