def __init__(self, pil_image, scale):
self.pil_image = pil_image
self.cv_scale = scale
self.numpy_image = shrunken_numpy_image(pil_image, scale)
self.raw_face_locations = api._raw_face_locations(self.numpy_image)
self._largest_face_encoding = None
self._largest_face_location = None
def test_raw_face_locations_32bit_image(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', '32bit.png'))
detected_faces = api._raw_face_locations(img)
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0].top(), 290)
self.assertEqual(detected_faces[0].bottom(), 558)
def test_cnn_raw_face_locations_32bit_image(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', '32bit.png'))
detected_faces = api._raw_face_locations(img, model="cnn")
self.assertEqual(len(detected_faces), 1)
self.assertAlmostEqual(detected_faces[0].rect.top(), 259, delta=25)
self.assertAlmostEqual(detected_faces[0].rect.bottom(), 552, delta=25)
def test_cnn_raw_face_locations_32bit_image(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', '32bit.png'))
detected_faces = api._raw_face_locations(img, model="cnn")
self.assertEqual(len(detected_faces), 1)
self.assertAlmostEqual(detected_faces[0].rect.top(), 259, delta=25)
self.assertAlmostEqual(detected_faces[0].rect.bottom(), 552, delta=25)
def test_raw_face_locations(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
detected_faces = api._raw_face_locations(img)
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0].top(), 142)
self.assertEqual(detected_faces[0].bottom(), 409)
def test_raw_face_locations(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
detected_faces = api._raw_face_locations(img)
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0].top(), 142)
self.assertEqual(detected_faces[0].bottom(), 409)
def test_raw_face_locations_32bit_image(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', '32bit.png'))
detected_faces = api._raw_face_locations(img)
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0].top(), 290)
self.assertEqual(detected_faces[0].bottom(), 558)
def test_raw_face_locations_32bit_image(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), "test_images", "32bit.png"))
detected_faces = api._raw_face_locations(img)
assert len(detected_faces) == 1
assert detected_faces[0].top() == 290
assert detected_faces[0].bottom() == 558
def test_raw_face_locations(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), "test_images", "obama.jpg"))
detected_faces = api._raw_face_locations(img)
assert len(detected_faces) == 1
assert detected_faces[0].top() == 142
assert detected_faces[0].bottom() == 409
def run(cap, models, gamma, predictor):
# try:
# while(True):
ret, unknown_image = cap.read()
unknown_image = adjust_gamma(unknown_image, gamma)
# Искать прямоугольники (телефоны, рамки и т.п)
# Если лицо внутри рамки, то это подставочка
# log.info(find_phone(unknown_image))
# known_image = face_recognition.load_image_file("aaiCMWZqifg.jpg")
# known_image = imutils.resize(known_image, width=500)
# biden_encoding = face_encodings(known_image)[0]
# unknown_image = face_recognition.load_image_file("71lOOv_lN5A.jpg")
# unknown_image = imutils.resize(unknown_image, width=500)
_face_locations = _raw_face_locations(unknown_image)
unknown_encodings = face_encodings(unknown_image, face_locations=_face_locations, model=predictor)
for unknown_encoding in unknown_encodings:
for model_name, model in models.items():
results = face_recognition.compare_faces(model, unknown_encoding)
# file_name = str(uuid.uuid1()) + str(results) + '.jpg'
# cv2.imwrite(file_name, unknown_image)
log.info(model_name)
log.info(results)
log.info(np.mean(results))
if np.mean(results) > 0.7:
log.info(model_name)
yield model_name
def test_cnn_raw_face_loc(self):
img = api.load_img_file(
os.path.join(os.path.dirname(__file__), 'test_img', 'obama.jpg'))
detected_faces = api._raw_face_locations(img, model="cnn")
self.assertEqual(len(detected_faces), 1)
self.assertAlmostEqual(detected_faces[0].rect.top(), 144, delta=25)
self.assertAlmostEqual(detected_faces[0].rect.bottom(), 389, delta=25)
def test_raw_face_locations_32bit_image(self):
img = api.load_image_file(
os.path.join(os.path.dirname(__file__), "test_images",
"32bit.png"))
detected_faces = api._raw_face_locations(img)
assert len(detected_faces) == 1
assert detected_faces[0].top() == 290
assert detected_faces[0].bottom() == 558
def test_raw_face_locations(self):
img = api.load_image_file(
os.path.join(os.path.dirname(__file__), "test_images",
"obama.jpg"))
detected_faces = api._raw_face_locations(img)
assert len(detected_faces) == 1
assert detected_faces[0].top() == 142
assert detected_faces[0].bottom() == 409
def _raw_face_landmarks(face_image, face_locations=None, model="large"):
face_locations = face_locations or _raw_face_locations(face_image)
pose_predictor = {'small': pose_predictor_5_point, 'large': pose_predictor_68_point}[model]
return [pose_predictor(face_image, face_location) for face_location in face_locations]
