def testCameraVisualization(self):
metric = detection_3d_metrics.CameraVisualization()
batch_size = 4
num_preds = 10
images = np.random.rand(batch_size, 512, 1024, 3)
bbox_corners = np.random.rand(batch_size, num_preds, 8, 2)
bbox_scores = np.random.rand(batch_size, num_preds)
metric.Update(
py_utils.NestedMap({
'camera_images': images,
'bbox_corners': bbox_corners,
'bbox_scores': bbox_scores
}))
# Test that the metric runs.
_ = metric.Summary('test')
def CreateDecoderMetrics(self):
"""Decoder metrics for KITTI."""
p = self.params
kitti_metric_p = p.ap_metric.Copy().Set(
cls=kitti_ap_metric.KITTIAPMetrics)
apm = kitti_metric_p.Instantiate()
class_names = apm.metadata.ClassNames()
# Convert the list of class names to a dictionary mapping class_id -> name.
class_id_to_name = dict(enumerate(class_names))
top_down_transform = transform_util.MakeCarToImageTransform(
pixels_per_meter=32.,
image_ref_x=512.,
image_ref_y=1408.,
flip_axes=True)
decoder_metrics = py_utils.NestedMap({
'top_down_visualization':
(detection_3d_metrics.TopDownVisualizationMetric(
top_down_transform,
image_height=1536,
image_width=1024,
class_id_to_name=class_id_to_name)),
'num_samples_in_batch':
metrics.AverageMetric(),
'kitti_AP_v2':
apm,
})
decoder_metrics.mesh = detection_3d_metrics.WorldViewer()
if p.summarize_boxes_on_image:
decoder_metrics.camera_visualization = (
detection_3d_metrics.CameraVisualization(
bbox_score_threshold=p.
visualization_classification_threshold))
return decoder_metrics