Based in your tutorial I am trying to use It for creating a DataLoader for MaskRCNN:
class MaskRCNN(dict):
@classmethod
def create(cls, dictionary):
return cls(dict({x:dictionary[x] for x in dictionary.keys()}))
def show(self, ctx=None, **kwargs):
dictionary = self
boxes = dictionary["boxes"]
labels = dictionary["labels"]
masks = dictionary["masks"]
result = masks
return show_image(result, ctx=ctx, **kwargs)
def MaskRCNNBlock():
return TransformBlock(type_tfms=MaskRCNN.create, batch_tfms=IntToFloatTensor)
def get_bbox(o):
label_path = get_y_fn(o)
mask=PILMask.create(label_path)
pos = np.where(mask)
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
return TensorBBox.create([xmin, ymin, xmax, ymax])
def get_bbox_label(o):
return TensorCategory([1])
def get_mask(o):
label_path = get_y_fn(o)
mask=PILMask.create(label_path)
mask=image2tensor(mask)
return TensorMask(mask)
def get_dict(o):
return {"boxes": get_bbox(o), "labels": get_bbox_label(o),"masks": get_mask(o)}
getters = [lambda o: o, get_dict]
maskrccnnDataBlock = DataBlock(
blocks=(ImageBlock, MaskRCNNBlock),
get_items=partial(get_image_files,folders=[manual_name]),
getters=getters,
splitter=RandomSplitter(valid_pct=0.1,seed=2020),
item_tfms=Resize((size,size)),
batch_tfms=Normalize.from_stats(*imagenet_stats)
)
maskrccnnDataBlock.summary(path_images)
dls = maskrccnnDataBlock.dataloaders(path_images,bs=bs)
dls.show_batch()
However, show_batch is not working owing to the fact that Mask is not getting resized to the same size as Image