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2_generate_data_ip_ct_c16.py
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import numpy as np
import h5py
from scipy.io import loadmat
# def Patch(data,height_index,width_index,PATCH_SIZE): # PATCH_SIZE为一个patch（边长-1）的一半 data维度(H,W,C)
# height_slice = slice(height_index-PATCH_SIZE, height_index+PATCH_SIZE)
# width_slice = slice(width_index-PATCH_SIZE, width_index+PATCH_SIZE)
# # 由height_index和width_index定位patch中心像素
# patch = data[height_slice, width_slice,:]
# patch = patch.reshape(-1,patch.shape[0]*patch.shape[1]*patch.shape[2])
# # print(patch.shape) #为一行 (1, 243) 243 = 9*9*3
# return patch
import sys
seed_number = sys.argv[1]
# seed_number = "1234"
np.random.seed(int(seed_number))
# img = loadmat('D:\hyperspectral_data\Indian_pines.mat')['indian_pines_corrected']
# gt = loadmat('D:\hyperspectral_data\Indian_pines_gt.mat')['indian_pines_gt']
# # print(img.shape) #(610, 340, 103)
#
# # 间隔采样选取100个波段
# #band = list(range(0,200,2))
# # img = img[:, :, 0:100]
# print(img.shape)
#
# # 归一化
# img = ( img * 1.0 - img.min() ) / ( img.max() - img.min() )
#
# [m, n, b] = img.shape
# label_num = gt.max() #最大为9，即除0外包括9类
# PATCH_SIZE = 14 #每一个patch边长大小为9
#
# # padding the hyperspectral images
# img_temp = np.zeros((m + 2 * PATCH_SIZE, n + 2 * PATCH_SIZE, b), dtype=np.float32)
# img_temp[PATCH_SIZE:(m + PATCH_SIZE), PATCH_SIZE:(n + PATCH_SIZE), :] = img[:, :, :]
#
# for i in range(PATCH_SIZE):
# img_temp[i, :, :] = img_temp[2 * PATCH_SIZE - i, :, :]
# img_temp[m + PATCH_SIZE + i, :, :] = img_temp[m + PATCH_SIZE - i - 2, :, :]
#
# for i in range(PATCH_SIZE):
# img_temp[:, i, :] = img_temp[:, 2 * PATCH_SIZE - i, :]
# img_temp[:, n + PATCH_SIZE + i, :] = img_temp[:, n + PATCH_SIZE - i - 2, :]
#
# img = img_temp
#
# gt_temp = np.zeros((m + 2 * PATCH_SIZE, n + 2 * PATCH_SIZE), dtype=np.int8)
# gt_temp[PATCH_SIZE:(m + PATCH_SIZE), PATCH_SIZE:(n + PATCH_SIZE)] = gt[:, :]
# gt = gt_temp
#
# [m, n, b] = img.shape
# # count = 0 #统计有多少个中心像素类别不为0的patch
#
#
def preparation():
#
# f = open('./data/gt_index_IP.txt', 'w')
# f1 = open('./data/PU_label.txt', 'w')
# data = []
# label = []
#
# for i in range(PATCH_SIZE, m - PATCH_SIZE):
# for j in range(PATCH_SIZE, n - PATCH_SIZE):
# if gt[i, j] == 0:
# continue
# else:
# # count += 1
# temp_data = Patch(img, i, j, PATCH_SIZE)
# #temp_label = np.zeros((1, label_num), dtype=np.int8) # temp_label为一行九列[0,1,2,....,7,8]表示类别
# #temp_label[0, gt[i, j] - 1] = 1
# temp_label = gt[i, j] - 1 # 直接用0-8表示，不用独热编码
# data.append(temp_data) # 每一行表示一个patch
# label.append(temp_label)
# gt_index = ((i - PATCH_SIZE) * 145 + j - PATCH_SIZE) # 记录坐标，用于可视化分类预测结果
# f.write(str(gt_index) + '\n')
# f1.write(str(temp_label) + '\n')
#
# # print(count) #42776
#
# data = np.array(data)
# print(data.shape) # (42776, 1, 8100)
# data = np.squeeze(data)
# print("squeeze : ", data.shape) # squeeze : (42776, 8100)
# label = np.array(label)
# print(label.shape) # (42776, 1, 9)
# label = np.squeeze(label)
# print("squeeze : ", label.shape) # squeeze : (42776, 9)
# print(np.unique(label)) # [0 1 2 3 4 5 6 7 8]
f = h5py.File('./data/IP_28_28_200_test.h5', 'r')
data = f['data'][:]
label = f['label'][:]
f.close()
# 每类随机采样num_s个生成支撑样本集
num_s = 5 # 支撑样本集数量
indices = np.arange(data.shape[0]) # list [0,.....,42775]
shuffled_indices = np.random.permutation(indices)
data = data[shuffled_indices]
label = label[shuffled_indices]
data_s = []
label_s = []
label_num = 16
for i in range(label_num): # 类别顺换 0123...8
count = 0
for j in range(10249): # 数量循环
if label[j] == i and count <= num_s-1: # 如果标记为第i类
data_s.append(data[j, :])
label_s.append(label[j])
count += 1
data_s = np.array(data_s)
label_s = np.array(label_s)
print(data_s.shape)
print(np.unique(label_s))
print(label_s)
PATH = './data/IP_28_28_200_support' + str(num_s) + '.h5'
f = h5py.File(PATH, 'w')
f['data_s'] = data_s # (45, 8100)
f['label_s'] = label_s # (45, 9)
f.close()
preparation()