def readPicSaveFace(sourcePath, objectPath, *suffix):
if not os.path.exists(objectPath):
os.mkdir(objectPath)
try:
#读取照片,注意第一个元素是文件名
resultArray, _ = readAllImg(sourcePath, *suffix)
#对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
face_cascade = cv2.CascadeClassifier(
"haarcascade_frontalface_default.xml")
for img in resultArray:
# if type(img) != str:
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if img.ndim == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
faces = face_cascade.detectMultiScale(gray, 1.1, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())),
str(count)] #以时间戳和读取的排序作为文件名称
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
cv2.imwrite(objectPath + os.sep + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
#Read image
resultArray = readAllImg(sourcePath, *suffix)
#Check the image in the list one by one. Find the face and put into the object path
count = 1
face_cascade = cv2.CascadeClassifier(
'D:\proj\opencv-master\opencv-master\data\haarcascades\haarcascade_frontalface_alt.xml'
)
for i in resultArray:
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())), str(count)]
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
cv2.imwrite(objectPath + os.sep + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
#读取照片,注意第一个元素是文件名
resultArray = readAllImg(sourcePath, *suffix)
#对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
face_cascade = cv2.CascadeClassifier(
'/Users/i037762/tf/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_alt.xml'
)
for i in resultArray:
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())),
str(count)] #以时间戳和读取的排序作为文件名称
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
cv2.imwrite(objectPath + os.sep + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
# 读取照片,注意第一个元素是文件名
resultArray = readAllImg(sourcePath, *suffix)
# 对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
# 加载人脸特征库
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_alt.xml')
for i in resultArray:
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
# 以时间戳和读取的排序作为文件名称
listStr = [str(int(time.time())), str(count)]
print(listStr)
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
cv2.imwrite(objectPath + os.sep + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('已经读取 ' + str(count - 1) + ' 张照片到 ' + objectPath + '目录。')
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
# Read the photo, note that the first element is the file name
resultArray = readAllImg(sourcePath, *suffix)
# Check the pictures in the list one by one, find the faces in them and write them to the target folder
count = 1
# Load face feature library
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_alt.xml')
for i in resultArray:
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
# File name with timestamp and reading order
listStr = [str(int(time.time())), str(count)]
print(listStr)
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
cv2.imwrite(objectPath + os.sep + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Photos to ' + objectPath +
'directory')
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
resultArray = readAllImg(sourcePath, *suffix)
count = 1
face_cascade = cv2.CascadeClassifier(
'models/haarcascade_frontalface_default.xml')
for i in resultArray:
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())),
str(count)] #以时间戳和读取的排序作为文件名称
fileName = ''.join(listStr)
f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
cv2.imwrite(objectPath + os.sep + '%s.png' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
# 读取照片,注意第一个元素是文件名
resultArray = readAllImg(sourcePath, *suffix)
# 对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
face_cascade = cv2.CascadeClassifier(
'./haarcascade_frontalface_alt.xml')
for i in resultArray:
if type(i) != str:
# gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
# 我也不知道为什么 。。。。。注释掉这行就可以了
faces = face_cascade.detectMultiScale(i, 1.3, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())),
str(count)] # 以时间戳和读取的排序作为文件名称
fileName = ''.join(listStr)
f = cv2.resize(i[y:(y + h), x:(x + w)], (92, 112))
cv2.imwrite(objectPath + os.sep + '%s.pgm' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def readPicSaveFace(sourcePath, objectPath, *suffix):
try:
#读取照片,注意第一个元素是文件名
resultArray = readAllImg(sourcePath, *suffix)
# print(resultArray)
#对list中图片逐一进行检查,找出其中的人脸然后写到目标文件夹下
count = 1
# face_cascade = cv2.CascadeClassifier('E:\openCV\opencv\sources\data\haarcascades\
# D:\Program Files (x86)\opencv\sources\data\haarcascades
# face_cascade = cv2.CascadeClassifier(r'D:/Program Files(x86)/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml')
# pathf = 'D:\\Program Files(x86)\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml'
# pathf = r'D:/Program Files(x86)/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml'
# face_cascade = cv2.CascadeClassifier(pathf)
# another
# face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# face_cascade = cv2.CascadeClassifier(r'D:/my_laboratory/face_detection20180516/source - cynthia/haarcascade_frontalface_alt.xml')
# face_cascade.load(r'D:/Program Files(x86)/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
# print(len(resultArray))
for i in resultArray:
print(type(i))
if type(i) != str:
gray = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
listStr = [str(int(time.time())),
str(count)] #以时间戳和读取的排序作为文件名称
fileName = ''.join(listStr)
# f = cv2.resize(gray[y:(y + h), x:(x + w)], (200, 200))
f = cv2.resize(gray[y:(y + h), x:(x + w)], (250, 250))
# # print(type(f))
# cv2.imshow("Output1",f)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# cv2.imwrite(objectPath+os.sep+'%s.jpg' % fileName, f)
cv2.imwrite(objectPath + '%s.jpg' % fileName, f)
count += 1
except IOError:
print("Error")
else:
print('Already read ' + str(count - 1) + ' Faces to Destination ' +
objectPath)
def test_onBatch(path):
model= Model(read_save=True)
model.load()
index = 0
img_list,img_name_list = readAllImg(path,'.jpg')
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
for img in img_list:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,1.1,5)
for (x, y, w, h) in faces:
face = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
picType,prob = model.predict(face)
name_list = read_name_list('G:\Python/FaceRecognition\datasets')
if picType != -1:
# if picType>=0 and picType<len(name_list):
print (img_name_list[index],":",name_list[picType],",",prob)
index += 1
else:
print (" Don't know this person")
return index
