def InitFromImgsAndMasks(self,
imgs,
masks,
classes,
dirSave,
resize=None,
preproc=None,
imgDataFmt=float,
maskDataFmt=int,
isMaskOneHot=False):
self.pathsImg = None
self.pathsMasks = None
if resize is not None:
# Use pure resizing to preserve data matchiing
imgs = transform.resize(imgs,
resize + (self.imgs.shape[2], ),
order=0,
clip=True,
preserve_range=True,
anti_aliasing=False)
masks = transform.resize(masks,
resize + (self.masks.shape[2], ),
order=0,
clip=True,
preserve_range=True,
anti_aliasing=False)
gc.collect()
if preproc is not None:
imgs, masks = preproc(imgs, masks, classes)
gc.collect()
imgs = imgs.astype(dtype=imgDataFmt)
masks = masks.astype(dtype=maskDataFmt)
# One-hot encoding target
if classes <= 1:
raise Exception("Class count = 1. Unable to run! ")
if not isMaskOneHot:
maxClass = np.max(np.unique(masks))
if maxClass > classes:
raise Exception("Not enough classes! MaxClassValue: " +
str(maxClass))
masks = CommonUtil.PackIntoOneHot(masks, classes)
gc.collect()
if np.any(np.isnan(imgs)):
raise Exception("NAN Warning!")
if np.any(np.isnan(masks)):
raise Exception("NAN Warning!")
# Save to npys
self.__SaveToNPYs(dirSave, imgs, masks)
def __init__(self,
arrImgs,
masks,
classes,
resize=None,
preproc=None,
imgDataFmt=float,
maskDataFmt=int,
isMaskOneHot=False):
self.arrImgs = arrImgs # ndarray[H, W, Type, Slices]
self.masks = masks
if resize is not None:
# Use pure resizing to preserve data matchiing
self.arrImgs = transform.resize(self.arrImgs,
(self.arrImgs.shape[0], ) +
resize + (self.arrImgs.shape[3], ),
order=0,
clip=True,
preserve_range=True,
anti_aliasing=False)
self.masks = transform.resize(self.masks,
resize + (self.masks.shape[2], ),
order=0,
clip=True,
preserve_range=True,
anti_aliasing=False)
gc.collect()
if preproc is not None:
self.arrImgs, self.masks = preproc(self.arrImgs, self.masks,
classes)
gc.collect()
self.arrImgs = self.arrImgs.astype(dtype=imgDataFmt)
self.masks = self.masks.astype(dtype=maskDataFmt)
# One-hot encoding target
if classes <= 1:
raise Exception("Class count = 1. Unable to run! ")
if not isMaskOneHot:
maxClass = np.max(np.unique(self.masks))
if maxClass > classes:
raise Exception("Not enough classes! MaxClassValue: " +
str(maxClass))
self.masks = CommonUtil.PackIntoOneHot(self.masks, classes)
gc.collect()
if np.any(np.isnan(self.arrImgs)):
raise Exception("NAN Warning!")
if np.any(np.isnan(self.masks)):
raise Exception("NAN Warning!")