Python mmcv.impad_to_multiple() Examples
The following are 30
code examples of mmcv.impad_to_multiple().
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Example #1
| Source File: transforms.py From AerialDetection with Apache License 2.0 | 6 votes |
|
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
Example #2
| Source File: transforms.py From AugFPN with Apache License 2.0 | 6 votes |
|
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
Example #3
| Source File: transforms.py From CenterNet with Apache License 2.0 | 6 votes |
|
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
Example #4
| Source File: transforms.py From hrnet with MIT License | 6 votes |
|
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
Example #5
| Source File: transforms.py From mmaction with Apache License 2.0 | 6 votes |
|
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
Example #6
| Source File: transforms.py From kaggle-imaterialist with MIT License | 6 votes |
|
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
Example #7
| Source File: transforms.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 6 votes |
|
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
Example #8
| Source File: transforms.py From Cascade-RPN with Apache License 2.0 | 6 votes |
|
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
Example #9
| Source File: transforms.py From FoveaBox with Apache License 2.0 | 6 votes |
|
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
Example #10
| Source File: transforms.py From Libra_R-CNN with Apache License 2.0 | 6 votes |
|
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
Example #11
| Source File: transforms.py From Reasoning-RCNN with Apache License 2.0 | 6 votes |
|
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
Example #12
| Source File: transforms.py From RDSNet with Apache License 2.0 | 6 votes |
|
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
Example #13
| Source File: transforms.py From Grid-R-CNN with Apache License 2.0 | 6 votes |
|
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
Example #14
| Source File: transforms.py From kaggle-kuzushiji-recognition with MIT License | 6 votes |
|
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
Example #15
| Source File: transforms.py From PolarMask with Apache License 2.0 | 6 votes |
|
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
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[np.newaxis,:,:]
return img
Example #16
| Source File: transforms.py From PolarMask with Apache License 2.0 | 6 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img, scale_factor = mmcv.imrescale(img, scale, return_scale=True, interpolation='nearest')
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
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[np.newaxis,:,:]
return img
Example #17
| Source File: transforms.py From mmdetection-annotated with Apache License 2.0 | 6 votes |
|
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
Example #18
| Source File: transforms.py From mmdetection_with_SENet154 with Apache License 2.0 | 6 votes |
|
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
Example #19
| Source File: transforms.py From GCNet with Apache License 2.0 | 6 votes |
|
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
Example #20
| Source File: transforms.py From PolarMask with Apache License 2.0 | 6 votes |
|
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
Example #21
| Source File: transforms.py From mmdetection-annotated with Apache License 2.0 | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #22
| Source File: test_geometric.py From mmcv with Apache License 2.0 | 5 votes |
|
def test_impad_to_multiple(self):
img = np.random.rand(11, 14, 3).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 4)
assert padded_img.shape == (12, 16, 3)
img = np.random.rand(20, 12).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 5)
assert padded_img.shape == (20, 15)
img = np.random.rand(20, 12).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 2)
assert padded_img.shape == (20, 12)
Example #23
| Source File: transforms.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #24
| Source File: transforms.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
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
Example #25
| Source File: transforms.py From kaggle-imaterialist with MIT License | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #26
| Source File: transforms.py From GCNet with Apache License 2.0 | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #27
| Source File: transforms.py From hrnet with MIT License | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #28
| Source File: transforms.py From AerialDetection with Apache License 2.0 | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #29
| Source File: transforms.py From CenterNet with Apache License 2.0 | 5 votes |
|
def __call__(self, img, scale, flip=False, keep_ratio=True):
if keep_ratio:
img = mmcv.imrescale(img, scale, interpolation='nearest')
else:
img = mmcv.imresize(img, scale, interpolation='nearest')
if flip:
img = mmcv.imflip(img)
if self.size_divisor is not None:
img = mmcv.impad_to_multiple(img, self.size_divisor)
return img
Example #30
| Source File: transforms.py From ttfnet with Apache License 2.0 | 5 votes |
|
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
