本文整理汇总了Python中face_recognition.face_encodings方法的典型用法代码示例。如果您正苦于以下问题:Python face_recognition.face_encodings方法的具体用法?Python face_recognition.face_encodings怎么用?Python face_recognition.face_encodings使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类face_recognition
的用法示例。
在下文中一共展示了face_recognition.face_encodings方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: loadKnown
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def loadKnown(self, label):
console.task('Loading known faces')
pa_g = Path('./known')
pathlist = []
for ext in ['.jpg', '.JPG', '.png', '.PNG', '.jpeg', '.JPEG', '.bmp', '.BMP']:
tmp_pl = pa_g.glob('**/*{}'.format(ext))
for t in tmp_pl:
pathlist.append(t)
for path in pathlist:
p_str = str(path)
delim = '/'
if platform == "win32":
delim = '\\'
console.subtask('Loading {0}'.format(p_str.split(delim)[1]))
im = face_recognition.load_image_file(p_str)
encoding = face_recognition.face_encodings(im, num_jitters=self.num_jitters)
for e in encoding:
self.known_face_encodings.append(e)
self.known_face_names.append(label)
示例2: encoding_images
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def encoding_images(path):
"""
对path路径下的子文件夹中的图片进行编码,
TODO:
对人脸数据进行历史库中的人脸向量进行欧式距离的比较,当距离小于某个阈值的时候提醒:
如果相似的是本人,则跳过该条记录,并提醒已经存在,否则警告人脸过度相似问题,
:param path:
:return:
"""
with open(name_and_encoding, 'w') as f:
subdirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]
for subdir in subdirs:
print('process image name :', subdir)
person_image_encoding = []
for y in os.listdir(subdir):
print("image name is ", y)
_image = face_recognition.load_image_file(os.path.join(subdir, y))
face_encodings = face_recognition.face_encodings(_image)
name = os.path.split(subdir)[-1]
if face_encodings and len(face_encodings) == 1:
if len(person_image_encoding) == 0:
person_image_encoding.append(face_encodings[0])
known_face_names.append(name)
continue
for i in range(len(person_image_encoding)):
distances = face_recognition.compare_faces(person_image_encoding, face_encodings[0], tolerance=image_thread)
if False in distances:
person_image_encoding.append(face_encodings[0])
known_face_names.append(name)
print(name, " new feature")
f.write(name + ":" + str(face_encodings[0]) + "\n")
break
# face_encoding = face_recognition.face_encodings(_image)[0]
# face_recognition.compare_faces()
known_face_encodings.extend(person_image_encoding)
bb = np.array(known_face_encodings)
print("--------")
np.save(KNOWN_FACE_ENCODINGS, known_face_encodings)
np.save(KNOWN_FACE_NANE, known_face_names)
示例3: dlib_encoder
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def dlib_encoder(image, boxes):
# Encode face into a 128-D representation (embeddings) using dlib.
# Convert image from BGR (OpenCV ordering) to dlib ordering (RGB).
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Generate encodings. Only one face is assumed so take the 1st element.
encoding = face_recognition.face_encodings(face_image=rgb,
known_face_locations=boxes, num_jitters=10)[0]
return encoding
# Loop over the image paths.
# NB: Its assumed that only one face is in each image.
示例4: test_load
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def test_load():
face_encodings, face_names = load_encodings()
print("===========face_encodings================")
print(face_encodings)
print("===========================")
print(face_names)
print("===========face_names================")
示例5: initialize_database
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def initialize_database(self):
"""
Reads the PNG images from ./people folder and
creates a list of peoples
The names of the image files are considered as their
real names.
For example;
/people
- mario.png
- jennifer.png
- melanie.png
Returns:
(tuple) (people_list, name_list) (features of people, names of people)
"""
filenames = glob.glob(cd + '/people/*.png')
people_list = []
name_list = []
for f in filenames:
im = cv2.imread(f, 1)
#cv2.imshow("Database Image", im)
#cv2.waitKey(500)
im = im.astype(np.uint8)
people_list.append(fr.face_encodings(im)[0])
name_list.append(f.split('/')[-1].split('.')[0])
#cv2.destroyAllWindows()
return (people_list, name_list)
示例6: constructIndexes
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def constructIndexes(self, label):
valid_links = []
console.section('Analyzing')
file_name = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
file_name += '.jpg'
tmp_path = os.path.join(tempfile.gettempdir(), file_name)
console.task("Storing Image in {0}".format(tmp_path))
for num, i in enumerate(self.profile_img):
console.task('Analyzing {0}...'.format(i.strip()[:90]))
urlretrieve(i, tmp_path)
frame = cv2.imread(tmp_path)
big_frame = cv2.resize(frame, (0, 0), fx=2.0, fy=2.0)
rgb_small_frame = big_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations, num_jitters=self.num_jitters)
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(self.known_face_encodings, face_encoding)
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 = self.known_face_names[first_match_index]
face_names.append(name)
for _, name in zip(face_locations, face_names):
if name == label:
valid_links.append(num)
if os.path.isfile(tmp_path):
console.task("Removing {0}".format(tmp_path))
os.remove(tmp_path)
return valid_links
示例7: predict
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def predict(X_frame, knn_clf=None, model_path=None, distance_threshold=0.5):
"""
Recognizes faces in given image using a trained KNN classifier
:param X_frame: frame to do the prediction on.
:param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
:param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
:param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
of mis-classifying an unknown person as a known one.
:return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
For faces of unrecognized persons, the name 'unknown' will be returned.
"""
if knn_clf is None and model_path is None:
raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
# Load a trained KNN model (if one was passed in)
if knn_clf is None:
with open(model_path, 'rb') as f:
knn_clf = pickle.load(f)
X_face_locations = face_recognition.face_locations(X_frame)
# If no faces are found in the image, return an empty result.
if len(X_face_locations) == 0:
return []
# Find encodings for faces in the test image
faces_encodings = face_recognition.face_encodings(X_frame, known_face_locations=X_face_locations)
# Use the KNN model to find the best matches for the test face
closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]
# Predict classes and remove classifications that aren't within the threshold
return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
示例8: predict
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
"""
Recognizes faces in given image using a trained KNN classifier
:param X_img_path: path to image to be recognized
:param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
:param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
:param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
of mis-classifying an unknown person as a known one.
:return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
For faces of unrecognized persons, the name 'unknown' will be returned.
"""
if not os.path.isfile(X_img_path) or os.path.splitext(X_img_path)[1][1:] not in ALLOWED_EXTENSIONS:
raise Exception("Invalid image path: {}".format(X_img_path))
if knn_clf is None and model_path is None:
raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
# Load a trained KNN model (if one was passed in)
if knn_clf is None:
with open(model_path, 'rb') as f:
knn_clf = pickle.load(f)
# Load image file and find face locations
X_img = face_recognition.load_image_file(X_img_path)
X_face_locations = face_recognition.face_locations(X_img)
# If no faces are found in the image, return an empty result.
if len(X_face_locations) == 0:
return []
# Find encodings for faces in the test iamge
faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)
# Use the KNN model to find the best matches for the test face
closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]
# Predict classes and remove classifications that aren't within the threshold
return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
示例9: recognize_face_from_image_file
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def recognize_face_from_image_file(image_file: str) -> Optional[ndarray]:
if PYROBOY_AVAILABLE:
return FaceRec.get_biggest_face_encoding(image_file)
else:
logger.warning("Falling back to basic Face Recognition functions, since Pyroboy is unavailable!")
image = fr.load_image_file(image_file)
faces = fr.face_encodings(image)
if faces:
return faces[0]
return None
示例10: get_processed_frame_object
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def get_processed_frame_object(frame_obj, scale=1.0):
"""Processes value produced by producer, returns prediction with png image.
:param frame_obj: frame dictionary with frame information and frame itself
:param scale: (0, 1] scale image before face recognition, speeds up processing, decreases accuracy
:return: A dict updated with faces found in that frame, i.e. their location and encoding.
"""
frame = np_from_json(frame_obj, prefix_name=ORIGINAL_PREFIX) # frame_obj = json
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
frame = cv2.cvtColor(frame.astype(np.uint8), cv2.COLOR_BGR2RGB)
if scale != 1:
# Resize frame of video to scale size for faster face recognition processing
rgb_small_frame = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
else:
rgb_small_frame = frame
with timer("PROCESS RAW FRAME {}".format(frame_obj["frame_num"])):
# Find all the faces and face encodings in the current frame of video
with timer("Locations in frame"):
face_locations = np.array(face_recognition.face_locations(rgb_small_frame))
face_locations_dict = np_to_json(face_locations, prefix_name="face_locations")
with timer("Encodings in frame"):
face_encodings = np.array(face_recognition.face_encodings(rgb_small_frame, face_locations))
face_encodings_dict = np_to_json(face_encodings, prefix_name="face_encodings")
frame_obj.update(face_locations_dict)
frame_obj.update(face_encodings_dict)
return frame_obj
示例11: calc_face_encoding
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def calc_face_encoding(image):
# Currently only use first face found on picture
loaded_image = face_recognition.load_image_file(image)
faces = face_recognition.face_encodings(loaded_image)
# If more than one face on the given image was found -> error
if len(faces) > 1:
raise Exception(
"Found more than one face in the given training image.")
# If none face on the given image was found -> error
if not faces:
raise Exception("Could not find any face in the given training image.")
return faces[0]
示例12: detect_faces_in_image
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def detect_faces_in_image(file_stream):
# Load the uploaded image file
img = face_recognition.load_image_file(file_stream)
# Get face encodings for any faces in the uploaded image
uploaded_faces = face_recognition.face_encodings(img)
# Defaults for the result object
faces_found = len(uploaded_faces)
faces = []
if faces_found:
face_encodings = list(faces_dict.values())
for uploaded_face in uploaded_faces:
match_results = face_recognition.compare_faces(
face_encodings, uploaded_face)
for idx, match in enumerate(match_results):
if match:
match = list(faces_dict.keys())[idx]
match_encoding = face_encodings[idx]
dist = face_recognition.face_distance([match_encoding],
uploaded_face)[0]
faces.append({
"id": match,
"dist": dist
})
return {
"count": faces_found,
"faces": faces
}
# <Picture functions> #
# <Controller>
示例13: extract_face_from_image
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def extract_face_from_image(X_img_path, detection_model, jitters, encoding_models):
# Load image data in a numpy array
try:
log.debug("extract_face_from_image | Loading image {}".format(X_img_path))
X_img, ratio = load_image_file(X_img_path)
except OSError:
log.error("extract_face_from_image | What have you uploaded ???")
return -2, -2, -1
log.debug("extract_face_from_image | Extracting faces locations ...")
try:
# TODO: Reduce size of the image at every iteration
X_face_locations = face_recognition.face_locations(
X_img, model=detection_model) # model="cnn")
except RuntimeError:
log.error(
"extract_face_from_image | GPU does not have enough memory: FIXME unload data and retry")
return None, None, ratio
log.debug("extract_face_from_image | Found {} face(s) for the given image".format(
len(X_face_locations)))
# If no faces are found in the image, return an empty result.
if len(X_face_locations) == 0:
log.warning("extract_face_from_image | Seems that no faces was found :( ")
return -3, -3, ratio
# Find encodings for faces in the test image
log.debug("extract_face_from_image | Encoding faces using [{}] jitters ...".format(jitters))
# num_jitters increase the distortion check
faces_encodings = face_recognition.face_encodings(
X_img, known_face_locations=X_face_locations, num_jitters=jitters, model=encoding_models)
log.debug("extract_face_from_image | Face encoded! | Let's ask to the neural network ...")
return faces_encodings, X_face_locations, ratio
示例14: init_dataset_core
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def init_dataset_core(detection_model, jitters, encoding_models, img_path=None):
"""
Delegated core method for parallelize work
:detection_model
:jitters
:param img_path:
:return:
"""
try:
image = load_image_file(img_path)
except OSError:
log.error(
"init_dataset | === FATAL === | Image {} is corrupted!!".format(img_path))
return None
# log.debug("initDataset | Image loaded! | Searching for face ...")
# Array of w,x,y,z coordinates
# NOTE: Can be used batch_face_locations in order to parallelize the image init, but unfortunately
# it's the only GPU that i have right now. And, of course, i'll try to don't burn it
face_bounding_boxes = face_locations(image, model=detection_model)
face_data = None
if len(face_bounding_boxes) == 1:
log.info(
"initDataset | Image {0} have only 1 face, loading for future training ...".format(img_path))
# Loading the X [data] using 300 different distortion
face_data = face_encodings(image, known_face_locations=face_bounding_boxes, num_jitters=jitters,
model=encoding_models)[0]
else:
log.error(
"initDataset | Image {0} not suitable for training!".format(img_path))
if len(face_bounding_boxes) == 0:
log.error("initDataset | I've not found any face :/ ")
else:
log.error(
"initDataset | Found more than one face, too much for me Sir :&")
return face_data
示例15: preprocess
# 需要导入模块: import face_recognition [as 别名]
# 或者: from face_recognition import face_encodings [as 别名]
def preprocess(self, imgs):
for img in imgs:
if img is None:
continue
h, w, c = img.shape
# img = cv2.resize(img, (64, 64))
code = face_recognition.face_encodings(img, [(0, w, h, 0)])[0]
self.img_encode_code.append(code)
# self.img_encode_code_array = np.array(self.img_encode_code)