def aug_image(self, img, mask=None):
if mask is not None:
Xi, Mi = from_pil(img, mask)
#print('>>>', Xi.shape, Mi.shape)
#print(Mi)
Xi, Mi = self.augment_with_target(Xi, Mi)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
Xi, Mi = to_pil(Xi, Mi)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi, Mi #torch.cat(Mi, dim=0)
else:
Xi = from_pil(img)
Xi = self.image_augment(Xi)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def test_augment():
img = os.path.join(settings.TRAIN_IMG_DIR, '003c477d7c.png')
mask = os.path.join(settings.TRAIN_MASK_DIR, '003c477d7c.png')
img = Image.open(img)
img = img.convert('RGB')
mask = Image.open(mask)
mask = mask.convert('L').point(lambda x: 0 if x < 128 else 255, '1')
print(type(mask))
mask = from_pil(mask)
Mi = [to_pil(mask == class_nr) for class_nr in [0, 1]]
img, *Mi = from_pil(img, *Mi)
aug = ImgAug(
crop_seq(crop_size=(settings.H, settings.W),
pad_size=(32, 32),
pad_method='reflect'))
aug2 = ImgAug(brightness_seq)
img, *Mi = aug(img, *Mi)
img = aug2(img)
img, *Mi = to_pil(img, Mi[0] * 255, Mi[1] * 255)
ImageDraw.Draw(img)
ImageDraw.Draw(Mi[0])
ImageDraw.Draw(Mi[1])
img.show()
Mi[0].show()
Mi[1].show()
def __getitem__(self, index):
if self.image_source == 'memory':
load_func = self.load_from_memory
elif self.image_source == 'disk':
load_func = self.load_from_disk
else:
raise NotImplementedError(
"Possible loading options: 'memory' and 'disk'!")
Xi = load_func(self.X, index, filetype='png', grayscale=False)
if self.y is not None:
Mi = self.load_target(self.y, index, load_func)
Xi, *Mi = from_pil(Xi, *Mi)
Xi, *Mi = self.image_augment_with_target(Xi, *Mi)
Xi = self.image_augment(Xi)
Xi, *Mi = to_pil(Xi, *Mi)
if self.mask_transform is not None:
Mi = [self.mask_transform(m) for m in Mi]
if self.image_transform is not None:
Xi = self.image_transform(Xi)
Mi = torch.cat(Mi, dim=0)
return Xi, Mi
else:
Xi = from_pil(Xi)
Xi = self.image_augment(Xi)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
Xi = self.X[0][index]
if self.y is not None:
Mi = self.y[0][index]
CTi = self.y[1][index]
CRi = self.y[2][index]
if self.train_mode and self.image_augment_with_target is not None:
Xi, Mi, CTi, CRi = from_pil(Xi, Mi, CTi, CRi)
Xi, Mi, CTi, CRi = self.image_augment_with_target(Xi, Mi, CTi, CRi)
Xi = self.image_augment(Xi)
Xi, Mi, CTi, CRi = to_pil(Xi, Mi, CTi, CRi)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
CTi = self.mask_transform(CTi)
CRi = self.mask_transform(CRi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi, Mi, CTi, CRi
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
if self.y is not None:
mask_filepath = self.y[index, 0]
contour_filepath = self.y[index, 1]
center_filepath = self.y[index, 2]
Mi = self.load_image(mask_filepath)
CTi = self.load_image(contour_filepath)
CRi = self.load_image(center_filepath)
if self.train_mode and self.image_augment_with_target is not None:
Xi, Mi, CTi, CRi = from_pil(Xi, Mi, CTi, CRi)
Xi, Mi, CTi, CRi = self.image_augment_with_target(Xi, Mi, CTi, CRi)
Xi = self.image_augment(Xi)
Xi, Mi, CTi, CRi = to_pil(Xi, Mi, CTi, CRi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
CTi = self.mask_transform(CTi)
CRi = self.mask_transform(CRi)
return Xi, Mi, CTi, CRi
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
if self.y is not None:
target_masks = []
for i in range(y.shape[1]):
filepath = self.y[index, i]
mask = self.load_image(filepath)
target_masks.append(mask)
target_masks = [target[index] for target in self.y]
data = [Xi] + target_masks
if self.train_mode and self.image_augment_with_target is not None:
data = from_pil(*data)
data = self.image_augment_with_target(*data)
data[0] = self.image_augment(data[0])
data = to_pil(*data)
if self.mask_transform is not None:
data[1:] = [self.mask_transform(mask) for mask in data[1:]]
if self.image_transform is not None:
data[0] = self.image_transform(data[0])
return data
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
if self.y is not None:
mask_filepath = self.y[index]
Mi = self.load_image(mask_filepath)
if self.train_mode and self.image_augment_with_target is not None:
Xi, Mi = from_pil(Xi, Mi)
Xi, Mi = self.image_augment_with_target(Xi, Mi)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
Xi, Mi = to_pil(Xi, Mi)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi, Mi
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def get_patches(self, X):
patches, patch_ids, tta_angles, patch_y_coords, patch_x_coords, image_h, image_w = [], [], [], [], [], [], []
for i, image in enumerate((X[0])):
image = from_pil(image)
h, w = image.shape[:2]
for y_coord, x_coord, image_patch in generate_patches(
image, self.dataset_params.h,
self.dataset_params.patching_stride):
for tta_rotation_angle, image_patch_tta in test_time_augmentation(
image_patch):
image_patch_tta = to_pil(image_patch_tta)
patches.append(image_patch_tta)
patch_ids.append(i)
tta_angles.append(tta_rotation_angle)
patch_y_coords.append(y_coord)
patch_x_coords.append(x_coord)
image_h.append(h)
image_w.append(w)
patch_ids = pd.DataFrame({
'patch_ids': patch_ids,
'tta_angles': tta_angles,
'y_coordinates': patch_y_coords,
'x_coordinates': patch_x_coords,
'image_h': image_h,
'image_w': image_w
})
return [patches], patch_ids
def __getitem__(self, index):
Xi = self.X[0][index]
if self.y is not None:
target_masks = [target[index] for target in self.y]
data = [Xi] + target_masks
if self.train_mode and self.image_augment_with_target is not None:
data = from_pil(*data)
data = self.image_augment_with_target(*data)
if self.image_augment is not None:
data[0] = self.image_augment(data[0])
data = to_pil(*data)
if self.mask_transform is not None:
data[1:] = [self.mask_transform(mask) for mask in data[1:]]
if self.image_transform is not None:
data[0] = self.image_transform(data[0])
return data
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
if self.y is not None:
mask_filepath = self.y[index]
Mi = self.load_image(mask_filepath)
distance_filepath = mask_filepath.replace("/masks/", "/distances/")
distance_filepath = os.path.splitext(distance_filepath)[0]
size_filepath = distance_filepath.replace("/distances/", "/sizes/")
Di = self.load_joblib(distance_filepath)
Di = Di.astype(np.uint16)
Si = self.load_joblib(size_filepath).astype(np.uint16)
Si = np.sqrt(Si).astype(np.uint16)
Xi, Mi = from_pil(Xi, Mi)
if self.image_augment_with_target is not None:
Xi, Mi, Di, Si = self.image_augment_with_target(Xi, Mi, Di, Si)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
Xi, Mi, Di, Si = to_pil(Xi, Mi, Di, Si)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
Di = self.mask_transform(Di)
Si = self.mask_transform(Si)
Mi = torch.cat((Mi, Di, Si), dim=0)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi, Mi
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def visualise_dataset(self):
"""
Sanity check.
"""
if self.config.mode == 'train':
dataset_names = ["train", "valid"]
loaders = [self.train_loader, self.valid_loader]
else:
dataset_names = ["test"]
loaders = [self.test_loader]
for name, loader in zip(dataset_names, loaders):
print(name)
x, y, has_target, target = loader.dataset[
loader.sampler.indices[0]]
print("y:", y.item())
print("attention target:",
target.numpy() if has_target else "none")
to_pil(x).save(f"{name}_0.png")
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
if self.y is not None:
mask_filepath = self.y[index]
Mi = self.load_image(mask_filepath)
distance_filepath = mask_filepath.replace("/masks/",
"/distances/")[:-4]
Di = self.load_distances(distance_filepath)
Di = np.sum(Di, axis=2).astype(
np.uint8
) # TODO: remove it when Di will be sum of distances to 2 closest objects
if self.train_mode and self.image_augment_with_target is not None:
Xi, Mi = from_pil(Xi, Mi)
Xi, Mi, Di = self.image_augment_with_target(Xi, Mi, Di)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
Xi, Mi, Di = to_pil(Xi, Mi, Di)
if not self.train_mode:
Di = to_pil(Di)
if self.mask_transform is not None:
Mi = self.mask_transform(Mi)
Di = self.mask_transform(Di)
Mi = torch.cat((Mi, Di), dim=0)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi, Mi
else:
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
img_filepath = self.X[index]
Xi = self.load_image(img_filepath)
Xi = from_pil(Xi)
if self.tta_params is not None:
tta_transform_specs = self.tta_params[index]
Xi = test_time_augmentation_transform(Xi, tta_transform_specs)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def __getitem__(self, index):
if self.image_source == 'memory':
load_func = self.load_from_memory
elif self.image_source == 'disk':
load_func = self.load_from_disk
else:
raise NotImplementedError(
"Possible loading options: 'memory' and 'disk'!")
Xi = load_func(self.X, index, filetype='png', grayscale=False)
Xi = from_pil(Xi)
if self.image_augment is not None:
Xi = self.image_augment(Xi)
if self.tta_params is not None:
tta_transform_specs = self.tta_params[index]
Xi = self.tta_transform(Xi, tta_transform_specs)
Xi = to_pil(Xi)
if self.image_transform is not None:
Xi = self.image_transform(Xi)
return Xi
def read_json(self, path):
with open(path, 'r') as file:
data = json.load(file)
masks = [to_pil(binary_from_rle(rle)) for rle in data]
return masks
def load_target(self, data_source, index, load_func):
Mi = load_func(data_source, index, filetype='png', grayscale=True)
Mi = from_pil(Mi)
target = [to_pil(Mi == class_nr) for class_nr in [0, 1]]
return target
