mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg

class mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg(size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), max_attempts=10)[source]

A combination of Crop, Decode and Resize using the simulation algorithm of Ascend series chip DVPP module.

The usage scenario is consistent with SoftDvppDecodeResizeJpeg. The input image size should be in range [32*32, 8192*8192]. The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16]. Only images with an even resolution can be output. The output of odd resolution is not supported.

Parameters
  • size (Union[int, sequence]) – The size of the output image. The size value(s) must be positive. If size is an integer, a square crop of size (size, size) is returned. If size is a sequence of length 2, it should be (height, width).

  • scale (list, tuple, optional) – Range [min, max) of respective size of the original size to be cropped, which must be non-negative (default=(0.08, 1.0)).

  • ratio (list, tuple, optional) – Range [min, max) of aspect ratio to be cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).

  • max_attempts (int, optional) – The maximum number of attempts to propose a valid crop_area (default=10). If exceeded, fall back to use center_crop instead. The max_attempts value must be positive.

Raises
Supported Platforms:

CPU

Examples

>>> # decode, randomly crop and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
...                                                 input_columns=["image"])
>>> # decode, randomly crop and resize to landscape style
>>> transforms_list2 = [c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
...                                                     input_columns=["image"])