Erase OperatorΒΆ
In this example we demonstrate the capabilities of the Erase
operator and the different ways to specify its arguments.
The Erase
operator can be used to remove parts of a tensor (for example, an image) by specifying one or more regions with a value to fill the erased regions.
Define a DALI pipeline that we will be using to demonstrate the different use cases.
[1]:
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.types as types
import matplotlib.pyplot as plt
batch_size = 1
image_filename = '../data/images'
def erase_pipeline(
anchor,
shape,
axis_names,
fill_value=0,
normalized_anchor=False,
normalized_shape=False,
centered_anchor=False):
pipe = Pipeline(batch_size=batch_size, num_threads=1, device_id=0)
with pipe:
jpegs, _ = fn.file_reader(device="cpu", file_root=image_filename)
images = fn.image_decoder(jpegs, device="cpu", output_type=types.RGB)
erased = fn.erase(
images,
device="cpu",
anchor=anchor,
shape=shape,
axis_names=axis_names,
fill_value=fill_value,
normalized_anchor=normalized_anchor,
normalized_shape=normalized_shape,
centered_anchor=centered_anchor)
pipe.set_outputs(erased)
return pipe
Write a simple utility function to run and display the output of a pipeline instance
[2]:
def show(pipe):
pipe.build()
out = pipe.run()
plt.imshow(out[0].at(0))
We can now use the pipeline to demonstrate the different use cases of the erase operator.
Specify a rectangular region with an anchor and a rectangular shape that is specified with absolute coordinates.
The order of the axes in the anchor
and shape
arguments is described by the axis_names
argument. The specified axis names also need to be present in the layout of the input. For instance, layout="HWC"
and axis_names="HW"
is equal to saying that the first coordinate corresponds to the axis with an index of 0, and the second coordinate corresponds to the axis with an index of 1. Alternatively, you can specify the axes
argument with a list of axis indexes, for example
axes=(0, 1)
.
Note: Using axis_names
is preferred over axes
.
[3]:
show(erase_pipeline(anchor=(40, 20), shape=(140, 50), axis_names="HW"))
If we change the
axis_names
value, the same region arguments might be interpreted differently.
For example, layout="HWC"
and axis_names="WH"
means that the first coordinate refers to the axis with an index of 1 and the second coordinate corresponds to the axis with an index of 0.
[4]:
show(erase_pipeline(anchor=(40, 20), shape=(140, 50), axis_names="WH"))
Specify a vertical or horizontal stripe by specifying only one dimension
[5]:
show(erase_pipeline(anchor=(350), shape=(50), axis_names="W"))
[6]:
show(erase_pipeline(anchor=(350), shape=(50), axis_names="H"))
Specify multiple regions by adding more points to the
anchor
andshape
arguments.
For instance, an anchor
and shape
with 4 points and the argument axis_names="HW"
representing 2 axes is interpreted as the following regions:
anchor=(y0, x0, y1, x1)
shape=(h0, w0, h1, w1)
[7]:
show(erase_pipeline(anchor=(30, 20, 350, 450), shape=(150, 40, 50, 150), axis_names="HW"))
Similarly, an anchor
and shape
with 3 elements representing only one axis (axis_names="W"
), corresponds to the following regions:
anchor=(x0, x1, x2)
shape=(w0, w1, w2)
[8]:
show(erase_pipeline(anchor=(50, 400, 550), shape=(60, 60, 60), axis_names="W"))
We can also change the default value for the erased regions. If one
fill_value
is provided, it is used in all the channels.
[9]:
show(erase_pipeline(anchor=(400), shape=(120), axis_names="W", fill_value=120))
Alternatively, we can specify a multi-channel fill value, for example
fill_value=(118, 185, 0)
.
In this case, the input layout needs to contain the C
channel, for example "HWC"
.
[10]:
show(erase_pipeline(anchor=(400), shape=(120), axis_names="W", fill_value=(118, 185, 0)))
Regions that fall totally, or partially out of the bounds of the image are ignored or trimmed respectively.
[11]:
show(erase_pipeline(anchor=(800), shape=(120), axis_names="W", fill_value=(118, 185, 0)))
[12]:
show(erase_pipeline(anchor=(500), shape=(500), axis_names="W", fill_value=(118, 185, 0)))
The region coordinates can also be specified by relative coordinates. In this case, to obtain the absolute coordinates, the relative coordinates are multiplied by the input dimensions.
[13]:
show(erase_pipeline(anchor=(0.8), shape=(0.15), axis_names="W", normalized_anchor=True, normalized_shape=True))
You can use relative and absolute coordinates to independently specify anchor
and shape
.
[14]:
show(erase_pipeline(anchor=(0.8), shape=(100), axis_names="W", normalized_anchor=True, normalized_shape=False))
[15]:
show(erase_pipeline(anchor=(450), shape=(0.22), axis_names="W", normalized_anchor=False, normalized_shape=True))
Specify that the regions should be centered at a specified
anchor
, instead of starting at it.For this, we can use the
centered_anchor
boolean argument.
[16]:
show(erase_pipeline(anchor=(0.5, 0.5), shape=(0.5, 0.5), axis_names="HW",
centered_anchor=True, normalized_anchor=True, normalized_shape=True))
[17]:
anchor = [k/10 for k in range(11)]
shape = [0.03] * 11
show(erase_pipeline(anchor=anchor, shape=shape, axis_names="W",
centered_anchor=True, normalized_anchor=True, normalized_shape=True))
Use tensor inputs, to specify the regions.
For instance, we can use the output of a random number generator to feed the
anchor
andshape
arguments of theErase
operator.
[18]:
def random_erase_pipeline(axis_names="W", nregions=5):
pipe = Pipeline(batch_size=batch_size, num_threads=1, device_id=0)
with pipe:
jpegs, _ = fn.file_reader(device="cpu", file_root=image_filename)
images = fn.image_decoder(jpegs, device="cpu", output_type=types.RGB)
ndims = len(axis_names)
args_shape=(ndims*nregions,)
random_anchor = fn.random.uniform(range=(0., 1.), shape=args_shape)
random_shape = fn.random.uniform(range=(20., 50), shape=args_shape)
erased = fn.erase(
images,
device="cpu",
anchor=random_anchor,
shape=random_shape,
axis_names=axis_names,
fill_value=(118, 185, 0),
normalized_anchor=True,
normalized_shape=False)
pipe.set_outputs(erased)
return pipe
[19]:
show(random_erase_pipeline(axis_names="W", nregions=1))
[20]:
show(random_erase_pipeline(axis_names="WH", nregions=4))
[21]:
show(random_erase_pipeline(axis_names="W", nregions=3))