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data_process.py
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import numpy as np
# 定位训练和测试样本
def chooose_train_and_test_point(train_data, test_data, true_data, num_classes):
number_train = []
pos_train = {}
number_test = []
pos_test = {}
number_true = []
pos_true = {}
#-------------------------for train data------------------------------------
for i in range(num_classes):
each_class = []
each_class = np.argwhere(train_data==(i+1))
number_train.append(each_class.shape[0])
pos_train[i] = each_class
total_pos_train = pos_train[0]
for i in range(1, num_classes):
total_pos_train = np.r_[total_pos_train, pos_train[i]] #(695,2)
total_pos_train = total_pos_train.astype(int)
#--------------------------for test data------------------------------------
for i in range(num_classes):
each_class = []
each_class = np.argwhere(test_data==(i+1))
number_test.append(each_class.shape[0])
pos_test[i] = each_class
total_pos_test = pos_test[0]
for i in range(1, num_classes):
total_pos_test = np.r_[total_pos_test, pos_test[i]] #(9671,2)
total_pos_test = total_pos_test.astype(int)
#--------------------------for true data------------------------------------
for i in range(num_classes+1):
#for i in range(num_classes):
each_class = []
each_class = np.argwhere(true_data==(i))
#each_class = np.argwhere(true_data==(i+1))
number_true.append(each_class.shape[0])
pos_true[i] = each_class
total_pos_true = pos_true[0]
for i in range(1, num_classes+1):
#for i in range(1, num_classes):
total_pos_true = np.r_[total_pos_true, pos_true[i]]
total_pos_true = total_pos_true.astype(int)
return total_pos_train, total_pos_test, total_pos_true, number_train, number_test, number_true
#-------------------------------------------------------------------------------
# 边界拓展：镜像
def mirror_hsi(height,width,band,input_normalize,patch=5):
padding=patch//2
mirror_hsi=np.zeros((height+2*padding,width+2*padding,band),dtype=float)
#中心区域
mirror_hsi[padding:(padding+height),padding:(padding+width),:]=input_normalize
#左边镜像
for i in range(padding):
mirror_hsi[padding:(height+padding),i,:]=input_normalize[:,padding-i-1,:]
#右边镜像
for i in range(padding):
mirror_hsi[padding:(height+padding),width+padding+i,:]=input_normalize[:,width-1-i,:]
#上边镜像
for i in range(padding):
mirror_hsi[i,:,:]=mirror_hsi[padding*2-i-1,:,:]
#下边镜像
for i in range(padding):
mirror_hsi[height+padding+i,:,:]=mirror_hsi[height+padding-1-i,:,:]
print("**************************************************")
print("patch is : {}".format(patch))
print("mirror_image shape : [{0},{1},{2}]".format(mirror_hsi.shape[0],mirror_hsi.shape[1],mirror_hsi.shape[2]))
print("**************************************************")
return mirror_hsi
#-------------------------------------------------------------------------------
# 获取patch的图像数据
def gain_neighborhood_pixel(mirror_image, point, i, patch=5):
x = point[i,0]
y = point[i,1]
temp_image = mirror_image[x:(x+patch),y:(y+patch),:]
return temp_image
def gain_neighborhood_band(x_train, band, band_patch, patch=5):
nn = band_patch // 2 # band_patch 3
pp = (patch*patch) // 2 # patch 7
x_train_reshape = x_train.reshape(x_train.shape[0], patch*patch, band) # band 144
x_train_band = np.zeros((x_train.shape[0], patch*patch*band_patch, band),dtype=float)
# 中心区域
x_train_band[:,nn*patch*patch:(nn+1)*patch*patch,:] = x_train_reshape
#左边镜像
for i in range(nn):
if pp > 0:
x_train_band[:,i*patch*patch:(i+1)*patch*patch,:i+1] = x_train_reshape[:,:,band-i-1:]
x_train_band[:,i*patch*patch:(i+1)*patch*patch,i+1:] = x_train_reshape[:,:,:band-i-1]
else:
x_train_band[:,i:(i+1),:(nn-i)] = x_train_reshape[:,0:1,(band-nn+i):]
x_train_band[:,i:(i+1),(nn-i):] = x_train_reshape[:,0:1,:(band-nn+i)]
#右边镜像
for i in range(nn):
if pp > 0:
x_train_band[:,(nn+i+1)*patch*patch:(nn+i+2)*patch*patch,:band-i-1] = x_train_reshape[:,:,i+1:]
x_train_band[:,(nn+i+1)*patch*patch:(nn+i+2)*patch*patch,band-i-1:] = x_train_reshape[:,:,:i+1]
else:
x_train_band[:,(nn+1+i):(nn+2+i),(band-i-1):] = x_train_reshape[:,0:1,:(i+1)]
x_train_band[:,(nn+1+i):(nn+2+i),:(band-i-1)] = x_train_reshape[:,0:1,(i+1):]
return x_train_band
#-------------------------------------------------------------------------------
# 汇总训练数据和测试数据
def train_and_test_data(mirror_image, band, train_point, test_point, true_point, patch=5, band_patch=3):
x_train = np.zeros((train_point.shape[0], patch, patch, band), dtype=float)
x_test = np.zeros((test_point.shape[0], patch, patch, band), dtype=float)
x_true = np.zeros((true_point.shape[0], patch, patch, band), dtype=float)
for i in range(train_point.shape[0]):
x_train[i,:,:,:] = gain_neighborhood_pixel(mirror_image, train_point, i, patch)
for j in range(test_point.shape[0]):
x_test[j,:,:,:] = gain_neighborhood_pixel(mirror_image, test_point, j, patch)
for k in range(true_point.shape[0]):
x_true[k,:,:,:] = gain_neighborhood_pixel(mirror_image, true_point, k, patch)
'''x_train = x_train.reshape(x_train.shape[0], patch*patch, band)
x_test = x_test.reshape(x_test.shape[0], patch*patch, band)
x_true = x_true.reshape(x_true.shape[0], patch*patch, band)'''
print("x_train shape = {}, type = {}".format(x_train.shape,x_train.dtype))
print("x_test shape = {}, type = {}".format(x_test.shape,x_test.dtype))
print("x_true shape = {}, type = {}".format(x_true.shape,x_test.dtype))
print("**************************************************")
#x_train_band = gain_neighborhood_band(x_train, band, band_patch, patch)
#x_test_band = gain_neighborhood_band(x_test, band, band_patch, patch)
#x_true_band = gain_neighborhood_band(x_true, band, band_patch, patch)
#print("x_train_band shape = {}, type = {}".format(x_train_band.shape,x_train_band.dtype))
#print("x_test_band shape = {}, type = {}".format(x_test_band.shape,x_test_band.dtype))
#print("x_true_band shape = {}, type = {}".format(x_true_band.shape,x_true_band.dtype))
#print("**************************************************")
#return x_train_band, x_test_band , x_true_band
return x_train, x_test, x_true
#-------------------------------------------------------------------------------
# 标签y_train, y_test
def train_and_test_label(number_train, number_test, number_true, num_classes):
y_train = []
y_test = []
y_true = []
for i in range(num_classes):
for j in range(number_train[i]):
y_train.append(i)
for k in range(number_test[i]):
y_test.append(i)
for i in range(num_classes+1):
#for i in range(num_classes):
for j in range(number_true[i]):
y_true.append(i)
y_train = np.array(y_train)
y_test = np.array(y_test)
y_true = np.array(y_true)
print("y_train: shape = {} ,type = {}".format(y_train.shape,y_train.dtype))
print("y_test: shape = {} ,type = {}".format(y_test.shape,y_test.dtype))
print("y_true: shape = {} ,type = {}".format(y_true.shape,y_true.dtype))
print("**************************************************")
return y_train, y_test, y_true
#-------------------------------------------------------------------------------