def __getitem__(self, index):
# read images, crop size
rgb = cv2.cvtColor(cv2.imread(self.data_lists[index]),
cv2.COLOR_BGR2RGB).astype(np.float32) / 255.
rgb = data_aug(rgb, mode=np.random.randint(0, 8))
h, w, c = rgb.shape
h = h // 2 * 2
w = w // 2 * 2
rgb = rgb[0:h, 0:w, :]
rgb = torch.from_numpy(
np.ascontiguousarray(np.transpose(rgb, [2, 0, 1]))).float()
# crop gt, keep even
wi = random.randint(0, w - self.patch_size)
hi = random.randint(0, h - self.patch_size)
# wi, hi, start point in gt
rgb = rgb[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
noise_level = max(self.min_noise,
np.random.rand(1) * self.max_noise)[0]
# raw_input + noise
noise = torch.randn([1, 1, self.patch_size,
self.patch_size]).mul_(noise_level)
rgb_noisy = rgb + noise
# cat noise_map
noise_map = torch.ones([1, 1, self.patch_size, self.patch_size
]) * noise_level
rgb_noisy = torch.cat((rgb_noisy, noise_map), 1)[0]
data = {}
data['input'] = torch.clamp(rgb_noisy, 0., 1.)
data['gt'] = torch.clamp(rgb, 0., 1.)
return data
def __getitem__(self, index):
# read images, crop size
rgb = cv2.cvtColor(cv2.imread(self.data_lists[index]),
cv2.COLOR_BGR2RGB).astype(np.float32) / 255.
rgb = data_aug(rgb, mode=np.random.randint(0, 8))
h, w, c = rgb.shape
h = h // 2 * 2
w = w // 2 * 2
rgb = rgb[0:h, 0:w, :]
if self.downsampler == 'bic':
lr_rgb = cv2.resize(rgb.copy(), (0, 0),
fx=1 / self.scale,
fy=1 / self.scale,
interpolation=cv2.INTER_CUBIC)
lr_rgb = torch.from_numpy(
np.ascontiguousarray(np.transpose(lr_rgb, [2, 0, 1]))).float()
rgb = torch.from_numpy(
np.ascontiguousarray(np.transpose(rgb, [2, 0, 1]))).float()
if self.downsampler == 'avg':
lr_rgb = F.avg_pool2d(rgb.clone(), self.scale, self.scale)
# crop gt, keep even
wi = random.randint(0, w - self.patch_size)
hi = random.randint(0, h - self.patch_size)
# wi, hi, start point in gt
rgb = rgb[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
lr_rgb = lr_rgb[:, hi // self.scale:hi // self.scale +
self.patch_size // self.scale,
wi // self.scale:wi // self.scale +
self.patch_size // self.scale]
if self.denoise:
noise_level = max(self.min_noise,
np.random.rand(1) * self.max_noise)[0]
# raw_input + noise
noise = torch.randn([
1, 1, self.patch_size // self.scale,
self.patch_size // self.scale
]).mul_(noise_level)
lr_rgb = lr_rgb + noise
# cat noise_map
noise_map = torch.ones([
1, 1, self.patch_size // self.scale,
self.patch_size // self.scale
]) * noise_level
lr_rgb = torch.cat((lr_rgb, noise_map), 1)[0]
data = {}
data['input'] = torch.clamp(lr_rgb, 0., 1.)
data['gt'] = torch.clamp(rgb, 0., 1.)
return data
def __getitem__(self, index):
# read images, crop size
# if you use huawei modelarts , use cv2.imdecode to read images
img = cv2.imdecode(np.fromstring(mox.file.read(self.data_lists[index], binary=True), np.uint8), cv2.IMREAD_COLOR)
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.
rgb = data_aug(rgb, mode=np.random.randint(0, 8))
h, w, c = rgb.shape
h = h // 8 * 8
w = w // 8 * 8
rgb = rgb[0:h, 0:w, :]
if self.downsampler == 'bic':
lr_rgb = cv2.resize(rgb.copy(), (0,0), fx=1/self.scale, fy=1/self.scale, interpolation=cv2.INTER_CUBIC)
lr_rgb = torch.from_numpy(np.ascontiguousarray(np.transpose(lr_rgb, [2, 0, 1]))).float()
rgb = torch.from_numpy(np.ascontiguousarray(np.transpose(rgb, [2, 0, 1]))).float()
if self.downsampler == 'avg':
lr_rgb = F.avg_pool2d(rgb.clone(), self.scale, self.scale)
lr_raw = rgb2raw(lr_rgb, is_tensor=True)
# crop gt, keep even
wi = random.randint(0, w - self.patch_size)
hi = random.randint(0, h - self.patch_size)
wi = wi - wi%(self.scale*2)
hi = hi - hi%(self.scale*2)
# wi, hi, start point in gt
rgb = rgb[:, hi: hi + self.patch_size, wi: wi + self.patch_size]
lr_raw = lr_raw[:, hi//self.scale: hi//self.scale + self.patch_size//self.scale,
wi//self.scale: wi//self.scale + self.patch_size//self.scale]
lr_raw = lr_raw.view(1, 1, lr_raw.shape[-2], lr_raw.shape[-1])
lr_raw = self.raw_stack(lr_raw)
if self.denoise:
noise_level = max(self.min_noise, np.random.rand(1)*self.max_noise)[0]
# raw_input + noise
noise = torch.randn([1, 1, self.patch_size//self.scale, self.patch_size//self.scale]).mul_(noise_level)
lr_raw = lr_raw + self.raw_stack(noise)
# cat noise_map
noise_map = torch.ones([1, 1, self.patch_size//(2*self.scale), self.patch_size//(2*self.scale)])*noise_level
lr_raw = torch.cat((lr_raw, noise_map), 1)
data = {}
data['input'] = torch.clamp(lr_raw, 0., 1.)[0]
data['gt'] = torch.clamp(rgb, 0., 1.)
if self.get2label:
data['raw_gt'] = torch.clamp(rgb2raw(rgb.clone(), is_tensor=True), 0., 1.)
return data
def __getitem__(self, index):
# read images, crop size
rggb = np.asarray(loadmat(self.data_lists[index])['ps']).astype(
np.float32) / 65535.
rggb = data_aug(rggb, mode=np.random.randint(0, 8))
h, w, c = rggb.shape
lr_raw, raw, rgb = rggb_prepro(rggb.copy(), self.scale)
# crop gt, keep even
wi = random.randint(0, w - self.patch_size)
hi = random.randint(0, h - self.patch_size)
wi = wi - wi % (self.scale * 2)
hi = hi - hi % (self.scale * 2)
# wi, hi, start point in gt
# in order to make rggb pattern, hi, hi must be
rgb = rgb[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
lr_raw = lr_raw[:, hi // self.scale:hi // self.scale +
self.patch_size // self.scale,
wi // self.scale:wi // self.scale +
self.patch_size // self.scale]
lr_raw = lr_raw.view(1, 1, lr_raw.shape[-2], lr_raw.shape[-1])
lr_raw = self.raw_stack(lr_raw)
if self.denoise:
noise_level = max(self.min_noise,
np.random.rand(1) * self.max_noise)[0]
# raw_input + noise
noise = torch.randn([
1, 1, self.patch_size // self.scale,
self.patch_size // self.scale
]).mul_(noise_level)
lr_raw = lr_raw + self.raw_stack(noise)
# cat noise_map
noise_map = torch.ones([
1, 1, self.patch_size // (2 * self.scale), self.patch_size //
(2 * self.scale)
]) * noise_level
lr_raw = torch.cat((lr_raw, noise_map), 1)
data = {}
data['input'] = torch.clamp(lr_raw, 0., 1.)[0]
data['gt'] = torch.clamp(rgb, 0., 1.)
if self.get2label:
raw = raw[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
data['raw_gt'] = torch.clamp(raw, 0., 1.)
return data
def __getitem__(self, index):
# read images, crop size
rgb = cv2.cvtColor(cv2.imread(self.data_lists[index]),
cv2.COLOR_BGR2RGB).astype(np.float32) / 255.
rgb = data_aug(rgb, mode=np.random.randint(0, 8))
h, w, c = rgb.shape
h = h // 2 * 2
w = w // 2 * 2
rgb = rgb[0:h, 0:w, :]
rgb = torch.from_numpy(
np.ascontiguousarray(np.transpose(rgb, [2, 0, 1]))).float()
raw = rgb2raw(rgb.clone(), is_tensor=True)
# crop gt, keep even
wi = random.randint(0, w - self.patch_size)
hi = random.randint(0, h - self.patch_size)
wi = wi - wi % 2
hi = hi - hi % 2
# wi, hi, start point in gt
rgb = rgb[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
raw = raw[:, hi:hi + self.patch_size, wi:wi + self.patch_size]
raw = raw.view(1, 1, raw.shape[-2], raw.shape[-1])
raw = self.raw_stack(raw)
if self.denoise:
noise_level = max(self.min_noise,
np.random.rand(1) * self.max_noise)[0]
# raw_input + noise
noise = torch.randn([1, 1, self.patch_size,
self.patch_size]).mul_(noise_level)
raw = raw + self.raw_stack(noise)
# cat noise_map
noise_map = torch.ones([
1, 1, self.patch_size // 2, self.patch_size // 2
]) * noise_level
raw = torch.cat((raw, noise_map), 1)
data = {}
data['input'] = torch.clamp(raw[0], 0., 1.)
data['gt'] = torch.clamp(rgb, 0., 1.)
return data