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Created July 2, 2022 18:09
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aug = iaa.Sharpen(alpha=(1.0), lightness=(1.5))
def adjust_data(img,mask):
#img = img[:,:,1]
img[img <0.2]=0.5
img = img / 255
mask = mask /255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
return (img,mask)
class Dataset:
# we will be modifying this CLASSES according to your data/problems
# the parameters needs to changed based on your requirements
# here we are collecting the file_names because in our dataset, both our images and maks will have same file name
# ex: fil_name.jpg file_name.mask.jpg
def __init__(self, dataframe):
self.ids = dataframe['Patient']
# the paths of images
self.images_fps = dataframe['img']
# the paths of segmentation images
self.masks_fps = dataframe['mask']
def __getitem__(self, i):
# read data
image = cv2.imread(self.images_fps[i], cv2.IMREAD_UNCHANGED)
image = aug.augment_image(image)
image= np.reshape(image, (256,256,1))
image = image.astype(np.float32)
mask = cv2.imread(self.masks_fps[i], cv2.IMREAD_UNCHANGED)
mask = np.reshape(mask, (256,256,1))
image_mask = mask
image_mask = image_mask.astype(np.float32)
image,image_mask= adjust_data(image, image_mask)
return (image,image_mask)
def __len__(self):
return len(self.ids)
class Dataloder(tf.keras.utils.Sequence):
def __init__(self, dataset, batch_size=1, shuffle=False):
self.dataset = dataset
self.batch_size = batch_size
self.shuffle = shuffle
self.indexes = np.arange(len(dataset))
def __getitem__(self, i):
# collect batch data
start = i * self.batch_size
stop = (i + 1) * self.batch_size
data = []
for j in range(start, stop):
batch = [np.stack(samples) for samples in zip(*data)]
return tuple(batch)
def __len__(self):
return len(self.indexes) // self.batch_size
def on_epoch_end(self):
if self.shuffle:
self.indexes = np.random.permutation(self.indexes)
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