def add_boxes(H,
orig_image,
confidences,
boxes,
use_stitching=False,
rnn_len=1,
min_conf=0.1,
show_removed=True,
tau=0.25,
color_removed=(0, 0, 255),
color_acc=(0, 0, 255)):
image = np.copy(orig_image[0])
num_cells = H["grid_height"] * H["grid_width"]
boxes_r = np.reshape(boxes,
(-1, H["grid_height"], H["grid_width"], rnn_len, 6))
confidences_r = np.reshape(
confidences,
(-1, H["grid_height"], H["grid_width"], rnn_len, H['num_classes']))
cell_pix_size = H['region_size']
all_boxes = [[[] for _ in range(H["grid_width"])]
for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = bbox[0]
abs_cy = bbox[1]
abs_cz = bbox[2]
w = bbox[3]
h = bbox[4]
d = bbox[5]
conf = np.max(confidences_r[0, y, x, n, 1:])
all_boxes[y][x].append(
Box(abs_cx, abs_cy, abs_cz, w, h, d, conf))
all_boxes_r = [r for row in all_boxes for cell in row for r in cell]
im = Image.fromarray(image.astype('uint8'))
image = np.array(im).astype('float32')
boxes = []
for box in all_boxes_r:
if box.true_confidence < min_conf:
continue
b = al.AnnoBox()
b.x, b.y, b.z = box.cx, box.cy, box.cz
b.w, b.h, b.d = box.width, box.height, box.depth
b.score = box.true_confidence
boxes.append(b)
return image, boxes
def read_kitti_anno(label_file, detect_truck):
""" Reads a kitti annotation file.
Args:
label_file: Path to file
Returns:
Lists of rectangels: Cars and don't care area.
"""
labels = [line.rstrip().split(' ') for line in open(label_file)]
box_list = []
for label in labels:
if not (label[0] == 'Car' or label[0] == 'Van' or
label[0] == 'Truck' or label[0] == 'DontCare'):
continue
notruck = not detect_truck
if notruck and label[0] == 'Truck':
continue
if label[0] == 'DontCare':
class_id = -1
else:
class_id = 1
x_dim=float(label[8])
y_dim=float(label[9])
z_dim=float(label[10])
x_pos=float(label[11])
y_pos=float(label[12])
z_pos=float(label[13])
if x_dim<0 or y_dim<0 or z_dim<0:
continue
object_rect = AnnoLib.AnnoBox(x=x_pos, y=y_pos, z=z_pos, w=x_dim, h=y_dim, d=z_dim)
object_rect.classID = class_id
box_list.append(object_rect)
return box_list
