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@ -32,12 +32,12 @@ from joblib import load |
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import paddlers |
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from .functions import normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, \ |
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horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, vertical_flip_rle, crop_poly, \ |
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crop_rle, expand_poly, expand_rle, resize_poly, resize_rle, de_haze, select_bands, \ |
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crop_rle, expand_poly, expand_rle, resize_poly, resize_rle, dehaze, select_bands, \ |
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to_intensity, to_uint8, img_flip, img_simple_rotate |
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__all__ = [ |
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"Compose", |
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"ImgDecoder", |
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"DecodeImg", |
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"Resize", |
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"RandomResize", |
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"ResizeByShort", |
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@ -50,19 +50,19 @@ __all__ = [ |
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"RandomCrop", |
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"RandomScaleAspect", |
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"RandomExpand", |
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"Padding", |
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"Pad", |
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"MixupImage", |
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"RandomDistort", |
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"RandomBlur", |
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"RandomSwap", |
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"Defogging", |
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"DimReducing", |
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"BandSelecting", |
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"Dehaze", |
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"ReduceDim", |
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"SelectBand", |
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"ArrangeSegmenter", |
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"ArrangeChangeDetector", |
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"ArrangeClassifier", |
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"ArrangeDetector", |
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"RandomFlipOrRotation", |
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"RandomFlipOrRotate", |
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] |
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interp_dict = { |
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@ -119,7 +119,7 @@ class Transform(object): |
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return sample |
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class ImgDecoder(Transform): |
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class DecodeImg(Transform): |
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""" |
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Decode image(s) in input. |
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Args: |
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@ -127,7 +127,7 @@ class ImgDecoder(Transform): |
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""" |
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def __init__(self, to_rgb=True, to_uint8=True): |
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super(ImgDecoder, self).__init__() |
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super(DecodeImg, self).__init__() |
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self.to_rgb = to_rgb |
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self.to_uint8 = to_uint8 |
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@ -254,7 +254,7 @@ class Compose(Transform): |
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'Length of transforms must not be less than 1, but received is {}' |
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.format(len(transforms))) |
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self.transforms = transforms |
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self.decode_image = ImgDecoder(to_uint8=to_uint8) |
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self.decode_image = DecodeImg(to_uint8=to_uint8) |
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self.arrange_outputs = None |
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self.apply_im_only = False |
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@ -544,7 +544,7 @@ class ResizeByLong(Transform): |
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return sample |
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class RandomFlipOrRotation(Transform): |
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class RandomFlipOrRotate(Transform): |
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""" |
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Flip or Rotate an image in different ways with a certain probability. |
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@ -561,7 +561,7 @@ class RandomFlipOrRotation(Transform): |
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# 定义数据增强 |
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train_transforms = T.Compose([ |
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T.RandomFlipOrRotation( |
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T.RandomFlipOrRotate( |
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probs = [0.3, 0.2] # 进行flip增强的概率是0.3，进行rotate增强的概率是0.2，不变的概率是0.5 |
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probsf = [0.3, 0.25, 0, 0, 0] # flip增强时，使用水平flip、垂直flip的概率分别是0.3、0.25，水平且垂直flip、对角线flip、反对角线flip概率均为0，不变的概率是0.45 |
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probsr = [0, 0.65, 0]), # rotate增强时，顺时针旋转90度的概率是0，顺时针旋转180度的概率是0.65，顺时针旋转90度的概率是0，不变的概率是0.35 |
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@ -574,7 +574,7 @@ class RandomFlipOrRotation(Transform): |
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probs=[0.35, 0.25], |
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probsf=[0.3, 0.3, 0.2, 0.1, 0.1], |
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probsr=[0.25, 0.5, 0.25]): |
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super(RandomFlipOrRotation, self).__init__() |
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super(RandomFlipOrRotate, self).__init__() |
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# Change various probabilities into probability intervals, to judge in which mode to flip or rotate |
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self.probs = [probs[0], probs[0] + probs[1]] |
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self.probsf = self.get_probs_range(probsf) |
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@ -1092,7 +1092,7 @@ class RandomExpand(Transform): |
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label_padding_value(int, optional): Filling value for the mask. Defaults to 255. |
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See Also: |
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paddlers.transforms.Padding |
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paddlers.transforms.Pad |
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""" |
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def __init__(self, |
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@ -1120,7 +1120,7 @@ class RandomExpand(Transform): |
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x = np.random.randint(0, w - im_w) |
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target_size = (h, w) |
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offsets = (x, y) |
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sample = Padding( |
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sample = Pad( |
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target_size=target_size, |
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pad_mode=-1, |
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offsets=offsets, |
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@ -1129,7 +1129,7 @@ class RandomExpand(Transform): |
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return sample |
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class Padding(Transform): |
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class Pad(Transform): |
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def __init__(self, |
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target_size=None, |
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pad_mode=0, |
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@ -1148,7 +1148,7 @@ class Padding(Transform): |
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label_padding_value(int, optional): Filling value for the mask. Defaults to 255. |
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size_divisor(int): Image width and height after padding is a multiple of coarsest_stride. |
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""" |
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super(Padding, self).__init__() |
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super(Pad, self).__init__() |
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if isinstance(target_size, (list, tuple)): |
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if len(target_size) != 2: |
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raise ValueError( |
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@ -1525,20 +1525,20 @@ class RandomBlur(Transform): |
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return sample |
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class Defogging(Transform): |
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class Dehaze(Transform): |
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""" |
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Defog input image(s). |
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Dehaze input image(s). |
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Args: |
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gamma (bool, optional): Use gamma correction or not. Defaults to False. |
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""" |
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def __init__(self, gamma=False): |
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super(Defogging, self).__init__() |
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super(Dehaze, self).__init__() |
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self.gamma = gamma |
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def apply_im(self, image): |
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image = de_haze(image, self.gamma) |
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image = dehaze(image, self.gamma) |
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return image |
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def apply(self, sample): |
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@ -1548,19 +1548,20 @@ class Defogging(Transform): |
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return sample |
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class DimReducing(Transform): |
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class ReduceDim(Transform): |
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""" |
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Use PCA to reduce input image(s) dimension. |
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Use PCA to reduce the dimension of input image(s). |
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Args: |
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joblib_path (str): Path of *.joblib about PCA. |
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joblib_path (str): Path of *.joblib file of PCA. |
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""" |
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def __init__(self, joblib_path): |
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super(DimReducing, self).__init__() |
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super(ReduceDim, self).__init__() |
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ext = joblib_path.split(".")[-1] |
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if ext != "joblib": |
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raise ValueError("`joblib_path` must be *.joblib, not *.{}.".format(ext)) |
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raise ValueError("`joblib_path` must be *.joblib, not *.{}.".format( |
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ext)) |
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self.pca = load(joblib_path) |
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def apply_im(self, image): |
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@ -1577,16 +1578,16 @@ class DimReducing(Transform): |
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return sample |
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class BandSelecting(Transform): |
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class SelectBand(Transform): |
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""" |
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Select the band of the input image(s). |
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Select a set of bands of input image(s). |
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Args: |
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band_list (list, optional): Bands of selected (Start with 1). Defaults to [1, 2, 3]. |
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band_list (list, optional): Bands to select (the band index starts with 1). Defaults to [1, 2, 3]. |
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""" |
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def __init__(self, band_list=[1, 2, 3]): |
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super(BandSelecting, self).__init__() |
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super(SelectBand, self).__init__() |
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self.band_list = band_list |
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def apply_im(self, image): |
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Bobholamovic
2 years ago