def createTestMat(dirName, classNum, testNum, ImageSize):
"""
创建测试集
:param dirName: 文件夹名称
:param classNum: 哪一类
:param testNum: 每类中用来测试的图像数量
:param ImageSize: 图像大小
:return: 第classNum类的测试集
"""
testMat = np.zeros((testNum, ImageSize))
index = 0
for i in range(1, testNum + 1):
dic = './' + str(dirName) + '/' + str(classNum) + '/' + 's' + str(
i) + '.bmp'
img = ImageSet.HistogramEqualization('./' + str(dirName) + '/' +
str(classNum) + '/' + 's' +
str(i) + '.bmp')
# cv2.imshow(str(classNum) + '/' + 's', img)
# name=str(classNum) + '/' + 's'+ str(i)
# print('name',name)
# cv2.waitKey(0)
tempImg = np.reshape(img, (-1, 1)) # 每张图转化为一列
if index == 0:
testMat = tempImg
index += 1
else:
testMat = np.column_stack((testMat, tempImg))
testMat = np.mat(testMat) # 列合并，然后转为矩阵
index += 1
return testMat
def createImageMat(dirName, classNum, classInNum, ImageNum, ImageSize):
"""
创建训练集矩阵
:param dirName:包含数据集的文件夹名称
:param classNum: 类数目
:param classInNum: 每类包含的图像数目
:param ImageNum: 图像总数
:param ImageSize: 每张图像的大小
:return: 图像矩阵，#每张图像对应的标签
"""
dataMat = np.zeros((ImageNum, ImageSize)) # 生成用0填充的数据集(数组),维度为图像数目*图像大小
# label = np.zeros((1,ImageNum))
label = []
index = 0
for classnum in range(1, classNum + 1):
for classinnum in range(1, classInNum + 1):
img = ImageSet.HistogramEqualization('./' + str(dirName) + '/' +
str(classnum) + '/' + 's' +
str(classinnum) + '.bmp')
# print('均衡化后的图像矩阵',img)
tempImg = np.reshape(img, (-1, 1)) # 每张图转化为一列
if index == 0:
dataMat = tempImg
label.append(str(classnum))
# label[:,index]=classnum
index += 1
else:
dataMat = np.column_stack((dataMat, tempImg))
dataMat = np.mat(dataMat) # 列合并，然后转为矩阵
label.append(str(classnum))
# label[:, index] = classnum
index += 1
# print('index',index)
return dataMat, label
def compare(dataMat, testImg, label):
"""
:param dataMat:样本矩阵
:param testImg: 测试图像矩阵,最原始形式（未均衡化处理）
:param label: 标签矩阵
:return: 结果文件名
"""
testImg = ImageSet.HistogramEqualization(testImg)
testImg = np.reshape(testImg, (-1, 1))
lowMat, redVects = PCA.pca(dataMat, 0.99)
print(' 变换矩阵维度', redVects.shape)
print('测试图像维度', testImg.shape)
# redVects=np.reshape(redVects, (-1, 1))
testImg = redVects.T * testImg #########
print('样本变化后的维度', testImg.shape)
disList = []
testVec = np.reshape(testImg, (1, -1))
print('testVec', testVec.shape)
for sample in lowMat.T:
disList.append(np.linalg.norm(testVec - sample))
print('disList', disList)
sortIndex = np.argsort(disList)
return label[sortIndex[0]]
def testPic(dataMat, label):
# def testPic(dataMat, label, disc_set, disc_value, redVects, Train_LDA):
print("thread")
j = 0
isRight = 0
isRight2 = 0
testTimes = 0
while True:
testImgSet = './pic/s0.bmp'
if not os.path.isfile(testImgSet):
continue
disc_set, disc_value, meanFace = LDA.pca(dataMat, 40)
redVects, Train_LDA = LDA.lda(dataMat, label, 40, 17, 11,
11 * 17) # LDA投影空间，最终的训练集
# testImgSet = createImageSet.createTestMat('Yale', testInClass, testNum, testInClass, 100 * 100)
testImgSet = ImageSet.HistogramEqualization(testImgSet)
# print("shape", testImgSet.shape)
testImgSet = np.reshape(testImgSet, (-1, 1))
testImgSet = disc_set.T.dot(testImgSet)
testImgSet = redVects.T.dot(testImgSet)
disList = []
testVec = np.reshape(testImgSet, (1, -1))
for sample in Train_LDA.T:
disList.append(np.linalg.norm(testVec - sample))
# print('disList', disList)
sortIndex = np.argsort(disList)
print(label[sortIndex[0]])
if 16 == int(label[sortIndex[0]]):
isRight = isRight + 1
if 17 == int(label[sortIndex[0]]):
isRight2 = isRight2 + 1
os.remove('./pic/s0.bmp')
j = j + 1
# j = j + 1
# 在脸上检测眼睛 (40, 40)是设置最小尺寸，再小的部分会不检测
# eyes = eye_cascade.detectMultiScale(roi_gray, 1.03, 5, 0, (40, 40))
# 把眼睛画出来
# for(ex, ey, ew, eh) in eyes:
# cv2.rectangle(img, (x+ex, y+ey), (x+ex+ew, y+ey+eh), (0, 255, 0), 2)
if j == 5:
if isRight >= 4 or isRight2 >= 4:
if isRight >= 4:
print("欢迎你，史长顺！")
# break
camera.release()
cv2.destroyAllWindows()
break
if isRight2 >= 4:
print("欢迎你，饶丝雨！")
# break
camera.release()
cv2.destroyAllWindows()
break
else:
if isRight < 4:
isRight = 0
testTimes += 1
print("测试失败")
if testTimes >= 5:
# break
camera.release()
j = 0
if isRight2 < 4:
isRight2 = 0
testTimes += 1
print("测试失败2")
if testTimes >= 5:
# break
camera.release()
j = 0
def upload():
if (g.currentUser.acctype == 'Participant'):
err = "You don't have permissions to upload"
return index(err)
setname = request.form.get('filename')
setname = re.sub('[^A-Za-z0-9]+', '', setname)
imagefiles = request.files.getlist('imagefile')
study = request.form.get('study')
for dicom in imagefiles:
fullname = setname + '-' + str(dicom.filename.strip('.dcm'))
# save locally temp to convert from dicom to jpg format
tempsaved = path.dirname(path.realpath(__file__)) + '/temp/'
if not path.exists(tempsaved):
makedirs(tempsaved)
dicom.save(tempsaved + fullname)
try:
output = subprocess.check_output([
'identify', '-format', '%[dcm:PixelSpacing],',
str(tempsaved + fullname)
])
matched_lines = [line for line in output.split(',')]
rows, cols = matched_lines[0].strip().split('\\')
rows = float(rows)
cols = float(cols)
resize = ""
if (rows == cols):
# no resize needed
resize = "100%x100%"
elif (cols > rows):
factor = (cols / rows) * 100
resize = str(factor) + "%x100%"
elif (rows > cols):
factor = (rows / cols) * 100
resize = "100%x" + str(factor) + "%"
except:
resize = "100%x100%"
convert = subprocess.call([
'mogrify', '-resize', resize, '-format', 'jpg',
tempsaved + fullname
])
set_size = len(fnmatch.filter(listdir(tempsaved), '*.jpg'))
if convert != 0:
return load_upload_page(
error="Unable to convert image, needs a DICOM (.dcm) format")
# create UNIQUE image set for this user and setname in database
imageset = ImageSet(user_id=g.currentUser.userID,
name=fullname,
study=study)
try:
imageset.create()
except Exception as e:
err = e[1]
if "Duplicate" in err:
err = "Image set name and DICOM already used for this user"
# empty the temp files dir and return error message
for img in glob.glob(tempsaved + fullname + '*'):
remove(img)
return load_upload_page(error=err)
# save all in set as blobs in images table in database in correct order
for index in range(set_size):
if (set_size > 1):
picture = tempsaved + fullname + "-" + str(index) + ".jpg"
else:
picture = tempsaved + fullname + ".jpg"
picture = path.abspath(picture)
contents = file_get_contents(picture)
image = Image(set_id=imageset.id, image=contents)
image.create()
if set_size > 2:
remove(picture) # delete temp files
remove(tempsaved + fullname) # delete original
return redirect("/" + "?search=" + setname, code=302)
for classnum in range(1, classNum + 1):
print('第', classnum, '类')
Count += compare(disc_set, Train_LDA, redVects, label, testNum, classnum, 5)
print('Final correctCount:', Count / 16)
if __name__ == '__main__':
# for i in range(1, 11):
#
dataMat, label = createImageSet.createImageMat('Yale', 16, 11, 176, 100 * 100)
# getResult(dataMat, label, 5, 16)
# disc_set, disc_value = LDA.pca(dataMat, 50)
# redVects, Train_LDA = LDA.lda(dataMat, label, 50, 16, 11, 11 * 16) # LDA投影空间，最终的训练集
testImgSet = './pic/s0.bmp'
testImg = ImageSet.HistogramEqualization(testImgSet)
testImg = np.reshape(testImg, (-1, 1))
lowMat, redVects = PCA.pca(dataMat, 0.99)
testImg = redVects.T * testImg
disList = []
testVec = np.reshape(testImg, (1, -1))
for sample in lowMat.T:
disList.append(np.linalg.norm(testVec - sample))
sortIndex = np.argsort(disList)
# # testImgSet = './Yale/16/s1.bmp'
# # testImgSet = createImageSet.createTestMat('Yale', testInClass, testNum, testInClass, 100 * 100)
# testImgSet = ImageSet.HistogramEqualization(testImgSet)
# testImgSet = np.reshape(testImgSet, (-1, 1))
# testImgSet = disc_set.T.dot(testImgSet)
# testImgSet = redVects.T.dot(testImgSet)
# disList = []