def test_image(known_names, known_face_encodings):
cam = cv2.VideoCapture(0)
ret, image_to_check = cam.read()
cam.release()
cv2.destroyAllWindows()
unknown_image = image_to_check
# Scale down image if it's giant so things run a little faster
if max(unknown_image.shape) > 1600:
pil_img = PIL.Image.fromarray(unknown_image)
pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS)
unknown_image = np.array(pil_img)
unknown_encodings = face_recognition.face_encodings(unknown_image)
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
result = list(distances <= 0.5625) #0.5625 is tolerance
if True in result:
continue
else:
# No match
sys.exit()
if not unknown_encodings:
# No faces were found in image
sys.exit()
def test_image(image_to_check,
known_names,
known_face_encodings,
tolerance=0.6,
show_distance=False):
unknown_image = face_recognition.load_image_file(image_to_check)
# Scale down image if it's giant so things run a little faster
if max(unknown_image.shape) > 1600:
pil_img = PIL.Image.fromarray(unknown_image)
pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS)
unknown_image = np.array(pil_img)
unknown_encodings = face_recognition.face_encodings(unknown_image)
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
result = list(distances <= tolerance)
#if True in result:
#[print_result(image_to_check, name, distance, show_distance) for is_match, name, distance in zip(result, known_names, distances) if is_match]
#else:
#print_result(image_to_check, "unknown_person", None, show_distance)
if not unknown_encodings:
# print out fact that no faces were found in image
print_result(image_to_check, "no_persons_found", None, show_distance)
def test_image(image_to_check,
known_names,
known_face_encodings,
tolerance=0.6,
show_distance=False):
unknown_image = face_recognition.load_image_file(image_to_check)
if max(unknown_image.shape) > 1600:
pil_img = PIL.Image.fromarray(unknown_image)
pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS)
unknown_image = np.array(pil_img)
unknown_encodings = face_recognition.face_encodings(unknown_image)
faces = []
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
result = list(distances <= tolerance)
res = []
if True in result:
[
res.append(
print_result(image_to_check, name, distance,
show_distance)) for is_match, name, distance
in zip(result, known_names, distances) if is_match
]
else:
res.append(
print_result(image_to_check, "unknown", None, show_distance))
mat = defaultdict(int)
mat = Counter(res)
res = max(mat.items(), key=lambda x: x[1])
faces.append(res[0])
if not unknown_encodings:
print_result(image_to_check, "no_persons_found", None, show_distance)
return faces