def testDrawBBoxes(self):
bs = 5
nbboxes = 2
class_id_to_name = {0: 'foo', 1: 'bar'}
loc_weights = np.ones(shape=(bs, nbboxes))
labels = np.ones(shape=(bs, nbboxes))
cbboxes = np.zeros(shape=(bs, nbboxes, 4))
# Ensures ymin/xmin/ymax/ymin.
cbboxes[:, :, :2] = 100. * np.random.normal(size=(bs, nbboxes, 2))
cbboxes[:, :, 2:4] = cbboxes[:, :, :2] + 10. * np.random.uniform(
size=(bs, nbboxes, 2))
images = np.zeros(shape=(bs, 100, 100, 1), dtype=np.uint8)
summary.DrawBBoxesOnImages(images, cbboxes, loc_weights, labels,
class_id_to_name, True)
def _EvaluateIfNecessary(self, name):
"""Create a top down image summary, if not already created."""
if self._summary is not None:
return
tf.logging.info('Generating top down summary.')
ret = tf.Summary()
transform = self._top_down_transform
for batch_idx, batch_sample in enumerate(self._sampler.samples):
batch_size = batch_sample.labels.shape[0]
visualization_labels = batch_sample.visualization_labels
predicted_bboxes = batch_sample.predicted_bboxes
visualization_weights = batch_sample.visualization_weights
points_xyz = batch_sample.points_xyz
points_padding = batch_sample.points_padding
gt_bboxes_2d = batch_sample.gt_bboxes_2d
gt_bboxes_2d_weights = batch_sample.gt_bboxes_2d_weights
labels = batch_sample.labels
difficulties = batch_sample.difficulties
source_ids = batch_sample.source_ids
# Create base images for entire batch that we will update.
images = np.zeros(
[batch_size, self._image_height, self._image_width, 3],
dtype=np.uint8)
# Draw lasers first, so that bboxes can be on top.
self._DrawLasers(images, points_xyz, points_padding, transform)
# Draw ground-truth bboxes.
gt_bboxes_2d = np.where(
np.expand_dims(gt_bboxes_2d_weights > 0, -1), gt_bboxes_2d,
np.zeros_like(gt_bboxes_2d))
transformed_gt_bboxes_2d = summary.TransformBBoxesToTopDown(
gt_bboxes_2d, transform)
summary.DrawBBoxesOnImages(images,
transformed_gt_bboxes_2d,
gt_bboxes_2d_weights,
labels,
self._class_id_to_name,
groundtruth=True)
# Draw predicted bboxes.
predicted_bboxes = np.where(
np.expand_dims(visualization_weights > 0, -1),
predicted_bboxes, np.zeros_like(predicted_bboxes))
transformed_predicted_bboxes = summary.TransformBBoxesToTopDown(
predicted_bboxes, transform)
summary.DrawBBoxesOnImages(images,
transformed_predicted_bboxes,
visualization_weights,
visualization_labels,
self._class_id_to_name,
groundtruth=False)
# Draw the difficulties on the image.
self.DrawDifficulty(images, transformed_gt_bboxes_2d,
gt_bboxes_2d_weights, difficulties)
for idx in range(batch_size):
source_id = source_ids[idx]
def AnnotateImage(fig, axes, source_id=source_id):
"""Add source_id to image."""
del fig
# Draw in top middle of image.
text = axes.text(500,
15,
source_id,
fontsize=16,
color='blue',
fontweight='bold',
horizontalalignment='center')
text.set_path_effects([
path_effects.Stroke(linewidth=3,
foreground='lightblue'),
path_effects.Normal()
])
image_summary = plot.Image(name='{}/{}/{}'.format(
name, batch_idx, idx),
aspect='equal',
figsize=self._figsize,
image=images[idx, ...],
setter=AnnotateImage)
ret.value.extend(image_summary.value)
tf.logging.info('Done generating top down summary.')
self._summary = ret