本文整理汇总了Python中datasets.dataset_catalog.RAW_DIR属性的典型用法代码示例。如果您正苦于以下问题：Python dataset_catalog.RAW_DIR属性的具体用法？Python dataset_catalog.RAW_DIR怎么用？Python dataset_catalog.RAW_DIR使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类datasets.dataset_catalog的用法示例。
在下文中一共展示了dataset_catalog.RAW_DIR属性的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: evaluate_masks
# 需要导入模块: from datasets import dataset_catalog [as 别名]
# 或者: from datasets.dataset_catalog import RAW_DIR [as 别名]
def evaluate_masks(
json_dataset,
all_boxes,
all_segms,
output_dir,
use_salt=True,
cleanup=False
):
if cfg.CLUSTER.ON_CLUSTER:
# On the cluster avoid saving these files in the job directory
output_dir = '/tmp'
res_file = os.path.join(
output_dir, 'segmentations_' + json_dataset.name + '_results')
if use_salt:
res_file += '_{}'.format(str(uuid.uuid4()))
res_file += '.json'
results_dir = os.path.join(output_dir, 'results')
if not os.path.exists(results_dir):
os.mkdir(results_dir)
os.environ['CITYSCAPES_DATASET'] = DATASETS[json_dataset.name][RAW_DIR]
os.environ['CITYSCAPES_RESULTS'] = output_dir
# Load the Cityscapes eval script *after* setting the required env vars,
# since the script reads their values into global variables (at load time).
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling \
as cityscapes_eval
roidb = json_dataset.get_roidb()
for i, entry in enumerate(roidb):
im_name = entry['image']
basename = os.path.splitext(os.path.basename(im_name))[0]
txtname = os.path.join(output_dir, basename + 'pred.txt')
with open(txtname, 'w') as fid_txt:
if i % 10 == 0:
logger.info('i: {}: {}'.format(i, basename))
for j in range(1, len(all_segms)):
clss = json_dataset.classes[j]
clss_id = cityscapes_eval.name2label[clss].id
segms = all_segms[j][i]
boxes = all_boxes[j][i]
if segms == []:
continue
masks = mask_util.decode(segms)
for k in range(boxes.shape[0]):
score = boxes[k, -1]
mask = masks[:, :, k]
pngname = os.path.join(
'results',
basename + '_' + clss + '_{}.png'.format(k))
# write txt
fid_txt.write('{} {} {}\n'.format(pngname, clss_id, score))
# save mask
cv2.imwrite(os.path.join(output_dir, pngname), mask * 255)
logger.info('Evaluating...')
cityscapes_eval.main([])
return None