landmarks_table = landmarks_excel.sheet_by_index(0)
CASIA_test_excel = xlrd.open_workbook('list.xlsx')
CASIA_test_table = CASIA_test_excel.sheet_by_index(0)
x_data = np.zeros([202599, 39, 39], dtype=np.float32) #input imagematrix_data
y_data = np.ones([202599, 10],
dtype=np.float32) #correct output landmarks_data
CASIA_test = np.zeros([4442, 39, 39],
dtype=np.float32) #dataset to be processed
newlandmarks = np.zeros(10, dtype=np.float32)
## handle train data
for i in range(202599): #train data part 1
#get 39*39 numpy matrix of a single image
imagename = landmarks_table.cell(i + 1, 0).value
imagematrix = nl.ImageToMatrix(IMAGESAVEURL_F1_CelebA + '\\' + imagename)
#extract image size and rawlandmarks data for normalized newlandmarks
width = 89
height = 89
rawlandmarks = landmarks_table.row_slice(i + 1, start_colx=1, end_colx=11)
#get ten normalized newlandmarks(coordinates of LE,RE,N,LM,RM)
for j in range(0, 9, 2):
newlandmarks[j] = (rawlandmarks[j].value - 44) / width * 39
for k in range(1, 10, 2):
newlandmarks[k] = (rawlandmarks[k].value - 90) / height * 39
#one dimension which represents one grey picture, set the first dimension as index
x_data[i, :, :] = imagematrix
y_data[i, :] = newlandmarks
# read actual test data
for i in range(4442):
train_table = train.sheet_by_index(0)
test = xlrd.open_workbook('testImageList.xlsx')
test_table = test.sheet_by_index(0)
x_data = np.zeros([10000, 31, 39], dtype=np.float32) #input imagematrix_data
y_data = np.ones([10000, 6], dtype=np.float32) #correct output landmarks_data
x_test = np.zeros([3466, 31, 39], dtype=np.float32)
y_test = np.ones([3466, 6], dtype=np.float32)
newlandmarks = np.zeros(6, dtype=np.float32)
## handle train data
for i in range(4151): #train data part 1
#get 39*39 numpy matrix of a single image
imagename = train_table.cell(i + 1, 0).value
true_imagename = imagename[9:]
imagematrix = nl.ImageToMatrix(IMAGESAVEURL_NM1_lfw + '\\' +
true_imagename)
#extract image size and rawlandmarks data for normalized newlandmarks
width = train_table.cell(i + 1, 4).value - train_table.cell(i + 1, 3).value
height = train_table.cell(i + 1, 2).value - train_table.cell(i + 1,
1).value
rawlandmarks = train_table.row_slice(i + 1, start_colx=9, end_colx=15)
#get ten normalized newlandmarks(coordinates of LE,RE,N,LM,RM)
for j in range(0, 5, 2):
newlandmarks[j] = (rawlandmarks[j].value - train_table.cell(
i + 1, 3).value + 0.05 * width) / (1.1 * width) * 39
for k in range(1, 6, 2):
newlandmarks[k] = (rawlandmarks[k].value - train_table.cell(
i + 1, 1).value - 0.18 * height) / (0.87 * height) * 39
#one dimension which represents one grey picture, set the first dimension as index
x_data[i, :, :] = imagematrix
y_data[i, :] = newlandmarks
train_table = train.sheet_by_index(0)
CASIA_test_excel = xlrd.open_workbook('list.xlsx')
CASIA_test_table = CASIA_test_excel.sheet_by_index(0)
x_data = np.zeros([10000, 31, 39], dtype=np.float32) #input imagematrix_data
y_data = np.ones([10000, 6], dtype=np.float32) #correct output landmarks_data
CASIA_test = np.zeros([4442, 31, 39],
dtype=np.float32) #dataset to be processed
newlandmarks = np.zeros(6, dtype=np.float32)
## handle train data
for i in range(4151): #train data part 1
#get 39*39 numpy matrix of a single image
imagename = train_table.cell(i + 1, 0).value
true_imagename = imagename[9:]
imagematrix = nl.ImageToMatrix(IMAGESAVEURL_EN1_lfw + '\\' +
true_imagename)
#extract image size and rawlandmarks data for normalized newlandmarks
width = train_table.cell(i + 1, 4).value - train_table.cell(i + 1, 3).value
height = train_table.cell(i + 1, 2).value - train_table.cell(i + 1,
1).value
rawlandmarks = train_table.row_slice(i + 1, start_colx=5, end_colx=11)
#get ten normalized newlandmarks(coordinates of LE,RE,N,LM,RM)
for j in range(0, 5, 2):
newlandmarks[j] = (rawlandmarks[j].value - train_table.cell(
i + 1, 3).value + 0.05 * width) / (1.1 * width) * 39
for k in range(1, 6, 2):
newlandmarks[k] = (rawlandmarks[k].value - train_table.cell(
i + 1, 1).value + 0.04 * height) / (0.88 * height) * 39
#one dimension which represents one grey picture, set the first dimension as index
x_data[i, :, :] = imagematrix
y_data[i, :] = newlandmarks
