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 __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 __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 __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='./model/face.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 = "venv/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml"
def run(self):
user_num = len(os.listdir('./data/'))
dataset = Dataset('./data/')
#self.pushButton_2.setText('训练中')
dataset.load()
model1 = Model()
model1.build_model(dataset, nb_classes=user_num)
# 先前添加的测试build_model()函数的代码
model1.build_model(dataset, nb_classes=user_num)
# 测试训练函数的代码
model1.train(dataset)
model1.save_model(file_path='./model/aggregate.face.model.h5')
def train_click(self):
#if __name__ == '__main__':
user_num = len(os.listdir('./data/'))
dataset = Dataset('./data/')
self.btn2.setText('训练中')
dataset.load()
model = Model()
model.build_model(dataset, nb_classes=user_num)
# 先前添加的测试build_model()函数的代码
model.build_model(dataset, nb_classes=user_num)
# 测试训练函数的代码
model.train(dataset)
model.save_model(file_path='./model/aggregate.face.model.h5')
self.btn2.setText('训练结束')
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)
#from post_detection import show_image
if __name__ == '__main__':
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Cant connect to the camera")
sys.exit(0)
# TODO add the paths to the config file
cascade_path = "/home/kartikeya/TensorFlow/Packages/opencv/data/haarcascades/haarcascade_frontalface_default.xml"
eye_cascade_path = '/home/kartikeya/TensorFlow/Packages/opencv/data/haarcascades/haarcascade_eye.xml'
model = Model()
model.load()
# Continue the loop until the escapce key
while True:
is_read, frame = cap.read()
if not is_read:
print("Unable to read the frame from the camera");
sys.exit(0);
# convert to gray scale - reduce complexity and S2N
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# face recognition using the frontal face cascade
# image = apply_gabor_filter(image)
print(image.shape)
result = model.predict(image)
print(result)
if result == 0: # KK
print('Image match! KK identified')
correct_detected_count += 1
else:
print('No match yet')
print("Total correctly detected images : ", correct_detected_count)
#cv2.destroyAllWindows()
if __name__ == '__main__':
# TODO add the paths to the config file
cascade_path = "/home/kartikeya/TensorFlow/Packages/opencv/data/haarcascades/haarcascade_frontalface_default.xml"
eye_cascade_path = '/home/kartikeya/TensorFlow/Packages/opencv/data/haarcascades/haarcascade_eye.xml'
model = Model()
model.load()
image_path = "/home/kartikeya/TensorFlow/Projects/FBFaceDetection/data/user_pics/"
# crop_face(image_path)
# verify_model_from_hdd_images(image_path)
gabor_filter_to_pics(image_path)
import cv2
import sys
from face_train import Model
if __name__ == '__main__':
if len(sys.argv) != 1:
print("Usage:%s camera_id\r\n" % (sys.argv[0]))
sys.exit(0)
model = Model()
model.load_model()
color = (0, 255, 0)
cap = cv2.VideoCapture(0)
cascade_path = "D:\\open_cv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
while cap.isOpened():
ret, frame = cap.read()
if ret:
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
continue
cascade = cv2.CascadeClassifier(cascade_path)
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
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)])
# -*- coding: utf-8 -*-
import cv2
import sys
import os
from face_train import Model
if __name__ == '__main__':
project_path = os.path.abspath(os.path.join(os.getcwd(), "../../.."))
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=project_path+'model/sakura.face.model.h5')
# 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)
# 人脸识别分类器本地存储路径
cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml'
# 循环检测识别人脸
while True:
ret, frame = cap.read() # 读取一帧视频
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
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='D:/model/ZCM.face.model.h5')
# 框住人脸的边框颜色
color = (0, 255, 0)
# 捕获指定摄像头的实时视频流
cap = cv2.VideoCapture(0)
# 人脸识别分类器本地存储路径
cascade_path = 'C:\\projects\\opencv-python\\opencv\modules\\objdetect\\src\\cascadedetect\\haarcascade_frontalface_default.xml'
# 循环识别人脸
while True:
ret, frame = cap.read() # 读取一帧视频
if ret is True:
# 图像灰度化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
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()
import cv2
import sys
import gc
from face_train import Model
from load_data import get_names, get_length
if __name__ == '__main__':
if len(sys.argv) != 1:
print("Usage:%s camera_id\r\n" % (sys.argv[0]))
sys.exit(0)
label_last2 = get_names("C:\\Users\\HP\\Desktop\\faceweb\\data")
length = get_length("C:\\Users\\HP\\Desktop\\faceweb\\data")
#加载模型
model = Model()
model.load_model(file_path='./model/zx.face.model.h5')
#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)
#人脸识别分类器本地存储路径
cascade_path = "S:\\opencv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
#定义分类器（人眼识别）
eye_cascade = cv2.CascadeClassifier(
"S:\\opencv\\opencv\\build\\etc\\haarcascades\\haarcascade_eye.xml")
#定义分类器（嘴巴识别）
mouth_cascade = cv2.CascadeClassifier(
import cv2
import sys
import os
from face_train import Model
from tkinter import *
from PIL import Image, ImageTk
model = Model()
model.load_model(file_path='./model/zhenghuireid.model.h5')
cascade_path = "/Users/zhenghuiup/anaconda3/pkgs/libopencv-3.4.2-h7c891bd_1/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml"
cascade = cv2.CascadeClassifier(cascade_path)
faceRects = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(32, 32))
image = cv2.imread('./GUI/my_image.jpg')
faceID = model.face_predict(image)
class Window(Frame):
def __init__(self, master=None):
Frame.__init__(self, master)
self.master = master
self.init_window()
def init_window(self):
# -*- coding: utf-8 -*-
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
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='D:\\data\\liuhaozhe.face.model.h5')
# 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)
# 人脸识别分类器本地存储路径
cascade_path = "D:\\Firefoxdownloads\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
# 循环检测识别人脸
while True:
ret, frame = cap.read() # 读取一帧视频
if ret is True:
#-*- coding: utf-8 -*-
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
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 = 'D:\\model\\sjq.face.model.h5')
#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)
#人脸识别分类器本地存储路径
cascade_path = "D:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
#循环检测识别人脸
while True:
ret, frame = cap.read() #读取一帧视频
if ret is True:
import cv2
import sys
#import importlib
from face_train import Model # module_path
module_path = './model/my.face.model.h5'
if __name__ == '__main__':
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=module_path)
#框住人脸的矩形边框颜色
color = (0, 230, 0)
#捕获指定摄像头的实时视频流
cap = cv2.VideoCapture(0)
#("http://*****:*****@192.168.43.1:8081/")
#人脸识别分类器本地存储路径
cascade_path = "E:\\Anaconda3\\pkgs\\libopencv-3.4.2-h20b85fd_0\\Library\\etc\\haarcascades\\haarcascade_frontalface_alt.xml"
#循环检测识别人脸
while True:
ret, frame = cap.read() #读取一帧视频
#-*- coding: utf-8 -*-
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
if len(sys.argv) != 1:
print("Usage:%s camera_id\r\n" % (sys.argv[0]))
sys.exit(0)
#載入模型
model = Model()
model.load_model(file_path='./model/liziqiang.face.model.h5')
#框住人臉的矩形邊框顏色
color = (0, 255, 0)
#捕獲指定攝像頭的實時視訊流
cap = cv2.VideoCapture(0)
#人臉識別分類器本地儲存路徑
cascade_path = "H:\\opencv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml"
#迴圈檢測識別人臉
while True:
ret, frame = cap.read() #讀取一幀視訊
if ret is True:
#-*- coding: utf-8 -*-
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
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/bochendong.face.model.h5')
#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)
#人脸识别分类器本地存储路径
cascade_path = "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/cv2/data/haarcascade_frontalface_default.xml"
#循环检测识别人脸
while True:
ret, frame = cap.read() #读取一帧视频
if ret is True:
# -*- coding: utf-8 -*-
# coding=gbk
################下面的计划还是不用自己写的cnn,用vgg16卷积模型来进行图片的训练######################
################人脸的图片对光的影响很大，粗腰对图片进行预处理#####################################
import cv2
import sys
from face_train import Model
if __name__ == '__main__':
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='./lbo.face.model.h5')
# 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)
# 人脸识别分类器本地存储路径
cascade_path = "F:\\Anaconda\\Library\\etc\\haarcascades\\haarcascade_frontalface_default.xml"
# 循环检测识别人脸
while True:
ret, frame = cap.read() # 读取一帧视频
if ret is True:
image = cv2.imread(full_path)
image = resize_image(image, IMAGE_SIZE,
IMAGE_SIZE) # 从我们的文件夹中读取图片然后进行统一规格的处理
return image
# 从指定路径读取训练数据
def load_data(path_name):
images = read_path(path_name)
# 将输入的所有图片转成四维数组，尺寸为(图片数量*IMAGE_SIZE*IMAGE_SIZE*3)
# 图片，IMAGE_SIZE为64，故对我来说尺寸为1000 * 64 * 64 * 3
# 图片为64 * 64像素,一个像素3个颜色值(RGB)
images = np.array(images)
return images
# 加载模型，训练好模型。
model = Model()
model.load_model(file_path='./lbo.face.model.h5')
def detectFaces(image_path):
img = read_path(image_path) # 读取读片
# 加载人脸识别的文件
face_cascade = cv2.CascadeClassifier(
"F:\\Anaconda\\Library\\etc\\haarcascades\\haarcascade_frontalface_default.xml"
)
# if语句：如果img维度为3，说明不是灰度图，先转化为灰度图gray，如果不为3，也就是2，原图就是灰度图
if img.ndim == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
# -*- coding: utf-8 -*-
import cv2
import sys
import gc
from face_train import Model
if __name__ == '__main__':
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/manwanting.face.model.h5')
# 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)
# 人脸识别分类器本地存储路径
cascade_path = "D:\\openvc\\haarcascade_frontalface_default.xml"
# 循环检测识别人脸
while True:
ret, frame = cap.read() # 读取一帧视频
if ret is True:
#-*- coding: utf-8 -*-
import cv2
from face_train import Model
if __name__ == '__main__':
#加载模型
model = Model()
model.load_model(file_path='face_model_re.h5')
color = (0, 255, 0)
cap = cv2.VideoCapture(0)
cascade_path = "E:\cs\opencv\sources\data\haarcascades\haarcascade_frontalface_alt2.xml"
#循环检测识别人脸
while True:
_, frame = cap.read() #读取一帧视频
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cascade = cv2.CascadeClassifier(cascade_path)
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
image = frame[y - 10:y + h + 10, x - 10:x + w + 10]
faceID = model.face_predict(image)
textf = ""
