def get_test_face():
# video = "http://*****:*****@192.168.43.1:8081/"
video = 0
capture = cv2.VideoCapture(video)
begin = False
input = mkpath('tmp/input/')
count = 0
while True:
ret, frame = capture.read()
cv2.imshow('video', frame)
k = cv2.waitKey(1)
if k == ord('q') or count > 100:
break
if k == ord('s'):
begin = True
if begin:
str_time = str(time()).replace('.', '')[4:13]
path = input + '/' + str_time + '.jpg'
cv2.imwrite(path, frame)
print(count, path)
count += 1
capture.release()
cv2.destroyAllWindows()
def merge(source, target):
print("准备合成视频")
last_source = find_last_path(source)[0]
target_path = mkpath(target)
images_to_video(last_source, target_path)
plt.rcParams['axes.unicode_minus'] = False
fontpath = r"C:/Windows/Fonts/simfang.ttf" # 宋体字体文件
# 压缩图片， 基本不用了， 已废除
# compress()
# 加载训练模型
known_face_encodings, known_face_names = load_face()
# # Create arrays of known face encodings and their names
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
does, select = selection()
if select == '3':
ouput = mkpath('tmp/output/')
begin = time()
video_capture = cv2.VideoCapture(does)
msc_start = time()
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
try:
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
except Exception as result:
print("The error is ", result)
break
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]