![]() Blurs image with randomly chosen Gaussian blur. Randomly convert image or videos to grayscale with a probability of p (default 0.1).īlurs image with randomly chosen Gaussian blur. Randomly convert image to grayscale with a probability of p (default 0.1). Randomly distorts the image or video as used in SSD: Single Shot MultiBox Detector. Randomly change the brightness, contrast, saturation and hue of an image or video. Randomly change the brightness, contrast, saturation and hue of an image. Vertically flip the input with a given probability. Vertically flip the given image randomly with a given probability. Horizontally flip the input with a given probability. Horizontally flip the given image randomly with a given probability. Transform the input with elastic transformations. Transform a tensor image with elastic transformations. Perform a random perspective transformation of the input with a given probability. Performs a random perspective transformation of the given image with a given probability. Random affine transformation the input keeping center invariant. Random affine transformation of the image keeping center invariant. "Zoom out" transformation from "SSD: Single Shot MultiBox Detector". Pad the input on all sides with the given "pad" value. Pad the given image on all sides with the given "pad" value. Crop the image or video into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). Crop the image or video into four corners and the central crop.Ĭrop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). Random IoU crop transformation from "SSD: Single Shot MultiBox Detector".Ĭrop the given image into four corners and the central crop. Crop a random portion of the input and resize it to a given size. RandomResizedCrop(size)Ĭrop a random portion of image and resize it to a given size. RandomCrop(size)Ĭrop the given image at a random location. Perform Large Scale Jitter on the input according to "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation". Resize the input image to the given size. that work with torch.Tensor and does not requireįor any custom transformations to be used with, they should be derived from torch.nn.Module. Make sure to use only scriptable transformations, i.e. The following examples illustrate the use of the available transforms: For reproducible transformations across calls, you may use Images of a given batch, but they will produce different transformationsĪcross calls. Randomized transformations will apply the same transformation to all the Have values in where MAX_DTYPE is the largest value Tensor images with an integer dtype are expected to Tensor images with a float dtype are expected to have ![]() ![]() The expected range of the values of a tensor image is implicitly defined by Tensor Images is a tensor of (B, C, H, W) shape, where B is a number Number of channels, H and W are image height and width. A Tensor Image is a tensor with (C, H, W) shape, where C is a The transformations that accept tensor images also accept batches of tensor PIL images, or for converting dtypes and ranges. The Conversion may be used to convert to and from Most transformations accept both PIL imagesĪnd tensor images, although some transformations are PIL-only and some are ![]() This is useful if you have to build a more complex transformation pipeline Transforms give fine-grained control over the Most transform classes have a function equivalent: functional Transforms are common image transformations available in the More about the APIs that we suspect might involve future changes. Please submit any feedback you may have here, and you can also check Note that these transforms are still BETA, and while we don’t expect majorīreaking changes in the future, some APIs may still change according to userįeedback. Transforms v2: End-to-end object detection example. These transformsĪre fully backward compatible with the current ones, and you’ll see themĭocumented below with a v2. Not just images but also bounding boxes, masks, or videos. 2 namespace, which add support for transforming # include const char* shape_of(torch::Tensor const & tensor), torch:: kByte) printf( "shape of tframe is %s, \n ", shape_of(tframe)) įrame = cv::Mat( 2, 2, CV_8UC3, data) std::cout ()) std::cout ()) std::cout << "cout show(tres.In 0.15, we released a new set of transforms available in the
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