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 is developed with Python PIL.

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.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.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 reigion of the input PIL image to the given size.
mindspore.dataset.vision.py_transforms.Cutout	Randomly cut (mask) out a given number of square patches from the input NumPy image array 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	Equalize the histogram of input PIL image.
mindspore.dataset.vision.py_transforms.FiveCrop	Generate 5 cropped images (one central image and four corners images).
mindspore.dataset.vision.py_transforms.Grayscale	Convert the input PIL image to grayscale image.
mindspore.dataset.vision.py_transforms.HsvToRgb	Convert a NumPy HSV image or one batch NumPy HSV images to RGB images.
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 colors of input PIL image.
mindspore.dataset.vision.py_transforms.LinearTransformation	Apply linear transformation to the input NumPy image array, given a square transformation matrix and a mean vector.
mindspore.dataset.vision.py_transforms.MixUp	Apply mix up transformation to the input image and label.
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 PIL image according to 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 of the input PIL image by a random degree.
mindspore.dataset.vision.py_transforms.RandomColorAdjust	Perform a random brightness, contrast, saturation, and hue adjustment on 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	Erase the pixels, within a selected rectangle region, to the given value.
mindspore.dataset.vision.py_transforms.RandomGrayscale	Randomly convert the input image into grayscale image 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	Extract crop from the input image and resize it to a random size and aspect ratio.
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 fixed or 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 a NumPy RGB image or a batch of NumPy RGB images to HSV images.
mindspore.dataset.vision.py_transforms.TenCrop	Generate 10 cropped images (first 5 images from FiveCrop, second 5 images from their flipped version as per input flag to flip vertically or horizontally).
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 and apply a number of transforms sequentially from a list of transforms.