def __init__(self, data_dir, use_transforms=False, pytorch=True):
"""
Args:
data_dir: Directory including both gray image directory and ground truth directory.
"""
super().__init__()
# Loop through the files in red folder and combine, into a dictionary, the other bands
self.files = self.create_dict(data_dir)
self.pytorch = pytorch
self.use_transforms = use_transforms
self.augmenter = Augmenter()
def get_augmenter(train_raw, domain):
if OPTIONS.augment:
aug_types = OPTIONS.augment.split('+')
augmenter = Augmenter(domain, train_raw, aug_types)
return augmenter
else:
return None
def plot_with_augmentations(data, idx):
aug = Augmenter()
fig, ax = plt.subplots(1, 2)
fig1, fig2 = data.remove_image_borders(idx)
ax[0].imshow(fig1)
ax[1].imshow(fig2)
plt.show()
class DatasetLoader(Dataset):
def __init__(self, data_dir, use_transforms=False, pytorch=True):
"""
Args:
data_dir: Directory including both gray image directory and ground truth directory.
"""
super().__init__()
# Loop through the files in red folder and combine, into a dictionary, the other bands
self.files = self.create_dict(data_dir)
self.pytorch = pytorch
self.use_transforms = use_transforms
self.augmenter = Augmenter()
def create_dict(self, data_dir):
"""
Args:
data_dir: Directory including both gray image directory and ground truth directory.
"""
return
def combine_files(self, gray_file: Path, gt_dir):
return
def __len__(self):
return len(self.files)
def open_as_array(self, idx, invert=False):
return
def open_mask(self, idx, add_dims=False):
return
def __getitem__(self, idx):
#get the image and mask as arrays
img_as_array = self.open_as_array(idx)
mask_as_array = self.open_mask(idx, add_dims=False)
if self.use_transforms:
img_as_array, mask_as_array = self.augmenter.transform_image(
image=img_as_array,
mask=mask_as_array,
transform=cfg.TRAINING.TRANSFORM)
if self.pytorch:
img_as_array = img_as_array.transpose((2, 0, 1))
# squeeze makes sure we get the right shape for the mask
x = torch.tensor(img_as_array, dtype=torch.float32)
y = torch.tensor(np.squeeze(mask_as_array), dtype=torch.torch.int64)
return x, y
def get_as_pil(self, idx): #fjernes?
#get an image for visualization
arr = 256 * self.open_mask(idx)
return Image.fromarray(arr.astype(np.uint8), 'RGB')
def main():
random.seed(0)
base_data = gen_nested()
base_train, base_test = base_data[:100], base_data[-500:]
write_data('train_base100.tsv', base_train)
write_data('test_base500.tsv', base_test)
domain = domains.new('artificial')
augmenter_entity = Augmenter(domain, base_train, ['entity'])
augmenter_nesting = Augmenter(domain, base_train, ['nesting', 'entity'])
deeper = sample_nested(depth=4, num=500)
entity_data = augmenter_entity.sample(500)
nesting_data = augmenter_nesting.sample(500)
aug_nums = (25, 50, 75, 100, 150, 200, 250, 300, 400, 500)
for n in aug_nums:
write_data('train_base%d.tsv' % (100 + n), base_data[:(100 + n)])
write_data('train_base100_entity%d.tsv' % n,
base_train + entity_data[:n])
write_data('train_base100_nesting%d.tsv' % n,
base_train + nesting_data[:n])
write_data('train_base100_deeper%d.tsv' % n, base_train + deeper[:n])
def num_classes(self):
return 3
if __name__ == '__main__':
from augmentation import get_augumentation, Resizer, Normalizer, Augmenter
# from augmentation import get_augumentation
# dataset = FLIRDataset(root_dir='/data_host/FLIR_ADAS/FLIR_ADAS_1_3', set_name='train',
# transform=get_augumentation(phase='train'))
# dataset = FLIRDataset(root_dir='/data_host/FLIR_ADAS/FLIR_ADAS', set_name='train',
# transform=transforms.Compose([Normalizer(),Augmenter(),Resizer()]))
dataset = FLIRDataset(root_dir='/data_host/FLIR_ADAS/FLIR_ADAS',
set_name='val',
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
# rand_id = 0
rand_id = random.randint(0, len(dataset) - 1)
sample = dataset[rand_id]
# print('sample: ', sample)
dataset.flir.info()
img = sample['img'].numpy()
annot = sample['annot'].numpy()
print('img:')
print(img)
print(img.shape)
print('annot:')
print(annot)
def __init__(self,
data_dir="data",
img_size=48,
rotate_angle=360,
shift=4,
n_epochs=200,
parallel=True,
pad=False,
normalize=True,
rng=np.random.RandomState(123)):
self.data_dir = data_dir
self.class_labels = {}
self._num_label = 0
self.img_size = img_size
self.train_file = os.path.join("data", "tidy",
"train_%d.npy" % img_size)
self.trainlabels_file = os.path.join("data", "tidy",
"train_labels_%d.npy" % img_size)
self.test_file = os.path.join("data", "tidy", "test_%d.npy" % img_size)
self.vanilla_file = os.path.join("data", "tidy",
"vtrain_%d.npy" % img_size)
self.vanillalabels_file = os.path.join(
"data", "tidy", "vtrain_labels_%d.npy" % img_size)
self.mapfile = os.path.join("data", "tidy", "train_labmapping.pkl")
self.trainfile = os.path.join("data", "tidy", "train.pkl")
self.n_testimages = 130400
# filenames in the testset order
self.testfilenames = []
self.rng = rng
self.normalize = normalize
X, y = self.load_images()
self.X_train, self.X_valid, self.y_train, self.y_valid = self.train_test_split(
X, y)
del X, y
if pad:
self.resize_f = functools.partial(square,
output_shape=(self.img_size,
self.img_size),
flatten=False)
else:
self.resize_f = lambda x: resize(x,
output_shape=(self.img_size, self.
img_size),
mode='constant',
cval=1.)
self.X_train_resized = np.vstack(
tuple([
x.reshape(1, self.img_size, self.img_size)
for x in map(self.resize_f, self.X_train)
]))
self.X_valid_resized = np.vstack(
tuple([
x.reshape(1, self.img_size * self.img_size)
for x in map(self.resize_f, self.X_valid)
]))
del self.X_train, self.X_valid
if parallel:
self.queue = Queue(min(1, n_epochs + 1))
self.augmenter = Augmenter(self.queue,
self.X_train_resized,
max_items=n_epochs + 1,
random_seed=self.rng.randint(9999),
max_angle=rotate_angle,
max_shift=shift,
normalize=normalize,
flatten=True)
self.augmenter.start()
class DataSetLoader:
def __init__(self,
data_dir="data",
img_size=48,
rotate_angle=360,
shift=4,
n_epochs=200,
parallel=True,
pad=False,
normalize=True,
rng=np.random.RandomState(123)):
self.data_dir = data_dir
self.class_labels = {}
self._num_label = 0
self.img_size = img_size
self.train_file = os.path.join("data", "tidy",
"train_%d.npy" % img_size)
self.trainlabels_file = os.path.join("data", "tidy",
"train_labels_%d.npy" % img_size)
self.test_file = os.path.join("data", "tidy", "test_%d.npy" % img_size)
self.vanilla_file = os.path.join("data", "tidy",
"vtrain_%d.npy" % img_size)
self.vanillalabels_file = os.path.join(
"data", "tidy", "vtrain_labels_%d.npy" % img_size)
self.mapfile = os.path.join("data", "tidy", "train_labmapping.pkl")
self.trainfile = os.path.join("data", "tidy", "train.pkl")
self.n_testimages = 130400
# filenames in the testset order
self.testfilenames = []
self.rng = rng
self.normalize = normalize
X, y = self.load_images()
self.X_train, self.X_valid, self.y_train, self.y_valid = self.train_test_split(
X, y)
del X, y
if pad:
self.resize_f = functools.partial(square,
output_shape=(self.img_size,
self.img_size),
flatten=False)
else:
self.resize_f = lambda x: resize(x,
output_shape=(self.img_size, self.
img_size),
mode='constant',
cval=1.)
self.X_train_resized = np.vstack(
tuple([
x.reshape(1, self.img_size, self.img_size)
for x in map(self.resize_f, self.X_train)
]))
self.X_valid_resized = np.vstack(
tuple([
x.reshape(1, self.img_size * self.img_size)
for x in map(self.resize_f, self.X_valid)
]))
del self.X_train, self.X_valid
if parallel:
self.queue = Queue(min(1, n_epochs + 1))
self.augmenter = Augmenter(self.queue,
self.X_train_resized,
max_items=n_epochs + 1,
random_seed=self.rng.randint(9999),
max_angle=rotate_angle,
max_shift=shift,
normalize=normalize,
flatten=True)
self.augmenter.start()
def load_images(self):
# get cached data
if os.path.isfile(self.trainfile) and os.path.isfile(self.mapfile):
with open(self.mapfile, 'r') as lfile:
self.class_labels = cPickle.load(lfile)
with open(self.trainfile, 'r') as tfile:
images, y = cPickle.load(tfile)
return pd.Series(images), np.array(y, dtype='int32')
images = []
y = []
for directory in sorted(
glob.iglob(os.path.join(self.data_dir, "train", "*"))):
print("processing %s" % directory)
files = os.listdir(directory)
n_images = len(files)
# the last directory is a class label
self.class_labels[self._num_label] = os.path.split(directory)[-1]
# create labels list
y.extend([self._num_label] * n_images)
self._num_label += 1
for i, image in enumerate(files):
images.append(
imread(os.path.join(directory, image), as_grey=True))
# cache images as array for future use
with open(self.mapfile, 'w') as lfile:
cPickle.dump(self.class_labels, lfile)
with open(self.trainfile, 'w') as tfile:
cPickle.dump((images, y), tfile)
return pd.Series(images), np.array(y, dtype='int32')
def train_gen(self, padded=False, augment=False):
assert len(self.X_train_resized) == len(self.y_train)
n_samples = len(self.X_train_resized)
# xs = np.zeros((n_samples, self.img_size * self.img_size), dtype='float32')
# yield train set permutations indefinately
while True:
shuff_ind = self.rng.permutation(n_samples)
if augment:
#yield self.X_train_resized[shuff_ind].astype('float32'), self.y_train[shuff_ind]
rotated = self.queue.get().astype(theano.config.floatX)
if self.normalize:
rotated = (rotated - np.mean(rotated, axis=1, keepdims=True)) \
/(rotated.std(axis=1, keepdims=True) + 1e-5)
yield rotated[shuff_ind], self.y_train[shuff_ind]
else:
reshaped = self.X_train_resized.reshape(
self.X_train_resized.shape[0],
self.img_size * self.img_size)
yield reshaped[shuff_ind].astype(
theano.config.floatX), self.y_train[shuff_ind]
#transform the training set
# xs = np.vstack(tuple(
# map(functools.partial(transform,
# rng=self.rng,
# image_size=(self.img_size, self.img_size)),
# self.X_train)))
def valid_gen(self, padded=False):
# will return same shuffled images
while True:
shuff_ind = self.rng.permutation(len(self.X_valid_resized))
xs = self.X_valid_resized
if self.normalize:
xs = (xs - np.mean(xs, axis=1, keepdims=True)) \
/(xs.std(axis=1, keepdims=True) + 1e-5)
yield xs[shuff_ind].astype(
theano.config.floatX), self.y_valid[shuff_ind]
def load_train(self):
# check if a dataset with the given image size has already been processed
if os.path.isfile(self.train_file) and os.path.isfile(
self.trainlabels_file):
X = np.load(self.train_file)
y = np.load(self.trainlabels_file)
with open(
os.path.join("data", "tidy",
"train_%d_labmapping.npy" % self.img_size),
'r') as lfile:
self.class_labels = json.load(lfile)
return X, y
x = []
y = []
for directory in sorted(
glob.iglob(os.path.join(self.data_dir, "train", "*"))):
print("processing %s" % directory)
files = os.listdir(directory)
# set up the array to store images and labels
n_images = len(files)
images = np.zeros((n_images, self.img_size * self.img_size),
dtype='float32')
# the last directory is a class label
self.class_labels[self._num_label] = os.path.split(directory)[-1]
# create labels list
y.extend([self._num_label] * n_images)
self._num_label += 1
for i, image in enumerate(files):
img_array = imread(os.path.join(directory, image),
as_grey=True)
images[i, ...] = resize(
img_array, (self.img_size, self.img_size)).reshape(1, -1)
x.append(images)
# concatenate the arrays from all classes and append labels
x = np.vstack(tuple(x))
y = np.array(y, dtype='int32')
# save the processed files
np.save(self.train_file, x)
np.save(self.trainlabels_file, y)
# also save label to index mapping
return x, y
def load_test(self):
testdir = os.path.join(self.data_dir, "test")
# if a test dataset is present load it from file
if os.path.isfile(self.test_file):
self.testfilenames = os.listdir(testdir)
return np.load(self.test_file)
# read test images
images = np.zeros((self.n_testimages, self.img_size * self.img_size),
dtype='float32')
for i, imfile in enumerate(os.listdir(testdir)):
img_array = imread(os.path.join(testdir, imfile), as_grey=True)
images[i, ...] = resize(img_array,
(self.img_size, self.img_size)).reshape(
1, -1)
self.testfilenames.append(imfile)
assert len(images) == len(
self.testfilenames
), "Number of files doesn't match number of images"
if self.normalize:
images = (images - np.mean(images, axis=1, keepdims=True)) \
/(images.std(axis=1, keepdims=True) + 1e-5)
# cache the resulting array for future use
np.save(self.test_file, images)
return images
def train_test_split(self, X, y, test_size=0.1):
sss = StratifiedShuffleSplit(y,
n_iter=1,
random_state=self.rng,
test_size=test_size)
# we only split once so do not use iter, just convert to list and get first split
train, test = list(sss).pop()
return X[train], X[test], y[train], y[test]
def save_submission(self, y_pred, file_suffix=""):
# sanity-check
h, w = y_pred.shape
assert w == len(self.class_labels), "Not all class labels present"
# number of test cases
assert h == len(
self.testfilenames), "Not all test observations present"
colnames = [self.class_labels[ind] for ind in xrange(121)]
dfr = pd.DataFrame(y_pred, index=self.testfilenames, columns=colnames)
dfr.to_csv(os.path.join(self.data_dir, "submissions",
"submission-%s.csv" % file_suffix),
format="%f",
index_label="image")
->masks_orig
->train
->val
->augs
->img
->mask
'''
# Augment original training set of images
in_img = r'C:\Users\ahls_st\Documents\MasterThesis\IKONOS\With_Hedges\FourBands\Splits\imgs\train' #change
aug_img_dir = r'D:\Steve\IKONOS\4band_geo_only\imgs' #change
in_mask = r'C:\Users\ahls_st\Documents\MasterThesis\IKONOS\With_Hedges\ThreeBands\Splits\BGR\Masks\train' #change
aug_mask_dir = r'D:\Steve\IKONOS\4band_geo_only\masks' #change
# Creates augmented versions of the training data. Can choose to do single, or combined augmentations
augmenter = Augmenter(in_img, in_mask, '.png')
#augmenter.augment(aug_img_dir, aug_mask_dir, n_bands=3) # performs all augmentations as singles
augmenter.augment_combo(aug_img_dir,
aug_mask_dir,
n_bands=4,
times=23,
n_geo=2,
n_spec=0) #performs geo_only augmentations
augmenter.random_crop(aug_img_dir, aug_mask_dir, n_bands=4,
num=8) #randomly crops
# Get rid of hedge masks where only a little bit is showing at the edge of the image
# Best to inspect the removed files before continuing to make sure the erase threshold is ok
if not os.path.exists(os.path.join(aug_mask_dir, 'Cleaned')):
os.mkdir(os.path.join(aug_mask_dir, 'Cleaned'))