def load_lidar_tile(self, normalize=True):
'''Load a point cloud into memory from file
'''
self.lidar_filepath = self.fetch_lidar_filename()
if self.lidar_filepath == None:
print("Lidar file {} cannot be found".format(self.row["tile"]))
return None
self.lidar_tile = Lidar.load_lidar(self.lidar_filepath, normalize)
return self.lidar_tile
draw_caption(draw, b, caption)
#only pass score threshold boxes
quality_boxes = []
for box, score, label in zip(boxes[0], scores[0], labels[0]):
quality_boxes.append(box)
# scores are sorted so we can break
if score < args.score_threshold:
break
#drape boxes
#get image name and load point cloud
image_name = os.path.splitext(os.path.basename(image_path))[0]
point_cloud_filename = os.path.join(DeepForest_config["lidar_path"] +
image_name) + ".laz"
pc = Lidar.load_lidar(point_cloud_filename)
pc = postprocessing.drape_boxes(boxes=quality_boxes, pc=pc)
#Skip if point density is too low
if pc:
#Get new bounding boxes
new_boxes = postprocessing.cloud_to_box(pc)
#expends 3dim
new_boxes = np.expand_dims(new_boxes, 0)
# visualize detections
for box, score, label in zip(new_boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < args.score_threshold:
break