本文整理匯總了Python中datasets.dataset_catalog.RAW_DIR屬性的典型用法代碼示例。如果您正苦於以下問題：Python dataset_catalog.RAW_DIR屬性的具體用法？Python dataset_catalog.RAW_DIR怎麽用？Python dataset_catalog.RAW_DIR使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類datasets.dataset_catalog的用法示例。
在下文中一共展示了dataset_catalog.RAW_DIR屬性的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: evaluate_masks
# 需要導入模塊: from datasets import dataset_catalog [as 別名]
# 或者: from datasets.dataset_catalog import RAW_DIR [as 別名]
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