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from __future__ import print_function, division
import numpy as np
from torch.utils.data import Dataset
import torch
class BaseDataset(Dataset):
def __init__(self, opt):
self.crop_size = 512
self.debug_mode = opt.debug_mode
self.data_path = opt.data_path # dataset path. e.g., ./data/
self.camera_name = opt.camera
self.gamma = opt.gamma
def norm_img(self, img, max_value):
img = img / float(max_value)
return img
def pack_raw(self, raw):
# pack Bayer image to 4 channels
im = np.expand_dims(raw, axis=2)
H, W = raw.shape[0], raw.shape[1]
# RGBG
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
def np2tensor(self, array):
return torch.Tensor(array).permute(2, 0, 1)
def center_crop(self, img, crop_size=None):
H = img.shape[0]
W = img.shape[1]
if crop_size is not None:
th, tw = crop_size[0], crop_size[1]
else:
th, tw = self.crop_size, self.crop_size
x1_img = int(round((W - tw) / 2.0))
y1_img = int(round((H - th) / 2.0))
if img.ndim == 3:
input_patch = img[y1_img : y1_img + th, x1_img : x1_img + tw, :]
else:
input_patch = img[y1_img : y1_img + th, x1_img : x1_img + tw]
return input_patch
def load(self, is_train=True):
# ./data
# ./data/NIKON D700/RAW, ./data/NIKON D700/RGB
# ./data/Canon EOS 5D/RAW, ./data/Canon EOS 5D/RGB
# ./data/NIKON D700_train.txt, ./data/NIKON D700_test.txt
# ./data/NIKON D700_train.txt: a0016, ...
input_RAWs_WBs = []
target_RGBs = []
data_path = self.data_path # ./data/
if is_train:
txt_path = data_path + self.camera_name + "_train.txt"
else:
txt_path = data_path + self.camera_name + "_test.txt"
with open(txt_path, "r") as f_read:
# valid_camera_list = [os.path.basename(line.strip()).split('.')[0] for line in f_read.readlines()]
valid_camera_list = [line.strip() for line in f_read.readlines()]
if self.debug_mode:
valid_camera_list = valid_camera_list[:10]
for i, name in enumerate(valid_camera_list):
full_name = data_path + self.camera_name
input_RAWs_WBs.append(full_name + "/RAW/" + name + ".npz")
target_RGBs.append(full_name + "/RGB/" + name + ".jpg")
return input_RAWs_WBs, target_RGBs
def __len__(self):
return 0
def __getitem__(self, idx):
return None
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