def predict(dirName, testFileName):
"""
:param dirName: 包含训练数据集的文件夹路径
:param testFileName: 测试图像文件名
:return: 预测结果
"""
# testImg=cv2.imread(testFileName)
# cv2.imshow("testImg",testImg)
dataMat, label = createImageSet.createImageMat(dirName, 16, 11, 176, 100 * 100)
print('加载数据集矩阵', dataMat)
print('加载图片标签', label)
ans = compare(dataMat, testFileName, label)
return ans
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
if __name__ == '__main__':
dataMat, label = createImageSet.createImageMat('Yale', 17, 11, 11 * 17,
100 * 100)
detect(dataMat, label)
disc_set,x,y=LDA.pca(dataMat,PCA_dim)
Total=classNum*classInNum
redVects, Train_LDA = LDA.lda(dataMat, label,PCA_dim, classNum, classInNum, Total) # LDA投影空间，最终的训练集
for classnum in range(1, classNum + 1):
print('第',classnum,'类')
Count += compare(disc_set, Train_LDA, redVects, label, testNum,classnum, 7)
print('Final correctCount:', Count/classNum)
if __name__ == '__main__':
PCA_dim=40 #####从22开始识别率提升为1.0
testNum=10
classNum=17
classInNum=11
Train_Total=classNum*classInNum
dataMat, label = createImageSet.createImageMat('Yale', classNum, classInNum, Train_Total, 100 * 100)
getResult(dataMat, label, PCA_dim, testNum , classNum ,classInNum)
# disc_set, disc_value = LDA.pca(dataMat, 50)
# redVects, Train_LDA = LDA.lda(dataMat, 50, 15, 11, 165) # LDA投影空间，最终的训练集
# testImgSet = './Yale/1/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 = []
# testVec = np.reshape(testImgSet, (1, -1))
# print('testVec', testVec.shape)
#
# for sample in Train_LDA.T:
# disList.append(np.linalg.norm(testVec - sample))