import torch
import torch.nn as nn
from torchvision import transforms
def extract_pc(image, pc=3):
channel, height, width = image.shape # input float tensor image with CxHxW
# data = image.view(channel, height*width)
data = image.contiguous().view(channel, height * width)
data_c = data - data.mean(dim=1).unsqueeze(1)
u, s, vt = torch.svd(data_c.matmul(data_c.T))
sorted_data, indices = s.sort(descending=True)
image_pc = u[:, indices[0:pc]].T.matmul(data)
return image_pc.view(pc, height, width)
def std_norm(image):
image = image.permute(1, 2, 0).numpy()
trans = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(torch.tensor(image).mean(dim=[0, 1]), torch.tensor(image).std(dim=[0, 1]))
])
return trans(image)
def one_zero_norm(image):
channel, height, width = image.shape
data = image.view(channel, height*width)
data_max = data.max(dim=1)[0]
data_min = data.min(dim=1)[0]
data = (data-data_min.unsqueeze(1))/(data_max.unsqueeze(1) - data_min.unsqueeze(1))
return data.view(channel, height, width)
def construct_sample(image, windows=27):
_, height, width = image.shape
half_window = int(windows/2)
pad = nn.ReplicationPad2d(half_window)
pad_image = pad(image.unsqueeze(0)).squeeze(0)
batch_image_indices = torch.zeros((height*width, 4), dtype=torch.long)
t = 0
for h in range(height):
for w in range(width):
batch_image_indices[t, :] = torch.tensor([h, h+windows, w, w+windows])
t += 1
return pad_image, batch_image_indices
def label_transform(gt):
label = torch.unique(gt)
gt_new = torch.zeros_like(gt)
for each in range(len(label)):
indices = torch.where(gt == label[each])
if label[0] == 0:
gt_new[indices] = each - 1
else:
gt_new[indices] = each
label_new = torch.unique(gt_new)
return gt_new
def label_inverse_transform(predict_result, gt):
label_origin = torch.unique(gt)
label_predict = torch.unique(predict_result)
predict_result_origin = torch.zeros_like(predict_result)
for each in range(len(label_predict)):
indices = torch.where(predict_result == label_predict[each])
if len(label_predict) != len(label_origin):
predict_result_origin[indices] = label_origin[each + 1]
else:
predict_result_origin[indices] = label_origin[each]
return predict_result_origin
def select_sample(gt, ntr):
gt_vector = gt.reshape(-1, 1).squeeze(1)
label = torch.unique(gt)
for each in range(len(label)):
indices_vector = torch.where(gt_vector == label[each]) # 1 tuple 返回一维的索引
indices = torch.where(gt == label[each]) # 2 tuple 返回二维的索引
indices_vector = indices_vector[0]
indices_row = indices[0]
indices_column = indices[1]
# 背景 -1
# 10776
if label[each] == -1:
no_gt_indices = torch.cat([indices_vector.unsqueeze(1),
indices_row.unsqueeze(1),
indices_column.unsqueeze(1)],
dim=1
)
no_gt_num = torch.tensor(len(indices_vector))
else:
class_num = torch.tensor(len(indices_vector))
rand_indices0 = torch.randperm(class_num)
rand_indices = indices_vector[rand_indices0]
# indian_pines
# num_workers = [30, 150, 150, 100, 150, 150, 20, 150, 15, 150, 150, 150, 150, 150, 50, 50]
# paviau
# num_workers = [150, 150, 150, 150, 150, 150, 150, 150, 150]
# KSC
num_workers = [33, 23, 24, 24, 15, 22, 9, 38, 51, 39, 41, 49, 91]
sel_num = num_workers[each - 1]
sel_num = torch.tensor(sel_num)
tr_ind0 = rand_indices0[0:sel_num]
te_ind0 = rand_indices0[sel_num:]
tr_ind = rand_indices[0:sel_num]
te_ind = rand_indices[sel_num:]
# 训练集:索引+坐标
sel_tr_ind = torch.cat([
tr_ind.unsqueeze(1),
indices_row[tr_ind0].unsqueeze(1),
indices_column[tr_ind0].unsqueeze(1)
], dim=1)
# 测试集：
sel_te_ind = torch.cat([
te_ind.unsqueeze(1),
indices_row[te_ind0].unsqueeze(1),
indices_column[te_ind0].unsqueeze(1)
], dim=1)
if each == 1:
train_indices = sel_tr_ind
train_num = sel_num.unsqueeze(0)
test_indices = sel_te_ind
test_num = (class_num - sel_num).unsqueeze(0)
else:
train_indices = torch.cat([train_indices, sel_tr_ind], dim=0)
train_num = torch.cat([train_num, sel_num.unsqueeze(0)])
test_indices = torch.cat([test_indices, sel_te_ind], dim=0)
test_num = torch.cat([test_num, (class_num - sel_num).unsqueeze(0)])
# 训练集
rand_tr_ind = torch.randperm(train_num.sum())
train_indices = train_indices[rand_tr_ind, ]
# 测试集
rand_te_ind = torch.randperm(test_num.sum()) # torch.Size([42506])
test_indices = test_indices[rand_te_ind, ]
# 背景图
rand_no_gt_ind = torch.randperm(no_gt_num.sum())
no_gt_indices = no_gt_indices[rand_no_gt_ind, ]
data_sample = {'train_indices': train_indices, 'train_num': train_num,
'test_indices': test_indices, 'test_num': test_num,
'no_gt_indices': no_gt_indices, 'no_gt_num': no_gt_num.unsqueeze(0)}
return data_sample
