def getdata():
video_capture = cv2.VideoCapture(0)
# 语音模块 voice model
speaker = win32com.client.Dispatch("SAPI.SpVoice")
name = "Unknown"
current_names = []
last_time = time.time()
known_face_names = []
known_face_encodings = []
known_face_encodings, known_face_names = encoding_images.load_encodings()
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True #
TIME_DIFF = 20 # 持久化的时间间隔,当设置为 0 时候,每次识别的结果直接进行保存.
name_record = "C:\workspace\\facefront\\face\dataset\\face_record.txt" # 持久化识别出的人脸结果
def process_face_records(name):
"""
处理每一条识别的记录 ,并在一定时间之后将数据持久化到文件中
:param name:
:return:
"""
global current_names, last_time
print("global current_names {}, last_time {}".format(current_names, last_time))
# 判断是不是在识别的列表中,不在的话就进行问候
if name not in current_names:
print("ts ====", last_time, time.time())
current_names.append(name)
print("Hello {}, nice to meet you! ".format(name))
speaker.Speak("Hello {}, nice to meet you! ".format(name))
# 在一定时间内,清空已经识别的人, 并进行
if last_time < time.time() - TIME_DIFF: # 每隔一段时间清空一下检测到的人
last_time = time.time()
time_format = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print(time_format + " update last_time and clear current_names.")
with open(name_record, 'a') as f:
if len(current_names) > 0:
f.writelines("{}:{} \n".format(time_format, str(current_names)))
# print("======", current_names)
current_names.clear()
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
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
# face_locations = face_recognition.face_locations(rgb_small_frame, model="cnn")
face_locations = face_recognition.face_locations(rgb_small_frame, model="hog")
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
ts1 = (int(round(time.time() * 1000)))
matches = face_recognition.compare_faces( \
\
known_face_encodings=known_face_encodings, face_encoding_to_check=face_encoding, tolerance=0.3)
ts2 = (int(round(time.time() * 1000)))
print("matches ts1 {} ts2{} cost {}ms".format(ts1, ts2, ts2 - ts1)) # matches
# 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 = known_face_names[first_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
print("process_face_records====")
# say hello
process_face_records(name)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
return name
# face_recognition.api.compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6)
# face_recognition.api.face_distance(face_encodings, face_to_compare)[source]
# face_recognition.api.face_encodings(face_image, known_face_locations=None, num_jitters=1)[source]
# face_recognition.api.face_landmarks(face_image, face_locations=None)[source]
# face_recognition.api.face_locations(img, number_of_times_to_upsample=1, model='hog')[source]
# face_recognition.api.load_image_file(file, mode='RGB')[source]
# 语音模块 voice model
speaker = win32com.client.Dispatch("SAPI.SpVoice")
name = "Unknown"
current_names = [name]
last_time = time.time()
known_face_names = []
known_face_encodings = []
known_face_encodings, known_face_names = encoding_images.load_encodings()
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True #
TIME_DIFF = 20 # 持久化的时间间隔,当设置为 0 时候,每次识别的结果直接进行保存.
name_record = "./dataset/face_record.txt" # 持久化识别出的人脸结果
NAME_DF = pd.DataFrame(known_face_names, columns=["name"])
last_ts = time.time()
lock = threading.Lock()
def myprint(log, ts):
global lock, last_ts