def collect_wh_data(cfg, args, stop_count):
# stop_count 防止数据太大，要很久才能跑完
dataset = build_dataset(cfg.data.train) # 这样才能考虑到数据增强带来的图片比例改变
dataloader = build_dataloader(dataset, args.samples_per_gpu,
args.workers_per_gpu)
print('----开始遍历数据集----')
wh_all = []
for count in range(args.repeat_count):
progress_bar = cv_core.ProgressBar(len(dataloader))
for i, data_batch in enumerate(dataloader):
if i > stop_count:
break
gt_bboxes = data_batch['gt_bboxes'].data[0]
gt_bboxes = torch.cat(gt_bboxes, dim=0).numpy()
if len(gt_bboxes) == 0:
continue
w = (gt_bboxes[:, 2] - gt_bboxes[:, 0])
h = gt_bboxes[:, 3] - gt_bboxes[:, 1]
wh = np.stack((w, h), axis=1)
wh_all.append(wh)
progress_bar.update()
wh_all = np.concatenate(wh_all, axis=0)
print(wh_all.shape)
return wh_all
def main():
args = parse_args()
cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
dataset = build_dataset(cfg.data.train)
progress_bar = mmcv.ProgressBar(len(dataset))
for item in dataset:
filename = os.path.join(args.output_dir,
Path(item['filename']).name
) if args.output_dir is not None else None
gt_masks = item.get('gt_masks', None)
if gt_masks is not None:
gt_masks = mask2ndarray(gt_masks)
imshow_det_bboxes(item['img'],
item['gt_bboxes'],
item['gt_labels'],
gt_masks,
class_names=dataset.CLASSES,
show=not args.not_show,
wait_time=args.show_interval,
out_file=filename,
bbox_color=(255, 102, 61),
text_color=(255, 102, 61))
progress_bar.update()
def show_featuremap_from_datalayer(featurevis, feature_indexs, is_show, output_dir):
if not isinstance(feature_indexs, (list, tuple)):
feature_indexs = [feature_indexs]
dataset = build_dataset(cfg.data.test)
for item in dataset:
img_tensor = item['img']
img_metas = item['img_metas'][0].data
filename = img_metas['filename']
img_norm_cfg = img_metas['img_norm_cfg']
img = img_tensor[0].cpu().numpy().transpose(1, 2, 0) # 依然是归一化后的图片
img_orig = imdenormalize(img, img_norm_cfg['mean'], img_norm_cfg['std']).astype(np.uint8)
_show_save_data(featurevis, img, img_orig, feature_indexs, filename, is_show, output_dir)
def main():
args = parse_args()
cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
if 'gt_semantic_seg' in cfg.train_pipeline[-1]['keys']:
cfg.data.train.pipeline = [
p for p in cfg.data.train.pipeline if p['type'] != 'SegRescale'
]
dataset = build_dataset(cfg.data.train)
progress_bar = mmcv.ProgressBar(len(dataset))
for item in dataset:
filename = os.path.join(args.output_dir,
Path(item['filename']).name
) if args.output_dir is not None else None
gt_bboxes = item['gt_bboxes']
gt_labels = item['gt_labels']
gt_masks = item.get('gt_masks', None)
if gt_masks is not None:
gt_masks = mask2ndarray(gt_masks)
gt_seg = item.get('gt_semantic_seg', None)
if gt_seg is not None:
pad_value = 255 # the padding value of gt_seg
sem_labels = np.unique(gt_seg)
all_labels = np.concatenate((gt_labels, sem_labels), axis=0)
all_labels, counts = np.unique(all_labels, return_counts=True)
stuff_labels = all_labels[np.logical_and(counts < 2,
all_labels != pad_value)]
stuff_masks = gt_seg[None] == stuff_labels[:, None, None]
gt_labels = np.concatenate((gt_labels, stuff_labels), axis=0)
gt_masks = np.concatenate((gt_masks, stuff_masks.astype(np.uint8)),
axis=0)
# If you need to show the bounding boxes,
# please comment the following line
gt_bboxes = None
imshow_det_bboxes(item['img'],
gt_bboxes,
gt_labels,
gt_masks,
class_names=dataset.CLASSES,
show=not args.not_show,
wait_time=args.show_interval,
out_file=filename,
bbox_color=dataset.PALETTE,
text_color=(200, 200, 200),
mask_color=dataset.PALETTE)
progress_bar.update()
def __init__(self, dataset_cfgs, dataset_sampling_weights=None):
if dataset_sampling_weights is None:
self.dataset_sampling_probs = [1. / len(dataset_cfgs)
] * len(dataset_cfgs)
else:
for x in dataset_sampling_weights:
assert x >= 0.
prob_total = float(sum(dataset_sampling_weights))
assert prob_total > 0.
self.dataset_sampling_probs = [
x / prob_total for x in dataset_sampling_weights
]
datasets = [build_dataset(cfg) for cfg in dataset_cfgs]
# add an attribute `CLASSES` for the calling in `tools/train.py`
self.CLASSES = datasets[0].CLASSES
super().__init__(datasets)
def main():
args = parse_args()
cfg = retrieve_data_cfg(args.config, args.skip_type)
dataset = build_dataset(cfg.data.train)
progress_bar = cv_core.ProgressBar(len(dataset))
for item in dataset:
filename = os.path.join(args.output_dir,
Path(item['filename']).name
) if args.output_dir is not None else None
cv_core.imshow_det_bboxes(item['img'],
item['gt_bboxes'],
item['gt_labels'],
class_names=dataset.CLASSES,
show=not args.not_show,
out_file=filename,
wait_time=args.show_interval)
progress_bar.update()
def main():
args = parse_args()
os.makedirs(args.output_root, exist_ok=True)
cfg = retrieve_data_cfg(args.config_path, args.fold, args.skip_type)
dataset: WheatDataset = build_dataset(cfg.data.train)
from IPython import embed
embed()
heights = []
widths = []
for i, data in tqdm(enumerate(dataset), total=len(dataset)):
image_id = osp.basename(dataset.data_infos[i]["file_name"])
image = data["img"]
bboxes = data["gt_bboxes"]
ignore_bboxes = data["gt_bboxes_ignore"]
if len(bboxes) == 0:
print(image.shape)
widths.append(bboxes[:, 2] - bboxes[:, 0])
heights.append(bboxes[:, 3] - bboxes[:, 1])
draw_bounding_boxes_on_image(image,
bboxes,
use_normalized_coordinates=False,
thickness=5)
if len(ignore_bboxes):
draw_bounding_boxes_on_image(
image,
ignore_bboxes,
label2colors={None: {
"bbox": (0, 255, 0)
}},
use_normalized_coordinates=False,
thickness=5,
)
cv2.imwrite(osp.join(args.output_root, f"{i}_{image_id}"), image)
widths = np.concatenate(widths)
heights = np.concatenate(heights)
clusters = kmeans(np.stack([heights, widths], axis=1), k=10)
print(f"aspect rations: {clusters[:, 0] / clusters[:, 1]}")
print(f"sizes: {np.sqrt(clusters[:, 0] * clusters[:, 1])}")
plot_statistics(widths, heights, args.output_root)