def main():
args, cfg = parse_config()
logger = common_utils.create_logger()
logger.info(
'-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
training=False,
root_path=Path(args.data_path),
ext=args.ext,
logger=logger)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
logger.info(f'Visualized sample index: \t{idx + 1}')
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
V.draw_scenes(points=data_dict['points'][:, 1:],
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'])
#mlab.show(stop=True)
mlab.savefig(filename='test.png')
logger.info('Demo done.')
def main():
args, cfg = parse_config()
logger = common_utils.create_logger()
logger.info('-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(
dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,
root_path=Path(args.data_path), ext=args.ext, logger=logger
)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
if args.saved_pred == "":
model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
logger.info(f'Visualized sample index: \t{idx + 1}')
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
with open('../saved_pred/curr_pickle.pkl', 'wb+') as f:
data_ = {
"data_dict": data_dict['points'][:, 1:],
"pred_dicts": pred_dicts
}
pkl.dump(data_, f)
else:
with open('../saved_pred/curr_pickle.pkl', 'rb') as f:
data_ = pkl.load(f)
data_dict = data_["data_dict"]
pred_dicts = data_["pred_dicts"]
vdisplay = Xvfb(width=1920, height=1080)
vdisplay.start()
V.draw_scenes(
points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'], ref_labels=pred_dicts[0]['pred_labels']
)
vdisplay.stop()
mlab.show(stop=True)
mlab.savefig("./test_eg.png")
logger.info('Demo done.')
def main():
args, cfg = parse_config()
logger = common_utils.create_logger()
logger.info(
'-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
training=False,
root_path=Path(args.data_path),
ext=args.ext,
logger=logger)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
data_name_list = demo_dataset.sample_file_list
# print(data_name_list)
print('evaluation data size=', len(data_name_list))
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
# logger.info(f'Visualized sample index: \t{idx + 1}')
logger.info(f'Detecte sample: \t{data_name_list[idx]}')
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
print(pred_dicts)
# print(data_dict)
# print(type(pred_dicts[0]['pred_boxes']))
# print(pred_dicts[0]['pred_boxes'])
res = pred_dicts[0]['pred_boxes'].cpu().numpy().round(8)
save_filename = str(data_name_list[idx])
np.savetxt('evaluation/' +
save_filename[save_filename.rfind('/') + 1:].replace(
'.bin', '.txt'),
res,
fmt='%.08f')
# test_f.writelines(pred_dicts[0]['pred_boxes'])
V.draw_scenes(points=data_dict['points'][:, 1:],
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'])
mlab.show(stop=True)
logger.info('Demo done.')
def __vis_fake__(points, gt_boxes, ref_boxes=None, scores=None, use_fakelidar=True):
import visual_utils.visualize_utils as vis
import mayavi.mlab as mlab
gt_boxes = copy.deepcopy(gt_boxes)
if use_fakelidar:
gt_boxes = box_utils.boxes3d_kitti_lidar_to_fakelidar(gt_boxes)
if ref_boxes is not None:
ref_boxes = copy.deepcopy(ref_boxes)
if use_fakelidar:
ref_boxes = box_utils.boxes3d_kitti_lidar_to_fakelidar(ref_boxes)
vis.draw_scenes(points, gt_boxes, ref_boxes=ref_boxes, ref_scores=scores)
mlab.show(stop=True)
def main():
args, cfg = parse_config()
logger = common_utils.create_logger() #logger记录日志
logger.info(
'-----------------Quick Demo of OpenPCDet-------------------------')
#建立一个DemoDataset类，其中储存关于输入数据的所有信息，包含六个参数
# dataset_cfg=cfg.DATA_CONFIG # 数据参数
# 包含数据集 / 数据路径 / 信息路径 / 数据处理器 / 数据增强器等
# class_names=cfg.CLASS_NAMES # 类别名
# training=False # 是否训练
# root_path=Path(args.data_path) # 数据路径
# ext=args.ext # 扩展
# logger=logger # 日志
demo_dataset = DemoDataset(dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
training=False,
root_path=Path(args.data_path),
ext=args.ext,
logger=logger)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
with torch.no_grad(): #目的是使得其中的数据不需要计算梯度，也不会进行反向传播
for idx, data_dict in enumerate(demo_dataset):
logger.info(f'Visualized sample index: \t{idx + 1}')
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
V.draw_scenes(points=data_dict['points'][:, 1:],
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'])
mlab.show(stop=True)
logger.info('Demo done.')
def main():
# 1 输入参数
args, cfg = parse_config() # cfg的参数在tools/cfg/kitti_models/pv-rcnn.yaml
logger = common_utils.create_logger()
logger.info(
'-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
training=False,
root_path=Path(args.data_path),
ext=args.ext,
logger=logger)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
# 2 调用的这些包就是pcdet/models/detectors下的各个py文件，
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=demo_dataset)
# 3 参数加载
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
# cuda( ) 和 eval( ) 都是数据处理
model.cuda()
model.eval()
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
logger.info(f'Visualized sample index: \t{idx + 1}') # 样本数
# 4. collate_batch
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict) # 传递数据给gpu的
pred_dicts, _ = model.forward(
data_dict
) # 在神经网络中向前传递数据data_dict，得到预测数据pred_dicts 定位到forward，因为是PVRCNN类下的函数，先看__init__ /home/hcq/pointcloud/PCDet/pcdet/models/detectors/pv_rcnn.py
# 可视化V
V.draw_scenes(points=data_dict['points'][:, 1:],
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'])
mlab.show(stop=True)
logger.info('Demo done.')
def main():
args, cfg = parse_config()
# Creating output dir if it does not already exist
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
for res_file in os.listdir(args.saved_pred):
file_name_parts_ = res_file.split('.')
if file_name_parts_[-1] == 'pkl':
with open('%s/%s.pkl' % (args.saved_pred, file_name_parts_[0]),
'rb') as f:
data_ = pkl.load(f)
data_dict = data_["data_dict"]
# pred_dicts = data_["pred_dicts"]
pred_boxes = data_["pred_boxes"]
pred_labels = data_["pred_labels"]
pred_scores = data_["pred_scores"]
gt_boxes = data_["gt_boxes"]
pred_dicts = list()
d = dict()
d["pred_boxes"] = pred_boxes
d["pred_labels"] = pred_labels
d["pred_scores"] = pred_scores
pred_dicts.append(d)
Rot_matrix = np.array([[0, 0, 1], [-1, 0, 0], [0, -1, 0]])
gt_boxes[:, 0:3] = (Rot_matrix @ gt_boxes[:, 0:3].T).T
gt_boxes[:, 3:6] = gt_boxes[:, [4, 5, 3]]
gt_boxes[:, 2] = gt_boxes[:, 2] + gt_boxes[:, 5] / 2
fig = V.draw_scenes(points=data_dict,
gt_boxes=gt_boxes,
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'])
mlab.show(stop=True)
mlab.savefig("%s/%s.png" % (args.output_dir, file_name_parts_[0]))
def main():
args, cfg = parse_config()
if args.saved_pred == "":
logger = common_utils.create_logger()
logger.info('-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(
dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,
root_path=Path(args.seq_path), ext=args.ext, logger=logger
)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
logger.info(f'Visualized sample index: \t{idx + 1}')
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
# Creating output dir if it does not already exist
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
with open('%s/curr_pickle_%s.pkl' % (args.output_dir, str(idx)), 'wb+') as f:
data_ = {
"data_dict": data_dict['points'][:, 1:].cpu().detach().numpy(),
"pred_boxes": pred_dicts[0]["pred_boxes"].cpu().detach().numpy(),
"pred_labels": pred_dicts[0]["pred_labels"].cpu().detach().numpy(),
"pred_scores": pred_dicts[0]["pred_scores"].cpu().detach().numpy()
}
pkl.dump(data_, f)
else:
# Creating output dir if it does not already exist
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
# ctr = 0
for res_file in os.listdir(args.saved_pred):
# ctr = ctr + 1
# if ctr == 15:
# break
file_name_parts_ = res_file.split('.')
if file_name_parts_[-1] == 'pkl':
with open('%s/%s.pkl' % (args.saved_pred, file_name_parts_[0]), 'rb') as f:
data_ = pkl.load(f)
data_dict = data_["data_dict"]
# pred_dicts = data_["pred_dicts"]
pred_boxes=data_["pred_boxes"]
pred_labels=data_["pred_labels"]
pred_scores=data_["pred_scores"]
gt_boxes=data_["gt_boxes"]
pred_dicts=list()
d=dict()
d["pred_boxes"] = pred_boxes
d["pred_labels"] = pred_labels
d["pred_scores"] = pred_scores
pred_dicts.append(d)
Rot_matrix = np.array([
[0, 0, 1],
[-1, 0, 0],
[0, -1, 0]
])
gt_boxes[:, 0:3] = (Rot_matrix @ gt_boxes[:, 0:3].T).T
gt_boxes[:, 3:6] = gt_boxes[:, [4, 5, 3]]
gt_boxes[:, 2] = gt_boxes[:, 2] + gt_boxes[:, 5]/2
# rot_y_matrix = np.array([
# [np.cos(gt_boxes[:, 6]), 0, -np.sin(gt_boxes[:, 6])],
# [0, 1, 0],
# [np.sin(gt_boxes[:, 6]), 0, np.cos(gt_boxes[:, 6])]
# ])
# gt_boxes[:, 6] = (Rot_matrix @ gt_boxes[:, 0:3].T).T
fig = V.draw_scenes(
points=data_dict,
gt_boxes=gt_boxes,
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels']
)
mlab.show(stop=True)
mlab.savefig("%s/%s.png"%(args.output_dir, file_name_parts_[0]))
def main():
args, cfg = parse_config()
logger = common_utils.create_logger()
logger.info('-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = NuScenesDataset(
dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,
root_path=Path(args.data_path), logger=logger
)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
start = time.time()
with torch.no_grad():
data_dict = demo_dataset[args.idx]
print(type(data_dict))
print(data_dict)
logger.info(f'Visualized sample index: \t{args.idx}')
data_dict = demo_dataset.collate_batch([data_dict])
# print(type(data_dict))
# print(data_dict)
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
# for bb in pred_dicts[0]['pred_boxes']:
# for x in bb:
# print(float(x), end=" ")
# print()
# pred_dicts[0]['pred_boxes'][:,0] = 1
# pred_dicts[0]['pred_boxes'][:,1] = 0
# pred_dicts[0]['pred_boxes'][:,2] = 1
# pred_dicts[0]['pred_boxes'][:,3] = 1
# pred_dicts[0]['pred_boxes'][:,4] = 1
# pred_dicts[0]['pred_boxes'][:,5] = 1
# pred_dicts[0]['pred_boxes'][:,6] = 0
# pred_dicts[0]['pred_boxes'] = pred_dicts[0]['pred_boxes'].cpu().numpy()
# foo = np.zeros((len(pred_dicts[0]['pred_boxes']), 2), dtype=pred_dicts[0]['pred_boxes'].dtype)
# pred_dicts[0]['pred_boxes'] = np.concatenate((pred_dicts[0]['pred_boxes'], foo), axis=1)
# pred_dicts[0]['pred_boxes'][:,7] = 0
# pred_dicts[0]['pred_boxes'][:,8] = 0
mask = (pred_dicts[0]['pred_scores']>0.3).float()
indices = torch.nonzero(mask)
V.draw_scenes(
points=data_dict['points'][:, 1:],
ref_boxes=pred_dicts[0]['pred_boxes'][indices].reshape(-1, 9),
ref_scores=pred_dicts[0]['pred_scores'][indices].reshape(-1), ref_labels=pred_dicts[0]['pred_labels'][indices].reshape(-1)
)
# mask = (pred_dicts[0]['pred_scores']>0.5).float()
# indices = torch.nonzero(mask)
# V.draw_scenes(
# points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'][indices].reshape(-1, 7),
# ref_scores=pred_dicts[0]['pred_scores'][indices].reshape(-1), ref_labels=pred_dicts[0]['pred_labels'][indices].reshape(-1)
# )
# print(pred_dicts[0]['pred_boxes'][indices])
# V.draw_scenes(
# points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'],
# ref_scores=pred_dicts[0]['pred_scores'], ref_labels=pred_dicts[0]['pred_labels']
# )
mlab.show(stop=True)
end = time.time()
print(end-start)
logger.info('Demo done.')
def main():
args, cfg = parse_config()
cfg.ROOT_DIR = Path(cfg.DATA_CONFIG.DATA_PATH)
logger = common_utils.create_logger()
dist_test = False
total_gpus = 1
if args.batch_size is None:
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
else:
assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
args.batch_size = args.batch_size // total_gpus
logger.info(
'-----------------Quick Demo of OpenPCDet-------------------------')
test_set, test_loader, sampler = build_dataloader(
dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
batch_size=args.batch_size,
dist=dist_test,
workers=args.workers,
logger=logger,
training=False)
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=test_set)
model.load_params_from_file(filename=args.ckpt,
logger=logger,
to_cpu=False)
model.cuda()
model.eval()
with torch.no_grad():
for idx, batch_dict in enumerate(test_loader):
logger.info(f'Visualized sample index: \t{idx + 1}')
load_data_to_gpu(batch_dict)
pred_dicts, _ = model(batch_dict)
filtered_gt_boxes = batch_dict['gt_boxes'][0].cpu().numpy()
mask = box_utils.mask_boxes_outside_range_numpy(
filtered_gt_boxes, test_loader.dataset.point_cloud_range)
filtered_gt_boxes = filtered_gt_boxes[mask]
if args.show_heatmap:
pass
if 'pred_keypoints' in pred_dicts[0]:
pred_keypoints = pred_dicts[0]['pred_keypoints']
else:
pred_keypoints = None
V.draw_scenes(points=batch_dict['points'][:, 1:],
gt_boxes=filtered_gt_boxes[:, :-1],
ref_boxes=pred_dicts[0]['pred_boxes'],
ref_scores=pred_dicts[0]['pred_scores'],
ref_labels=pred_dicts[0]['pred_labels'],
gt_labels=filtered_gt_boxes[:, -1],
class_names=test_loader.dataset.class_names,
pred_keypoints=pred_keypoints)
mlab.show(stop=True)
logger.info('Demo done.')