def __getitem__(self, index):
normal_path, img_list, dirs, reflectance_path, mask_path = self._getInputPath(index)
normal = sio.loadmat(normal_path)['normal'].astype(np.float32)
reflectance = imread(reflectance_path).astype(np.float32) / 255.0
if(reflectance.shape[2] == 4):
reflectance = reflectance[:,:,:3]
imgs = []
for i in img_list:
img = imread(i).astype(np.float32) / 255.0
if(img.shape[2] == 4):
img = img[:,:,:3]
imgs.append(img)
img = np.concatenate(imgs, 2)
h, w, c = img.shape
crop_h, crop_w = self.args.crop_h, self.args.crop_w
if self.args.rescale and not (crop_h == h):
sc_h = np.random.randint(crop_h, h) if self.args.rand_sc else self.args.scale_h
sc_w = np.random.randint(crop_w, w) if self.args.rand_sc else self.args.scale_w
img, normal = pms_transforms.rescale(img, normal, [sc_h, sc_w])
if self.args.crop:
img, normal = pms_transforms.randomCrop(img, normal, [crop_h, crop_w])
# if self.args.color_aug:
# img = img * np.random.uniform(1, self.args.color_ratio)
if self.args.int_aug:
ints = pms_transforms.getIntensity(len(imgs))
img = np.dot(img, np.diag(ints.reshape(-1)))
else:
ints = np.ones(c)
# if self.args.noise_aug:
# img = pms_transforms.randomNoiseAug(img, self.args.noise)
mask = sio.loadmat(mask_path)['mask'].astype(np.float32)
norm = np.sqrt((normal * normal).sum(2, keepdims=True))
normal = normal / (norm + 1e-10) # Rescale normal to unit length
item = {'normal': normal, 'img': img, 'reflectance': reflectance}
for k in item.keys():
item[k] = pms_transforms.arrayToTensor(item[k])
#item['dirs'] = torch.from_numpy(dirs).view(-1, 1, 1).float()
item['lights'] = torch.from_numpy(dirs).view(-1, 1, 1).float()
item['ints'] = torch.from_numpy(ints).view(-1, 1, 1).float()
item['mask'] = torch.from_numpy(mask).unsqueeze(0)
# normal : torch.Size([3, 128, 128])
# img : torch.Size([6, 128, 128])
# mask : torch.Size([1, 128, 128])
# dirs : torch.Size([6, 1, 1])
# ints : torch.Size([6, 1, 1])
return item
def __getitem__(self, index):
normal_path, img_list, lights = self._getInputPath(index)
normal = imread(normal_path).astype(np.float32) / 255.0 * 2 - 1
imgs = []
shadows = []
for i in img_list:
img = imread(i).astype(np.float32) / 255.0
if self.args.shadow:
s = imread(i.replace('/l_', '/s_')).astype(np.float32) / 255.0
shadows.append(s[:, :, 0:1])
imgs.append(img)
img = np.concatenate(imgs, 2)
shadows = np.concatenate(shadows, 2)
img = np.concatenate([img, shadows], 2)
h, w, c = img.shape
crop_h, crop_w = self.args.crop_h, self.args.crop_w
if self.args.rescale:
sc_h = np.random.randint(crop_h, h)
sc_w = np.random.randint(crop_w, w)
img, normal = pms_transforms.rescale(img, normal, [sc_h, sc_w])
if self.args.crop:
img, normal = pms_transforms.randomCrop(img, normal,
[crop_h, crop_w])
if self.args.shadow:
shadow = np.empty_like(img[:, :, 96:])
shadow[:] = img[:, :, 96:]
if self.args.color_aug:
img = (img * np.random.uniform(1, 3)).clip(0, 2)
if self.args.noise_aug:
img = pms_transforms.randomNoiseAug(img, self.args.noise)
mask = pms_transforms.normalToMask(normal)
normal = normal * mask.repeat(3, 2)
item = {'N': normal, 'img': img, 'mask': mask}
for k in item.keys():
item[k] = pms_transforms.arrayToTensor(item[k])
if self.args.in_light:
item['light'] = torch.from_numpy(lights).view(-1, 1, 1).float()
if self.args.shadow:
item['shadow'] = shadow
return item
def __getitem__(self, index):
normal_path, img_list, dirs = self._getInputPath(index)
normal = imread(normal_path).astype(np.float32) / 255.0 * 2 - 1
imgs = []
for i in img_list:
img = imread(i).astype(np.float32) / 255.0
imgs.append(img)
img = np.concatenate(imgs, 2)
h, w, c = img.shape
crop_h, crop_w = self.args.crop_h, self.args.crop_w
if self.args.rescale and not (crop_h == h):
sc_h = np.random.randint(
crop_h, h) if self.args.rand_sc else self.args.scale_h
sc_w = np.random.randint(
crop_w, w) if self.args.rand_sc else self.args.scale_w
img, normal = pms_transforms.rescale(img, normal, [sc_h, sc_w])
if self.args.crop:
img, normal = pms_transforms.randomCrop(img, normal,
[crop_h, crop_w])
if self.args.color_aug:
img = img * np.random.uniform(1, self.args.color_ratio)
if self.args.int_aug:
ints = pms_transforms.getIntensity(len(imgs))
img = np.dot(img, np.diag(ints.reshape(-1)))
else:
ints = np.ones(c)
if self.args.noise_aug:
img = pms_transforms.randomNoiseAug(img, self.args.noise)
mask = pms_transforms.normalToMask(normal)
normal = normal * mask.repeat(3, 2)
norm = np.sqrt((normal * normal).sum(2, keepdims=True))
normal = normal / (norm + 1e-10) # Rescale normal to unit length
item = {'normal': normal, 'img': img, 'mask': mask}
for k in item.keys():
item[k] = pms_transforms.arrayToTensor(item[k])
item['dirs'] = torch.from_numpy(dirs).view(-1, 1, 1).float()
item['ints'] = torch.from_numpy(ints).view(-1, 1, 1).float()
return item
def __getitem__(self, index):
normal_path, img_list, lights = self._getInputPath(index)
normal = imread(normal_path).astype(np.float32) / 255.0 * 2 - 1
imgs = []
for i in img_list:
img = imread(i).astype(np.float32) / 255.0
imgs.append(img)
img = np.concatenate(imgs, 2)
h, w, c = img.shape
crop_h, crop_w = self.args.crop_h, self.args.crop_w
if self.args.rescale:
sc_h = np.random.randint(crop_h, h)
sc_w = np.random.randint(crop_w, w)
img, normal = pms_transforms.rescale(img, normal, [sc_h, sc_w])
if self.args.crop:
img, normal = pms_transforms.randomCrop(img, normal,
[crop_h, crop_w])
if self.args.color_aug and not self.args.normalize:
img = (img * np.random.uniform(1, 3)).clip(0, 2)
if self.args.normalize:
imgs = np.split(img, img.shape[2] // 3, 2)
imgs = pms_transforms.normalize(imgs)
img = np.concatenate(imgs, 2)
if self.args.noise_aug:
img = pms_transforms.randomNoiseAug(img, self.args.noise)
mask = pms_transforms.normalToMask(normal)
normal = normal * mask.repeat(3, 2)
norm = np.sqrt((normal * normal).sum(2, keepdims=True))
normal = normal / (norm + 1e-10)
item = {'N': normal, 'img': img, 'mask': mask}
for k in item.keys():
item[k] = pms_transforms.arrayToTensor(item[k])
if self.args.in_light:
item['light'] = torch.from_numpy(lights).view(-1, 1, 1).float()
return item