class UnitTestMtcnnFaceDetector(tf.test.TestCase):
def setUp(self):
tf.reset_default_graph()
with self.test_session() as sess:
self.detector = MtcnnFaceDetector(sess, )
self.image = TestUtils.get_image_and_bbox("one_face.jpg")["image"]
def tearDown(self):
pass
def test_should_detect_one_face(self):
bboxes, face_landmarks, scores, dnorm_scores = self.detector.detect(self.image)
self.assertEqual(len(bboxes), 1)
def test_should_not_detect_a_face(self):
zero_image = np.zeros(self.image.shape)
bboxes, face_landmarks, scores, dnorm_scores = self.detector.detect(zero_image)
self.assertEqual(len(bboxes), 0)
def test_should_crop_a_face(self):
bboxes, face_landmarks, scores, dnorm_scores = self.detector.detect(self.image)
raw_faces = self.detector.crop_faces(self.image, bboxes, False)
square_faces = self.detector.crop_faces(self.image, bboxes, True)
self.assertEqual(len(bboxes), len(raw_faces))
self.assertEqual(len(bboxes), len(square_faces))
def test_should_initialize_with_internal_session(self):
_ = MtcnnFaceDetector()
def test_should_work_with_two_instances(self):
_ = MtcnnFaceDetector()
_ = MtcnnFaceDetector()
def test_nms_from_pnet(self):
total_boxes = np.empty((0, 9))
with self.test_session() as sess:
detector = MtcnnFaceDetector(sess)
scale = SCALES_ASSET[-1]
hs = int(np.ceil(self.image.shape[0] * scale))
ws = int(np.ceil(self.image.shape[1] * scale))
im = cv2.resize(self.image, (ws, hs), interpolation=cv2.INTER_AREA)
boxes = _run_Pnet_one_scale(detector.pnet, im, scale, 0.6)
total_boxes = np.append(total_boxes, boxes, axis=0)
filtered_boxes = nms(total_boxes.copy(), 0.5, 'Union')
self.assertEqual(filtered_boxes.shape[0], 1)
def test_run_nets(self):
with self.test_session() as sess:
detector = MtcnnFaceDetector(sess)
boxes_Pnet, proposals_batch = _run_Pnet(detector.pnet, self.image, SCALES_ASSET, 0.6)
self.assertEqual(proposals_batch.shape, (24, 24, 3, 43))
boxes_RNet, refined_proposals_batch = _run_Rnet(detector.rnet, boxes_Pnet, self.image, proposals_batch, 0.7)
self.assertEqual(refined_proposals_batch.shape, (48, 48, 3, 3))
boxes_ONet, landmarks, features = _run_Onet(detector.onet, boxes_RNet, refined_proposals_batch, 0.9)
self.assertEqual(landmarks.shape, (10, 1))
self.assertEqual(features.shape, (1, 256))
def test_run_pnet_one_scale(self):
scale = SCALES_ASSET[-1]
hs = int(np.ceil(self.image.shape[0] * scale))
ws = int(np.ceil(self.image.shape[1] * scale))
print("Scale h: ", hs)
im = cv2.resize(self.image, (ws, hs), interpolation=cv2.INTER_AREA)
with self.test_session() as sess:
detector = MtcnnFaceDetector(sess)
boxes = _run_Pnet_one_scale(detector.pnet, im, scale, 0.6)
self.assertEqual(boxes.shape[0], 5)
self.assertEqual(boxes.shape[1], 9)
self.assertArrayNear(boxes[0, :], PNET_BOX_0, 0.000001)
def test_should_work_with_two_instances(self):
_ = MtcnnFaceDetector()
_ = MtcnnFaceDetector()
def test_should_initialize_with_internal_session(self):
_ = MtcnnFaceDetector()
def setUp(self):
tf.reset_default_graph()
with self.test_session() as sess:
self.detector = MtcnnFaceDetector(sess, )
self.image = TestUtils.get_image_and_bbox("one_face.jpg")["image"]