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"""
self supervise dataset making Script ver： Aug 21th 21:50
todo
提供了一个简单的思路
先做成单进程的，后续做成多进程的
"""
import torch
import numpy as np
import os
import shutil
from utils.tools import to_2tuple, find_all_files
def convert_to_npy(a_data_path):
# 处理转换
# 传回npy
numpy_img = 0
return numpy_img
def cut_to_patch(numpy_img, save_root, resize_infor, patch_size=384):
pass
def read_and_convert(data_root,save_root, resize_infor, suffix=None, patch_size=384):
# 一次处理只一个数据集, 每个数据集的处理方式可能有不同
# 读入所有数据
all_files = find_all_files(data_root)
# 把所有数据转换为同一个格式
for img in all_files:
numpy_img = convert_to_npy(img)
cut_to_patch(numpy_img, save_root, resize_infor, patch_size)
pass
class to_patch:
"""
Split a image into patches, each patch with the size of patch_size
"""
def __init__(self, patch_size=(16, 16)):
patch_size = to_2tuple(patch_size)
self.patch_h = patch_size[0]
self.patch_w = patch_size[1]
def __call__(self, x):
c, h, w = x.shape
assert h // self.patch_h == h / self.patch_h and w // self.patch_w == w / self.patch_w
num_patches = (h // self.patch_h) * (w // self.patch_w)
# patch encoding
# (c, h, w)
# -> (c, h // self.patch_h, self.patch_h, w // self.patch_w, self.patch_w)
# -> (h // self.patch_h, w // self.patch_w, self.patch_h, self.patch_w, c)
# -> (n_patches, patch_size^2*c)
patches = x.view(
c,
h // self.patch_h,
self.patch_h,
w // self.patch_w,
self.patch_w).permute(1, 3, 2, 4, 0).reshape(num_patches, -1) # it can also used in transformer Encoding
# patch split
# (n_patches, patch_size^2*c)
# -> (num_patches, self.patch_h, self.patch_w, c)
# -> (num_patches, c, self.patch_h, self.patch_w)
patches = patches.view(num_patches,
self.patch_h,
self.patch_w,
c).permute(0, 3, 1, 2)
'''
# check
for i in range(len(patches)):
recons_img = ToPILImage()(patches[i])
recons_img.save(os.path.join('./patch_play', 'recons_target'+str(i)+'.jpg'))
# patch compose to image
# (num_patches, c, self.patch_h, self.patch_w)
# -> (h // self.patch_h, w // self.patch_w, c, self.patch_h, self.patch_w)
# -> (c, h // self.patch_h, self.patch_h, w // self.patch_w, self.patch_w)
# -> (c, h, w)
patches = patches.view(h // self.patch_h,
w // self.patch_w,
c,
self.patch_h,
self.patch_w).permute(2, 0, 3, 1, 4).reshape(c, h, w)
'''
'''
# visual check
# reshape
composed_patches = patches.view(h // self.patch_h,
w // self.patch_w,
c,
self.patch_h,
self.patch_w).permute(2, 0, 3, 1, 4).reshape(c, h, w)
# view pic
from torchvision.transforms import ToPILImage
composed_img = ToPILImage()(bag_image[0]) # transform tensor image to PIL image
composed_img.save(os.path.join('./', 'composed_img.jpg'))
'''
return patches
img = np.ones([3, 224, 224])
patchfy=to_patch(patch_size=(16, 16))
patch=patchfy(img) |