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test.py
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test.py
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import argparse
import cv2
import numpy as np
import os
import torch
import pdb
from utils import setup_seed, read_points, read_calib, read_label, \
keep_bbox_from_image_range, keep_bbox_from_lidar_range, vis_pc, \
vis_img_3d, bbox3d2corners_camera, points_camera2image, \
bbox_camera2lidar
from model import PointPillars
def point_range_filter(pts, point_range=[0, -39.68, -3, 69.12, 39.68, 1]):
'''
data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty)
point_range: [x1, y1, z1, x2, y2, z2]
'''
flag_x_low = pts[:, 0] > point_range[0]
flag_y_low = pts[:, 1] > point_range[1]
flag_z_low = pts[:, 2] > point_range[2]
flag_x_high = pts[:, 0] < point_range[3]
flag_y_high = pts[:, 1] < point_range[4]
flag_z_high = pts[:, 2] < point_range[5]
keep_mask = flag_x_low & flag_y_low & flag_z_low & flag_x_high & flag_y_high & flag_z_high
pts = pts[keep_mask]
return pts
def main(args):
CLASSES = {
'Pedestrian': 0,
'Cyclist': 1,
'Car': 2
}
LABEL2CLASSES = {v:k for k, v in CLASSES.items()}
pcd_limit_range = np.array([0, -40, -3, 70.4, 40, 0.0], dtype=np.float32)
if not args.no_cuda:
model = PointPillars(nclasses=len(CLASSES)).cuda()
model.load_state_dict(torch.load(args.ckpt))
else:
model = PointPillars(nclasses=len(CLASSES))
model.load_state_dict(
torch.load(args.ckpt, map_location=torch.device('cpu')))
if not os.path.exists(args.pc_path):
raise FileNotFoundError
pc = read_points(args.pc_path)
pc = point_range_filter(pc)
pc_torch = torch.from_numpy(pc)
if os.path.exists(args.calib_path):
calib_info = read_calib(args.calib_path)
else:
calib_info = None
if os.path.exists(args.gt_path):
gt_label = read_label(args.gt_path)
else:
gt_label = None
if os.path.exists(args.img_path):
img = cv2.imread(args.img_path, 1)
else:
img = None
model.eval()
with torch.no_grad():
if not args.no_cuda:
pc_torch = pc_torch.cuda()
result_filter = model(batched_pts=[pc_torch],
mode='test')[0]
if calib_info is not None and img is not None:
tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32)
r0_rect = calib_info['R0_rect'].astype(np.float32)
P2 = calib_info['P2'].astype(np.float32)
image_shape = img.shape[:2]
result_filter = keep_bbox_from_image_range(result_filter, tr_velo_to_cam, r0_rect, P2, image_shape)
result_filter = keep_bbox_from_lidar_range(result_filter, pcd_limit_range)
lidar_bboxes = result_filter['lidar_bboxes']
labels, scores = result_filter['labels'], result_filter['scores']
vis_pc(pc, bboxes=lidar_bboxes, labels=labels)
if calib_info is not None and img is not None:
bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes']
bboxes_corners = bbox3d2corners_camera(camera_bboxes)
image_points = points_camera2image(bboxes_corners, P2)
img = vis_img_3d(img, image_points, labels, rt=True)
if calib_info is not None and gt_label is not None:
tr_velo_to_cam = calib_info['Tr_velo_to_cam'].astype(np.float32)
r0_rect = calib_info['R0_rect'].astype(np.float32)
dimensions = gt_label['dimensions']
location = gt_label['location']
rotation_y = gt_label['rotation_y']
gt_labels = np.array([CLASSES.get(item, -1) for item in gt_label['name']])
sel = gt_labels != -1
gt_labels = gt_labels[sel]
bboxes_camera = np.concatenate([location, dimensions, rotation_y[:, None]], axis=-1)
gt_lidar_bboxes = bbox_camera2lidar(bboxes_camera, tr_velo_to_cam, r0_rect)
bboxes_camera = bboxes_camera[sel]
gt_lidar_bboxes = gt_lidar_bboxes[sel]
gt_labels = [-1] * len(gt_label['name']) # to distinguish between the ground truth and the predictions
pred_gt_lidar_bboxes = np.concatenate([lidar_bboxes, gt_lidar_bboxes], axis=0)
pred_gt_labels = np.concatenate([labels, gt_labels])
vis_pc(pc, pred_gt_lidar_bboxes, labels=pred_gt_labels)
if img is not None:
bboxes_corners = bbox3d2corners_camera(bboxes_camera)
image_points = points_camera2image(bboxes_corners, P2)
gt_labels = [-1] * len(gt_label['name'])
img = vis_img_3d(img, image_points, gt_labels, rt=True)
if calib_info is not None and img is not None:
cv2.imshow(f'{os.path.basename(args.img_path)}-3d bbox', img)
cv2.waitKey(0)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Configuration Parameters')
parser.add_argument('--ckpt', default='pretrained/epoch_160.pth', help='your checkpoint for kitti')
parser.add_argument('--pc_path', help='your point cloud path')
parser.add_argument('--calib_path', default='', help='your calib file path')
parser.add_argument('--gt_path', default='', help='your ground truth path')
parser.add_argument('--img_path', default='', help='your image path')
parser.add_argument('--no_cuda', action='store_true',
help='whether to use cuda')
args = parser.parse_args()
main(args)