本文整理匯總了Python中mmcv.impad方法的典型用法代碼示例。如果您正苦於以下問題：Python mmcv.impad方法的具體用法？Python mmcv.impad怎麽用？Python mmcv.impad使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mmcv的用法示例。
在下文中一共展示了mmcv.impad方法的8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: __call__
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def __call__(self, results):
if results['keep_ratio']:
gt_seg = mmcv.imrescale(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
else:
gt_seg = mmcv.imresize(
results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
if results['flip']:
gt_seg = mmcv.imflip(gt_seg)
if gt_seg.shape != results['pad_shape']:
gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
if self.scale_factor != 1:
gt_seg = mmcv.imrescale(
gt_seg, self.scale_factor, interpolation='nearest')
results['gt_semantic_seg'] = gt_seg
return results 示例2: _pad_img
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def _pad_img(self, results):
"""Pad images according to ``self.size``."""
for key in results.get('img_fields', ['img']):
if self.size is not None:
padded_img = mmcv.impad(
results[key], shape=self.size, pad_val=self.pad_val)
elif self.size_divisor is not None:
padded_img = mmcv.impad_to_multiple(
results[key], self.size_divisor, pad_val=self.pad_val)
results[key] = padded_img
results['pad_shape'] = padded_img.shape
results['pad_fixed_size'] = self.size
results['pad_size_divisor'] = self.size_divisor 示例3: _pad_seg
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def _pad_seg(self, results):
"""Pad semantic segmentation map according to
``results['pad_shape']``."""
for key in results.get('seg_fields', []):
results[key] = mmcv.impad(
results[key], shape=results['pad_shape'][:2]) 示例4: pad
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def pad(self, out_shape, pad_val=0):
"""See :func:`BaseInstanceMasks.pad`."""
if len(self.masks) == 0:
padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
else:
padded_masks = np.stack([
mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
for mask in self.masks
])
return BitmapMasks(padded_masks, *out_shape) 示例5: __call__
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def __call__(self, masks, pad_shape, scale_factor, flip=False):
masks = [
mmcv.imrescale(mask, scale_factor, interpolation='nearest')
for mask in masks
]
if flip:
masks = [mask[:, ::-1] for mask in masks]
padded_masks = [
mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
]
padded_masks = np.stack(padded_masks, axis=0)
return padded_masks 示例6: __call__
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def __call__(self, masks, pad_shape, scale_factor, flip=False):
# aspect ratio unchanged
if isinstance(scale_factor, float):
masks = [
mmcv.imrescale(mask, scale_factor, interpolation='nearest')
for mask in masks
]
# aspect ratio changed
else:
w_ratio, h_ratio = scale_factor[:2]
if masks:
h, w = masks[0].shape[:2]
new_h = int(np.round(h * h_ratio))
new_w = int(np.round(w * w_ratio))
new_size = (new_w, new_h)
masks = [
mmcv.imresize(mask, new_size, interpolation='nearest')
for mask in masks
]
if flip:
masks = [mask[:, ::-1] for mask in masks]
padded_masks = [
mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
]
padded_masks = np.stack(padded_masks, axis=0)
return padded_masks 示例7: _pad_img
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def _pad_img(self, results):
if self.size is not None:
padded_img = mmcv.impad(results['img'], self.size)
elif self.size_divisor is not None:
padded_img = mmcv.impad_to_multiple(
results['img'], self.size_divisor, pad_val=self.pad_val)
results['img'] = padded_img
results['pad_shape'] = padded_img.shape
results['pad_fixed_size'] = self.size
results['pad_size_divisor'] = self.size_divisor 示例8: _pad_masks
# 需要導入模塊: import mmcv [as 別名]
# 或者: from mmcv import impad [as 別名]
def _pad_masks(self, results):
pad_shape = results['pad_shape'][:2]
for key in results.get('mask_fields', []):
padded_masks = [
mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
for mask in results[key]
]
results[key] = np.stack(padded_masks, axis=0)