def test_image(src,
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)
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(src, image_to_check, name, distance,
show_distance) for is_match, name, distance in
zip(result, known_names, distances) if is_match
]
else:
print_result(src, image_to_check, "unknown_person", None,
show_distance)
if not unknown_encodings:
print_result(image_to_check, "no_persons_found", None, show_distance)
def test_face_distance(self):
img_a1 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
img_a2 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama2.jpg'))
img_a3 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama3.jpg'))
img_b1 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'biden.jpg'))
face_encoding_a1 = api.face_encodings(img_a1)[0]
face_encoding_a2 = api.face_encodings(img_a2)[0]
face_encoding_a3 = api.face_encodings(img_a3)[0]
face_encoding_b1 = api.face_encodings(img_b1)[0]
faces_to_compare = [
face_encoding_a2,
face_encoding_a3,
face_encoding_b1]
distance_results = api.face_distance(faces_to_compare, face_encoding_a1)
# 0.6 is the default face distance match threshold. So we'll spot-check that the numbers returned
# are above or below that based on if they should match (since the exact numbers could vary).
self.assertEqual(type(distance_results), np.ndarray)
self.assertLessEqual(distance_results[0], 0.6)
self.assertLessEqual(distance_results[1], 0.6)
self.assertGreater(distance_results[2], 0.6)
def compare_faces(profile, person, target_encoding, threshold=0.6):
profilelink, profilepic, distance = profile
match = None
try:
image = urllib.request.urlopen(profilepic)
unknown_image = face_recognition.load_image_file(image)
unknown_encoding = face_recognition.face_encodings(unknown_image)
if len(unknown_encoding) > 0:
results = face_recognition.face_distance(target_encoding,
unknown_encoding[0])
for result in results:
if result < float(threshold):
person.instagram = encoding.smart_str(profilelink,
encoding='ascii',
errors='ignore')
person.instagramimage = encoding.smart_str(
profilepic, encoding='ascii', errors='ignore')
logging.info("Match found: " + person.full_name +
" ,Instagram: " + person.instagram)
if args.vv == True:
print("\tMatch found: " + person.full_name)
print("\tInstagram: " + person.instagram)
match = person
except Exception as e:
logging.error("compare_faces: " + str(e))
print("ERROR")
print(e)
return match
def process_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)
result_list = list()
# 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)
recognized_name = ""
if True in result:
for is_match, name, distance in zip(result, known_names,
distances):
if is_match:
recognized_name = name
else:
recognized_name = "unknown_person"
result_list.append(recognized_name)
return image_to_check, result_list
def recognition_faces_in_image(knownfile_stream, detectfile_stream):
# 载入用户上传的图片
knownimg = face_recognition.load_image_file(knownfile_stream)
detectimg = face_recognition.load_image_file(detectfile_stream)
# 为用户上传的图片中的人脸编码
knownface_encodings = face_recognition.face_encodings(knownimg)
detectface_encodings = face_recognition.face_encodings(detectimg)
if len(knownface_encodings) > 1:
result = {"ret": 1, "msg": "knownface has more than one face"}
return jsonify(result)
if not knownface_encodings or not detectface_encodings:
result = {"ret": 2, "msg": "knownface or detectface has no face"}
return jsonify(result)
checked_results = []
for detectface_encoding in detectface_encodings:
distances = face_recognition.face_distance(knownface_encodings,
detectface_encoding)
checked_result = list(distances <= 0.6)
checked_results.append(distances.tolist())
# 讲识别结果以json键值对的数据结构输出
result = {"ret": 0, "results": checked_results}
return jsonify(result)
def test_face_distance(self):
img_a1 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
img_a2 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama2.jpg'))
img_a3 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama3.jpg'))
img_b1 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'biden.jpg'))
face_encoding_a1 = api.face_encodings(img_a1)[0]
face_encoding_a2 = api.face_encodings(img_a2)[0]
face_encoding_a3 = api.face_encodings(img_a3)[0]
face_encoding_b1 = api.face_encodings(img_b1)[0]
faces_to_compare = [
face_encoding_a2,
face_encoding_a3,
face_encoding_b1]
distance_results = api.face_distance(faces_to_compare, face_encoding_a1)
# 0.6 is the default face distance match threshold. So we'll spot-check that the numbers returned
# are above or below that based on if they should match (since the exact numbers could vary).
self.assertEqual(type(distance_results), np.ndarray)
self.assertLessEqual(distance_results[0], 0.6)
self.assertLessEqual(distance_results[1], 0.6)
self.assertGreater(distance_results[2], 0.6)
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
]
def test_image(image_to_check, known_names, known_face_encodings):
unknown_image = face_recognition.load_image_file(image_to_check)
# Scale down image if it's giant so things run a little faster
if unknown_image.shape[1] > 1600:
scale_factor = 1600.0 / unknown_image.shape[1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
unknown_image = scipy.misc.imresize(unknown_image, scale_factor)
unknown_encodings = face_recognition.face_encodings(unknown_image)
print("unknown_encodings " + str(unknown_encodings))
if len(unknown_encodings) == 1:
for unknown_encoding in unknown_encodings:
result = face_recognition.compare_faces(known_face_encodings,
unknown_encoding)
distance = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
print(distance[0])
print("True") if True in result else print("False ")
return distance[0], result[0]
else:
return "0", "Many Faces or No Faces"
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 unknown_image.shape[1] > 1600:
scale_factor = 1600.0 / unknown_image.shape[1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
unknown_image = scipy.misc.imresize(unknown_image, scale_factor)
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)
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
]
#put IMAGE into firebase unknown_image?
else:
#no matches
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)
# 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)
face_names = []
for unknown_encoding in unknown_encodings:
distances = face_recognition.face_distance(known_face_encodings,
unknown_encoding)
result = list(distances <= tolerance)
match = face_recognition.compare_faces(known_face_encodings,
unknown_encoding, 0.5)
name = "Unknown"
for k in range(len(match)):
if match[k]:
name = known_names[k]
face_names.append(name)
if not unknown_encodings:
# print out fact that no faces were found in image
print(image_to_check, "no_persons_found", None, show_distance)
return face_names
def test(image1,encoding1,image2,encoding2):
for encodings2 in encoding2:
distance = face_recognition.face_distance(encoding1,encodings2)
similarity = distance_to_similarity(distance[0])
similarity = float("{0:.2f}".format(similarity))
return similarity
def test_face_distance_empty_lists(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'biden.jpg'))
face_encoding = api.face_encodings(img)[0]
# empty python list
faces_to_compare = []
distance_results = api.face_distance(faces_to_compare, face_encoding)
self.assertEqual(type(distance_results), np.ndarray)
self.assertEqual(len(distance_results), 0)
# empty numpy list
faces_to_compare = np.array([])
distance_results = api.face_distance(faces_to_compare, face_encoding)
self.assertEqual(type(distance_results), np.ndarray)
self.assertEqual(len(distance_results), 0)
def test_face_distance_empty_lists(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'biden.jpg'))
face_encoding = api.face_encodings(img)[0]
# empty python list
faces_to_compare = []
distance_results = api.face_distance(faces_to_compare, face_encoding)
self.assertEqual(type(distance_results), np.ndarray)
self.assertEqual(len(distance_results), 0)
# empty numpy list
faces_to_compare = np.array([])
distance_results = api.face_distance(faces_to_compare, face_encoding)
self.assertEqual(type(distance_results), np.ndarray)
self.assertEqual(len(distance_results), 0)
def get_face_name(faces_to_compare,face_encodings):
orgin_name,targe_name,simily='','',0
for o in faces_to_compare:
name=o["name"]
_face_encodings=o["face_encodings"]
max_distance=max(api.face_distance([face_encodings],_face_encodings))
if max_distance > 0.8 :
return name,max_distance
else :
simily= simily if simily >max_distance else max_distance
targe_name= targe_name if simily >max_distance else name
return targe_name,simily
def test(encoding1, encoding2):
"""
Compare document with Person Image Encodings.
"""
if len(encoding1) < 1 or len(encoding2) < 1:
print("No Face Identified")
sys.exit(0)
for encodings2 in encoding2:
distance = face_recognition.face_distance(encoding1, encodings2)
similarity = distance_to_similarity(distance[0])
similarity = float("{0:.2f}".format(similarity))
return similarity
def save_keypoints_video(video_path, input_folder, output_folder, tolerance,
batch_size):
video = cv2.VideoCapture(str(video_path))
out_path = move_path(video_path, input_folder, output_folder)
out_path = out_path.with_suffix('')
known_faces = []
frame_count = 0
frames = []
while video.isOpened():
ret, frame = video.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = scale_frame(frame)
frames.append(frame)
if (len(frames) == batch_size) or not ret:
batch_of_face_locations = face_recognition.batch_face_locations(
frames,
number_of_times_to_upsample=upsample,
batch_size=batch_size)
# Now let's list all the faces we found in all batch_size frames
for i, face_locations in enumerate(batch_of_face_locations):
frame_count += 1
encodings, landmarks = face_recognition.face_encodings_and_landmarks(
frames[i], known_face_locations=locations)
num_faces = landmarks.shape[0]
if num_faces == 0:
continue
for face_idx, (encoding, landmark_array) in enumerate(
zip(encodings, landmarks)):
distances = face_recognition.face_distance(
known_faces, encoding)
if len(distances) == 0 or distances.min() > tolerance:
known_idx = len(known_faces)
known_faces.append(encoding)
else:
known_idx = int(np.argmin(distances))
known_faces[known_idx] = np.mean(
[known_faces[known_idx], encoding], axis=0)
file_path = Path(out_path, F"{known_idx}",
F"{frame_count}.npy")
file_path.parent.mkdir(parents=True, exist_ok=True)
np.save(file_path, landmark_array)
# Clear the frames array to start the next batch
frames = []
print(F"finished video {out_path}")
def recognize(self, faces):
faces = [
_trim_css_to_bounds(_rect_to_css(face), self.sample.shape)
for face in faces
]
unknown_encodings = face_recognition.face_encodings(self.sample, faces)
distances = face_recognition.face_distance(unknown_encodings,
self.person_picture)
for i, distance in enumerate(distances):
self.reco_distance = distance
if self.reco_distance <= self.reco_tolerance:
return i
return -1
def recognize_faces(face_image_path):
# Read image from disk
image = api.load_image_file(face_image_path)
# Get face locations in image
face_locations = api.face_locations(image, model="hog")
# Create encodings from image
face_encodings = api.face_encodings(image, known_face_locations=face_locations)
# Get encodings from DB
existing_face_encoding_objects = [list(g) for _, g in groupby(FaceEncoding.query.order_by(FaceEncoding.player_id).all(), attrgetter('player_id'))]
outputs = []
for i, face_encoding in enumerate(face_encodings):
distances = {}
for face_encoding_group in existing_face_encoding_objects:
# Calculate distance to every encoding for this group (person)
existing_face_encodings = np.array([fe.encoding for fe in face_encoding_group])
face_distances = api.face_distance(existing_face_encodings, face_encoding)
distances[face_encoding_group[0].player] = sum(face_distances) / len(face_distances)
recognized_player = None
if len(distances) > 0:
recognized_player = min(distances, key=distances.get)
if distances[recognized_player] > THRESHOLD:
recognized_player = None
# Create a crop of the face
top, right, bottom, left = face_locations[i]
image_crop = image[top:bottom, left:right]
im = Image.fromarray(image_crop)
with io.BytesIO() as output:
im.save(output, format="JPEG")
contents = output.getvalue()
outputs.append(
{
"image": base64.b64encode(contents).decode("utf-8"),
"embedding": face_encoding.tolist(),
"player": recognized_player.serialize() if recognized_player else None
})
return outputs
def compute_distances(image_to_check, known_face_encodings):
unknown_encodings = face_recognition.face_encodings(image_to_check,
num_jitters=1)
#skimage.io.imsave(open('try0.png','wb'), image_to_check, plugin='pil', format_str='png')
if len(unknown_encodings) == 0:
rotated = skimage.transform.rotate(image_to_check, 90, resize=True)
unknown_encodings = face_recognition.face_encodings(rotated,
num_jitters=3)
if len(unknown_encodings) == 0:
rotated = skimage.transform.rotate(image_to_check, -90, resize=True)
unknown_encodings = face_recognition.face_encodings(rotated,
num_jitters=3)
return [
list(
face_recognition.face_distance(known_face_encodings,
unknown_encoding))
for unknown_encoding in unknown_encodings
]
def test_bad_face_matching_confidence(self):
self.tolerance = 0.50
self.threshold = 0.80
ic_path = os.path.join(test_data_path, 'my ic.jpg')
passport_path = os.path.join(test_data_path, 'passport.jpg')
image_ic = api.load_image_file(ic_path)
image_passport = api.load_image_file(passport_path)
face_encoding_ic = api.face_encodings(image_ic)[0]
face_encoding_passport = api.face_encodings(image_passport)[0]
face_distances = api.face_distance([face_encoding_ic],
face_encoding_passport)
confidence = self.face_distance_to_conf(face_distances, self.tolerance)
self.assertLessEqual(confidence, self.threshold)
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('hello biotch')
[
print_result(image_to_check, name, distance, show_distance) for
is_match, name, distance in zip(result, known_names, distances)
if is_match
]
for is_match, name, distance in zip(result, known_names,
distances):
if is_match:
print('hello again bitch')
call([
"python", "GUI.py", "found", name,
str(distance),
str(tolerance)
])
else:
print_result(image_to_check, "unknown_person", None, show_distance)
call(["python", "GUI.py", "not_found"])
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)
call(["python", "GUI.py", "unknown"])
def test_face_matching_confidence(self):
self.tolerance = 0.50
self.threshold = 0.80
ic_path = os.path.join(test_data_path, 'my ic.jpg')
driving_license_path = os.path.join(test_data_path,
'my driving license.jpg')
image_ic = api.load_image_file(ic_path)
image_driving_license = api.load_image_file(driving_license_path)
face_encoding_ic = api.face_encodings(image_ic)[0]
face_encoding_driving = api.face_encodings(image_driving_license)[0]
face_distances = api.face_distance([face_encoding_ic],
face_encoding_driving)
confidence = self.face_distance_to_conf(face_distances, self.tolerance)
self.assertGreaterEqual(confidence, self.threshold)
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 unknown_image.shape[1] > 1600:
scale_factor = 1600.0 / unknown_image.shape[1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
unknown_image = scipy.misc.imresize(unknown_image, scale_factor)
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)
def match_face_encodings(self, known_face, face_to_compare):
"""
Match face encodings
:param known_face: Face object as the basis for verification
:param face_to_compare: Face object to verify
:return: Value of face distance as computed
"""
known_face_encoding = self.get_encoding(known_face)
face_encoding_to_compare = self.get_encoding(face_to_compare)
if known_face_encoding.size < 1:
known_face_encoding = self.encode_image(known_face)
if face_encoding_to_compare.size < 1:
face_encoding_to_compare = self.encode_image(face_to_compare)
face_distance = api.face_distance(
face_encodings=[known_face_encoding], face_to_compare=face_encoding_to_compare
)
return face_distance[0]
def save_keypoints_video(video_path, input_folder, output_folder, model,
tolerance):
video = cv2.VideoCapture(str(video_path))
out_path = move_path(video_path, input_folder, output_folder)
out_path = out_path.with_suffix('')
known_faces = []
frame_count = 0
while video.isOpened():
ret, frame = video.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
locations = face_recognition.face_locations(frame, model=model)
encodings, landmarks = face_recognition.face_encodings_and_landmarks(
frame, known_face_locations=locations)
num_faces = landmarks.shape[0]
if num_faces == 0:
continue
for face_idx, (encoding,
landmark_array) in enumerate(zip(encodings, landmarks)):
distances = face_recognition.face_distance(known_faces, encoding)
if len(distances) == 0 or distances.min() > tolerance:
known_idx = len(known_faces)
known_faces.append(encoding)
else:
known_idx = int(np.argmin(distances))
known_faces[known_idx] = np.mean(
[known_faces[known_idx], encoding], axis=0)
file_path = Path(out_path, F"{known_idx}", F"{frame_count}.npy")
file_path.parent.mkdir(parents=True, exist_ok=True)
np.save(file_path, landmark_array)
frame_count += 1
print(F"finished video {out_path}")
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 compare_faces_in_image(knownface, detectface):
knownface_encodings = []
detectface_encodings = []
knownface_encodings.append(np.array(knownface))
detectface_encodings.append(np.array(detectface))
if len(knownface_encodings) > 1:
result = {"ret": 1, "msg": "knownface has more than one face"}
return jsonify(result)
if not knownface_encodings or not detectface_encodings:
result = {"ret": 2, "msg": "knownface or detectface has no face"}
return jsonify(result)
checked_results = []
for detectface_encoding in detectface_encodings:
distances = face_recognition.face_distance(knownface_encodings,
detectface_encoding)
checked_result = list(distances <= 0.6)
checked_results.append(distances.tolist())
# 讲识别结果以json键值对的数据结构输出
result = {"ret": 0, "results": checked_results}
return jsonify(result)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings,
face_encoding,
tolerance=0.35)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_ids[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
# print(face_distances)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_ids[best_match_index]
face_names.append(name)
if (sanity_count == 0):
prev_name = name
sanity_count += 1
elif (sanity_count < 60):
if (prev_name == name and name != "Unknown"):
sanity_count += 1
prev_name = name
else:
sanity_count = 0
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument(
"-i",
"--new-image",
required=True,
help=
"sciezka do obrazu, z ktorym porownywane beda zdjecia z bazy dancyh")
ap.add_argument("-e",
"--encodings-file",
required=True,
help="sciezka do pliku z kodowaniem twarzy")
args = vars(ap.parse_args())
new_encoded_image = get_encoding_for_image(args['new_image'])
# odczytywanie kodowania twarzy z bazy danych
results = pickle.loads(open(args['encodings_file'], "rb").read())
encodings = results['encodings']
names = results['names']
# obliczanie odleglosci - czyli podobienstwa twarzy i wypisywanie ich
distance_results = face_distance(encodings, new_encoded_image)
results = list(zip(distance_results, names))
sorted_list = sorted(results, key=lambda x: x[0])
for x in sorted_list:
print(x)
def mark_your_attendance():
mpl.rcParams['toolbar'] = 'None'
STORAGE_PATH = "/home/harsh/face-recognition-attendance-system/storage"
try:
with open(os.path.join(STORAGE_PATH, "known_face_ids.pickle"),
"rb") as fp:
known_face_ids = pickle.load(fp)
with open(os.path.join(STORAGE_PATH, "known_face_encodings.pickle"),
"rb") as fp:
known_face_encodings = pickle.load(fp)
# known_face_ids = np.load("known_face_ids.npy")
# known_face_encodings = np.load("known_face_encodings.npy")
except:
known_face_encodings = []
known_face_ids = []
# CSV_PATH = "/home/harsh/Backup/face-recognition/data/attendance.csv"
CSV_PATH = "/home/harsh/face-recognition-attendance-system/static/data/attendance.csv"
if (os.path.exists(CSV_PATH)):
csv_file = open(CSV_PATH, "a+")
writer = csv.writer(csv_file)
else:
os.mknod(CSV_PATH)
csv_file = open(CSV_PATH, "w+")
writer = csv.writer(csv_file)
writer.writerow(["Student ID", "Date", "Time of Entry"])
name = "Unknown"
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
sanity_count = 0
unknown_count = 0
marked = True
video_capture = cv2.VideoCapture(0)
ret, frame = video_capture.read()
plot = plt.subplot(1, 1, 1)
plt.title("Detecting Face")
plt.axis('off')
im1 = plot.imshow(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# print("FRAME READ WORKS")
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings,
face_encoding,
tolerance=0.35)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_ids[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
# print(face_distances)
try:
best_match_index = np.argmin(face_distances)
except:
# print("No students have been marked")
video_capture.release()
cv2.destroyAllWindows()
marked = False
return marked
if matches[best_match_index]:
name = known_face_ids[best_match_index]
face_names.append(name)
if (name == "Unknown"):
unknown_count += 1
else:
unknown_count = 0
if (unknown_count == 600):
# video_capture.release()
# cv2.destroyAllWindows()
# print("You haven't been registered")
marked = False
unknown_count = 0
break
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations,
face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.7,
(255, 255, 255), 1)
# print("BEFORE sHOWING")
# Display the resulting image
# cv2.imshow('Video', frame)
# if cv2.waitKey(20) == 27:
# break
plt.ion()
im1.set_data(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
plt.pause(0.001)
# as opencv loads in BGR format by default, we want to show it in RGB.
plt.show()
# print("AFTER SHOWING")
# Hit 'q' on the keyboard to quit!
if (sanity_count == 0):
prev_name = name
sanity_count += 1
elif (sanity_count < 60):
if (prev_name == name and name != "Unknown"):
sanity_count += 1
prev_name = name
else:
sanity_count = 0
elif (sanity_count == 60):
# print("Face registered")
# video_capture.release()
# cv2.destroyAllWindows()
sanity_count = 0
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
date = dt_string.split(" ")[0]
time = dt_string.split(" ")[1]
writer.writerow([name, date, time])
# print(name + date + time)
break
# Release handle to the webcam
plt.close()
video_capture.release()
cv2.destroyAllWindows()
return marked
import csv
dir='face_photos_small'
files=os.listdir(dir)
csv=open('dataset.csv',mode='w')
encodingmap={}
for file in files:
filepath=dir+"/"+file
image=fr.load_image_file(filepath)
encoding=fr.face_encodings(image)[0]
encodingmap[file]=encoding
# print(file,encoding)
csv.write('"'+file+'"')
for e in encoding:
csv.write(','+str(e))
csv.write('\n')
csv.close()
for f1 in encodingmap.keys():
for f2 in encodingmap.keys():
if f1==f2:
continue
e1=encodingmap[f1]
e2=encodingmap[f2]
# print(f1,e1,f2,e2)
dist=fr.face_distance([e1],e2)
print(f1,f2,dist)
def analyseFootage(clipname):
CLIP_PATH = FOOTAGES_PATH + "/" + clipname
if os.path.isfile(CLIP_PATH) == False:
return 0
#Load the known face IDs and encodings for facial recognition
try:
with open(os.path.join(STORAGE_PATH, "known_face_ids.pickle"),
"rb") as fp:
known_face_ids = pickle.load(fp)
with open(os.path.join(STORAGE_PATH, "known_face_encodings.pickle"),
"rb") as fp:
known_face_encodings = pickle.load(fp)
except:
known_face_encodings = []
known_face_ids = []
#Start the Video Stream
fvs = FileVideoStream(CLIP_PATH).start()
time.sleep(1.0)
print("[INFO] Loading the facial detector")
detector = dlib.get_frontal_face_detector()
#predictor = dlib.shape_predictor(LANDMARK_PATH)
#fa = FaceAligner(predictor, desiredFaceWidth = 96)
name = "Unknown"
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
#sanity_count = 0
unknown_count = 0
marked = True
print("[INFO] Initializing CCTV Footage")
while fvs.more():
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
frame = fvs.read()
if frame is None:
break
frame = imutils.resize(frame, width=600)
frame = adjust_gamma(frame, gamma=1.5)
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#To store the faces
#This will detect all the images in the current frame, and it will return the coordinates of the faces
#Takes in image and some other parameter for accurate result
faces = detector(gray_frame, 0)
#In above 'faces' variable there can be multiple faces so we have to get each and every face and draw a rectangle around it.
#sampleNum = sampleNum+1
for face in faces:
#num_frames = num_frames + 1
#print("inside for loop")
if face is None:
print("face is none")
continue
#face_aligned = fa.align(frame,gray_frame,face)
#face_aligned = imutils.resize(face_aligned ,width = 600)
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(frame)
face_encodings = face_recognition.face_encodings(
frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(
known_face_encodings, face_encoding, tolerance=0.35)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_ids[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
# print(face_distances)
try:
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_ids[best_match_index]
except:
# print("No students have been marked")
#video_capture.release()
#cv2.destroyAllWindows()
marked = False
#return marked
#if matches[best_match_index]:
# name = known_face_ids[best_match_index]
face_names.append(name)
if name == "Unknown":
unknown_count += 1
else:
unknown_count = 0
if unknown_count == 600:
# video_capture.release()
# cv2.destroyAllWindows()
# print("You haven't been registered")
marked = False
unknown_count = 0
break
process_this_frame = not process_this_frame
for (top, right, bottom,
left), name in zip(face_locations, face_names):
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255),
1)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom + 15), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom + 15), font, 0.4,
(255, 255, 255), 1)
#Showing the image in another window
#Creates a window with window name "Face" and with the image img
#cv2.imshow("Video feed (PRESS Q TO QUIT",frame)
frame = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
#if cv2.waitKey(1) == ord("q") :
# break
print("here")
# do a bit of cleanup
cv2.destroyAllWindows()
#fvs.stop()
return
def mark_your_attendance(location, known_face_encodings, known_face_ids):
mpl.rcParams['toolbar'] = 'None'
if (os.path.exists(DB_PATH)):
#rdbms='sqlite'
#conn = psycopg2.connect(DB_PATH)
#c=conn.cursor()
rdbms = 'postgresql'
conn = psycopg2.connect(host="localhost",
database="face_rec_db",
user="******",
password="******")
c = conn.cursor()
else:
#os.mknod(DB_PATH)
conn = sqlite3.connect(DB_PATH)
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS ATTENDANCE
(ID TEXT NOT NULL,
TIMESTAMP TEXT NOT NULL,
LOCATION TEXT);''')
conn.commit()
name = "Unknown"
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
sanity_count = 0
unknown_count = 0
marked = True
video_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
#_,frame = video_capture.read()
while True:
# Grab a single frame of video
_, frame = video_capture.read()
#Applying face enhancement steps
frame = imageEnhancement.adjust_gamma(frame, gamma=1.5)
# Resize frame of video to 1/4 size for faster face recognition processing
#small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
small_frame = frame
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings,
face_encoding,
tolerance=0.35)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_ids[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding)
# print(face_distances)
try:
best_match_index = np.argmin(face_distances)
except:
# print("No students have been marked")
video_capture.release()
cv2.destroyAllWindows()
marked = False
return marked
if matches[best_match_index]:
name = known_face_ids[best_match_index]
face_names.append(name)
if name == "Unknown":
unknown_count += 1
else:
unknown_count = 0
if unknown_count == 600:
# video_capture.release()
# cv2.destroyAllWindows()
# print("You haven't been registered")
marked = False
unknown_count = 0
break
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations,
face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
#top *= 4
#right *= 4
#bottom *= 4
#left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom),
(0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.7,
(255, 255, 255), 1)
# print("AFTER SHOWING")
# Hit 'q' on the keyboard to quit!
if (sanity_count == 0):
prev_name = name
sanity_count += 1
elif sanity_count < 60:
if (prev_name == name and name != "Unknown"):
sanity_count += 1
prev_name = name
else:
sanity_count = 0
elif sanity_count == 60:
# print("Face registered")
# video_capture.release()
# cv2.destroyAllWindows()
sanity_count = 0
# now = datetime.now()
# if(entry_or_exit==0):
# c.execute("INSERT INTO ATTENDANCE VALUES (?,datetime('now'),'IN');",(name, ))
# else:
# c.execute("INSERT INTO ATTENDANCE VALUES (?,datetime('now'),'OUT');",(name, ))
if (rdbms == 'sqlite'):
c.execute(
"INSERT INTO ATTENDANCE VALUES (?,datetime('now'),?);", (
name,
location,
))
elif (rdbms == 'postgresql'):
c.execute("INSERT INTO attendance VALUES (%s,now(),%s);",
(name, location))
conn.commit()
break
#OpenCV's implementation to show an image in window(doesn't work on production server)
#cv2.imshow("Marking Attendance (PRESS Q TO QUIT)",frame)
#Encoding the frame to be stream into browser
frame = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
#if cv2.waitKey(20) == ord("q"):
# break
# Release handle to the webcam
#plt.close()
video_capture.release()
cv2.destroyAllWindows()
conn.close()
return marked