def test_predict_return_data_types(self):
self.test_engine.fit([self.bubbles1], [0])
image = imread(self.bubbles1)
bbs, extra = self.test_engine.find_faces(image, limit=1)
embeddings = self.test_engine.compute_embeddings(image, bbs, **extra)
data = self.test_engine.predict(embeddings)
self.assertEqual(len(data), 2)
self.assertIsInstance(data, tuple)
self.assertIsInstance(data[0], list)
self.assertIsInstance(data[1], list)
def get_face_dataset(dir, engine, shuffle=False):
"""Generator of FaceImages"""
from face_engine.exceptions import FaceNotFoundError
from face_engine.tools import imread
dataset = get_image_dataset(dir, shuffle)
if shuffle:
np.random.shuffle(dataset)
for image in dataset:
bounding_boxes = []
embeddings = []
image_paths = image.image_paths
for i, image_path in enumerate(image_paths):
img = imread(image_path)
try:
bounding_box = engine.find_face(img)[1]
bounding_boxes.append(bounding_box)
embeddings.append(engine.compute_embedding(img, bounding_box))
except FaceNotFoundError:
del image_paths[i]
continue
yield FaceImage(image.name, image_paths, bounding_boxes, embeddings)
def test_compute_embeddings_vector_dimension(self):
bubbles1 = imread(self.bubbles1)
bbs, _ = self.test_engine.find_faces(bubbles1)
embeddings = self.test_engine.compute_embeddings(bubbles1, bbs)
self.assertEqual(embeddings.size,
self.test_engine._embedder.embedding_dim)
def test_find_faces_with_image_content(self):
content = imread(self.family)
data = self.test_engine.find_faces(content)
self.assertIsNotNone(data)
def test_predict_before_fit_raises_train_error(self):
image = imread(self.bubbles1)
bbs, extra = self.test_engine.find_faces(image)
embeddings = self.test_engine.compute_embeddings(image, bbs, **extra)
with self.assertRaises(TrainError):
self.test_engine.predict(embeddings)
def setUp(self):
self.image = imread(self.bubbles1)
self.bbs = np.array([[278, 132, 618, 471]])
self.embedder = Embedder()
def test_detect_raises_face_not_found_error(self):
with self.assertRaises(FaceNotFoundError):
self.detector.detect(imread(self.book_stack))
def test_detect_return_multiple_bbs(self):
bbs, _ = self.detector.detect(imread(self.family))
self.assertGreater(len(bbs), 1)
def test_detect_return_bbs_type(self):
bbs, _ = self.detector.detect(imread(self.bubbles1))
self.assertIsInstance(bbs, np.ndarray)
def test_detect_return_data_is_tuple(self):
data = self.detector.detect(imread(self.bubbles1))
self.assertIsNotNone(data)