def __init__(self, profile_activated):
self.profile_activated = profile_activated
self.model_path = BASE_DIR + SPERATOR + "facedatabase" + SPERATOR
self.svm_model_name = "trained_classifier.pkl"
self.image_rep_name = "faces.npy"
self.people_rep_name = "people.npy"
self.image_folder_path = "images" + SPERATOR
if self.profile_activated is True:
print("FaceRecognition: initialize face recognizer")
self.svm = None
self.training = False
self.training_id = 0
self.training_name = "Unknown"
self.trainingInProgress = False
self.identify_threshold = 0.8
self.face_classfier = None
if not os.path.exists(self.model_path):
os.makedirs(self.model_path)
if self.profile_activated is True:
print("FaceRecognition: loading prepresentations of kownen persons")
try:
self.images = np.load(self.model_path + SPERATOR + self.image_rep_name,allow_pickle=True).item()
except IOError:
if self.profile_activated is True:
print("Warning: no image representation file found")
self.images = {}
try:
self.people = np.load(self.model_path + SPERATOR + self.people_rep_name,allow_pickle=True).item()
except IOError:
if self.profile_activated is True:
print("Warning: no people representation file found")
self.people = {}
self.people["0"] = "Unknown"
if self.profile_activated is True:
print ("FaceRecognition: try to load saved svm for estimation")
try:
self.face_classfier = FaceClassifier(self.model_path + SPERATOR + self.svm_model_name)
except IOError:
if self.profile_activated is True:
print ("Warning: no svm saved")
self.face_classfier = None
if self.profile_activated is True:
print ("FaceRecognition: initialisation done")
self.tracker_sort = Sort(25,3)
def trainSVM(self):
self.trainingInProgress = True
if self.profile_activated is True:
print ("FaceRecognition: Training SVM on {} labeled images.".format(len(self.images)))
d = self.getData()
if d is None:
self.face_classfier = None
if self.profile_activated is True:
print ("FaceRecognition: at least 2 persons needed ..")
self.trainingInProgress = False
return
else:
(X, y) = d
numIdentities = len(set(y + [-1]))
if numIdentities <= 1:
self.trainingInProgress = False
return
self.face_classfier = FaceClassifier()
self.face_classfier.train(X, y, model='svm', save_model_path=(self.model_path + SPERATOR + self.svm_model_name))
self.saveImageDataAndTrainedNet()
print ("training done!")
self.trainingInProgress = False
import cv2
import time
import numpy as np
from detection.FaceDetector import FaceDetector
from recognition.FaceRecognition import FaceRecognition
from classifier.FaceClassifier import FaceClassifier
face_detector = FaceDetector()
face_recognition = FaceRecognition()
face_classfier = FaceClassifier('./classifier/trained_classifier.pkl')
video_capture = cv2.VideoCapture(0)
print('Start Recognition!')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0, 0), fx=1, fy=1) # resize frame (optional)
#time.sleep(1)
curTime = time.time() # calc fps
find_results = []
frame = frame[:, :, 0:3]
boxes, scores = face_detector.detect(frame)
face_boxes = boxes[np.argwhere(scores>0.3).reshape(-1)]
face_scores = scores[np.argwhere(scores>0.3).reshape(-1)]
print('Detected_FaceNum: %d' % len(face_boxes))
if len(face_boxes) > 0:
for i in range(len(face_boxes)):
box = face_boxes[i]
cropped_face = frame[box[0]:box[2], box[1]:box[3], :]
return names[ind]
def _update_attendance(attendance_list):
'''
Updates attendance
Args:
attend
'''
with open(args.attendance_file,"w") as f:
attendance_list=[a+"\n" for a in attendance_list]
f.writelines(attendance_list)
face_detector = FaceDetector(PATH_TO_CKPT=detect_ckpt)
face_recognition = FaceRecognition(PATH_TO_CKPT=recog_ckpt)
face_classfier = FaceClassifier(args.trained_classifier)
face_classfier_svm=FaceClassifier('./classifier/trained_svm.pkl')
face_classfier_knn7=FaceClassifier('./classifier/knn_7.pkl')
face_classfier_knn7=FaceClassifier('./classifier/knn_5.pkl')
face_classfier_rf=FaceClassifier('./classifier/random_forests.pkl')
#video_capture = cv2.VideoCapture(args.camera)
video_capture = cv2.VideoCapture(0)
print('Start Recognition!')
prevTime = 0
count=0
attendance_list=[]
skip=0
while True:
ret, frame = video_capture.read()
"all"
]
input_folder = path.join(path.abspath(path.curdir), "data", "input")
people_folders = None
number_imgs_list = []
embeddings = []
embeddings_ids = []
embeddings_df = None
people_df = None
vector_size = None
face_classifier = FaceClassifier()
def download(file_path, url):
import requests
import math
r = requests.get(url, stream=True)
total_size = int(r.headers.get('content-length'))
block_size = 1024
with open(file_path, 'wb') as f:
for data in tqdm(r.iter_content(block_size), total=math.ceil(total_size // block_size), desc="Download",
unit='B', unit_scale=True, unit_divisor=1024):
f.write(data)
import cv2
import time
import numpy as np
from detection.FaceDetector import FaceDetector
from recognition.FaceRecognition import FaceRecognition
from classifier.FaceClassifier import FaceClassifier
VIDEO_INPUT_FILE = './media/test_video/Zidane_1.avi'
VIDEO_OUTPUT_FILE = './media/test_video_output/Zidane_Recognition_1.avi'
FACE_CLASSIFIER_MODEL = './classifier/trained_classifier_lfw.pkl'
face_detector = FaceDetector()
face_recognition = FaceRecognition()
face_classfier = FaceClassifier(FACE_CLASSIFIER_MODEL)
video_capture = cv2.VideoCapture(VIDEO_INPUT_FILE)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(VIDEO_OUTPUT_FILE, fourcc, 24.0,
(int(video_capture.get(3)), int(video_capture.get(4))))
print('Start Recognition!')
prevTime = 0
while video_capture.isOpened():
ret, frame = video_capture.read()
curTime = time.time() # calc fps
find_results = []
frame = frame[:, :, 0:3]
boxes, scores = face_detector.detect(frame)
face_boxes = boxes[np.argwhere(scores > 0.3).reshape(-1)]
def main(args):
face_detector = FaceDetector()
face_recognition = FaceRecognition(args.model)
face_classfier = FaceClassifier(args.classifier_filename)
video_capture = cv2.VideoCapture(args.video_input)
output_file = './media/result/' + os.path.basename(
args.video_input) + '_result.avi'
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(
output_file, fourcc, 24.0,
(int(video_capture.get(3)), int(video_capture.get(4))))
print('Start Recognition!')
prevTime = 0
while video_capture.isOpened():
ret, frame = video_capture.read()
curTime = time.time() # calc fps
find_results = []
frame = frame[:, :, 0:3]
boxes, scores = face_detector.detect(frame)
face_boxes = boxes[np.argwhere(scores > 0.3).reshape(-1)]
face_scores = scores[np.argwhere(scores > 0.3).reshape(-1)]
print('Detected_FaceNum: %d' % len(face_boxes))
if len(face_boxes) > 0:
for i in range(len(face_boxes)):
box = face_boxes[i]
cropped_face = frame[box[0]:box[2], box[1]:box[3], :]
cropped_face = cv2.resize(cropped_face, (160, 160),
interpolation=cv2.INTER_AREA)
feature = face_recognition.recognize(cropped_face)
name = face_classfier.classify(feature)
cv2.rectangle(frame, (box[1], box[0]), (box[3], box[2]),
(0, 255, 0), 2)
# plot result idx under box
text_x = box[1]
text_y = box[2] + 20
cv2.putText(frame,
name, (text_x, text_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255),
thickness=1,
lineType=2)
else:
print('Unable to align')
sec = curTime - prevTime
prevTime = curTime
fps = 1 / (sec)
str = 'FPS: %2.3f' % fps
text_fps_x = len(frame[0]) - 150
text_fps_y = 20
cv2.putText(frame,
str, (text_fps_x, text_fps_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 0),
thickness=1,
lineType=2)
out.write(frame)
video_capture.release()
out.release()
cv2.destroyAllWindows()
def _update_attendance(attendance_list):
'''
Updates attendance
Args:
attend
'''
with open(args.attendance_file, "w") as f:
attendance_list = [a + "\n" for a in attendance_list]
f.writelines(attendance_list)
face_detector = FaceDetector(PATH_TO_CKPT=detect_ckpt)
face_recognition = FaceRecognition(PATH_TO_CKPT=recog_ckpt)
face_classfier = FaceClassifier(args.trained_classifier)
#video_capture = cv2.VideoCapture(args.camera)
print('Start Recognition!')
prevTime = 0
count = 0
attendance_list = []
i = 1
while True:
j = 37 + i
path = './media/train_classifier/milind/'
frame = cv2.imread(path +
'WhatsApp Image 2018-11-01 at 9.23.%d PM.jpeg' % j)
print('%dth recognition' % i)
i += 1
import numpy as np
from classifier.FaceClassifier import FaceClassifier
#Training classifiers after obtaining the feaure vectors from the recognition model
features = np.load('./npy files/features.npy')
labels = np.load('./npy files/labels.npy')
face_classifier = FaceClassifier('./classifier/trained_svm.pkl')
Svm = face_classifier.train(
features,
labels,
model='SVM',
save_model_path='./classifier/new_classifiers/trained_svm.pkl')
#KNN = face_classifier.train(features,labels,model='knn',save_model_path='./classifier/trained_classifier.pkl')
KNN_5 = face_classifier.train(
features,
labels,
model='knn',
knn_neighbours=5,
save_model_path='./classifier/new_classifiers/knn_5.pkl')
KNN_7 = face_classifier.train(
features,
labels,
model='knn',
knn_neighbours=7,
save_model_path='./classifier/new_classifiers/knn_7.pkl')
random_forests = face_classifier.train(
features,
labels,
model='random-forests',
save_model_path='./classifier/new_classifiers/random_forests.pkl')
Svm_poly = face_classifier.train(
features,
import time
import cv2
import numpy as np
from classifier.FaceClassifier import FaceClassifier
from detection.FaceDetector import FaceDetector
from embeddings.FaceEmbeddings import FaceEmbeddings
model = input("Choose a model> ")
# model = "knn"
face_detector = FaceDetector()
face_recognition = FaceEmbeddings()
face_classifier = FaceClassifier(
f'./classifier/{model.lower()}_classifier.pkl')
video_capture = cv2.VideoCapture(0)
prevTime = 0
cv2.namedWindow("Video", cv2.WINDOW_NORMAL)
print('Start Recognition!')
while True:
ret, frame = video_capture.read()
# if not ret:
# break
frame = cv2.resize(frame, (0, 0), fx=1, fy=1) # resize frame (optional)
curTime = time.time() # calc fps