def __init__(self, data_dir, out_size=800):
if 'train' in data_dir:
self.bg_dir = data_dir[0:data_dir.find('train')] + 'background'
else:
self.bg_dir = data_dir[0:data_dir.find('test')] + 'background'
self.bg_name = [
name for name in os.listdir(self.bg_dir)
if os.path.isfile(os.path.join(self.bg_dir, name))
]
self.num_bg = len(self.bg_name)
self.bg_object = [
open_img(os.path.join(self.bg_dir, bg_name)).convert('RGB')
for bg_name in self.bg_name
]
self.bg_scale = (0.5, 1.0)
self.obj_scale = (0.2, 0.4, 0.6, 0.8, 1)
#self.obj_size = obj_size
self.bg_size = out_size
self.rrc = RandomResizedCrop(size=out_size, scale=self.bg_scale)
self.rrc_obj = self.class_resize(min_size=500,
max_size=(600, 575, 550))
self.color_jig = ColorJitter()
def __getitem__(self, idx):
img_name = os.path.join(self.data_dir_img, self.img_name[idx])
image = open_img(img_name).convert('RGB')
#print(self.size, image.size)
i, j, h, w = self.get_params(image, self.size)
#print("image size : {} ; {}, {}, {}, {}".format(image.size, i, j, h, w))
image = F.crop(image, i, j, h, w)
boxes = torch.as_tensor(self.gt[idx]).reshape(
-1, 4) # guard against no boxes
#print(boxes, self.gt[idx])
#print("Boxes :", boxes.shape)
boxes = boxes - torch.as_tensor([i, j, 0, 0])
#print(boxes)
target = BoxList(boxes, image.size, mode="xywh").convert("xyxy")
classes = torch.as_tensor(self.cls_idx[idx]).reshape(-1)
#print("Classes :", classes.shape)
target.add_field("labels", classes)
masks = torch.as_tensor(1 * self.gen_mask_from_img(image))
masks = SegmentationMask(masks, image.size, mode='mask')
target.add_field("masks", masks)
target = target.clip_to_image(remove_empty=True)
if self._transform:
image, target = self._transform(image, target)
return image, target, idx
def test_secure_filename(self):
self.assertEqual('My_cool_movie.mov',
secure_filename('My cool movie.mov'))
self.assertEqual('etc_passwd', secure_filename('../../../etc/passwd'))
self.assertEqual('i_contain_cool_umlauts.txt',
secure_filename(u'i contain cool \xfcml\xe4uts.txt'))
self.assertEqual('i_contain_weird_characters.txt',
secure_filename(u'i contain weird châräctérs.txt'))
with self.assertNoException():
size = open_img(
join(settings.CREME_ROOT, 'static', 'common', 'images',
secure_filename('500_200.png'))).size
self.assertEqual((200, 200), size)
def augment_img(image_path, amount, width=IMAGE_WIDTH, height=IMAGE_HEIGHT):
"""Returns an array of `amount` generated images"""
if amount <= 1:
return [transform_img(image_path, width, height)]
img = np.array(open_img(image_path))
# The array has shape (amount, width, height, 3)
# and dtype uint8.
images = np.array([img for _ in range(amount)], dtype=np.uint8)
seq = iaa.Sequential(
[
iaa.Fliplr(0.5), # horizontal flips
iaa.Crop(percent=(0, 0.1)), # random crops
# Small gaussian blur with random sigma between 0 and 0.5.
# But we only blur about 50% of all images.
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 0.5))),
# Strengthen or weaken the contrast in each image.
iaa.ContrastNormalization((0.75, 1.5)),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# Make some images brighter and some darker.
# In 20% of all cases, we sample the multiplier once per channel,
# which can end up changing the color of the images.
iaa.Multiply((0.8, 1.2), per_channel=0.2),
# Apply affine transformations to each image.
# Scale/zoom them, translate/move them, rotate them and shear them.
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
rotate=(-25, 25),
shear=(-8, 8))
],
random_order=True) # apply augmenters in random order
images_aug = seq.augment_images(images)
res = [transform_img(image_path, width, height)]
for img in images_aug:
res.append(transform(fromarray(img), width, height))
return res
def __init__(self, data_dir):
if 'train' in data_dir:
self.bg_dir = data_dir[0:data_dir.find('train')] + 'background'
else:
self.bg_dir = data_dir[0:data_dir.find('test')] + 'background'
self.bg_name = [
name for name in os.listdir(self.bg_dir)
if os.path.isfile(os.path.join(self.bg_dir, name))
]
self.num_bg = len(self.bg_name)
#print(self.num_bg)
self.bg_object = [
open_img(os.path.join(self.bg_dir, bg_name)).convert('RGB')
for bg_name in self.bg_name
]
self.bg_scale = [0.1, 0.2, 0.4]
def __getitem__(self, idx):
img_name = os.path.join(self.data_dir_img, self.img_name[idx])
image = open_img(img_name).convert('RGB')
boxes = torch.as_tensor(self.gt[idx]).reshape(
-1, 4) # guard against no boxes
target = BoxList(boxes, image.size, mode="xywh").convert("xyxy")
classes = torch.as_tensor(self.cls_idx[idx])[None]
target.add_field("labels", classes)
masks = torch.as_tensor(1 * self.gen_mask_from_img(image))
masks = SegmentationMask(masks, image.size, mode='mask')
#print(masks)
target.add_field("masks", masks)
target = target.clip_to_image(remove_empty=True)
if self._transform:
image, target = self._transform(image, target)
return image, target, idx
def image_size(*, path: str) -> Tuple[int, int]:
"""Get the size of a image from it's file path."""
with open_img(path) as img:
size = img.size
return size
def transform_img(image_path, width=IMAGE_WIDTH, height=IMAGE_HEIGHT):
"""Returns resized image"""
return transform(open_img(image_path), width, height)
def get_img_info(self, index):
img_name = os.path.join(self.data_dir_img, self.img_name[index])
img = open_img(img_name)
img_height, img_width = img.size
return {"height": img_height, "width": img_width}
