def write(x, batches, results):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
#print("c1 is {}, c2 is {}".format(c1,c2))
img = results[int(x[0])]
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2, color, 1)
cv2.imshow('rect1', img)
cv2.waitKey(3000)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 30
cv2.rectangle(img, c1, c2, color, -1)
cv2.imshow('rect2', img)
cv2.waitKey(3000)
ft = ft2.put_chinese_text('msyh.ttf')
#print("label is {}".format(label))
ft.draw_text(image=img,
pos=(c1[0], c1[1] + t_size[1] - 7),
text=label,
text_size=20,
text_color=[255, 255, 255])
# cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1)
cv2.imshow('hello', img)
cv2.waitKey(3000)
return img
def write(x, img):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
conf = str(x[-2].data)
label = "{} {}%".format(classes[cls], float(conf[7:11])*100)
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
#c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
#cv2.rectangle(img, c1, c2,color, -1)
#cv2.rectangle(img, c1, c2, color, -1)
ft = ft2.put_chinese_text('msyh.ttf')
ft.draw_text(image=img, pos=(c1[0], c1[1] + t_size[1] - 7), text=label, text_size=15, text_color=[255, 255, 255])
#cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [225,255,255], 1);
return img
def write(x, img):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
#label = "{0}".format(classes[cls])
label = classes[cls]
color = random.choice(colors)
cv2.rectangle(img, c1, c2, color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2, color, -1)
ft = ft2.put_chinese_text('msyh.ttf')
ft.draw_text(image=img,
pos=(c1[0], c1[1] + t_size[1] - 7),
text=label,
text_size=15,
text_color=[255, 255, 255])
#cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
return img
def data_init(self):
config = Read_config()
self.my_config = config.my_config
self.font_path = "font//msyh.ttc"
self.font_size = 18
self.font_color = (255, 255, 255)
self.font_position = {
"start_x": 800,
"start_y": 10,
"add_x": 0,
"add_y": 30
}
self.video_num = 4
self.video_path = "video/"
self.video_x_num = 2
self.video_y_num = 2
self.video_width = 500
self.video_height = 300
self.ft = ft2.put_chinese_text(self.font_path)
self.detector = [Detector(self.video_width, self.video_height, self.my_config) for i in range(self.video_num)]
def Run(self, input_dir, output_dir):
if (not os.path.exists(output_dir)):
os.makedirs(output_dir)
self.ft = ft2.put_chinese_text(os.path.join(ft_path, 'msyh.ttf'))
image_list = glob.glob(os.path.join(input_dir, "*.jpg"))
for start in range(0, len(image_list), batch_size):
try:
cur_image_list = image_list[
start:min(start + batch_size, len(image_list))]
l = [cv2.imread(image) for image in cur_image_list]
start = time.time()
ocr_result = self.ocr(l)
print('cost', (time.time() - start))
for image, result, name in zip(l, ocr_result['result'],
cur_image_list):
cur_image = numpy.zeros(image.shape)
print(result['rect'])
for rect in result['rect']:
if not rect[5]: continue
cur_image = cv2.rectangle(cur_image, tuple(rect[0:2]),
tuple(rect[2:4]),
(0, 255, 0), 1)
cur_image = self.ft.draw_text(cur_image, rect[0:2],
rect[5], 15, (0, 255, 0))
new_name = os.path.join(
output_dir,
os.path.splitext(os.path.basename(name))[0] +
"_debug.jpg")
cur_image = numpy.hstack((image, cur_image))
cv2.imwrite(new_name, cur_image)
json_name = os.path.splitext(new_name)[0] + ".json"
f = open(json_name, "wb")
json.dump(ocr_result['result'], f)
f.close()
except Exception as e:
print(e)
# -*- coding: UTF-8 -*-
import face_recognition
import cv2
import os
import ft2
#中文支持,加载微软雅黑字体
ft = ft2.put_chinese_text('msyh.ttf')
# 获取摄像头# 0（默认）
video_capture = cv2.VideoCapture(0)
# 加载待识别人脸图像并识别。
basefacefilespath = "images" #faces文件夹中放待识别任务正面图,文件名为人名,将显示于结果中
baseface_titles = [] #图片名字列表
baseface_face_encodings = [] #识别所需人脸编码结构集
#读取人脸资源
for fn in os.listdir(basefacefilespath): #fn 人脸文件名
baseface_face_encodings.append(
face_recognition.face_encodings(
face_recognition.load_image_file(basefacefilespath + "/" + fn))[0])
fn = fn[:(len(fn) - 4)]
baseface_titles.append(fn) #
while True:
# 获取一帧视频
ret, frame = video_capture.read()
# 人脸检测,并获取帧中所有人脸编码
face_locations = face_recognition.face_locations(frame)
face_encodings = face_recognition.face_encodings(frame, face_locations)
# 遍历帧中所有人脸编码
for (top, right, bottom,
left), face_encoding in zip(face_locations, face_encodings):
# 与baseface_face_encodings匹配否?
for i, v in enumerate(baseface_face_encodings):
def show_camera(self): #展示摄像头画面并进行人脸识别的功能
print('show_camera is open ')
if self.video_btn == 0: #在前面就设置了video_btn为0 为了在人脸识别的时候直接把这个值给改了 这样人脸识别和摄像头展示就分开了
if self.source == "rtsp://*****:*****@192.168.1.61/Streaming/Channels/1":
self.pushButton_8.setText(u'关闭摄像头1')
else:
self.pushButton_6.setText(u'关闭摄像头2')
while (self.cap.isOpened()):
ret, self.image = self.cap.read()
QApplication.processEvents(
) #这句代码告诉QT处理来处理任何没有被处理的事件，并且将控制权返回给调用者 让代码变的没有那么卡
show = cv2.resize(self.image, (800, 494))
show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB) # 这里指的是显示原图
# opencv 读取图片的样式，不能通过Qlabel进行显示，需要转换为Qimage QImage(uchar * data, int width,
self.showImage = QImage(show.data, show.shape[1],
show.shape[0], QImage.Format_RGB888)
self.label_5.setPixmap(QPixmap.fromImage(self.showImage))
# 因为他最后会存留一张 图像在lable上需要对 lable_5进行清理
self.label_5.clear()
print('打开摄像头时间', time() - self.t3)
elif self.video_btn == 1:
#这段代码是 获取photo文件夹中 人的信息
filepath = 'photo'
filename_list = listdir(filepath)
known_face_names = []
known_face_encodings = []
a = 0
print('2')
for filename in filename_list: #依次读入列表中的内容
a += 1
QApplication.processEvents()
if filename.endswith('jpg'): # 后缀名'jpg'匹对
known_face_names.append(
filename[:-4]) #把文件名字的后四位.jpg去掉获取人名
file_str = 'photo\\' + filename
a_images = face_recognition.load_image_file(file_str)
print(file_str)
a_face_encoding = face_recognition.face_encodings(
a_images)[0]
known_face_encodings.append(a_face_encoding)
print(known_face_names, a)
#knowe_face_names里面放着每个人的名字 known_face_encodings里面放着提取出来的每个人的人脸特征信息
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while (self.cap.isOpened()):
ret, frame = self.cap.read()
QApplication.processEvents()
# 改变摄像头图像的大小，图像小，所做的计算就少
small_frame = cv2.resize(frame, (0, 0), fx=0.33, fy=0.33)
# print('3 is running')
# opencv的图像是BGR格式的，而我们需要是的RGB格式的，因此需要进行一个转换。
rgb_small_frame = small_frame[:, :, ::-1]
#print('4 is running')
if process_this_frame:
QApplication.processEvents()
# 根据encoding来判断是不是同一个人，是就输出true，不是为flase
face_locations = face_recognition.face_locations(
rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
# print('5 is running')
for face_encoding in face_encodings:
# 默认为unknown
matches = face_recognition.compare_faces(
known_face_encodings,
face_encoding,
tolerance=0.42)
#阈值太低容易造成无法成功识别人脸，太高容易造成人脸识别混淆 默认阈值tolerance为0.6
#print(matches)
name = "Unknown"
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
# 将捕捉到的人脸显示出来
self.set_name = set(face_names)
self.set_names = tuple(
self.set_name
) # 把名字先设为了一个 集合 把重复的去掉 再设为tuple 以便于下面显示其他信息和记录 调用
voice_syn = str()
print(self.set_names) #把人脸识别检测到的人 用set_names 这个集合收集起来
self.write_record() #把名字记录到excel中去
#self.video_announce()
for (top, right, bottom,
left), name in zip(face_locations, face_names):
#由于我们检测到的帧被缩放到1/4大小，所以要缩小面位置
top *= 3
right *= 3
bottom *= 3
left *= 3
# 矩形框
cv2.rectangle(frame, (left, top), (right, bottom),
(0, 0, 255), 3)
ft = ft2.put_chinese_text('msyh.ttf')
#引入ft2中的字体
# cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)这是不输入汉字时可以用的代码
frame = ft.draw_text(frame, (left + 10, bottom), name, 25,
(0, 0, 255))
print('face recognition is running')
#def draw_text(self, image, pos, text, text_size, text_color)
self.show_picture() # 调用显示详细信息的函数
show_video = cv2.resize(frame, (800, 494))
show_video = cv2.cvtColor(show_video,
cv2.COLOR_BGR2RGB) # 这里指的是显示原图
# opencv 读取图片的样式，不能通过Qlabel进行显示，需要转换为Qimage QImage(uchar * data, int width,
self.showImage = QImage(show_video.data, show_video.shape[1],
show_video.shape[0],
QImage.Format_RGB888)
self.label_5.setPixmap(QPixmap.fromImage(self.showImage))
print('打开人脸识别所需要的时间', time() - self.t2)
if not q_frame_main2face_detect.empty(): #非空
frame = q_frame_main2face_detect.get() #get()后,大小减1
# 使用opencv的人脸检测
face_bbox = face_opencv_detect(frame)
if (len(face_bbox) > 0):
q_frame_face_detect2face_recognize.put(frame)
q_face_bbox.put(face_bbox)
print 'detected'
else:
print 'not detected'
else:
time.sleep(0.02) #空,sleep 20ms
import ft2
ft = ft2.put_chinese_text('simhei.ttf')
id_more = []
count = 0
#创建进程
p1 = multiprocessing.Process(target=Face_detect)
p2 = Face_recongnize()
p1.daemon = True #设置为守护进程,主进程结束,子进程强制结束
p2.daemon = True
p2.start() #开始
p1.start()
while (1):
start = time.time()
ret, frame = videoCapture.read()
input=True,
frames_per_buffer=CHUNK,
input_device_index=4)
camera1 = cv2.VideoCapture(0) # capture camera
#camera2 = cv2.VideoCapture(1)
camera2 = camera1
cv2.namedWindow("Camera", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("Camera", cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
video_default = cv2.VideoCapture(VIDEO_DEFAULT)
video_60 = cv2.VideoCapture(VIDEO_60)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1
ft = ft2.put_chinese_text('wqy-zenhei.ttc')
max_volume = 0
while (True):
data = stream.read(CHUNK, exception_on_overflow=False)
volume = volume_of(data)
if volume > max_volume:
max_volume = volume
#print max_volume
if volume < SWITCH:
playing_video = video_default
else:
playing_video = video_60
def __init__(self):
self.ft = ft2.put_chinese_text(os.path.join(ft_path, 'msyh.ttf'))
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
right = right * 2 + 10
top = top * 2 - 10
bottom = bottom * 2 + 10
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# 画出一个带名字的标签，放在框下
cv2.rectangle(frame, (left, bottom + 35), (right, bottom), (0, 0, 255),
cv2.FILLED)
line = name
color = (255, 255, 255)
pos = (left + 40, bottom + 5)
text_size = 25
# ft = put_chinese_text('wqy-zenhei.ttc')
ft = put_chinese_text(
'/home/nvidia/.local/share/fonts/KaiTi_GB2312.ttf')
frame = ft.draw_text(frame, pos, line, text_size, color)
#font = cv2.FONT_HERSHEY_DUPLEX
#cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)
# 显示结果图像
cv2.imshow('人脸识别系统', frame)
# 按q退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#p暂停，c恢复
elif cv2.waitKey(1) & 0xFF == ord('p'):
while True:
if cv2.waitKey(1) & 0xFF == ord('c'):
break