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data.py
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data.py
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import torch
from torch.utils.data import Dataset
import os
import numpy as np
import glob
import cv2
import rawpy
import random
from PIL import Image
def pack_raw(raw):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512, 0) / (16383 - 512) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate(
(
im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :],
),
axis=2,
)
# print (np.max(out))
return out
def pack_nikon(raw, resize=False):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 600, 0) / (16383 - 600) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate(
(
im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :],
),
axis=2,
)
if resize:
out = cv2.resize(out, (out.shape[1] // 4, out.shape[0] // 4))
return out
def pack_canon(raw):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 2048, 0) / (16383 - 2048) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate(
(
im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :],
),
axis=2,
)
return out
class SonyDataset(Dataset):
def __init__(self, input_dir, gt_dir, ps=512):
self.input_dir = input_dir
self.gt_dir = gt_dir
self.ps = ps
self.fns = glob.glob(gt_dir + "0*.ARW") # file names
self.ids = [int(os.path.basename(fn)[0:5]) for fn in self.fns]
# Raw data takes long time to load. Keep them in memory after loaded.
self.gt_images = [None] * len(self.ids)
self.input_images = [None] * len(self.ids)
self.ratios = [None] * len(self.ids)
print("Loading Sony images onto RAM....")
for i in range(len(self.ids)):
# input
index = self.ids[i]
print(index, i)
in_files = glob.glob(self.input_dir + "%05d_00*.ARW" % index)
in_path = in_files[np.random.random_integers(0, len(in_files) - 1)]
in_fn = os.path.basename(in_path)
# ground truth
gt_files = glob.glob(self.gt_dir + "%05d_00*.ARW" % index)
gt_path = gt_files[0]
gt_fn = os.path.basename(gt_path)
# ratio
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure / in_exposure, 300)
self.ratios[i] = ratio
# load images
raw = rawpy.imread(in_path)
self.input_images[i] = np.expand_dims(pack_raw(raw), axis=0) * ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(
use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16
)
self.gt_images[i] = np.expand_dims(np.float32(im / 65535.0), axis=0)
print(f"Loaded all {len(self.ids)} Sony images onto RAM....")
def __len__(self):
return len(self.ids)
def __getitem__(self, ind):
index = self.ids[ind]
# crop
ratio = self.ratios[ind]
H = self.input_images[ind].shape[1]
W = self.input_images[ind].shape[2]
# np.random.seed()
xx = np.random.randint(0, W - self.ps)
yy = np.random.randint(0, H - self.ps)
# print (xx, yy, ind)
input_patch = self.input_images[ind][:, yy : yy + self.ps, xx : xx + self.ps, :]
# gt_jpeg_patch = self.gt_jpeg_images[ind][:, yy * 2:yy * 2 + self.ps * 2, xx * 2:xx * 2 + self.ps * 2, :]
gt_patch = self.gt_images[ind][
:, yy * 2 : yy * 2 + self.ps * 2, xx * 2 : xx * 2 + self.ps * 2, :
]
# data augmentation
# random flip vertically
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=1).copy()
gt_patch = np.flip(gt_patch, axis=1).copy()
# gt_jpeg_patch = np.flip(gt_jpeg_patch, axis=1).copy()
# random flip horizontally
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=2).copy()
gt_patch = np.flip(gt_patch, axis=2).copy()
# gt_jpeg_patch = np.flip(gt_jpeg_patch, axis=2).copy()
# random transpose
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.transpose(input_patch, (0, 2, 1, 3)).copy()
gt_patch = np.transpose(gt_patch, (0, 2, 1, 3)).copy()
# gt_jpeg_patch = np.transpose(gt_jpeg_patch, (0, 2, 1, 3)).copy()
input_patch = np.minimum(input_patch, 1.0)
gt_patch = np.maximum(gt_patch, 0.0)
# gt_jpeg_patch = np.maximum(gt_jpeg_patch, 0.0)
input_patch = torch.from_numpy(input_patch)
input_patch = torch.squeeze(input_patch)
input_patch = input_patch.permute(2, 0, 1)
gt_patch = torch.from_numpy(gt_patch)
gt_patch = torch.squeeze(gt_patch)
gt_patch = gt_patch.permute(2, 0, 1)
return input_patch, gt_patch, index, ratio
class NikonTrainSet(Dataset):
"""Training as target domain with Fuji as source"""
def __init__(
self,
input_dir,
gt_dir,
no_of_items=1,
set_num=1,
ps=512,
random_sample=False,
seed=0,
stratify=False,
):
self.input_dir = input_dir
self.gt_dir = gt_dir
self.ps = ps
self.fns = glob.glob(gt_dir + "0*.NEF")
self.ids = [int(os.path.basename(fn)[0:5]) for fn in self.fns]
if no_of_items == 1:
if set_num == 1:
self.ids = [4]
elif set_num == 2:
self.ids = [29]
elif set_num == 3:
self.ids = [14]
elif no_of_items == 2:
if set_num == 1:
self.ids = [4, 15]
elif set_num == 2:
self.ids = [24, 29]
elif set_num == 3:
self.ids = [22, 33]
elif no_of_items == 4:
if set_num == 1:
self.ids = [4, 15, 24, 29]
elif set_num == 2:
self.ids = [4, 22, 33, 57]
elif set_num == 3:
self.ids = [14, 22, 38, 54]
elif no_of_items == 6:
if set_num == 1:
self.ids = [4, 15, 24, 29, 33, 57]
elif set_num == 2:
self.ids = [4, 22, 33, 57, 24, 13]
elif set_num == 3:
self.ids = [14, 22, 38, 54, 25, 30]
elif no_of_items == 8:
if set_num == 1:
self.ids = [4, 22, 33, 57, 24, 13, 29, 49]
elif set_num == 2:
self.ids = [7, 26, 39, 38, 44, 15, 29, 49] # 39
elif set_num == 3:
self.ids = [25, 30, 46, 54, 6, 16, 14, 26] # 25, 30, 54, 6, 16
elif no_of_items == 10 and not random_sample:
if set_num == 1:
self.ids = [4, 22, 33, 57, 24, 13, 29, 49, 15, 14]
elif set_num == 2:
self.ids = [7, 26, 39, 38, 44, 15, 29, 49, 33, 57] # 39
elif set_num == 3:
self.ids = [25, 30, 46, 54, 6, 16, 14, 26, 33, 13] # 25, 30, 54, 6, 16
elif no_of_items == 10 and random_sample:
if set_num == 1:
set1_seed = seed - 1
random.seed(set1_seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
10,
)
elif set_num == 2:
random.seed(seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
10,
)
elif set_num == 3:
set3_seed = seed + 1
random.seed(set3_seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
10,
)
elif no_of_items == 16 and random_sample == False:
if set_num == 1:
self.ids = [
4,
13,
22,
24,
14,
15,
29,
49,
33,
57,
7,
26,
39,
19,
59,
17,
]
elif set_num == 2:
self.ids = [
25,
30,
22,
24,
14,
15,
29,
49,
33,
57,
7,
26,
38,
44,
6,
16,
]
elif set_num == 3:
self.ids = [4, 13, 22, 24, 39, 15, 29, 46, 54, 17, 7, 26, 38, 44, 6, 16]
elif no_of_items == 16 and random_sample == True:
if set_num == 1:
set1_seed = seed - 1
random.seed(set1_seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
16,
)
elif set_num == 2:
random.seed(seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
16,
)
elif set_num == 3:
set3_seed = seed + 1
random.seed(set3_seed)
self.ids = random.sample(
set(list(range(4, 34)) + list(range(37, 43)) + list(range(44, 61))),
16,
)
# Raw data takes long time to load. Keep them in memory after loaded.
self.gt_images = [None] * len(self.ids)
self.ratios = [None] * len(self.ids)
self.input_images = [None] * len(self.ids)
print("Loading Nikon images onto RAM....")
for i in range(len(self.ids)):
index = self.ids[i]
in_files = glob.glob(self.input_dir + "%05d_0*.NEF" % id)
in_files = sorted(in_files)
in_path = random.sample(in_files, 1)[0]
in_fn = os.path.basename(in_path)
gt_files = glob.glob(self.gt_dir + "%05d_0*.NEF" % id)
gt_path = gt_files[0]
gt_fn = os.path.basename(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure / in_exposure, 300)
self.ratios[i] = ratio
print(set_num, index, i, ratio)
raw = rawpy.imread(in_path)
self.input_images[i] = np.expand_dims(pack_nikon(raw), axis=0) * ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(
use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16
)
self.gt_images[i] = np.expand_dims(np.float32(im / 65535.0), axis=0)
print(
f"Loaded Nikon images for {no_of_items} images - set {set_num} onto RAM..."
)
def __len__(self):
return len(self.ids)
def __getitem__(self, ind):
ind = ind % 4
index = self.ids[ind]
# crop
H = self.input_images[ind].shape[1]
W = self.input_images[ind].shape[2]
xx = np.random.randint(0, W - self.ps)
yy = np.random.randint(0, H - self.ps)
input_patch = self.input_images[ind][:, yy : yy + self.ps, xx : xx + self.ps, :]
gt_patch = self.gt_images[ind][
:, yy * 2 : yy * 2 + self.ps * 2, xx * 2 : xx * 2 + self.ps * 2, :
]
# Data augmentation
# random flip vertically
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=1).copy()
gt_patch = np.flip(gt_patch, axis=1).copy()
# random flip horizontally
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=2).copy()
gt_patch = np.flip(gt_patch, axis=2).copy()
# random transpose
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.transpose(input_patch, (0, 2, 1, 3)).copy()
gt_patch = np.transpose(gt_patch, (0, 2, 1, 3)).copy()
# gt_jpeg_patch = np.transpose(gt_jpeg_patch, (0, 2, 1, 3)).copy()
input_patch = np.minimum(input_patch, 1.0)
gt_patch = np.maximum(gt_patch, 0.0)
input_patch = torch.from_numpy(input_patch)
input_patch = torch.squeeze(input_patch)
input_patch = input_patch.permute(2, 0, 1)
gt_patch = torch.from_numpy(gt_patch)
gt_patch = torch.squeeze(gt_patch)
gt_patch = gt_patch.permute(2, 0, 1)
return input_patch, gt_patch, index, self.ratios[ind]
class CanonTrainSet(Dataset):
"""Training as target domain with Fuji as source"""
def __init__(self, input_dir, gt_dir, no_of_items=1, set_num=1, ps=512):
self.input_dir = input_dir
self.gt_dir = gt_dir
self.ps = ps
self.fns = glob.glob(gt_dir + "1*.CR2") # file names
self.ids = [int(os.path.basename(fn)[0:5]) for fn in self.fns]
if no_of_items == 1:
if set_num == 1:
self.ids = [10513]
elif set_num == 2:
self.ids = [10510]
elif set_num == 3:
self.ids = [10518]
elif no_of_items == 3:
if set_num == 1:
self.ids = [10513, 10519, 10502]
elif set_num == 2:
self.ids = [10506, 10510, 10520]
elif set_num == 3:
self.ids = [10514, 10507, 10518]
elif no_of_items == 6:
if set_num == 1:
self.ids = [10513, 10519, 10502, 10506, 10510, 10520]
elif set_num == 2:
self.ids = [10516, 10518, 10504, 10507, 10514, 10517]
elif set_num == 3:
self.ids = [10503, 10524, 10501, 10500, 10522, 10523]
elif no_of_items == 9:
if set_num == 1:
self.ids = [
10507,
10520,
10510,
10502,
10506,
10519,
10513,
10514,
10517,
]
elif set_num == 2:
self.ids = [
10513,
10519,
10502,
10506,
10510,
10520,
10516,
10518,
10504,
]
elif set_num == 3:
self.ids = [
10516,
10518,
10504,
10507,
10514,
10517,
10513,
10519,
10502,
]
elif no_of_items == 12:
if set_num == 1:
self.ids = [
10516,
10518,
10504,
10507,
10514,
10517,
10513,
10519,
10502,
10506,
10510,
10520,
]
elif set_num == 2:
self.ids = [
10516,
10523,
10507,
10524,
10501,
10503,
10500,
10517,
10504,
10518,
10514,
10522,
]
elif set_num == 3:
self.ids = [
10513,
10519,
10502,
10506,
10510,
10520,
10503,
10524,
10501,
10500,
10522,
10523,
]
# Raw data takes long time to load. Keep them in memory after loaded.
self.input_images = [None] * len(self.ids)
self.gt_images = [None] * len(self.ids)
self.ratios = [None] * len(self.ids)
print("Loading Canon images into RAM....")
for i in range(len(self.ids)):
index = self.ids[i]
print(index, i)
# Input
in_files = glob.glob(self.input_dir + "%05d_00*.CR2" % id)
in_files = sorted(in_files)
in_path = random.sample(in_files, 1)[0]
in_fn = os.path.basename(in_path)
# Ground truth
gt_files = glob.glob(self.gt_dir + "%05d_00*.CR2" % id)
gt_path = gt_files[0]
gt_fn = os.path.basename(gt_path)
# Ratio
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure / in_exposure, 300)
self.ratios[i] = ratio
raw = rawpy.imread(in_path)
self.input_images[i] = np.expand_dims(pack_canon(raw), axis=0) * ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(
use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16
)
self.gt_images[i] = np.expand_dims(np.float32(im / 65535.0), axis=0)
print(
f"Loaded Canon images for {no_of_items} images - set {set_num} onto RAM..."
)
def __len__(self):
return len(self.ids)
def __getitem__(self, ind):
index = self.ids[ind]
# crop
H = self.input_images[ind].shape[1]
W = self.input_images[ind].shape[2]
xx = np.random.randint(0, W - self.ps)
yy = np.random.randint(0, H - self.ps)
input_patch = self.input_images[ind][:, yy : yy + self.ps, xx : xx + self.ps, :]
gt_patch = self.gt_images[ind][
:, yy * 2 : yy * 2 + self.ps * 2, xx * 2 : xx * 2 + self.ps * 2, :
]
# Data augmentation
# Random flip vertically
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=1).copy()
gt_patch = np.flip(gt_patch, axis=1).copy()
# Random flip horizontally
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=2).copy()
gt_patch = np.flip(gt_patch, axis=2).copy()
# Random transpose
if np.random.randint(2, size=1)[0] == 1:
input_patch = np.transpose(input_patch, (0, 2, 1, 3)).copy()
gt_patch = np.transpose(gt_patch, (0, 2, 1, 3)).copy()
input_patch = np.minimum(input_patch, 1.0)
gt_patch = np.maximum(gt_patch, 0.0)
input_patch = torch.from_numpy(input_patch)
input_patch = torch.squeeze(input_patch)
input_patch = input_patch.permute(2, 0, 1)
gt_patch = torch.from_numpy(gt_patch)
gt_patch = torch.squeeze(gt_patch)
gt_patch = gt_patch.permute(2, 0, 1)
return input_patch, gt_patch, index, self.ratios[ind]