def add_image_with_boxes(self,
tag,
img_tensor,
box_tensor,
global_step=None,
walltime=None,
dataformats='CHW',
**kwargs):
"""Add image and draw bounding boxes on the image.
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time()) of event
Shape:
img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformat`` agrument.
e.g. CHW or HWC
box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4, where N is the number of
boxes and each 4 elememts in a row represents (xmin, ymin, xmax, ymax).
"""
if self._check_caffe2(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
if self._check_caffe2(box_tensor):
box_tensor = workspace.FetchBlob(box_tensor)
self.file_writer.add_summary(
image_boxes(tag,
img_tensor,
box_tensor,
dataformats=dataformats,
**kwargs), global_step, walltime)
def add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None,
walltime=None, **kwargs):
"""Add image boxes data to summary (useful for models such as Detectron).
Args:
tag (string): Data identifier
img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)
global_step (int): Global step value to record
walltime (float): Optional override default walltime (time.time()) of event
"""
if self._check_caffe2(img_tensor):
img_tensor = workspace.FetchBlob(img_tensor)
if self._check_caffe2(box_tensor):
box_tensor = workspace.FetchBlob(box_tensor)
self.file_writer.add_summary(image_boxes(tag, img_tensor, box_tensor, **kwargs), global_step, walltime)
def test_image_with_boxes(self):
summary.image_boxes('dummy',
np.random.rand(3, 32, 32).astype(np.float32),
np.array([[10, 10, 40, 40]]))
def test_image_with_boxes(self):
compare_proto(summary.image_boxes('dummy',
tensor_N(shape=(3, 32, 32)),
np.array([[10, 10, 40, 40]])), self)
