def Predict_Dense():
# 从摄像头中读取视频流
video_capture = cv2.VideoCapture(0)
check_weight(DENSE_WEIGHT_PATH)
model = dense_models()
# 加载权重
model.load_weights(DENSE_WEIGHT_PATH)
while True: # 读取每一帧
ret, frame = video_capture.read() # 0.01s
small_frame = cv2.resize(frame, (0, 0), fx=0.75, fy=0.75)
rgb_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_frame) # 0.17s
if face_locations == []:
continue
face_encodings = face_recognition.face_encodings(
rgb_frame, face_locations) # 找到了当前帧中所有面部的编码 #0.51s
for face_location in face_locations: # 画矩形时间：不足0.001s
top, right, bottom, left = face_location
cv2.rectangle(small_frame, (left, top), (right, bottom),
(0, 0, 255), 2)
out = model.predict(np.array(face_encodings)) # 神经网络预测时间：0.001s
for o in out:
print("Welcome people：{}".format(o.argmax() + 1))
cv2.imshow('Video', small_frame) # 展示一帧时间：不足0.001s
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows
def Predict_CNN():
# 从摄像头中读取视频流
video_capture = cv2.VideoCapture(0)
check_weight(CNN_WEIGHT_PATH)
model = cnn_models()
# 加载权重
model.load_weights(CNN_WEIGHT_PATH)
while True: # 读取每一帧
face_image = []
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_locations = face_recognition.face_locations(
gray) # 0.18s 其余所有时间为0.012s
if face_locations == []:
continue
for face_location in face_locations:
top, right, bottom, left = face_location
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
face = gray[top:bottom, left:right]
face_image.append(cv2.resize(face, (weight, height)))
data = np.array(face_image).astype(np.float32).reshape(-1, 64, 64,
1) / 255
out = model.predict(data) # 神经网络预测时间：0.001s
for o in out:
print("Welcome people：{}".format(o.argmax() + 1))
cv2.imshow('Video', frame) # 展示一帧时间：不足0.001s
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows
def test():
print("begin testing")
check_weight(CNN_WEIGHT_PATH)
model = cnn_models()
model.load_weights(CNN_WEIGHT_PATH)
test_x, test_y = get_pictures(Is_Train=False)
out = model.test_on_batch(test_x, test_y)
print("loss = {:.4f}, accuracy = {:.4f}".format(out[0], out[1]))
print("test finish\n")
def test():
check_weight(DENSE_WEIGHT_PATH)
model = dense_models()
model.load_weights(DENSE_WEIGHT_PATH)
data = []
test_x, test_y = get_encodings(False)
data.append(test_x)
data = np.array(data).astype(np.float32).reshape(-1, 128)
out = model.test_on_batch(data, test_y)
print("loss = {:.4f}, accuracy = {:.4/}".format(out[0], out[1]))
def Predict_CNN():
video_capture = cv2.VideoCapture(0) # 从摄像头中读取视频流
check_weight(CNN_WEIGHT_PATH) #核查是否存在权重
model = cnn_models() #模型
model.load_weights(CNN_WEIGHT_PATH) # 加载权重
face_cascade = cv2.CascadeClassifier('D:\Program files\python36\Lib\site-packages\cv2\data'
'/haarcascade_frontalface_default.xml')
i = 0 # 每20帧输出一次，i作循环变量
who = {} # 记录每个人出现的概率
numbers = {} # 记录每个人出现的次数
number = 0 # 总人数
while True: # 读取每一帧
face_locations = []
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 灰度化
face_locations = face_cascade.detectMultiScale(gray, 1.2, 5) # 第二个参数是每次检测放大的百分比，
# face_locations = face_recognition.face_locations(gray) # 0.18s 其余所有时间为0.012s
if len(face_locations) != 0: # 检测到视频帧中有人脸
number += len(face_locations) # 每帧中人脸的总数
for face_location in face_locations:
# top, right, bottom, left = face_location # face_recognition的边框
# cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# face = gray[top:bottom, left:right]
face_image = []
x, y, h, w = face_location # cv2 的边框，矩形的表示方法却不同
cv2.rectangle(frame, (x, y), (x + h, y + w), (0, 0, 255), 2)
face = gray[y:y + h, x:x + w]
try:
face_image.append(cv2.resize(face, (WIDTH, HEIGHT)))
data = np.array(face_image).astype(np.float32).reshape(-1, WIDTH, HEIGHT, 1) / 255
results = model.predict(data) # 神经网络预测时间：0.001s
if who == {}:
for m in range(len(face_locations)):
temp = results[m].argmax()
who[temp] = results[m].take(temp)
numbers[temp] = 1
else:
for m in range(len(face_locations)):
temp = results[m].argmax()
if temp in who.keys():
who[temp] += results[m].take(temp)
numbers[temp] += 1
else:
who[temp] = results[m].take(temp)
numbers[temp] = 1
i += 1
if i % 20 == 0:
print(str(who))
print(number)
for m in range(len(who)):
print("Welcome people:{}".format(list(who.keys())[m]),end='\t')
print(who[list(who.keys())[m]] / numbers[list(numbers.keys())[m]],end='\t')
print(numbers[list(numbers.keys())[m]], '\n')
# a = int(numbers[list(numbers.key())[m]])
# b = int(number // 3)
# c = who[list(who.keys())[m]] / numbers[list(numbers.keys())[m]]
# if a >= b:
# if c >= 0.6:
# print("Welcome :{}".format(list(who.keys())[m]))
who.clear()
numbers.clear()
i = 0
number = 0
except:
continue # 这里有一定几率产生一个奇怪的错误。。。暂时只能continue
cv2.imshow("Video", frame) # 展示一帧时间：不足0.001s
if cv2.waitKey(1) & 0xFF == ord('q'): # 1ms一帧 q：退出
break
video_capture.release() # 释放资源
cv2.destroyAllWindows