def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine'
, enable_face_analysis = True
, source = 0, time_threshold = 5, frame_threshold = 5):
"""
This function applies real time face recognition and facial attribute analysis
Parameters:
db_path (string): facial database path. You should store some .jpg files in this folder.
model_name (string): VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib or Ensemble
distance_metric (string): cosine, euclidean, euclidean_l2
enable_facial_analysis (boolean): Set this to False to just run face recognition
source: Set this to 0 for access web cam. Otherwise, pass exact video path.
time_threshold (int): how many second analyzed image will be displayed
frame_threshold (int): how many frames required to focus on face
"""
if time_threshold < 1:
raise ValueError("time_threshold must be greater than the value 1 but you passed "+str(time_threshold))
if frame_threshold < 1:
raise ValueError("frame_threshold must be greater than the value 1 but you passed "+str(frame_threshold))
functions.initialize_detector(detector_backend = 'opencv')
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis
, source = source, time_threshold = time_threshold, frame_threshold = frame_threshold)
def stream(db_path='',
model_name='VGG-Face',
distance_metric='cosine',
enable_face_analysis=True,
source=0,
time_threshold=5,
frame_threshold=5):
if time_threshold < 1:
raise ValueError(
"time_threshold must be greater than the value 1 but you passed " +
str(time_threshold))
if frame_threshold < 1:
raise ValueError(
"frame_threshold must be greater than the value 1 but you passed "
+ str(frame_threshold))
functions.initialize_detector(detector_backend='opencv')
realtime.analysis(db_path,
model_name,
distance_metric,
enable_face_analysis,
source=source,
time_threshold=time_threshold,
frame_threshold=frame_threshold)
def stream(
db_path="",
model_name="VGG-Face",
distance_metric="cosine",
enable_face_analysis=True,
):
realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
def stream(db_path='',
model_name='VGG-Face',
distance_metric='cosine',
enable_face_analysis=True):
functions.initialize_detector(detector_backend='opencv')
realtime.analysis(db_path, model_name, distance_metric,
enable_face_analysis)
def stream(db_path='',
model_name='Facenet',
distance_metric='cosine',
enable_face_analysis=False):
realtime.analysis(db_path, model_name, distance_metric,
enable_face_analysis)
def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True): realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)
import logging
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" # FATAL
logging.getLogger("tensorflow").setLevel(logging.FATAL)
from deepface import DeepFace
from deepface.commons import realtime
import youtube_dl
import cv2
from cv2 import VideoCapture
###########################################YOUTUBE
f_list = glob("nested/*/*")
ids = defaultdict(list)
for f in f_list:
ids[f.split('/')[1]].append(f)
dfs = DeepFace.find(img_path=f_list, db_path="nested")
models = ["VGG-Face", "Facenet", "OpenFace", "DeepFace", "DeepID"]
realtime.analysis(cap=cap,
out=out,
db_path="friends",
model_name=models[1],
distance_metric='cosine',
enable_face_analysis=False,
embd_saved=True)
