DL-Art-School/codes/data/Downsample_dataset.py
James Betker 46f550e42b Change downsample_dataset to do no image modification
I'm  preprocessing the images myself now. There's no need to have
the dataset do this processing as well.
2020-04-28 11:50:04 -06:00

120 lines
5.9 KiB
Python

import random
import numpy as np
import cv2
import lmdb
import torch
import torch.utils.data as data
import data.util as util
class DownsampleDataset(data.Dataset):
"""
Reads an unpaired HQ and LQ image. Clips both images to the expected input sizes of the model. Produces a
downsampled LQ image from the HQ image and feeds that as well.
"""
def __init__(self, opt):
super(DownsampleDataset, self).__init__()
self.opt = opt
self.data_type = self.opt['data_type']
self.paths_LQ, self.paths_GT = None, None
self.sizes_LQ, self.sizes_GT = None, None
self.LQ_env, self.GT_env = None, None # environments for lmdb
self.doCrop = self.opt['doCrop']
self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'])
self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
self.data_sz_mismatch_ok = opt['mismatched_Data_OK']
assert self.paths_GT, 'Error: GT path is empty.'
assert self.paths_LQ, 'LQ is required for downsampling.'
if not self.data_sz_mismatch_ok:
assert len(self.paths_LQ) == len(
self.paths_GT
), 'GT and LQ datasets have different number of images - {}, {}.'.format(
len(self.paths_LQ), len(self.paths_GT))
self.random_scale_list = [1]
def _init_lmdb(self):
# https://github.com/chainer/chainermn/issues/129
self.GT_env = lmdb.open(self.opt['dataroot_GT'], readonly=True, lock=False, readahead=False,
meminit=False)
self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
meminit=False)
def __getitem__(self, index):
if self.data_type == 'lmdb' and (self.GT_env is None or self.LQ_env is None):
self._init_lmdb()
scale = self.opt['scale']
GT_size = self.opt['target_size'] * scale
# get GT image
GT_path = self.paths_GT[index]
resolution = [int(s) for s in self.sizes_GT[index].split('_')
] if self.data_type == 'lmdb' else None
img_GT = util.read_img(self.GT_env, GT_path, resolution)
if self.opt['phase'] != 'train': # modcrop in the validation / test phase
img_GT = util.modcrop(img_GT, scale)
if self.opt['color']: # change color space if necessary
img_GT = util.channel_convert(img_GT.shape[2], self.opt['color'], [img_GT])[0]
# get LQ image
lqind = index % len(self.paths_LQ)
LQ_path = self.paths_LQ[index % len(self.paths_LQ)]
resolution = [int(s) for s in self.sizes_LQ[index].split('_')
] if self.data_type == 'lmdb' else None
img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
# Create a downsampled version of the HQ image using matlab imresize.
img_Downsampled = util.imresize_np(img_GT, 1 / scale)
assert img_Downsampled.ndim == 3
if self.opt['phase'] == 'train':
H, W, _ = img_GT.shape
assert H >= GT_size and W >= GT_size
H, W, C = img_LQ.shape
LQ_size = GT_size // scale
if self.doCrop:
# randomly crop
rnd_h = random.randint(0, max(0, H - LQ_size))
rnd_w = random.randint(0, max(0, W - LQ_size))
img_LQ = img_LQ[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
img_Downsampled = img_Downsampled[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
rnd_h_GT, rnd_w_GT = int(rnd_h * scale), int(rnd_w * scale)
img_GT = img_GT[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
else:
img_LQ = cv2.resize(img_LQ, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
img_Downsampled = cv2.resize(img_Downsampled, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
# augmentation - flip, rotate
img_LQ, img_GT, img_Downsampled = util.augment([img_LQ, img_GT, img_Downsampled], self.opt['use_flip'],
self.opt['use_rot'])
if self.opt['color']: # change color space if necessary
img_Downsampled = util.channel_convert(C, self.opt['color'],
[img_Downsampled])[0] # TODO during val no definition
# BGR to RGB, HWC to CHW, numpy to tensor
if img_GT.shape[2] == 3:
img_GT = img_GT[:, :, [2, 1, 0]]
img_LQ = img_LQ[:, :, [2, 1, 0]]
img_Downsampled = img_Downsampled[:, :, [2, 1, 0]]
img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
img_Downsampled = torch.from_numpy(np.ascontiguousarray(np.transpose(img_Downsampled, (2, 0, 1)))).float()
img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()
# This may seem really messed up, but let me explain:
# The goal is to re-use existing code as much as possible. SRGAN_model was coded to supersample, not downsample,
# but it can be retrofitted. To do so, we need to "trick" it. In this case the "input" is the HQ image and the
# "output" is the LQ image. SRGAN_model will be using a Generator and a Discriminator which already know this,
# we just need to trick its logic into following this rules.
# Do this by setting LQ(which is the input into the models)=img_GT and GT(which is the expected outpuut)=img_LQ.
# PIX is used as a reference for the pixel loss. Use the manually downsampled image for this.
return {'LQ': img_GT, 'GT': img_LQ, 'PIX': img_Downsampled, 'LQ_path': LQ_path, 'GT_path': GT_path}
def __len__(self):
return len(self.paths_GT)