def __getitem__(self, i):
g = imread(random.choice(self.d)) / 255
g = rgb2gray(g).astype(np.float32)
g = rand_crop(g, self.gs + self.ks - 1)
g = torch.from_numpy(g).view(1, *g.shape)
k = gen_kernel(self.ks).astype(np.float32)
k = torch.from_numpy(k)
s = random.uniform(1, 3)
s = torch.tensor((s, ), dtype=torch.float)
y = F.conv2d(g.view(1, *g.shape), k.view(1, 1, *k.shape)).squeeze(0)
assert y.shape[-1] == self.gs
# noise
if self.noise:
y += torch.randn_like(y) * s / 255
# show(y[0])
# edgetaping, todo convert to torch, and move to model stage
if self.edgetaper:
y = y.permute(1, 2, 0)
y = to_tensor(
edgetaper(pad_for_kernel(y.numpy(), k.numpy(), "edge"),
k.numpy())).astype(np.float32)
y = torch.from_numpy(y).squeeze(-1)
g = center_crop(g, self.gs)
# [1,284,284] [1,320,320] [37,37] [1]
return g, y, k, s
def __getitem__(self, i):
print(self.d[i])
mat = loadmat(self.d[i])
g = mat["x"].astype(np.float32)
y = mat["y"].astype(np.float32)
k = mat["f"].astype(np.float32)
# flip kernel
k = k[::-1, ::-1]
k = np.clip(k, 0, 1)
k /= np.sum(k)
y = to_tensor(edgetaper(pad_for_kernel(y, k, "edge"),
k)).astype(np.float32)
g = torch.from_numpy(g).unsqueeze(0)
y = torch.from_numpy(y).squeeze(-1)
k = torch.from_numpy(k)
s = torch.tensor((1.5, ), dtype=torch.float)
return g, y, k, s
def __getitem__(self, i):
i = self.d[i]
print(i)
p = str(i.name)
g, k = p.split("_")[:2]
g = imread(i.parent / f"img{g}_groundtruth_img.png")
k = imread(i.parent / f"kernel{k}_groundtruth_kernel.png")
y = imread(i)
[g, k, y] = [i.astype(np.float32) / 255 for i in [g, k, y]]
# k = k[::-1, ::-1]
k = np.clip(k, 0, 1)
k /= np.sum(k)
y = to_tensor(edgetaper(pad_for_kernel(y, k, "edge"),
k)).astype(np.float32)
g = torch.from_numpy(g).unsqueeze(0)
y = torch.from_numpy(y).squeeze(-1)
k = torch.from_numpy(k)
s = torch.tensor((2.55, ), dtype=torch.float)
return g, y, k, s
def __getitem__(self, i):
# [img1xkernel1, img1xkernel2,...,img2xkernel1]
g = imread(self.d[i // 8]) / 255
g = rgb2gray(g).astype(np.float32)
k = self.k[i % 8]
g = torch.from_numpy(g)
s = torch.tensor((self.s, ), dtype=torch.float)
g = g.view(1, *g.shape)
# blur
y = F.conv2d(g.view(1, *g.shape), k.view(1, 1, *k.shape))
g = center_crop(g, 250)
y = center_crop(y, 250)[0]
# show(torch.cat((g.detach().cpu()[0], y.detach().cpu()[0,0]), 0))
# noise
# y += torch.randn_like(y) * s / 255
# edgetaping, todo convert to torch, and move to model stage
y = y.permute(1, 2, 0)
y = to_tensor(
edgetaper(pad_for_kernel(y.numpy(), k.numpy(), "edge"),
k.numpy())).astype(np.float32)
y = torch.from_numpy(y).squeeze(-1)
# [1,250,250] [1,267,267] [1,13,13] [1]
return g, y, k, s
