def __init__(self,
df,
img_path='/home/rliu/TDD-Net/data/',
window_size=50,
pad_size=50,
mask=create_circular_mask(224, 224),
transforms=None):
"""
Args:
df: dataframes of training data
transform: pytorch transforms for transforms and tensor conversion
"""
self.data = df
self.img_path = img_path
self.transforms = transforms
self.window_size = window_size
self.pad_size = pad_size
self.mask = mask
def __init__(
self,
csv_path='/home/rliu/yolo2/v2_pytorch_yolo2/data/an_data/VOCdevkit/VOC2007/csv_labels/train.csv',
img_path='/home/rliu/TDD-Net/data/',
window_size=50,
pad_size=50,
mask=create_circular_mask(224, 224),
transforms=None):
"""
Args:
csv_path (string): path to csv file
transform: pytorch transforms for transforms and tensor conversion
"""
self.data = pd.read_csv(csv_path, sep=" ")
self.img_path = img_path
self.transforms = transforms
self.window_size = window_size
self.pad_size = pad_size
self.mask = mask
def __init__(self,
image_index=6501,
img_path='/home/rliu/TDD-Net/data/',
window_size=45,
mask=create_circular_mask(224, 224),
stride=2,
transforms=None):
"""
Args:
image_index: index of image being processed
window_size: size of sliding window
transform: pytorch transforms for transforms and tensor conversion
"""
self.image = Image.open(img_path +
'%06.0f.jpg' % image_index).convert('L')
coord_list = np.empty([0, 2], dtype=int)
for i in np.arange(0, self.image.size[0], stride):
for j in np.arange(0, self.image.size[1], stride):
coord_list = np.append(coord_list, [[i, j]], axis=0)
self.coords = coord_list
self.mask = mask
self.window_size = window_size
self.transforms = transforms