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multiscanning strategy tool.py
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import numpy as np
import torch
from einops import rearrange
def direction1(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(h):
if (i + 1) % 2 == 1:
result.extend(input_HW[i])
else:
result.extend(input_HW[i][::-1])
return result
def direction2(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(w):
if (i + 1) % 2 == 1:
result.extend(input_HW[:, i])
else:
result.extend(input_HW[:, i][::-1])
return result
def direction3(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(h):
if (i + 1) % 2 == 0:
result.extend(input_HW[h - 1 - i])
else:
result.extend(input_HW[h - 1 - i][::-1])
return result
def direction4(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(w):
if (i + 1) % 2 == 0:
result.extend(input_HW[:, w - 1 - i])
else:
result.extend(input_HW[:, w - 1 - i][::-1])
return result
def direction5(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(h):
if (i + 1) % 2 == 0:
result.extend(input_HW[i])
else:
result.extend(input_HW[i][::-1])
return result
def direction6(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(w):
if (i + 1) % 2 == 1:
result.extend(input_HW[:, w - 1 - i])
else:
result.extend(input_HW[:, w - 1 - i][::-1])
return result
def direction7(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(h):
if (i + 1) % 2 == 1:
result.extend(input_HW[h - 1 - i])
else:
result.extend(input_HW[h - 1 - i][::-1])
return result
def direction8(input_HW):
h = input_HW.shape[0]
w = input_HW.shape[1]
result = []
for i in range(w):
if (i + 1) % 2 == 0:
result.extend(input_HW[:, i])
else:
result.extend(input_HW[:, i][::-1])
return result
method_map = {1: direction1, 2: direction2, 3: direction3, 4: direction4, 5: direction5, 6: direction6, 7: direction7,
8: direction8}
# img_test: batch size,H,W,C
# method_index:方法下标：1-8
def multiscan(img_test, method_index):
input = img_test.numpy()
batchsize = input.shape[0]
h = img_test.shape[1]
w = input.shape[2]
c = input.shape[3]
# print(h, w, c)
sample_all = []
for i in range(batchsize):
sample_pic = []
for j in range(c):
sample_pic.append(method_map[method_index](input[i][:, :, j]))
sample_all.append(sample_pic)
sample_all_array = np.array(sample_all)
sample_all_torch = torch.from_numpy(sample_all_array)
sample_all_rearrange = rearrange(sample_all_torch, 'b c s -> b s c')
return sample_all_rearrange
# batchsize:2 H:2 w:3 c:2
img_test = torch.randn(1, 5, 5, 2)
print(img_test.shape, img_test)
result = multiscan(img_test, 4)
print(result.shape, result)
print("trans shape:", result.shape)