# mindspore.dataset.vision¶

This module is to support vision augmentations. It includes two parts: c_transforms and py_transforms. C_transforms is a high performance image augmentation module which is developed with c++ opencv. Py_transforms provide more kinds of image augmentations which are developed with Python PIL.

Common imported modules in corresponding API examples are as follows:

import mindspore.dataset.vision.c_transforms as c_vision
import mindspore.dataset.vision.py_transforms as py_vision
from mindspore.dataset.transforms import c_transforms


## mindspore.dataset.vision.c_transforms¶

 mindspore.dataset.vision.c_transforms.AutoContrast Apply automatic contrast on input image. mindspore.dataset.vision.c_transforms.BoundingBoxAugment Apply a given image transform on a random selection of bounding box regions of a given image. mindspore.dataset.vision.c_transforms.CenterCrop Crop the input image at the center to the given size. mindspore.dataset.vision.c_transforms.ConvertColor Change the color space of the image. mindspore.dataset.vision.c_transforms.Crop Crop the input image at a specific location. mindspore.dataset.vision.c_transforms.CutMixBatch Apply CutMix transformation on input batch of images and labels. mindspore.dataset.vision.c_transforms.CutOut Randomly cut (mask) out a given number of square patches from the input image array. mindspore.dataset.vision.c_transforms.Decode Decode the input image in RGB mode(default) or BGR mode(deprecated). mindspore.dataset.vision.c_transforms.Equalize Apply histogram equalization on input image. mindspore.dataset.vision.c_transforms.GaussianBlur Blur input image with the specified Gaussian kernel. mindspore.dataset.vision.c_transforms.HorizontalFlip Flip the input image horizontally. mindspore.dataset.vision.c_transforms.HWC2CHW Transpose the input image from shape (H, W, C) to shape (C, H, W). mindspore.dataset.vision.c_transforms.Invert Apply invert on input image in RGB mode. mindspore.dataset.vision.c_transforms.MixUpBatch Apply MixUp transformation on input batch of images and labels. mindspore.dataset.vision.c_transforms.Normalize Normalize the input image with respect to mean and standard deviation. mindspore.dataset.vision.c_transforms.NormalizePad Normalize the input image with respect to mean and standard deviation then pad an extra channel with value zero. mindspore.dataset.vision.c_transforms.Pad Pad the image according to padding parameters. mindspore.dataset.vision.c_transforms.RandomAffine Apply Random affine transformation to the input image. mindspore.dataset.vision.c_transforms.RandomColor Adjust the color of the input image by a fixed or random degree. mindspore.dataset.vision.c_transforms.RandomColorAdjust Randomly adjust the brightness, contrast, saturation, and hue of the input image. mindspore.dataset.vision.c_transforms.RandomCrop Crop the input image at a random location. mindspore.dataset.vision.c_transforms.RandomCropDecodeResize A combination of Crop, Decode and Resize. mindspore.dataset.vision.c_transforms.RandomCropWithBBox Crop the input image at a random location and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.RandomHorizontalFlip Randomly flip the input image horizontally with a given probability. mindspore.dataset.vision.c_transforms.RandomHorizontalFlipWithBBox Flip the input image horizontally randomly with a given probability and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.RandomPosterize Reduce the number of bits for each color channel to posterize the input image randomly with a given probability. mindspore.dataset.vision.c_transforms.RandomResize Resize the input image using a randomly selected interpolation mode. mindspore.dataset.vision.c_transforms.RandomResizedCrop Crop the input image to a random size and aspect ratio. mindspore.dataset.vision.c_transforms.RandomResizedCropWithBBox Crop the input image to a random size and aspect ratio and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.RandomResizeWithBBox Tensor operation to resize the input image using a randomly selected interpolation mode and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.RandomRotation Rotate the input image randomly within a specified range of degrees. mindspore.dataset.vision.c_transforms.RandomSelectSubpolicy Choose a random sub-policy from a policy list to be applied on the input image. mindspore.dataset.vision.c_transforms.RandomSharpness Adjust the sharpness of the input image by a fixed or random degree. mindspore.dataset.vision.c_transforms.RandomSolarize Randomly selects a subrange within the specified threshold range and sets the pixel value within the subrange to (255 - pixel). mindspore.dataset.vision.c_transforms.RandomVerticalFlip Randomly flip the input image vertically with a given probability. mindspore.dataset.vision.c_transforms.RandomVerticalFlipWithBBox Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.Rescale Rescale the input image with the given rescale and shift. mindspore.dataset.vision.c_transforms.Resize Resize the input image to the given size with a given interpolation mode. mindspore.dataset.vision.c_transforms.ResizeWithBBox Resize the input image to the given size and adjust bounding boxes accordingly. mindspore.dataset.vision.c_transforms.Rotate Rotate the input image by specified degrees. mindspore.dataset.vision.c_transforms.SlicePatches Slice Tensor to multiple patches in horizontal and vertical directions. mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg A combination of Crop, Decode and Resize using the simulation algorithm of Ascend series chip DVPP module. mindspore.dataset.vision.c_transforms.SoftDvppDecodeResizeJpeg Decode and resize JPEG image using the simulation algorithm of Ascend series chip DVPP module. mindspore.dataset.vision.c_transforms.UniformAugment Perform randomly selected augmentation on input image. mindspore.dataset.vision.c_transforms.VerticalFlip Flip the input image vertically.

## mindspore.dataset.vision.py_transforms¶

 mindspore.dataset.vision.py_transforms.AutoContrast Automatically maximize the contrast of the input PIL Image. mindspore.dataset.vision.py_transforms.CenterCrop Crop the central region of the input PIL Image with the given size. mindspore.dataset.vision.py_transforms.Cutout Randomly apply a given number of square patches of zeros to a location within the input numpy.ndarray image of shape (C, H, W). mindspore.dataset.vision.py_transforms.Decode Decode the input raw image to PIL Image format in RGB mode. mindspore.dataset.vision.py_transforms.Equalize Apply histogram equalization on the input PIL Image. mindspore.dataset.vision.py_transforms.FiveCrop Crop the given image into one central crop and four corners. mindspore.dataset.vision.py_transforms.Grayscale Convert the input PIL Image to grayscale. mindspore.dataset.vision.py_transforms.HsvToRgb Convert one or more numpy.ndarray images from HSV to RGB. mindspore.dataset.vision.py_transforms.HWC2CHW Transpose the input numpy.ndarray image of shape (H, W, C) to (C, H, W). mindspore.dataset.vision.py_transforms.Invert Invert the colors of the input PIL Image. mindspore.dataset.vision.py_transforms.LinearTransformation Transform the input numpy.ndarray image with a given square transformation matrix and a mean vector. mindspore.dataset.vision.py_transforms.MixUp Randomly mix up a batch of images together with its labels. mindspore.dataset.vision.py_transforms.Normalize Normalize the input numpy.ndarray image of shape (C, H, W) with the specified mean and standard deviation. mindspore.dataset.vision.py_transforms.NormalizePad Normalize the input numpy.ndarray image of shape (C, H, W) with the specified mean and standard deviation, then pad an extra channel filled with zeros. mindspore.dataset.vision.py_transforms.Pad Pad the input image on all sides with the given padding parameters. mindspore.dataset.vision.py_transforms.RandomAffine Apply random affine transformation to the input PIL Image. mindspore.dataset.vision.py_transforms.RandomColor Adjust the color balance of the input PIL Image by a random degree. mindspore.dataset.vision.py_transforms.RandomColorAdjust Randomly adjust the brightness, contrast, saturation, and hue of the input PIL Image. mindspore.dataset.vision.py_transforms.RandomCrop Crop the input PIL Image at a random location with the specified size. mindspore.dataset.vision.py_transforms.RandomErasing Randomly erase the pixels within a random selected rectangle region with a given probability. mindspore.dataset.vision.py_transforms.RandomGrayscale Randomly convert the input image into grayscale with a given probability. mindspore.dataset.vision.py_transforms.RandomHorizontalFlip Randomly flip the input image horizontally with a given probability. mindspore.dataset.vision.py_transforms.RandomPerspective Randomly apply perspective transformation to the input PIL Image with a given probability. mindspore.dataset.vision.py_transforms.RandomResizedCrop Randomly crop the image and resize it to a given size. mindspore.dataset.vision.py_transforms.RandomRotation Rotate the input PIL Image by a random angle. mindspore.dataset.vision.py_transforms.RandomSharpness Adjust the sharpness of the input PIL Image by a random degree. mindspore.dataset.vision.py_transforms.RandomVerticalFlip Randomly flip the input image vertically with a given probability. mindspore.dataset.vision.py_transforms.Resize Resize the input PIL Image to the given size. mindspore.dataset.vision.py_transforms.RgbToHsv Convert one or more numpy.ndarray images from RGB to HSV. mindspore.dataset.vision.py_transforms.TenCrop Crop the given image into one central crop and four corners plus the flipped version of these. mindspore.dataset.vision.py_transforms.ToPIL Convert the input decoded numpy.ndarray image to PIL Image. mindspore.dataset.vision.py_transforms.ToTensor Convert the input PIL Image or numpy.ndarray of shape (H, W, C) in the range [0, 255] to numpy.ndarray of shape (C, H, W) in the range [0.0, 1.0] with the desired dtype. mindspore.dataset.vision.py_transforms.ToType Convert the input numpy.ndarray image to the desired dtype. mindspore.dataset.vision.py_transforms.UniformAugment Uniformly select a number of transformations from a sequence and apply them sequentially and randomly, which means that there is a chance that a chosen transformation will not be applied.

## mindspore.dataset.vision.utils¶

 mindspore.dataset.vision.Border Padding Mode, Border Type. mindspore.dataset.vision.ImageBatchFormat Data Format of images after batch operation. mindspore.dataset.vision.Inter Interpolation Modes. mindspore.dataset.vision.SliceMode Mode to Slice Tensor into multiple parts.