def detections(self, face_data, rotation=None):
face_detector = FaceDetector(enable_tracking=False)
# for test image set this needs to be zero, in normal use it doesn't matter because of filtering
face_detector._FACE_DETECTOR_PYRAMID_LAYERS = 0
emotion_classifier = EmotionClassifier(apply_mean_filter=False)
if rotation == "cw":
expected_rotation_angle = -self._face_rotate_angle
elif rotation == "ccw":
expected_rotation_angle = self._face_rotate_angle
else:
expected_rotation_angle = 0
for i, (frame, width) in enumerate(face_data):
face = face_detector(frame)
self.face_assertions(
face=face,
expected_rotation_angle=expected_rotation_angle,
width=width)
emotion = emotion_classifier(face)
self.emotion_assertions(emotion=emotion,
expected_emotion_data=emotion_data[i],
frame=frame)
def test_no_face(self):
face_detector = FaceDetector(enable_tracking=False)
# for test image set this needs to be zero, in normal use it doesn't matter because of filtering
face_detector._FACE_DETECTOR_PYRAMID_LAYERS = 0
emotion_classifier = EmotionClassifier(apply_mean_filter=False)
frame = self._blank_cv_frame.copy()
face = face_detector(frame)
self.assertFalse(face.found)
self.assertIsNone(face.center)
self.assertIsNone(face.angle)
self.assertIsNone(face.features)
self.assertIsNone(face.rectangle)
self.assertIsNone(face.mouth_dimensions)
self.assertIsNone(face.left_eye_dimensions)
self.assertIsNone(face.right_eye_dimensions)
self.assertIsNone(face.left_eye_center)
self.assertIsNone(face.right_eye_center)
self.assertIsNone(face.nose_bottom)
self.assertIsNone(face.pupil_distance)
self.assertEqual(face.robot_view.shape[0], self._height)
self.assertEqual(face.robot_view.shape[1], self._width)
# check nothing has been drawn to robot view
comparison = face.robot_view == frame
self.assertTrue(comparison.all())
emotion = emotion_classifier(face)
self.assertIsNone(emotion.type)
self.assertEqual(emotion.confidence, 0.0)
self.assertIsInstance(emotion.robot_view, np.ndarray)
# check nothing has been drawn to robot view
comparison = emotion.robot_view == frame
self.assertTrue(comparison.all())
def test_calculated_face_data_68_landmark(self):
face_detector = FaceDetector(enable_tracking=False)
# for test image set this needs to be zero, in normal use it doesn't matter because of filtering
face_detector._FACE_DETECTOR_PYRAMID_LAYERS = 0
test_image = self._face_image_data[0][0]
face = face_detector(test_image)
self.assertAlmostEqual(face.angle, -0.5, delta=0.1)
self.assertAlmostEqual(face.pupil_distance, 112.0, delta=3)
x, y = face.left_eye_center
self.assertAlmostEqual(x, 371, delta=3)
self.assertAlmostEqual(y, 197, delta=3)
x, y = face.right_eye_center
self.assertAlmostEqual(x, 259, delta=3)
self.assertAlmostEqual(y, 195, delta=3)
w, h = face.left_eye_dimensions
self.assertAlmostEqual(w, 42.2, delta=3)
self.assertAlmostEqual(h, 18.1, delta=3)
w, h = face.right_eye_dimensions
self.assertAlmostEqual(w, 44.2, delta=3)
self.assertAlmostEqual(h, 18.3, delta=3)
x, y = face.mouth_center
self.assertAlmostEqual(x, 312, delta=3)
self.assertAlmostEqual(y, 311, delta=3)
w, h = face.mouth_dimensions
self.assertAlmostEqual(w, 88.0, delta=3)
self.assertAlmostEqual(h, 24.1, delta=3)
x, y = face.nose_bottom
self.assertAlmostEqual(x, 314, delta=3)
self.assertAlmostEqual(y, 272, delta=3)
def test_calculated_face_data_5_landmark(self):
face_detector = FaceDetector(
dlib_landmark_predictor_filename=
"shape_predictor_5_face_landmarks.dat",
enable_tracking=False,
)
# for test image set this needs to be zero, in normal use it doesn't matter because of filtering
face_detector._FACE_DETECTOR_PYRAMID_LAYERS = 0
test_image = self._face_image_data[0][0]
face = face_detector(test_image)
self.assertAlmostEqual(face.angle, -1.1, delta=1.0)
self.assertAlmostEqual(face.pupil_distance, 108.0, delta=3)
x, y = face.left_eye_center
self.assertAlmostEqual(x, 371, delta=3)
self.assertAlmostEqual(y, 197, delta=3)
x, y = face.right_eye_center
self.assertAlmostEqual(x, 259, delta=3)
self.assertAlmostEqual(y, 195, delta=3)
w, h = face.left_eye_dimensions
self.assertAlmostEqual(w, 42.2, delta=3)
self.assertIsNone(h)
w, h = face.right_eye_dimensions
self.assertAlmostEqual(w, 44.2, delta=3)
self.assertIsNone(h)
self.assertRaises(ValueError, lambda: face.mouth_center)
self.assertRaises(ValueError, lambda: face.mouth_dimensions)
x, y = face.nose_bottom
self.assertAlmostEqual(x, 314, delta=3)
self.assertAlmostEqual(y, 276, delta=3)
from signal import pause
import cv2
from pitop import Camera
from pitop.processing.algorithms.faces import FaceDetector
def find_faces(frame):
face = face_detector(frame)
robot_view = face.robot_view
cv2.imshow("Faces", robot_view)
cv2.waitKey(1)
if face.found:
print(f"Face angle: {face.angle} \n"
f"Face center: {face.center} \n"
f"Face rectangle: {face.rectangle} \n")
else:
print("Cannot find face!")
camera = Camera(resolution=(640, 480), flip_top_bottom=True)
face_detector = FaceDetector()
camera.on_frame = find_faces
pause()