calib = Calibration(
img_path.replace(".png", ".txt").replace("image_2", "calib"))
bev_map = (bev_map.transpose(1, 2, 0) * 255).astype(np.uint8)
bev_map = cv2.resize(bev_map, (cnf.BEV_HEIGHT, cnf.BEV_WIDTH))
for box_idx, (cls_id, x, y, z, h, w, l, yaw) in enumerate(labels):
# Draw rotated box
yaw = -yaw
y1 = int((x - cnf.boundary['minX']) / cnf.DISCRETIZATION)
x1 = int((y - cnf.boundary['minY']) / cnf.DISCRETIZATION)
w1 = int(w / cnf.DISCRETIZATION)
l1 = int(l / cnf.DISCRETIZATION)
drawRotatedBox(bev_map, x1, y1, w1, l1, yaw,
cnf.colors[int(cls_id)])
# Rotate the bev_map
bev_map = cv2.rotate(bev_map, cv2.ROTATE_180)
labels[:, 1:] = lidar_to_camera_box(labels[:, 1:], calib.V2C, calib.R0,
calib.P2)
img_rgb = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
img_rgb = show_rgb_image_with_boxes(img_rgb, labels, calib)
out_img = merge_rgb_to_bev(img_rgb,
bev_map,
output_width=configs.output_width)
cv2.imshow('bev_map', out_img)
if cv2.waitKey(0) & 0xff == 27:
break
RGB_Map=None)
img_rgb = show_image_with_boxes(img_rgb, objects_pred, calib,
False)
# Rescale target
targets[:, 2:6] *= configs.img_size
# Get yaw angle
targets[:, 6] = torch.atan2(targets[:, 6], targets[:, 7])
img_bev = imgs.squeeze() * 255
img_bev = img_bev.permute(1, 2, 0).numpy().astype(np.uint8)
img_bev = cv2.resize(img_bev, (configs.img_size, configs.img_size))
for c, x, y, w, l, yaw in targets[:, 1:7].numpy():
# Draw rotated box
bev_utils.drawRotatedBox(img_bev, x, y, w, l, yaw,
cnf.colors[int(c)])
img_bev = cv2.rotate(img_bev, cv2.ROTATE_180)
if configs.mosaic and configs.show_train_data:
cv2.imshow('mosaic_sample', img_bev)
else:
out_img = merge_rgb_to_bev(img_rgb,
img_bev,
output_width=configs.output_width)
cv2.imshow('single_sample', out_img)
if cv2.waitKey(0) & 0xff == 27:
break
# Draw rotated box
kitti_bev_utils.drawRotatedBox(img_bev, x, y, w, l, yaw,
cnf.colors[int(cls_pred)])
img_rgb = cv2.imread(img_paths[0])
calib = kitti_data_utils.Calibration(img_paths[0].replace(
".png", ".txt").replace("image_2", "calib"))
objects_pred = predictions_to_kitti_format(img_detections, calib,
img_rgb.shape,
configs.img_size)
img_rgb = show_image_with_boxes(img_rgb, objects_pred, calib,
False)
img_bev = cv2.flip(cv2.flip(img_bev, 0), 1)
out_img = merge_rgb_to_bev(img_rgb, img_bev, output_width=608)
print(
'\tDone testing the {}th sample, time: {:.1f}ms, speed {:.2f}FPS'
.format(batch_idx, (t2 - t1) * 1000, 1 / (t2 - t1)))
if configs.save_test_output:
if configs.output_format == 'image':
img_fn = os.path.basename(img_paths[0])[:-4]
cv2.imwrite(
os.path.join(configs.results_dir,
'{}.jpg'.format(img_fn)), out_img)
elif configs.output_format == 'video':
if out_cap is None:
out_cap_h, out_cap_w = out_img.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
