def recFace():
# 加载模型
counter = 0
for dir_item in os.listdir(path_name):
counter += 1
if dir_item.endswith('.face.model.h5'):
model = Model()
model.load_model(file_path=path_name + dir_item)
# 框住人脸的矩形边框颜色
color = (0, 255, 0)
# 捕获指定摄像头的实时视频流
cap = cv2.VideoCapture(0)
# 循环检测识别人脸
while True:
try:
ret, frame = cap.read() # 读取一帧视频
# 图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(
r"./openCv/opencv/data/haarcascades/" +
"haarcascade_frontalface_default.xml")
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(70, 70))
for (x, y, w, h) in faceRects:
# 截取脸部图像提交给模型识别这是谁
image = frame[y - 10:y + h + 10, x - 10:x + w + 10]
faceID = model.face_predict(image)
print(faceID)
cv2.rectangle(frame, (x, y), (x + h, y + w), color, 2)
# 如果是“我”
if faceID == 0:
# 文字提示是谁
cv2.putText(frame,
dir_item.split('.')[0], (x, y + 1),
cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(115, 233, 86), 2)
else:
pass
cv2.imshow("Detect my face", frame)
# 等待10毫秒看是否有按键输入
k = cv2.waitKey(10)
# 如果输入q则退出循环
if k & 0xFF == ord('q'):
break
except BaseException:
continue
# 释放摄像头并销毁所有窗口
cap.release()
cv2.destroyAllWindows()
break
elif counter == len(os.listdir(path_name)):
print('No model has been found, please craft a model first!')
break
class Face_recognition():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def __init__(self):
with open('contrast_table', 'r') as f:
self.contrast_table = json.loads(f.read())
self.model = Model()
# 加载训练数据
self.model.load_model(file_path='F:/bs_data/lfb.h5')
# 框住人脸的矩形边框颜色
self.color = (0, 255, 0)
# 捕获指定摄像头的实时视频流
self.cap = cv2.VideoCapture(0)
# 调用人脸识别器
self.cascade_path = "D:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
def recongition(self):
while True:
ret, frame = self.cap.read() # 读取一帧视频
if ret is True:
# 图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
continue
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(self.cascade_path)
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
# 截取脸部图像提交给模型识别这是谁
image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
probability, name_number = self.model.face_predict(image)
print("name_number:", name_number)
name = self.contrast_table[str(name_number)]
print('name:', name)
cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), self.color, thickness=2)
# 标出识别者
# cv2.putText(frame, name, (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
if probability > 0.7:
cv2.putText(frame, name, (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
else:
cv2.putText(frame, 'unknow', (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
cv2.imshow("face_recognition", frame)
# 等待10毫秒看是否有按键输入
k = cv2.waitKey(10)
if k & 0xFF == ord('q'):
break
self.cap.release()
cv2.destroyAllWindows()
def upload():
model = Model()
model.load_model(file_path='./model/face.model')
file = request.files.get('file')
f = io.imread(file)
probability, name_number = model.face_predict(
transform.resize(f, (160, 160, 3)))
keyword_list = ['id', 'name', 'time']
path = "log.csv"
s = SaveCSV()
with open('contrast_table', 'r') as f:
contrast_table = json.loads(f.read())
res = {}
res['id'] = create_id()
res['name'] = contrast_table[str(name_number)]
res['time'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
s.save(keyword_list, path, res)
# res = "识别结果：" + contrast_table[str(name_number)]
return json.dumps(res, ensure_ascii=False)
continue
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(cascade_path)
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
# 截取脸部图像提交给模型识别这是谁
image = frame[y - 10:y + h + 10, x - 10:x + w + 10]
faceID = model.face_predict(image)
# 如果是“我”
if faceID == 0:
cv2.rectangle(frame, (x - 10, y - 10),
(x + w + 10, y + h + 10),
color,
thickness=2)
# 文字提示是谁
cv2.putText(
frame,
'manwanting',
(x + 30, y + 30), # 坐标
cv2.FONT_HERSHEY_SIMPLEX, # 字体
1, # 字号
class Face_recognition():
def __init__(self):
with open('contrast_table', 'r') as f:
self.contrast_table = json.loads(f.read())
self.model = Model()
self.model.load_model(file_path='G:\\face\\renlian\\faces.model')
# 框住人脸的矩形边框颜色
self.color = (0, 255, 0)
# 捕获指定摄像头的实时视频流
self.cap = cv2.VideoCapture(0)
# 人脸识别分类器本地存储路径
self.cascade_path = "haarcascade_frontalface_default.xml"
def recongition(self):
while True:
ret, frame = self.cap.read() # 读取一帧视频
if ret is True:
# 图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
continue
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(self.cascade_path)
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
# 截取脸部图像提交给模型识别这是谁
image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
probability, name_number = self.model.face_predict(image)
print(name_number)
name = self.contrast_table[str(name_number)]
# print('name_number:', name_number)
cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), self.color, thickness=2)
# 文字提示是谁
cv2.putText(frame, name, (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
# if probability > 0.7:
# cv2.putText(frame, name, (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
# else:
# cv2.putText(frame, 'unknow', (x + 30, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
cv2.imshow("face_recognition", frame)
# 等待10毫秒看是否有按键输入
k = cv2.waitKey(10)
# 如果输入q则退出循环
if k & 0xFF == 27:
break
# 释放摄像头并销毁所有窗口
self.cap.release()
cv2.destroyAllWindows()
def face_det(self):
if len(sys.argv) != 1:
print("Usage:%s camera_id\r\n" % (sys.argv[0]))
sys.exit(0)
#加载模型
model = Model()
model.load_model(file_path='./model/aggregate.face.model.h5')
# 框住人脸的矩形边框颜色
color = (0, 255, 0)
# 人脸识别分类器本地存储路径
cascade_path = r"D:\user\Software\Anaconda\Lib\site-packages\cv2\data\haarcascade_frontalface_alt2.xml"
# 循环检测识别人脸
while self.cap.isOpened():
ret, frame = self.cap.read() # 读取一帧视频
#facecolor = frame
if ret is True:
# 图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
continue
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(cascade_path)
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=2,
minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
# 截取脸部图像提交给模型识别这是谁
image = frame[y:y + h, x:x + w] #(改)
faceID = model.face_predict(image)
#print(model.)
cv2.rectangle(frame, (x - 10, y - 10),
(x + w + 10, y + h + 10),
color,
thickness=2)
#face_id判断（改）
for i in range(len(os.listdir('./data/'))):
if i == faceID:
#文字提示是谁
cv2.putText(
frame,
os.listdir('./data/')[faceID],
(x + 30, y + 30), # 坐标
cv2.FONT_HERSHEY_SIMPLEX, # 字体
1, # 字号
(255, 0, 255), # 颜色
2) # 字的线宽
else:
# 文字提示是谁
#cv2.putText(frame, '陌生人',
# (x + 30, y + 30), # 坐标
# cv2.FONT_HERSHEY_SIMPLEX, # 字体
# 1, # 字号
# (255, 0, 255), # 颜色
# 2) # 字的线宽
frame = self.cv2ImgAddText(frame, "陌生人", x + 30,
y + 30, (255, 0, 255),
25)
#frame = Image.blend(frame, img, 0.3)
#cv2.imshow('show', img)
show = cv2.cvtColor(frame,
cv2.COLOR_BGR2RGB) #视频色彩转换回RGB，这样才是现实的颜色
#show = cv2.resize(self.image,(840,680))
showImage = QtGui.QImage(
show.data, show.shape[1], show.shape[0],
QtGui.QImage.Format_RGB888) #把读取到的视频数据变成QImage形式
#hanziimg = QtGui.QImage(img.data,img.shape[1],img.shape[0],QtGui.QImage.Format_RGB888)
self.cameraLabel.setPixmap(
QtGui.QPixmap.fromImage(showImage)) #往显示视频的Label里 显示QImage
#self.cameraLabel.setPixmap(QtGui.QPixmap.fromImage(hanziimg))
#等待10毫秒看是否有按键输入,如果注释掉就无法显示视频
cv2.waitKey(10)
# 释放摄像头并销毁所有窗口
self.cap.release()
cv2.destroyAllWindows()
from face_train import Model
from skimage import io, transform
import json
if __name__ == '__main__':
model = Model()
model.load_model(file_path='./model/face.model')
img = io.imread("D:/python/workspace/Face1/data/lp/3.jpg")
print(img.shape)
probability, name_number = model.face_predict(img)
with open('contrast_table', 'r') as f:
contrast_table = json.loads(f.read())
print(probability)
print(contrast_table[str(name_number)])
continue
#使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(cascade_path)
#利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
#截取脸部图像提交给模型识别这是谁
image = frame[y - 10:y + h + 10, x - 10:x + w + 10]
result, result2 = model.face_predict(image)
#图像灰化，降低计算复杂度
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print('result:', result)
count = 0
for each in result:
for each1 in each:
if each1 <= 0.72:
count += 1
else:
pass
if count == length:
result2 = -1
