DL-Art-School/codes/data/LQGT_dataset.py

217 lines
9.9 KiB
Python
Raw Normal View History

2019-08-23 13:42:47 +00:00
import random
import numpy as np
import cv2
import lmdb
import torch
import torch.utils.data as data
import data.util as util
2020-05-30 02:34:00 +00:00
from PIL import Image, ImageOps
from io import BytesIO
import torchvision.transforms.functional as F
2019-08-23 13:42:47 +00:00
class LQGTDataset(data.Dataset):
"""
Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, etc) and GT image pairs.
If only GT images are provided, generate LQ images on-the-fly.
"""
def get_lq_path(self, i):
2020-05-07 05:16:35 +00:00
which_lq = random.randint(0, len(self.paths_LQ)-1)
return self.paths_LQ[which_lq][i]
2019-08-23 13:42:47 +00:00
def __init__(self, opt):
super(LQGTDataset, 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.paths_PIX, self.sizes_PIX = None, None
2020-05-02 23:46:30 +00:00
self.LQ_env, self.GT_env, self.PIX_env = None, None, None # environments for lmdbs
self.force_multiple = self.opt['force_multiple'] if 'force_multiple' in self.opt.keys() else 1
2019-08-23 13:42:47 +00:00
self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'], opt['dataroot_GT_weights'])
if 'dataroot_LQ' in opt.keys():
self.paths_LQ = []
if isinstance(opt['dataroot_LQ'], list):
# Multiple LQ data sources can be given, in case there are multiple ways of corrupting a source image and
# we want the model to learn them all.
for dr_lq in opt['dataroot_LQ']:
lq_path, self.sizes_LQ = util.get_image_paths(self.data_type, dr_lq)
self.paths_LQ.append(lq_path)
else:
lq_path, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])
self.paths_LQ.append(lq_path)
2020-05-02 23:46:30 +00:00
self.doCrop = opt['doCrop']
if 'dataroot_PIX' in opt.keys():
2020-04-22 06:40:13 +00:00
self.paths_PIX, self.sizes_PIX = util.get_image_paths(self.data_type, opt['dataroot_PIX'])
2019-08-23 13:42:47 +00:00
assert self.paths_GT, 'Error: GT path is empty.'
if self.paths_LQ and self.paths_GT:
assert len(self.paths_LQ[0]) == len(
2019-08-23 13:42:47 +00:00
self.paths_GT
), 'GT and LQ datasets have different number of images - {}, {}.'.format(
len(self.paths_LQ[0]), len(self.paths_GT))
2019-08-23 13:42:47 +00:00
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)
if 'dataroot_PIX' in self.opt.keys():
2020-04-22 06:40:13 +00:00
self.PIX_env = lmdb.open(self.opt['dataroot_PIX'], readonly=True, lock=False, readahead=False,
meminit=False)
2019-08-23 13:42:47 +00:00
def motion_blur(self, image, size, angle):
k = np.zeros((size, size), dtype=np.float32)
k[(size - 1) // 2, :] = np.ones(size, dtype=np.float32)
k = cv2.warpAffine(k, cv2.getRotationMatrix2D((size / 2 - 0.5, size / 2 - 0.5), angle, 1.0), (size, size))
k = k * (1.0 / np.sum(k))
return cv2.filter2D(image, -1, k)
2019-08-23 13:42:47 +00:00
def __getitem__(self, index):
if self.data_type == 'lmdb' and (self.GT_env is None or self.LQ_env is None):
self._init_lmdb()
GT_path, LQ_path = None, None
scale = self.opt['scale']
2020-04-22 06:37:41 +00:00
GT_size = self.opt['target_size']
2019-08-23 13:42:47 +00:00
# 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]
2020-04-22 06:40:13 +00:00
# get the pix image
if self.paths_PIX is not None:
PIX_path = self.paths_PIX[index]
2020-04-22 06:40:13 +00:00
img_PIX = util.read_img(self.PIX_env, PIX_path, resolution)
if self.opt['color']: # change color space if necessary
img_PIX = util.channel_convert(img_PIX.shape[2], self.opt['color'], [img_PIX])[0]
else:
img_PIX = img_GT
2020-04-22 06:40:13 +00:00
2019-08-23 13:42:47 +00:00
# get LQ image
if self.paths_LQ:
LQ_path = self.get_lq_path(index)
2019-08-23 13:42:47 +00:00
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)
else: # down-sampling on-the-fly
# randomly scale during training
if self.opt['phase'] == 'train':
random_scale = random.choice(self.random_scale_list)
H_s, W_s, _ = img_GT.shape
def _mod(n, random_scale, scale, thres):
rlt = int(n * random_scale)
rlt = (rlt // scale) * scale
return thres if rlt < thres else rlt
H_s = _mod(H_s, random_scale, scale, GT_size)
W_s = _mod(W_s, random_scale, scale, GT_size)
img_GT = cv2.resize(img_GT, (W_s, H_s), interpolation=cv2.INTER_LINEAR)
if img_GT.ndim == 2:
img_GT = cv2.cvtColor(img_GT, cv2.COLOR_GRAY2BGR)
H, W, _ = img_GT.shape
2019-08-23 13:42:47 +00:00
# using matlab imresize
img_LQ = util.imresize_np(img_GT, 1 / scale, True)
2019-08-23 13:42:47 +00:00
if img_LQ.ndim == 2:
img_LQ = np.expand_dims(img_LQ, axis=2)
# Enforce force_resize constraints.
h, w, _ = img_LQ.shape
if h % self.force_multiple != 0 or w % self.force_multiple != 0:
h, w = (w - w % self.force_multiple), (h - h % self.force_multiple)
img_LQ = cv2.resize(img_LQ, (h, w))
h *= scale
w *= scale
img_GT = cv2.resize(img_GT, (h, w))
img_PIX = cv2.resize(img_LQ, (h, w))
2019-08-23 13:42:47 +00:00
if self.opt['phase'] == 'train':
H, W, _ = img_GT.shape
assert H >= GT_size and W >= GT_size
2019-08-23 13:42:47 +00:00
H, W, C = img_LQ.shape
LQ_size = GT_size // scale
2020-05-02 23:46:30 +00:00
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, :]
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, :]
img_PIX = img_PIX[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_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
img_PIX = cv2.resize(img_PIX, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)
2019-08-23 13:42:47 +00:00
if self.opt['doResizeLoss']:
r = random.randrange(0, 10)
if r > 5:
img_LQ = cv2.resize(img_LQ, (int(LQ_size/2), int(LQ_size/2)), interpolation=cv2.INTER_LINEAR)
img_LQ = cv2.resize(img_LQ, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
2019-08-23 13:42:47 +00:00
# augmentation - flip, rotate
img_LQ, img_GT, img_PIX = util.augment([img_LQ, img_GT, img_PIX], self.opt['use_flip'],
2019-08-23 13:42:47 +00:00
self.opt['use_rot'])
if self.opt['use_blurring']:
# Pick randomly between gaussian, motion, or no blur.
blur_det = random.randint(0, 100)
2020-06-02 14:40:52 +00:00
blur_magnitude = 3 if 'blur_magnitude' not in self.opt.keys() else self.opt['blur_magnitude']
if blur_det < 40:
2020-06-02 14:40:52 +00:00
blur_sig = int(random.randrange(0, blur_magnitude))
img_LQ = cv2.GaussianBlur(img_LQ, (blur_magnitude, blur_magnitude), blur_sig)
elif blur_det < 70:
2020-06-02 14:40:52 +00:00
img_LQ = self.motion_blur(img_LQ, random.randrange(1, blur_magnitude * 3), random.randint(0, 360))
2020-05-24 17:56:39 +00:00
2019-08-23 13:42:47 +00:00
if self.opt['color']: # change color space if necessary
img_LQ = util.channel_convert(C, self.opt['color'],
[img_LQ])[0] # TODO during val no definition
# BGR to RGB, HWC to CHW, numpy to tensor
if img_GT.shape[2] == 3:
img_GT = cv2.cvtColor(img_GT, cv2.COLOR_BGR2RGB)
img_LQ = cv2.cvtColor(img_LQ, cv2.COLOR_BGR2RGB)
img_PIX = cv2.cvtColor(img_PIX, cv2.COLOR_BGR2RGB)
# LQ needs to go to a PIL image to perform the compression-artifact transformation.
img_LQ = (img_LQ * 255).astype(np.uint8)
img_LQ = Image.fromarray(img_LQ)
if self.opt['use_compression_artifacts'] and random.random() > .25:
qf = random.randrange(10, 70)
corruption_buffer = BytesIO()
img_LQ.save(corruption_buffer, "JPEG", quality=qf, optimice=True)
corruption_buffer.seek(0)
img_LQ = Image.open(corruption_buffer)
2020-05-30 02:34:00 +00:00
if self.opt['grayscale']:
2020-05-30 04:04:50 +00:00
img_LQ = ImageOps.grayscale(img_LQ).convert('RGB')
2020-05-30 02:34:00 +00:00
2019-08-23 13:42:47 +00:00
img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
img_PIX = torch.from_numpy(np.ascontiguousarray(np.transpose(img_PIX, (2, 0, 1)))).float()
img_LQ = F.to_tensor(img_LQ)
2019-08-23 13:42:47 +00:00
lq_noise = torch.randn_like(img_LQ) * 5 / 255
img_LQ += lq_noise
2019-08-23 13:42:47 +00:00
if LQ_path is None:
LQ_path = GT_path
return {'LQ': img_LQ, 'GT': img_GT, 'PIX': img_PIX, 'LQ_path': LQ_path, 'GT_path': GT_path}
2019-08-23 13:42:47 +00:00
def __len__(self):
return len(self.paths_GT)