def get_calib(self, idx):
calib_file = os.path.join(self.calib_dir, '{:06d}.txt'.format(idx))
# assert os.path.isfile(calib_file)
return Calibration(calib_file)
with torch.no_grad():
for sample_idx in range(len(demo_dataset)):
metadatas, bev_map, img_rgb = demo_dataset.load_bevmap_front(sample_idx)
detections, bev_map, fps = do_detect(configs, model, bev_map, is_front=True)
# Draw prediction in the image
bev_map = (bev_map.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT))
bev_map = draw_predictions(bev_map, detections, configs.num_classes)
# Rotate the bev_map
bev_map = cv2.rotate(bev_map, cv2.ROTATE_180)
img_path = metadatas['img_path'][0]
img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
calib = Calibration(configs.calib_path)
kitti_dets = convert_det_to_real_values(detections)
if len(kitti_dets) > 0:
kitti_dets[:, 1:] = lidar_to_camera_box(kitti_dets[:, 1:], calib.V2C, calib.R0, calib.P2)
img_bgr = show_rgb_image_with_boxes(img_bgr, kitti_dets, calib)
out_img = merge_rgb_to_bev(img_bgr, bev_map, output_width=configs.output_width)
write_credit(out_img, (80, 210), text_author='Cre: github.com/maudzung', org_fps=(80, 250), fps=fps)
if out_cap is None:
out_cap_h, out_cap_w = out_img.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out_path = os.path.join(configs.results_dir, '{}_front.avi'.format(configs.foldername))
print('Create video writer at {}'.format(out_path))
out_cap = cv2.VideoWriter(out_path, fourcc, 30, (out_cap_w, out_cap_h))
out_cap.write(out_img)
# lidar_aug = OneOf([
# Random_Rotation(limit_angle=np.pi / 4, p=1.),
# Random_Scaling(scaling_range=(0.95, 1.05), p=1.),
# ], p=1.)
lidar_aug = None
dataset = KittiDataset(configs,
mode='val',
lidar_aug=lidar_aug,
hflip_prob=0.,
num_samples=configs.num_samples)
print('\n\nPress n to see the next sample >>> Press Esc to quit...')
for idx in range(len(dataset)):
bev_map, labels, img_rgb, img_path = dataset.draw_img_with_label(idx)
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)
def evaluate_mAP(val_loader, model, configs, logger):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
progress = ProgressMeter(len(val_loader), [batch_time, data_time],
prefix="Evaluation phase...")
labels = []
sample_metrics = [] # List of tuples (TP, confs, pred)
# switch to evaluate mode
model.eval()
class_id = {0:'Car', 1:'Pedestrian', 2:'Cyclist'}
with torch.no_grad():
start_time = time.time()
for batch_idx, batch_data in enumerate(tqdm(val_loader)):
metadatas, targets= batch_data
batch_size = len(metadatas['img_path'])
voxelinput = metadatas['voxels']
coorinput = metadatas['coors']
numinput = metadatas['num_points']
dtype = torch.float32
voxelinputr = torch.tensor(
voxelinput, dtype=torch.float32, device=configs.device).to(dtype)
coorinputr = torch.tensor(
coorinput, dtype=torch.int32, device=configs.device)
numinputr = torch.tensor(
numinput, dtype=torch.int32, device=configs.device)
t1 = time_synchronized()
outputs = model(voxelinputr, coorinputr, numinputr)
outputs = outputs._asdict()
outputs['hm_cen'] = _sigmoid(outputs['hm_cen'])
outputs['cen_offset'] = _sigmoid(outputs['cen_offset'])
# detections size (batch_size, K, 10)
img_path = metadatas['img_path'][0]
#print(img_path)
calib = Calibration(img_path.replace(".png", ".txt").replace("image_2", "calib"))
detections = decode(outputs['hm_cen'], outputs['cen_offset'], outputs['direction'], outputs['z_coor'],
outputs['dim'], K=configs.K)
detections = detections.cpu().numpy().astype(np.float32)
detections = post_processing(detections, configs.num_classes, configs.down_ratio, configs.peak_thresh)
for i in range(configs.batch_size):
detections[i] = convert_det_to_real_values(detections[i])
img_path = metadatas['img_path'][i]
#rint(img_path)
datap = str.split(img_path,'/')
filename = str.split(datap[7],'.')
file_write_obj = open('../result/' + filename[0] + '.txt', 'w')
lidar_path = '/' + datap[1] + '/' + datap[2] + '/' + datap[3] + '/' + \
datap[4] + '/' + datap[5] + '/' + 'velodyne' + '/' + filename[0] + '.bin'
#print(lidar_path)
#show3dlidar(lidar_path, detections[i], calib.V2C, calib.R0, calib.P2)
dets = detections[i]
if len(dets) >0 :
dets[:, 1:] = lidar_to_camera_box(dets[:, 1:], calib.V2C, calib.R0, calib.P2)
for box_idx, label in enumerate(dets):
location, dim, ry = label[1:4], label[4:7], label[7]
if ry < -np.pi:
ry = 2*np.pi + ry
if ry > np.pi:
ry = -2*np.pi + ry
corners_3d = compute_box_3d(dim, location, ry)
corners_2d = project_to_image(corners_3d, calib.P2)
minxy = np.min(corners_2d, axis=0)
maxxy = np.max(corners_2d, axis=0)
bbox = np.concatenate([minxy, maxxy], axis=0)
if bbox[0] < 0 or bbox[2]<0:
continue
if bbox[1] > 1272 or bbox[3] > 375:
continue
oblist = ['Car',' ','0.0', ' ', '0', ' ', '-10', ' ','%.2f'%bbox[0], ' ', \
'%.2f' %bbox[1], ' ','%.2f'%bbox[2], ' ','%.2f'%bbox[3], ' ','%.2f'%dim[0], ' ','%.2f'%dim[1], ' ','%.2f'%dim[2], ' ', \
'%.2f' %location[0], ' ','%.2f'%location[1], ' ','%.2f'%location[2], ' ', '%.2f'%ry, '\n']
file_write_obj.writelines(oblist)
file_write_obj.close()
'''for sample_i in range(len(detections)):