def draw_gt(gt_objects, prop_2d_axes, prop_3d_axes, p_matrix):
# Draw filtered ground truth boxes
for obj in gt_objects:
# Draw 2D boxes
vis_utils.draw_box_2d(prop_2d_axes, obj, test_mode=True, color_tm='r')
# Draw 3D boxes
vis_utils.draw_box_3d(prop_3d_axes, obj, p_matrix,
show_orientation=False,
color_table=['r', 'y', 'r', 'w'],
line_width=2,
double_line=False)
def draw_proposals(filtered_gt_objs, p_matrix, num_of_proposals, proposal_objs,
proposal_boxes, prop_2d_axes, prop_3d_axes,
draw_orientations_on_prop):
# Draw filtered ground truth boxes
for obj in filtered_gt_objs:
# Draw 2D boxes
vis_utils.draw_box_2d(prop_2d_axes,
obj,
test_mode=True,
color_tm='r',
linewidth=linewidth)
# Draw 3D boxes
vis_utils.draw_box_3d(prop_3d_axes,
obj,
p_matrix,
show_orientation=draw_orientations_on_prop,
color_table=['r', 'y', 'r', 'w'],
line_width=line_width,
double_line=False)
# Overlay proposal boxes on images
for anchor_idx in range(num_of_proposals):
obj_label = proposal_objs[anchor_idx]
# Draw 2D boxes (can't use obj_label since 2D corners are not
# filled in)
rgb_box_2d = proposal_boxes[anchor_idx]
box_x1 = rgb_box_2d[0]
box_y1 = rgb_box_2d[1]
box_w = rgb_box_2d[2] - box_x1
box_h = rgb_box_2d[3] - box_y1
rect = patches.Rectangle((box_x1, box_y1),
box_w,
box_h,
linewidth=line_width,
edgecolor='cornflowerblue',
facecolor='none')
prop_2d_axes.add_patch(rect)
# Draw 3D boxes
vis_utils.draw_box_3d(prop_3d_axes,
obj_label,
p_matrix,
show_orientation=draw_orientations_on_prop,
color_table=['cornflowerblue', 'y', 'r', 'w'],
line_width=line_width,
double_line=False)
def main():
# Start of the Kitti demo code
print('=== Python Kitti Wrapper Demo ===')
# Setting Paths
data_set = 'training'
cam = 2
root_dir = '/notebooks/DATA/Kitti/object/' #os.path.expanduser('~') + '/Kitti/object/'
image_dir = os.path.join(root_dir, data_set) + '/image_' + str(cam)
label_dir = os.path.join(root_dir, data_set) + '/label_' + str(cam)
calib_dir = os.path.join(root_dir, data_set) + '/calib'
img_idx = int(random.random() * 100)
print('img_idx', img_idx)
# Run Visualization Function
f, ax1, ax2 = vis_utils.visualization(image_dir, img_idx)
# Run the main loop to run throughout the images
frame_calibration_info = calib_utils.read_calibration(calib_dir, img_idx)
p = frame_calibration_info.p2
# Load labels
objects = obj_utils.read_labels(label_dir, img_idx)
# For all annotated objects
for obj in objects:
# Draw 2D and 3D boxes
vis_utils.draw_box_2d(ax1, obj)
vis_utils.draw_box_3d(ax2, obj, p)
# Render results
plt.draw()
plt.show()
def draw_predictions(filtered_gt_objs, p_matrix, predictions_to_show,
prediction_objs, prediction_class, final_boxes,
pred_2d_axes, pred_3d_axes, draw_score, draw_iou,
gt_classes, draw_orientations_on_pred):
# Draw filtered ground truth boxes
gt_boxes = []
for obj in filtered_gt_objs:
# Draw 2D boxes
vis_utils.draw_box_2d(pred_2d_axes, obj, test_mode=True, color_tm='r')
# Draw 3D boxes
vis_utils.draw_box_3d(pred_3d_axes,
obj,
p_matrix,
show_orientation=draw_orientations_on_pred,
color_table=['r', 'y', 'r', 'w'],
line_width=2,
double_line=False)
if draw_iou:
gt_box_2d = [obj.x1, obj.y1, obj.x2, obj.y2]
gt_boxes.append(gt_box_2d)
if gt_boxes:
# the two_2 eval function expects np.array
gt_boxes = np.asarray(gt_boxes)
for pred_idx in range(predictions_to_show):
pred_obj = prediction_objs[pred_idx]
pred_class_idx = prediction_class[pred_idx]
rgb_box_2d = final_boxes[pred_idx]
box_x1 = rgb_box_2d[0]
box_y1 = rgb_box_2d[1]
box_w = rgb_box_2d[2] - box_x1
box_h = rgb_box_2d[3] - box_y1
box_cls = gt_classes[int(pred_class_idx)]
rect = patches.Rectangle((box_x1, box_y1),
box_w,
box_h,
linewidth=2,
edgecolor=BOX_COLOUR_SCHEME[box_cls],
facecolor='none')
pred_2d_axes.add_patch(rect)
# Draw 3D boxes
vis_utils.draw_box_3d(pred_3d_axes,
pred_obj,
p_matrix,
show_orientation=draw_orientations_on_pred,
color_table=['#00FF00', 'y', 'r', 'w'],
line_width=2,
double_line=False,
box_color=BOX_COLOUR_SCHEME[box_cls])
if draw_score or draw_iou:
box_x2 = rgb_box_2d[2]
box_y2 = rgb_box_2d[3]
pred_box_2d = [box_x1, box_y1, box_x2, box_y2]
info_text_x = (box_x1 + box_x2) / 2
info_text_y = box_y1
draw_prediction_info(pred_2d_axes, info_text_x, info_text_y,
pred_obj, pred_class_idx, pred_box_2d,
gt_boxes, draw_score, draw_iou, gt_classes)
