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paddleseg.transforms

Compose

class paddleseg.transforms.Compose(transforms, to_rgb = True)
Do transformation on input data with corresponding pre-processing and augmentation operations.
The shape of input data to all operations is [height, width, channels].

Args

  • transforms (list): A list contains data pre-processing or augmentation.
  • to_rgb (bool, optional): If converting image to RGB color space. Default: True.

Raises

  • TypeError: When 'transforms' is not a list.
  • ValueError: when the length of 'transforms' is less than 1.

RandomHorizontalFlip

class paddleseg.transforms.RandomHorizontalFlip(prob = 0.5)
Flip an image horizontally with a certain probability.

Args

  • prob (float, optional): A probability of horizontally flipping. Default: 0.5.

RandomVerticalFlip

class paddleseg.transforms.RandomVerticalFlip(prob = 0.1)
Flip an image vertically with a certain probability.

Args

  • prob (float, optional): A probability of vertical flipping. Default: 0.1.

Resize

class paddleseg.transforms.Resize(target_size = (512, 512), interp='LINEAR')
Resize an image.

Args

  • target_size (list|tuple, optional): The target size of image. Default: (512, 512).
  • interp (str, optional): The interpolation mode of resize is consistent with opencv. ['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM']. Note that when it is 'RANDOM', a random interpolation mode would be specified. Default: "LINEAR".

Raises

  • TypeError: When 'target_size' type is neither list nor tuple.
  • ValueError: When "interp" is out of pre-defined methods ('NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM').

ResizeByLong

class paddleseg.transforms.ResizeByLong(long_size)
Resize the long side of an image to given size, and then scale the other side proportionally.

Args

  • long_size (int): The target size of long side.

ResizeRangeScaling

class paddleseg.transforms.ResizeRangeScaling(min_value = 400, max_value = 600)
Resize the long side of an image into a range, and then scale the other side proportionally.

Args

  • min_value (int, optional): The minimum value of long side after resize. Default: 400.
  • max_value (int, optional): The maximum value of long side after resize. Default: 600.

ResizeStepScaling

class paddleseg.transforms.ResizeStepScaling(min_scale_factor = 0.75,
                 max_scale_factor = 1.25,
                 scale_step_size = 0.25)
Scale an image proportionally within a range.

Args

  • min_scale_factor (float, optional): The minimum scale. Default: 0.75.
  • max_scale_factor (float, optional): The maximum scale. Default: 1.25.
  • scale_step_size (float, optional): The scale interval. Default: 0.25.

Raises

  • ValueError: When min_scale_factor is smaller than max_scale_factor.

Normalize

class paddleseg.transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5))
Normalize an image.The specific operations are as follows:
1. Min_val is subtracted from the pixel value.
2. The pixel value is divided by (max_val-min_val) and normalized to the interval [0.0, 1.0].
3. Subtract the mean value and divide by the standard deviation operation on the image.

Note: In the mean and std parameters, the length of the list should be consistent with the number of image channels.

Args

  • mean (list, optional): The mean value of a data set. Default: [0.5, 0.5, 0.5].
  • std (list, optional): The standard deviation of a data set. Default: [0.5, 0.5, 0.5].

Raises

  • ValueError: When mean/std is not list or any value in std is 0.

Padding

class paddleseg.transforms.Padding(target_size,
                 im_padding_value = (127.5, 127.5, 127.5),
                 label_padding_value = 255)
Perform padding operation on the image or annotated image according to the provided value.Add bottom-right padding to a raw image or annotation image.

Args

  • target_size (list|tuple): The target size after padding.
  • im_padding_value (list, optional): The padding value of raw image. Default: [127.5, 127.5, 127.5].
  • label_padding_value (int, optional): The padding value of annotation image. Default: 255.

Raises

  • TypeError: When target_size is neither list nor tuple.
  • ValueError: When the length of target_size is not 2.

RandomPaddingCrop

class paddleseg.transforms.RandomPaddingCrop(crop_size = (512, 512),
                 im_padding_value = (127.5, 127.5, 127.5),
                 label_padding_value = 255)
Crop a sub-image from a raw image and annotation image randomly. If the target cropping size
is larger than original image, then the bottom-right padding will be added.

Args

  • crop_size (tuple, optional): The target cropping size. Default: (512, 512).
  • im_padding_value (list, optional): The padding value of raw image. Default: [127.5, 127.5, 127.5].
  • label_padding_value (int, optional): The padding value of annotation image. Default: 255.

Raises

  • TypeError: When crop_size is neither list nor tuple.
  • ValueError: When the length of crop_size is not 2.

RandomBlur

class paddleseg.transforms.RandomBlur(prob = 0.1)
Blurring an image by a Gaussian function with a certain probability.

Args

  • prob (float, optional): A probability of blurring an image. Default: 0.1.

RandomRotation

class paddleseg.transforms.RandomRotation(max_rotation = 15,
                 im_padding_value = (127.5, 127.5, 127.5),
                 label_padding_value = 255)
Randomly rotate the image. When there is annotated image, it will be synchronized, and the rotated image and the annotated image will be padding accordingly.

Args

  • max_rotation (float, optional): The maximum rotation degree. Default: 15.
  • im_padding_value (list, optional): The padding value of raw image. Default: [127.5, 127.5, 127.5].
  • label_padding_value (int, optional): The padding value of annotation image. Default: 255. Note: The length of the list of parameters im_padding_value and label_padding_value should be consistent with the number of image channels.

RandomScaleAspect

class paddleseg.transforms.RandomScaleAspect(min_scale = 0.5, aspect_ratio = 0.33)
Crop and resize the image back to the original size and the annotated image.
The image is cropped according to a certain area ratio and aspect ratio, and the reszie returns to the original image. When there is an annotated image, it is synchronized.

Args

  • min_scale (float, optional): The minimum value of the area ratio of the cropped image to the original image. The value range is [0, 1]. When it is equal to 0, the original image will be returned.Default: 0.5.
  • aspect_ratio (float, optional): The minimum value of the cropped image's aspect ratio range, non-negative value, returns to the original image when it is 0.Default: 0.33.

RandomDistort

class paddleseg.transforms.RandomDistort(brightness_range = 0.5,
                 brightness_prob = 0.5,
                 contrast_range = 0.5,
                 contrast_prob = 0.5,
                 saturation_range = 0.5,
                 saturation_prob = 0.5,
                 hue_range = 18,
                 hue_prob = 0.5)
Distort an image with random configurations.

Args

  • brightness_range (float, optional): A range of brightness. Default: 0.5.
  • brightness_prob (float, optional): A probability of adjusting brightness. Default: 0.5.
  • contrast_range (float, optional): A range of contrast. Default: 0.5.
  • contrast_prob (float, optional): A probability of adjusting contrast. Default: 0.5.
  • saturation_range (float, optional): A range of saturation. Default: 0.5.
  • saturation_prob (float, optional): A probability of adjusting saturation. Default: 0.5.
  • hue_range (int, optional): A range of hue. Default: 18.
  • hue_prob (float, optional): A probability of adjusting hue. Default: 0.5.