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cihebot-98 2 years ago
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  1. 143
      paddlers/transforms/operators.py

143
paddlers/transforms/operators.py
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@ -104,7 +104,7 @@ class Compose(object):
def __call__(self, sample):
"""
This is equivalent to sequentially calling compose_obj.apply_transforms()
This is equivalent to sequentially calling compose_obj.apply_transforms()
and compose_obj.arrange_outputs().
"""
@ -189,20 +189,20 @@ class Transform(object):
class DecodeImg(Transform):
"""
Decode image(s) in input.
Args:
to_rgb (bool, optional): If True, convert input image(s) from BGR format to
to_rgb (bool, optional): If True, convert input image(s) from BGR format to
RGB format. Defaults to True.
to_uint8 (bool, optional): If True, quantize and convert decoded image(s) to
to_uint8 (bool, optional): If True, quantize and convert decoded image(s) to
uint8 type. Defaults to True.
decode_bgr (bool, optional): If True, automatically interpret a non-geo image
decode_bgr (bool, optional): If True, automatically interpret a non-geo image
(e.g., jpeg images) as a BGR image. Defaults to True.
decode_sar (bool, optional): If True, automatically interpret a single-channel
geo image (e.g. geotiff images) as a SAR image, set this argument to
decode_sar (bool, optional): If True, automatically interpret a single-channel
geo image (e.g. geotiff images) as a SAR image, set this argument to
True. Defaults to True.
read_geo_info (bool, optional): If True, read geographical information from
read_geo_info (bool, optional): If True, read geographical information from
the image. Deafults to False.
use_stretch (bool, optional): Whether to apply 2% linear stretch. Valid only if
use_stretch (bool, optional): Whether to apply 2% linear stretch. Valid only if
`to_uint8` is True. Defaults to False.
"""
@ -384,13 +384,13 @@ class Resize(Transform):
Args:
target_size (int | list[int] | tuple[int]): Target size. If it is an integer, the
target height and width will be both set to `target_size`. Otherwise,
target height and width will be both set to `target_size`. Otherwise,
`target_size` represents [target height, target width].
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
Defaults to 'LINEAR'.
keep_ratio (bool, optional): If True, the scaling factor of width and height will
be set to same value, and height/width of the resized image will be not
keep_ratio (bool, optional): If True, the scaling factor of width and height will
be set to same value, and height/width of the resized image will be not
greater than the target width/height. Defaults to False.
Raises:
@ -518,8 +518,8 @@ class RandomResize(Transform):
Args:
target_sizes (list[int] | list[list|tuple] | tuple[list|tuple]):
Multiple target sizes, each of which should be int, list, or tuple.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
Defaults to 'LINEAR'.
Raises:
@ -558,8 +558,8 @@ class ResizeByShort(Transform):
short_size (int): Target size of the shorter side of the image(s).
max_size (int, optional): Upper bound of longer side of the image(s). If
`max_size` is -1, no upper bound will be applied. Defaults to -1.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
Defaults to 'LINEAR'.
Raises:
@ -598,10 +598,10 @@ class RandomResizeByShort(Transform):
Args:
short_sizes (list[int]): Target size of the shorter side of the image(s).
max_size (int, optional): Upper bound of longer side of the image(s).
max_size (int, optional): Upper bound of longer side of the image(s).
If `max_size` is -1, no upper bound will be applied. Defaults to -1.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
interp (str, optional): Interpolation method for resizing image(s). One of
{'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}.
Defaults to 'LINEAR'.
Raises:
@ -655,12 +655,12 @@ class RandomFlipOrRotate(Transform):
Flip or Rotate an image in different directions with a certain probability.
Args:
probs (list[float]): Probabilities of performing flipping and rotation.
probs (list[float]): Probabilities of performing flipping and rotation.
Default: [0.35,0.25].
probsf (list[float]): Probabilities of 5 flipping modes (horizontal,
vertical, both horizontal and vertical, diagonal, anti-diagonal).
probsf (list[float]): Probabilities of 5 flipping modes (horizontal,
vertical, both horizontal and vertical, diagonal, anti-diagonal).
Default: [0.3, 0.3, 0.2, 0.1, 0.1].
probsr (list[float]): Probabilities of 3 rotation modes (90°, 180°, 270°
probsr (list[float]): Probabilities of 3 rotation modes (90°, 180°, 270°
clockwise). Default: [0.25, 0.5, 0.25].
Examples:
@ -930,13 +930,13 @@ class Normalize(Transform):
3. im = im / std
Args:
mean (list[float] | tuple[float], optional): Mean of input image(s).
mean (list[float] | tuple[float], optional): Mean of input image(s).
Defaults to [0.485, 0.456, 0.406].
std (list[float] | tuple[float], optional): Standard deviation of input
std (list[float] | tuple[float], optional): Standard deviation of input
image(s). Defaults to [0.229, 0.224, 0.225].
min_val (list[float] | tuple[float], optional): Minimum value of input
min_val (list[float] | tuple[float], optional): Minimum value of input
image(s). If None, use 0 for all channels. Defaults to None.
max_val (list[float] | tuple[float], optional): Maximum value of input
max_val (list[float] | tuple[float], optional): Maximum value of input
image(s). If None, use 255. for all channels. Defaults to None.
apply_to_tar (bool, optional): Whether to apply transformation to the target
image. Defaults to True.
@ -996,7 +996,7 @@ class CenterCrop(Transform):
2. Crop the image.
Args:
crop_size (int, optional): Target size of the cropped image(s).
crop_size (int, optional): Target size of the cropped image(s).
Defaults to 224.
"""
@ -1030,26 +1030,26 @@ class CenterCrop(Transform):
class RandomCrop(Transform):
"""
Randomly crop the input.
1. Compute the height and width of cropped area according to `aspect_ratio` and
1. Compute the height and width of cropped area according to `aspect_ratio` and
`scaling`.
2. Locate the upper left corner of cropped area randomly.
3. Crop the image(s).
4. Resize the cropped area to `crop_size` x `crop_size`.
Args:
crop_size (int | list[int] | tuple[int]): Target size of the cropped area. If
crop_size (int | list[int] | tuple[int]): Target size of the cropped area. If
None, the cropped area will not be resized. Defaults to None.
aspect_ratio (list[float], optional): Aspect ratio of cropped region in
aspect_ratio (list[float], optional): Aspect ratio of cropped region in
[min, max] format. Defaults to [.5, 2.].
thresholds (list[float], optional): Iou thresholds to decide a valid bbox
thresholds (list[float], optional): Iou thresholds to decide a valid bbox
crop. Defaults to [.0, .1, .3, .5, .7, .9].
scaling (list[float], optional): Ratio between the cropped region and the
scaling (list[float], optional): Ratio between the cropped region and the
original image in [min, max] format. Defaults to [.3, 1.].
num_attempts (int, optional): Max number of tries before giving up.
num_attempts (int, optional): Max number of tries before giving up.
Defaults to 50.
allow_no_crop (bool, optional): Whether returning without doing crop is
allow_no_crop (bool, optional): Whether returning without doing crop is
allowed. Defaults to True.
cover_all_box (bool, optional): Whether to ensure all bboxes be covered in
cover_all_box (bool, optional): Whether to ensure all bboxes be covered in
the final crop. Defaults to False.
"""
@ -1243,7 +1243,7 @@ class RandomScaleAspect(Transform):
"""
Crop input image(s) and resize back to original sizes.
Args:
Args:
min_scale (float): Minimum ratio between the cropped region and the original
image. If 0, image(s) will not be cropped. Defaults to .5.
aspect_ratio (float): Aspect ratio of cropped region. Defaults to .33.
@ -1271,12 +1271,12 @@ class RandomExpand(Transform):
Randomly expand the input by padding according to random offsets.
Args:
upper_ratio (float, optional): Maximum ratio to which the original image
upper_ratio (float, optional): Maximum ratio to which the original image
is expanded. Defaults to 4..
prob (float, optional): Probability of apply expanding. Defaults to .5.
im_padding_value (list[float] | tuple[float], optional): RGB filling value
im_padding_value (list[float] | tuple[float], optional): RGB filling value
for the image. Defaults to (127.5, 127.5, 127.5).
label_padding_value (int, optional): Filling value for the mask.
label_padding_value (int, optional): Filling value for the mask.
Defaults to 255.
See Also:
@ -1329,17 +1329,17 @@ class Pad(Transform):
Pad image to a specified size or multiple of `size_divisor`.
Args:
target_size (list[int] | tuple[int], optional): Image target size, if None, pad to
target_size (list[int] | tuple[int], optional): Image target size, if None, pad to
multiple of size_divisor. Defaults to None.
pad_mode (int, optional): Pad mode. Currently only four modes are supported:
[-1, 0, 1, 2]. if -1, use specified offsets. If 0, only pad to right and bottom
If 1, pad according to center. If 2, only pad left and top. Defaults to 0.
offsets (list[int]|None, optional): Padding offsets. Defaults to None.
im_padding_value (list[float] | tuple[float]): RGB value of padded area.
im_padding_value (list[float] | tuple[float]): RGB value of padded area.
Defaults to (127.5, 127.5, 127.5).
label_padding_value (int, optional): Filling value for the mask.
label_padding_value (int, optional): Filling value for the mask.
Defaults to 255.
size_divisor (int): Image width and height after padding will be a multiple of
size_divisor (int): Image width and height after padding will be a multiple of
`size_divisor`.
"""
super(Pad, self).__init__()
@ -1465,9 +1465,9 @@ class MixupImage(Transform):
Mixup two images and their gt_bbbox/gt_score.
Args:
alpha (float, optional): Alpha parameter of beta distribution.
alpha (float, optional): Alpha parameter of beta distribution.
Defaults to 1.5.
beta (float, optional): Beta parameter of beta distribution.
beta (float, optional): Beta parameter of beta distribution.
Defaults to 1.5.
"""
super(MixupImage, self).__init__()
@ -1546,24 +1546,24 @@ class RandomDistort(Transform):
Random color distortion.
Args:
brightness_range (float, optional): Range of brightness distortion.
brightness_range (float, optional): Range of brightness distortion.
Defaults to .5.
brightness_prob (float, optional): Probability of brightness distortion.
brightness_prob (float, optional): Probability of brightness distortion.
Defaults to .5.
contrast_range (float, optional): Range of contrast distortion.
contrast_range (float, optional): Range of contrast distortion.
Defaults to .5.
contrast_prob (float, optional): Probability of contrast distortion.
contrast_prob (float, optional): Probability of contrast distortion.
Defaults to .5.
saturation_range (float, optional): Range of saturation distortion.
saturation_range (float, optional): Range of saturation distortion.
Defaults to .5.
saturation_prob (float, optional): Probability of saturation distortion.
saturation_prob (float, optional): Probability of saturation distortion.
Defaults to .5.
hue_range (float, optional): Range of hue distortion. Defaults to .5.
hue_prob (float, optional): Probability of hue distortion. Defaults to .5.
random_apply (bool, optional): Apply the transformation in random (yolo) or
fixed (SSD) order. Defaults to True.
count (int, optional): Number of distortions to apply. Defaults to 4.
shuffle_channel (bool, optional): Whether to swap channels randomly.
shuffle_channel (bool, optional): Whether to swap channels randomly.
Defaults to False.
"""
@ -1710,7 +1710,7 @@ class RandomBlur(Transform):
"""
Randomly blur input image(s).
Args:
Args:
prob (float): Probability of blurring.
"""
@ -1746,7 +1746,7 @@ class Dehaze(Transform):
"""
Dehaze input image(s).
Args:
Args:
gamma (bool, optional): Use gamma correction or not. Defaults to False.
"""
@ -1769,7 +1769,7 @@ class ReduceDim(Transform):
"""
Use PCA to reduce the dimension of input image(s).
Args:
Args:
joblib_path (str): Path of *.joblib file of PCA.
apply_to_tar (bool, optional): Whether to apply transformation to the target
image. Defaults to True.
@ -1804,8 +1804,8 @@ class SelectBand(Transform):
"""
Select a set of bands of input image(s).
Args:
band_list (list, optional): Bands to select (band index starts from 1).
Args:
band_list (list, optional): Bands to select (band index starts from 1).
Defaults to [1, 2, 3].
apply_to_tar (bool, optional): Whether to apply transformation to the target
image. Defaults to True.
@ -1923,7 +1923,7 @@ class RandomSwap(Transform):
Randomly swap multi-temporal images.
Args:
prob (float, optional): Probability of swapping the input images.
prob (float, optional): Probability of swapping the input images.
Default: 0.2.
"""
@ -1955,15 +1955,15 @@ class AppendIndex(Transform):
Append remote sensing index to input image(s).
Args:
index_type (str): Type of remote sensinng index. See supported
index types in
index_type (str): Type of remote sensinng index. See supported
index types in
https://github.com/PaddlePaddle/PaddleRS/tree/develop/paddlers/transforms/indices.py .
band_indices (dict, optional): Mapping of band names to band indices
(starting from 1). See band names in
band_indices (dict, optional): Mapping of band names to band indices
(starting from 1). See band names in
https://github.com/PaddlePaddle/PaddleRS/tree/develop/paddlers/transforms/indices.py .
Default: None.
satellite (str, optional): Type of satellite. If set,
band indices will be automatically determined accordingly. See supported satellites in
satellite (str, optional): Type of satellite. If set,
band indices will be automatically determined accordingly. See supported satellites in
https://github.com/PaddlePaddle/PaddleRS/tree/develop/paddlers/transforms/satellites.py .
Default: None.
"""
@ -2000,8 +2000,8 @@ class MatchRadiance(Transform):
Args:
method (str, optional): Method used to match the radiance of the
bi-temporal images. Choices are {'hist', 'lsr', 'fft}. 'hist'
stands for histogram matching, 'lsr' stands for least-squares
bi-temporal images. Choices are {'hist', 'lsr', 'fft}. 'hist'
stands for histogram matching, 'lsr' stands for least-squares
regression, and 'fft' replaces the low-frequency components of
the image to match the reference image. Default: 'hist'.
"""
@ -2104,3 +2104,8 @@ class ArrangeRestorer(Arrange):
return image, target
if self.mode == 'test':
return image,
class RandomRotate(Transform):
#TODO
def apply(self, sample):
pass

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