本文整理汇总了Python中mmcv.imrescale方法的典型用法代码示例。如果您正苦于以下问题：Python mmcv.imrescale方法的具体用法？Python mmcv.imrescale怎么用？Python mmcv.imrescale使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmcv的用法示例。
在下文中一共展示了mmcv.imrescale方法的6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: _resize_img
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
def _resize_img(self, results):
"""Resize images with ``results['scale']``."""
for key in results.get('img_fields', ['img']):
if self.keep_ratio:
img, scale_factor = mmcv.imrescale(
results[key], results['scale'], return_scale=True)
# the w_scale and h_scale has minor difference
# a real fix should be done in the mmcv.imrescale in the future
new_h, new_w = img.shape[:2]
h, w = results[key].shape[:2]
w_scale = new_w / w
h_scale = new_h / h
else:
img, w_scale, h_scale = mmcv.imresize(
results[key], results['scale'], return_scale=True)
results[key] = img
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img_shape'] = img.shape
# in case that there is no padding
results['pad_shape'] = img.shape
results['scale_factor'] = scale_factor
results['keep_ratio'] = self.keep_ratio 示例2: __call__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array(
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor 示例3: __call__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True)
else:
img, w_scale, h_scale = mmcv.imresize(
img, scale, return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img_shape = img.shape
img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
pad_shape = img.shape
else:
pad_shape = img_shape
img = img.transpose(2, 0, 1)
return img, img_shape, pad_shape, scale_factor 示例4: _resize_masks
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
def _resize_masks(self, results):
for key in results.get('mask_fields', []):
if results[key] is None:
continue
if self.keep_ratio:
masks = [
mmcv.imrescale(
mask, results['scale_factor'], interpolation='nearest')
for mask in results[key]
]
else:
mask_size = (results['img_shape'][1], results['img_shape'][0])
masks = [
mmcv.imresize(mask, mask_size, interpolation='nearest')
for mask in results[key]
]
results[key] = masks 示例5: __call__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
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6: _resize_seg
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import imrescale [as 别名]
def _resize_seg(self, results):
"""Resize semantic segmentation map with ``results['scale']``."""
for key in results.get('seg_fields', []):
if self.keep_ratio:
gt_seg = mmcv.imrescale(
results[key], results['scale'], interpolation='nearest')
else:
gt_seg = mmcv.imresize(
results[key], results['scale'], interpolation='nearest')
results['gt_semantic_seg'] = gt_seg