names=['goutham','lokesh','milind','nishant','pranjal','sumanth']
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:
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], :]
from recognition.FaceRecognition import FaceRecognition
from classifier.FaceClassifier import FaceClassifier
if os.name == 'nt':
SPERATOR = '\\'
else:
SPERATOR = '/'
BASE_DIR = os.path.dirname(__file__) + SPERATOR
PROFILE_ROUND = '.4f'
FACE_DETEC_THRESHOLD = 0.5
face_detector = FaceDetector()
face_recognition = FaceRecognition()
class Face:
def __init__(self, rep, identity):
self.rep = rep
self.identity = identity
def __repr__(self):
return "{{id: {}, rep[0:10]: {}}}".format(
str(self.identity),
self.rep[0:10],
)
import cv2
import numpy as np
from recognition.FaceRecognition import FaceRecognition
from detection.FaceDetector import FaceDetector
face_detector = FaceDetector()
face_recognition = FaceRecognition()
image_files = ['./media/1.jpg', './media/2.jpg']
for input_str in image_files:
img = cv2.imread(input_str)
boxes, scores = face_detector.detect(img)
face_boxes = boxes[np.argwhere(scores > 0.5).squeeze()]
print('Number of face in image:', len(face_boxes))
for box in face_boxes:
cropped_face = img[box[0]:box[2], box[1]:box[3], :]
cropped_face = cv2.resize(cropped_face, (160, 160),
interpolation=cv2.INTER_AREA)
print('Face descriptor:')
print(face_recognition.recognize(cropped_face), '\n')
cv2.imshow('image', cropped_face)
cv2.waitKey(0)
cv2.destroyAllWindows()
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()
import cv2
import os
import numpy as np
from recognition.facenet import get_dataset
from recognition.FaceRecognition import FaceRecognition
from detection.FaceDetector import FaceDetector
from classifier.FaceClassifier import FaceClassifier
datadir = './media/train_classifier'
face_detector = FaceDetector()
face_recognition = FaceRecognition()
face_classfier = FaceClassifier()
def get_image_paths_and_labels(dataset):
image_paths_flat = []
labels_flat = []
for i in range(len(dataset)):
image_paths_flat += dataset[i].image_paths
labels_flat += [dataset[i].name] * len(dataset[i].image_paths)
return image_paths_flat, labels_flat
dataset = get_dataset(datadir)
paths, labels = get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Run forward pass to calculate embeddings
recog_ckpt = 'model/inception_resnet_v1_20170512-110547.pb'
if args.classifier == "SVM":
model_type = "SVM"
elif args.classifier == "random-forests":
model_type = "random-forests"
elif args.classifier == "KNN":
model_type = "knn"
elif args.classifier == "DNN":
model_type = "dnn"
mobilenet = False
if args.recognition_model == "mobilenet_v2":
mobilenet = True
face_detector = FaceDetector(PATH_TO_CKPT=detect_ckpt)
face_recognition = FaceRecognition(PATH_TO_CKPT=recog_ckpt)
face_classfier = FaceClassifier(args.trained_classifier)
def get_image_paths_and_labels(dataset):
image_paths_flat = []
labels_flat = []
for i in range(len(dataset)):
image_paths_flat += dataset[i].image_paths
labels_flat += [dataset[i].name] * len(dataset[i].image_paths)
return image_paths_flat, labels_flat
dataset = get_dataset(datadir)
paths, labels = get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))