def vis(result_sha, data_root, result_root):
def show_image_with_boxes(img, objects_res, object_gt, calib, save_path, height_threshold=0):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple([int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=color_tmp)
text = 'ID: %d' % obj.id
if box3d_pts_2d is not None:
img2 = cv2.putText(img2, text, (int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color=color_tmp)
img = Image.fromarray(img2)
img = img.resize((width, height))
img.save(save_path)
for seq in seq_list:
image_dir = os.path.join(data_root, 'image_02/%s' % seq)
calib_file = os.path.join(data_root, 'calib/%s.txt' % seq)
result_dir = os.path.join(result_root, '%s/trk_withid/%s' % (result_sha, seq))
save_3d_bbox_dir = os.path.join(result_dir, '../../trk_image_vis/%s' % seq); mkdir_if_missing(save_3d_bbox_dir)
# load the list
images_list, num_images = load_list_from_folder(image_dir)
print('number of images to visualize is %d' % num_images)
start_count = 0
for count in range(start_count, num_images):
image_tmp = images_list[count]
if not is_path_exists(image_tmp):
count += 1
continue
image_index = int(fileparts(image_tmp)[1])
image_tmp = np.array(Image.open(image_tmp))
img_height, img_width, img_channel = image_tmp.shape
result_tmp = os.path.join(result_dir, '%06d.txt'%image_index) # load the result
if not is_path_exists(result_tmp): object_res = []
else: object_res = read_label(result_tmp)
print('processing index: %d, %d/%d, results from %s' % (image_index, count+1, num_images, result_tmp))
calib_tmp = Calibration(calib_file) # load the calibration
object_res_filtered = []
for object_tmp in object_res:
if object_tmp.type not in type_whitelist: continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold: continue
center = object_tmp.t
object_res_filtered.append(object_tmp)
num_instances = len(object_res_filtered)
save_image_with_3dbbox_gt_path = os.path.join(save_3d_bbox_dir, '%06d.jpg' % (image_index))
show_image_with_boxes(image_tmp, object_res_filtered, [], calib_tmp, save_path=save_image_with_3dbbox_gt_path)
print('number of objects to plot is %d' % (num_instances))
count += 1
def get_label(self, idx):
label_file = os.path.join(self.label_path, '%06d.txt' % idx)
assert os.path.exists(label_file)
return kitti_utils.read_label(label_file)
def vis(result_sha, data_root, result_root):
def show_image_with_boxes(img,
velo,
objects_res,
objects_res_det,
objects_res_raw,
labeldata,
object_gt,
calib,
save_path,
height_threshold=0,
show_lidar=True,
save_image=False):
img2 = np.copy(img)
for obj in objects_res:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 0, 255))
text = 'Tracked ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 0, 255))
for obj in objects_res_det:
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
color_tmp = tuple(
[int(tmp * 255) for tmp in colors[obj.id % max_color]])
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(0, 255, 0))
text = 'Detection ID: %d, Type: %s' % (obj.id, obj.type)
if box3d_pts_2d is not None:
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[3, 0]), int(box3d_pts_2d[3, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(0, 255, 0))
import itertools
labeldata, labeldata2 = itertools.tee(labeldata)
for obj in labeldata:
# print("here")
box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255, 0, 0))
text = 'GT, Type: %s' % (obj.type)
if box3d_pts_2d is not None:
# print("also")
print(text)
img2 = cv2.putText(
img2,
text,
(int(box3d_pts_2d[4, 0]), int(box3d_pts_2d[4, 1]) - 8),
cv2.FONT_HERSHEY_TRIPLEX,
0.5,
color=(255, 0, 0))
# for obj in objects_res_raw:
# box3d_pts_2d, _ = compute_box_3d(obj, calib.P)
# color_tmp = tuple([int(tmp * 255)
# for tmp in colors[obj.id % max_color]])
# img2 = draw_projected_box3d(img2, box3d_pts_2d, color=(255,0,0))
# text = 'Estimate ID: %d' % obj.id
# if box3d_pts_2d is not None:
# img2 = cv2.putText(img2, text, (int(box3d_pts_2d[2, 0]), int(
# box3d_pts_2d[2, 1]) - 8), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color=(255,0,0))
if show_lidar:
show_lidar_with_boxes(velo,
objects=labeldata2,
calib=calib,
objects_pred=objects_res)
img = Image.fromarray(img2)
img = img.resize((width, height))
cv2.imshow("Image", img2)
cv2.waitKey()
if save_image:
print("Saving Image at", save_path)
img.save(save_path)
return img2
for seq in seq_list:
image_dir = os.path.join(data_root, 'image_02/%s' % seq)
calib_file = os.path.join(data_root, 'calib/%s.txt' % seq)
label_file = os.path.join(data_root, 'label_02/%s.txt' % seq)
velo_dir = os.path.join(data_root, 'velodyne/%s' % seq)
result_dir = [
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[0], seq)),
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[1], seq)),
os.path.join(result_root,
'%s/trk_withid/%s' % (result_sha[2], seq))
]
save_3d_bbox_dir = os.path.join(
result_root,
'%s/trk_image_vis/%s' % ("Combined_Final_WithLabel", seq))
mkdir_if_missing(save_3d_bbox_dir)
# load the list
images_list, num_images = load_list_from_folder(image_dir)
velo_list, num_velo = load_list_from_folder(velo_dir)
print('number of images to visualize is %d' % num_images)
start_count = 0
filecontent = np.array([f.split() for f in open(label_file, 'r')])
# alllabels = np.unique(filecontent[:,2])
# labels = ['Car', 'Pedestrian', 'Cyclist']
# finallabelset = [x for x in alllabels if x not in labels]
# print(alllabels)
# print(finallabelset)
# for val in finallabelset:
# filecontent = filecontent[filecontent[:,2]!=val,:]
# print(np.unique(filecontent[:,2]))
size = (width, height)
out = cv2.VideoWriter(f'{result_root}/{seq}.avi',
cv2.VideoWriter_fourcc(*'DIVX'), 15, size)
num_images = 1
for count in range(start_count, num_images):
image_tmp = images_list[count]
velo_tmp = velo_list[count]
if not is_path_exists(image_tmp):
count += 1
continue
image_index = int(fileparts(image_tmp)[1])
image_tmp = np.array(Image.open(image_tmp))
img_height, img_width, img_channel = image_tmp.shape
filecontentframe = filecontent[filecontent[:, 0] ==
str(image_index), :]
print(len(filecontentframe))
print(f"Labels for frame {image_index}",
np.unique(filecontentframe[:, 2]))
labeldata = (Object3d(getstringfromarray(line[2:]))
for line in filecontentframe)
object_res = []
object_res_det = []
object_res_raw = []
for dirt in result_dir:
result_tmp = os.path.join(dirt, '%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp):
object_res = object_res + read_label(result_tmp)
result_tmp_det = os.path.join(dirt, 'det%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp_det):
object_res_det = object_res_det + \
read_label(result_tmp_det)
result_tmp_raw = os.path.join(dirt, 'raw%06d.txt' %
image_index) # load the result
if is_path_exists(result_tmp_raw):
object_res_raw = object_res_raw + \
read_label(result_tmp_raw)
print('processing index: %d, %d/%d, results from %s' %
(image_index, count + 1, num_images, result_tmp))
calib_tmp = Calibration(calib_file) # load the calibration
object_res_filtered = []
for object_tmp in object_res:
if object_tmp.type not in type_whitelist:
continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold:
continue
center = object_tmp.t
object_res_filtered.append(object_tmp)
object_res_filtered_det = []
for object_tmp in object_res_det:
if object_tmp.type not in type_whitelist:
continue
if hasattr(object_tmp, 'score'):
if object_tmp.score < score_threshold:
continue
center = object_tmp.t
object_res_filtered_det.append(object_tmp)
object_res_filtered_raw = []
for object_tmp in object_res_raw:
if object_tmp.type not in type_whitelist:
continue
# if hasattr(object_tmp, 'score'):
# if object_tmp.score < score_threshold:
# continue
center = object_tmp.t
object_res_filtered_raw.append(object_tmp)
num_instances = len(object_res_filtered)
save_image_with_3dbbox_gt_path = os.path.join(
save_3d_bbox_dir, '%06d.jpg' % (image_index))
velodyne_scan = load_velo_scan(velo_tmp, np.float32, n_vec=4)[:,
0:4]
img = show_image_with_boxes(
image_tmp,
velodyne_scan,
object_res_filtered,
object_res_filtered_det,
object_res_filtered_raw,
labeldata, [],
calib_tmp,
save_path=save_image_with_3dbbox_gt_path)
print('number of objects to plot is %d, %d, %d' %
(num_instances, len(object_res_filtered_det),
len(object_res_filtered_raw)))
count += 1
out.write(img)
out.release()
def get_label_objects(self, idx):
assert(idx<self.num_samples and self.split=='training')
label_filename = os.path.join(self.label_dir, '%06d.txt'%(idx))
return utils.read_label(label_filename)
def __getitem__(self, index):
# start time
get_item_start_time = time.time()
# get point cloud
lidar_filename = os.path.join(self.lidar_dir, '%06d.bin' % index)
lidar_data = kitti_utils.load_velo_scan(lidar_filename)
# time for loading point cloud
read_point_cloud_end_time = time.time()
read_point_cloud_time = read_point_cloud_end_time - get_item_start_time
# voxelize point cloud
voxel_point_cloud = torch.tensor(
kitti_utils.voxelize(point_cloud=lidar_data),
requires_grad=True,
device=self.device).float()
# time for voxelization
voxelization_end_time = time.time()
voxelization_time = voxelization_end_time - read_point_cloud_end_time
# channels along first dimensions according to PyTorch convention
voxel_point_cloud = voxel_point_cloud.permute([2, 0, 1])
# get image
if self.get_image:
image_filename = os.path.join(self.image_dir, '%06d.png' % index)
image = kitti_utils.get_image(image_filename)
# get current time
read_labels_start_time = time.time()
# get calibration
calib_filename = os.path.join(self.calib_dir, '%06d.txt' % index)
calib = kitti_utils.Calibration(calib_filename)
# get labels
label_filename = os.path.join(self.label_dir, '%06d.txt' % index)
labels = kitti_utils.read_label(label_filename)
read_labels_end_time = time.time()
read_labels_time = read_labels_end_time - read_labels_start_time
# compute network label
if self.split == 'training':
# get current time
compute_label_start_time = time.time()
# create empty pixel labels
regression_label = np.zeros(
(OUTPUT_DIM_0, OUTPUT_DIM_1, OUTPUT_DIM_REG))
classification_label = np.zeros(
(OUTPUT_DIM_0, OUTPUT_DIM_1, OUTPUT_DIM_CLA))
# iterate over all 3D label objects in list
for label in labels:
if label.type == 'Car':
# compute corners of 3D bounding box in camera coordinates
_, bbox_corners_camera_coord = kitti_utils.compute_box_3d(
label, calib.P, scale=1.0)
# get pixel label for classification and BEV bounding box
regression_label, classification_label = compute_pixel_labels\
(regression_label, classification_label, label, bbox_corners_camera_coord)
# stack classification and regression label
regression_label = torch.tensor(regression_label,
device=self.device).float()
classification_label = torch.tensor(classification_label,
device=self.device).float()
training_label = torch.cat(
(regression_label, classification_label), dim=2)
# get time for computing pixel label
compute_label_end_time = time.time()
compute_label_time = compute_label_end_time - compute_label_start_time
# total time for data loading
get_item_end_time = time.time()
get_item_time = get_item_end_time - get_item_start_time
if self.show_times:
print('---------------------------')
print('Get Item Time: {:.4f} s'.format(get_item_time))
print('---------------------------')
print('Read Point Cloud Time: {:.4f} s'.format(
read_point_cloud_time))
print('Voxelization Time: {:.4f} s'.format(voxelization_time))
print('Read Labels Time: {:.4f} s'.format(read_labels_time))
print(
'Compute Labels Time: {:.4f} s'.format(compute_label_time))
return voxel_point_cloud, training_label
else:
if self.get_image:
return image, voxel_point_cloud, labels, calib
else:
return voxel_point_cloud, labels, calib