Source code for mindspore.dataset.vision.utils

# Copyright 2019-2024 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Interpolation Mode, Resampling Filters
"""
from enum import Enum, IntEnum
from fractions import Fraction
import numbers

import numpy as np
from PIL import Image

import mindspore
import mindspore._c_dataengine as cde

# The following constants have been deprecated by Pillow since version 9.1.0
if int(Image.__version__.split(".")[0]) > 9 or Image.__version__ >= "9.1.0":
    FLIP_LEFT_RIGHT = Image.Transpose.FLIP_LEFT_RIGHT
    FLIP_TOP_BOTTOM = Image.Transpose.FLIP_TOP_BOTTOM
    PERSPECTIVE = Image.Transform.PERSPECTIVE
    AFFINE = Image.Transform.AFFINE
    NEAREST = Image.Resampling.NEAREST
    ANTIALIAS = Image.Resampling.LANCZOS
    LINEAR = Image.Resampling.BILINEAR
    CUBIC = Image.Resampling.BICUBIC
else:
    FLIP_LEFT_RIGHT = Image.FLIP_LEFT_RIGHT
    FLIP_TOP_BOTTOM = Image.FLIP_TOP_BOTTOM
    PERSPECTIVE = Image.PERSPECTIVE
    AFFINE = Image.AFFINE
    NEAREST = Image.NEAREST
    ANTIALIAS = Image.ANTIALIAS
    LINEAR = Image.LINEAR
    CUBIC = Image.CUBIC


[docs]class AutoAugmentPolicy(str, Enum): """ AutoAugment policy for different datasets. Possible enumeration values are: ``AutoAugmentPolicy.IMAGENET``, ``AutoAugmentPolicy.CIFAR10``, AutoAugmentPolicy.SVHN. Each policy contains 25 pairs of augmentation operations. When using AutoAugment, each image is randomly transformed with one of these operation pairs. Each pair has 2 different operations. The following shows all of these augmentation operations, including operation names with their probabilities and random params. - ``AutoAugmentPolicy.IMAGENET``: dataset auto augment policy for ImageNet. .. code-block:: Augmentation operations pair: [(("Posterize", 0.4, 8), ("Rotate", 0.6, 9)), (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), (("Equalize", 0.8, None), ("Equalize", 0.6, None)), (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)), (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), (("Equalize", 0.4, None), ("Rotate", 0.8, 8)), (("Solarize", 0.6, 3), ("Equalize", 0.6, None)), (("Posterize", 0.8, 5), ("Equalize", 1.0, None)), (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)), (("Equalize", 0.6, None), ("Posterize", 0.4, 6)), (("Rotate", 0.8, 8), ("Color", 0.4, 0)), (("Rotate", 0.4, 9), ("Equalize", 0.6, None)), (("Equalize", 0.0, None), ("Equalize", 0.8, None)), (("Invert", 0.6, None), ("Equalize", 1.0, None)), (("Color", 0.6, 4), ("Contrast", 1.0, 8)), (("Rotate", 0.8, 8), ("Color", 1.0, 2)), (("Color", 0.8, 8), ("Solarize", 0.8, 7)), (("Sharpness", 0.4, 7), ("Invert", 0.6, None)), (("ShearX", 0.6, 5), ("Equalize", 1.0, None)), (("Color", 0.4, 0), ("Equalize", 0.6, None)), (("Equalize", 0.4, None), ("Solarize", 0.2, 4)), (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)), (("Invert", 0.6, None), ("Equalize", 1.0, None)), (("Color", 0.6, 4), ("Contrast", 1.0, 8)), (("Equalize", 0.8, None), ("Equalize", 0.6, None))] - ``AutoAugmentPolicy.CIFAR10``: dataset auto augment policy for Cifar10. .. code-block:: Augmentation operations pair: [(("Invert", 0.1, None), ("Contrast", 0.2, 6)), (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)), (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)), (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)), (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)), (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)), (("Color", 0.4, 3), ("Brightness", 0.6, 7)), (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)), (("Equalize", 0.6, None), ("Equalize", 0.5, None)), (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)), (("Color", 0.7, 7), ("TranslateX", 0.5, 8)), (("Equalize", 0.8, None), ("Invert", 0.1, None)), (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)), (("Brightness", 0.9, 6), ("Color", 0.2, 8)), (("Solarize", 0.5, 2), ("Invert", 0.0, None)), (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)), (("Equalize", 0.2, None), ("Equalize", 0.6, None)), (("Color", 0.9, 9), ("Equalize", 0.6, None)), (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)), (("Brightness", 0.1, 3), ("Color", 0.7, 0)), (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)), (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)), (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)), (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)), (("Equalize", 0.2, None), ("AutoContrast", 0.6, None))] - ``AutoAugmentPolicy.SVHN``: dataset auto augment policy for SVHN. .. code-block:: Augmentation operations pair: [(("ShearX", 0.9, 4), ("Invert", 0.2, None)), (("ShearY", 0.9, 8), ("Invert", 0.7, None)), (("Equalize", 0.6, None), ("Solarize", 0.6, 6)), (("Invert", 0.9, None), ("Equalize", 0.6, None)), (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)), (("ShearY", 0.9, 8), ("Invert", 0.4, None)), (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)), (("Invert", 0.9, None), ("AutoContrast", 0.8, None)), (("Equalize", 0.6, None), ("Rotate", 0.9, 3)), (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)), (("ShearY", 0.8, 8), ("Invert", 0.7, None)), (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)), (("Invert", 0.9, None), ("Equalize", 0.6, None)), (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)), (("Invert", 0.8, None), ("TranslateY", 0.0, 2)), (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)), (("Invert", 0.6, None), ("Rotate", 0.8, 4)), (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)), (("ShearX", 0.1, 6), ("Invert", 0.6, None)), (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)), (("ShearY", 0.8, 4), ("Invert", 0.8, None)), (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)), (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)), (("ShearX", 0.7, 2), ("Invert", 0.1, None))] """ IMAGENET: str = "imagenet" CIFAR10: str = "cifar10" SVHN: str = "svhn" @staticmethod def to_c_type(policy): """ Function to return C type for AutoAugment policy. """ c_values = {AutoAugmentPolicy.IMAGENET: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_IMAGENET, AutoAugmentPolicy.CIFAR10: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_CIFAR10, AutoAugmentPolicy.SVHN: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_SVHN} value = c_values.get(policy) if value is None: raise RuntimeError("Unsupported AutoAugmentPolicy, only support IMAGENET, CIFAR10, and SVHN.") return value
[docs]class Border(str, Enum): """ Padding Mode, Border Type. Possible enumeration values are: ``Border.CONSTANT``, ``Border.EDGE``, ``Border.REFLECT``, ``Border.SYMMETRIC``. - ``Border.CONSTANT`` : means it fills the border with constant values. - ``Border.EDGE`` : means it pads with the last value on the edge. - ``Border.REFLECT`` : means it reflects the values on the edge omitting the last value of edge. For example, padding [1,2,3,4] with 2 elements on both sides will result in [3,2,1,2,3,4,3,2]. - ``Border.SYMMETRIC`` : means it reflects the values on the edge repeating the last value of edge. For example, padding [1,2,3,4] with 2 elements on both sides will result in [2,1,1,2,3,4,4,3]. Note: This class derived from class str to support json serializable. """ CONSTANT: str = "constant" EDGE: str = "edge" REFLECT: str = "reflect" SYMMETRIC: str = "symmetric" @staticmethod def to_python_type(border_type): """ Function to return Python type for Border Type. """ python_values = {Border.CONSTANT: 'constant', Border.EDGE: 'edge', Border.REFLECT: 'reflect', Border.SYMMETRIC: 'symmetric'} value = python_values.get(border_type) if value is None: raise RuntimeError("Unsupported Border type, only support CONSTANT, EDGE, REFLECT and SYMMETRIC.") return value @staticmethod def to_c_type(border_type): """ Function to return C type for Border Type. """ c_values = {Border.CONSTANT: cde.BorderType.DE_BORDER_CONSTANT, Border.EDGE: cde.BorderType.DE_BORDER_EDGE, Border.REFLECT: cde.BorderType.DE_BORDER_REFLECT, Border.SYMMETRIC: cde.BorderType.DE_BORDER_SYMMETRIC} value = c_values.get(border_type) if value is None: raise RuntimeError("Unsupported Border type, only support CONSTANT, EDGE, REFLECT and SYMMETRIC.") return value
[docs]class ConvertMode(IntEnum): """ The color conversion mode. Possible enumeration values are as follows: - ConvertMode.COLOR_BGR2BGRA: convert BGR format images to BGRA format images. - ConvertMode.COLOR_RGB2RGBA: convert RGB format images to RGBA format images. - ConvertMode.COLOR_BGRA2BGR: convert BGRA format images to BGR format images. - ConvertMode.COLOR_RGBA2RGB: convert RGBA format images to RGB format images. - ConvertMode.COLOR_BGR2RGBA: convert BGR format images to RGBA format images. - ConvertMode.COLOR_RGB2BGRA: convert RGB format images to BGRA format images. - ConvertMode.COLOR_RGBA2BGR: convert RGBA format images to BGR format images. - ConvertMode.COLOR_BGRA2RGB: convert BGRA format images to RGB format images. - ConvertMode.COLOR_BGR2RGB: convert BGR format images to RGB format images. - ConvertMode.COLOR_RGB2BGR: convert RGB format images to BGR format images. - ConvertMode.COLOR_BGRA2RGBA: convert BGRA format images to RGBA format images. - ConvertMode.COLOR_RGBA2BGRA: convert RGBA format images to BGRA format images. - ConvertMode.COLOR_BGR2GRAY: convert BGR format images to GRAY format images. - ConvertMode.COLOR_RGB2GRAY: convert RGB format images to GRAY format images. - ConvertMode.COLOR_GRAY2BGR: convert GRAY format images to BGR format images. - ConvertMode.COLOR_GRAY2RGB: convert GRAY format images to RGB format images. - ConvertMode.COLOR_GRAY2BGRA: convert GRAY format images to BGRA format images. - ConvertMode.COLOR_GRAY2RGBA: convert GRAY format images to RGBA format images. - ConvertMode.COLOR_BGRA2GRAY: convert BGRA format images to GRAY format images. - ConvertMode.COLOR_RGBA2GRAY: convert RGBA format images to GRAY format images. """ COLOR_BGR2BGRA = 0 COLOR_RGB2RGBA = COLOR_BGR2BGRA COLOR_BGRA2BGR = 1 COLOR_RGBA2RGB = COLOR_BGRA2BGR COLOR_BGR2RGBA = 2 COLOR_RGB2BGRA = COLOR_BGR2RGBA COLOR_RGBA2BGR = 3 COLOR_BGRA2RGB = COLOR_RGBA2BGR COLOR_BGR2RGB = 4 COLOR_RGB2BGR = COLOR_BGR2RGB COLOR_BGRA2RGBA = 5 COLOR_RGBA2BGRA = COLOR_BGRA2RGBA COLOR_BGR2GRAY = 6 COLOR_RGB2GRAY = 7 COLOR_GRAY2BGR = 8 COLOR_GRAY2RGB = COLOR_GRAY2BGR COLOR_GRAY2BGRA = 9 COLOR_GRAY2RGBA = COLOR_GRAY2BGRA COLOR_BGRA2GRAY = 10 COLOR_RGBA2GRAY = 11 @staticmethod def to_c_type(mode): """ Function to return C type for color mode. """ c_values = {ConvertMode.COLOR_BGR2BGRA: cde.ConvertMode.DE_COLOR_BGR2BGRA, ConvertMode.COLOR_RGB2RGBA: cde.ConvertMode.DE_COLOR_RGB2RGBA, ConvertMode.COLOR_BGRA2BGR: cde.ConvertMode.DE_COLOR_BGRA2BGR, ConvertMode.COLOR_RGBA2RGB: cde.ConvertMode.DE_COLOR_RGBA2RGB, ConvertMode.COLOR_BGR2RGBA: cde.ConvertMode.DE_COLOR_BGR2RGBA, ConvertMode.COLOR_RGB2BGRA: cde.ConvertMode.DE_COLOR_RGB2BGRA, ConvertMode.COLOR_RGBA2BGR: cde.ConvertMode.DE_COLOR_RGBA2BGR, ConvertMode.COLOR_BGRA2RGB: cde.ConvertMode.DE_COLOR_BGRA2RGB, ConvertMode.COLOR_BGR2RGB: cde.ConvertMode.DE_COLOR_BGR2RGB, ConvertMode.COLOR_RGB2BGR: cde.ConvertMode.DE_COLOR_RGB2BGR, ConvertMode.COLOR_BGRA2RGBA: cde.ConvertMode.DE_COLOR_BGRA2RGBA, ConvertMode.COLOR_RGBA2BGRA: cde.ConvertMode.DE_COLOR_RGBA2BGRA, ConvertMode.COLOR_BGR2GRAY: cde.ConvertMode.DE_COLOR_BGR2GRAY, ConvertMode.COLOR_RGB2GRAY: cde.ConvertMode.DE_COLOR_RGB2GRAY, ConvertMode.COLOR_GRAY2BGR: cde.ConvertMode.DE_COLOR_GRAY2BGR, ConvertMode.COLOR_GRAY2RGB: cde.ConvertMode.DE_COLOR_GRAY2RGB, ConvertMode.COLOR_GRAY2BGRA: cde.ConvertMode.DE_COLOR_GRAY2BGRA, ConvertMode.COLOR_GRAY2RGBA: cde.ConvertMode.DE_COLOR_GRAY2RGBA, ConvertMode.COLOR_BGRA2GRAY: cde.ConvertMode.DE_COLOR_BGRA2GRAY, ConvertMode.COLOR_RGBA2GRAY: cde.ConvertMode.DE_COLOR_RGBA2GRAY, } mode = c_values.get(mode) if mode is None: raise RuntimeError("Unsupported ConvertMode, see https://www.mindspore.cn/docs/zh-CN/r2.3.1/api_python/" "dataset_vision/mindspore.dataset.vision.ConvertColor.html for more details.") return mode
[docs]class ImageBatchFormat(IntEnum): """ Data Format of images after batch operation. Possible enumeration values are: ``ImageBatchFormat.NHWC``, ``ImageBatchFormat.NCHW``. - ``ImageBatchFormat.NHWC``: in orders like, batch N, height H, width W, channels C to store the data. - ``ImageBatchFormat.NCHW``: in orders like, batch N, channels C, height H, width W to store the data. """ NHWC = 0 NCHW = 1 @staticmethod def to_c_type(image_batch_format): """ Function to return C type for ImageBatchFormat. """ c_values = {ImageBatchFormat.NHWC: cde.ImageBatchFormat.DE_IMAGE_BATCH_FORMAT_NHWC, ImageBatchFormat.NCHW: cde.ImageBatchFormat.DE_IMAGE_BATCH_FORMAT_NCHW} value = c_values.get(image_batch_format) if value is None: raise RuntimeError("Unsupported ImageBatchFormat, only support NHWC and NCHW.") return value
[docs]class ImageReadMode(IntEnum): """ The read mode used for the image file. Possible enumeration values are: ``ImageReadMode.UNCHANGED``, ``ImageReadMode.GRAYSCALE``, ``ImageReadMode.COLOR``. - ``ImageReadMode.UNCHANGED``: remain the output in the original format. - ``ImageReadMode.GRAYSCALE``: convert the output into one channel grayscale data. - ``ImageReadMode.COLOR``: convert the output into three channels RGB color data. """ UNCHANGED = 0 GRAYSCALE = 1 COLOR = 2 @staticmethod def to_c_type(image_read_mode): """ Function to return C type for ImageReadMode. """ c_values = {ImageReadMode.UNCHANGED: cde.ImageReadMode.DE_IMAGE_READ_MODE_UNCHANGED, ImageReadMode.GRAYSCALE: cde.ImageReadMode.DE_IMAGE_READ_MODE_GRAYSCALE, ImageReadMode.COLOR: cde.ImageReadMode.DE_IMAGE_READ_MODE_COLOR} value = c_values.get(image_read_mode) if value is None: raise RuntimeError("Unsupported ImageReadMode, only support UNCHANGED, GRAYSCALE and COLOR.") return value
[docs]class Inter(IntEnum): """ Interpolation methods. Available values are as follows: - ``Inter.NEAREST`` : Nearest neighbor interpolation. - ``Inter.ANTIALIAS`` : Antialias interpolation. Supported only when the input is PIL.Image.Image. - ``Inter.LINEAR`` : Linear interpolation, the same as ``Inter.BILINEAR``. - ``Inter.BILINEAR`` : Bilinear interpolation. - ``Inter.CUBIC`` : Cubic interpolation, the same as ``Inter.BICUBIC``. - ``Inter.BICUBIC`` : Bicubic interpolation. - ``Inter.AREA`` : Pixel area interpolation. Supported only when the input is numpy.ndarray. - ``Inter.PILCUBIC`` : Pillow implementation of bicubic interpolation. Supported only when the input is numpy.ndarray. """ NEAREST = 0 ANTIALIAS = 1 BILINEAR = LINEAR = 2 BICUBIC = CUBIC = 3 AREA = 4 PILCUBIC = 5 @staticmethod def to_python_type(inter_type): """ Function to return Python type for Interpolation Mode. """ python_values = {Inter.NEAREST: NEAREST, Inter.ANTIALIAS: ANTIALIAS, Inter.LINEAR: LINEAR, Inter.CUBIC: CUBIC} value = python_values.get(inter_type) if value is None: raise RuntimeError("Unsupported interpolation, only support NEAREST, ANTIALIAS, LINEAR and CUBIC.") return value @staticmethod def to_c_type(inter_type): """ Function to return C type for Interpolation Mode. """ c_values = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR, Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR, Inter.CUBIC: cde.InterpolationMode.DE_INTER_CUBIC, Inter.AREA: cde.InterpolationMode.DE_INTER_AREA, Inter.PILCUBIC: cde.InterpolationMode.DE_INTER_PILCUBIC} value = c_values.get(inter_type) if value is None: raise RuntimeError("Unsupported interpolation, only support NEAREST, LINEAR, CUBIC, AREA and PILCUBIC.") return value
[docs]class SliceMode(IntEnum): """ Mode to Slice Tensor into multiple parts. Possible enumeration values are: ``SliceMode.PAD``, ``SliceMode.DROP``. - ``SliceMode.PAD``: pad some pixels before slice the Tensor if needed. - ``SliceMode.DROP``: drop remainder pixels before slice the Tensor if needed. """ PAD = 0 DROP = 1 @staticmethod def to_c_type(mode): """ Function to return C type for SliceMode. """ c_values = {SliceMode.PAD: cde.SliceMode.DE_SLICE_PAD, SliceMode.DROP: cde.SliceMode.DE_SLICE_DROP} value = c_values.get(mode) if value is None: raise RuntimeError("Unsupported SliceMode, only support PAD and DROP.") return value
[docs]def encode_jpeg(image, quality=75): """ Encode the input image as JPEG data. Args: image (Union[numpy.ndarray, mindspore.Tensor]): The image to be encoded. quality (int, optional): Quality of the resulting JPEG data, in range of [1, 100]. Default: ``75``. Returns: numpy.ndarray, one dimension uint8 data. Raises: TypeError: If `image` is not of type numpy.ndarray or mindspore.Tensor. TypeError: If `quality` is not of type int. RuntimeError: If the data type of `image` is not uint8. RuntimeError: If the shape of `image` is not <H, W> or <H, W, 1> or <H, W, 3>. RuntimeError: If `quality` is less than 1 or greater than 100. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> import numpy as np >>> # Generate a random image with height=120, width=340, channels=3 >>> image = np.random.randint(256, size=(120, 340, 3), dtype=np.uint8) >>> jpeg_data = vision.encode_jpeg(image) """ if not isinstance(quality, int): raise TypeError("Input quality is not of type {0}, but got: {1}.".format(int, type(quality))) if isinstance(image, np.ndarray): return cde.encode_jpeg(cde.Tensor(image), quality).as_array() if isinstance(image, mindspore.Tensor): return cde.encode_jpeg(cde.Tensor(image.asnumpy()), quality).as_array() raise TypeError("Input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, mindspore.Tensor, type(image)))
[docs]def encode_png(image, compression_level=6): """ Encode the input image as PNG data. Args: image (Union[numpy.ndarray, mindspore.Tensor]): The image to be encoded. compression_level (int, optional): The `compression_level` for encoding, in range of [0, 9]. Default: ``6``. Returns: numpy.ndarray, one dimension uint8 data. Raises: TypeError: If `image` is not of type numpy.ndarray or mindspore.Tensor. TypeError: If `compression_level` is not of type int. RuntimeError: If the data type of `image` is not uint8. RuntimeError: If the shape of `image` is not <H, W> or <H, W, 1> or <H, W, 3>. RuntimeError: If `compression_level` is less than 0 or greater than 9. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> import numpy as np >>> # Generate a random image with height=120, width=340, channels=3 >>> image = np.random.randint(256, size=(120, 340, 3), dtype=np.uint8) >>> png_data = vision.encode_png(image) """ if not isinstance(compression_level, int): raise TypeError("Input compression_level is not of type {0}, but got: {1}.".format(int, type(compression_level))) if isinstance(image, np.ndarray): return cde.encode_png(cde.Tensor(image), compression_level).as_array() if isinstance(image, mindspore.Tensor): return cde.encode_png(cde.Tensor(image.asnumpy()), compression_level).as_array() raise TypeError("Input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, mindspore.Tensor, type(image)))
[docs]def get_image_num_channels(image): """ Get the number of input image channels. Args: image (Union[numpy.ndarray, PIL.Image.Image]): Image to get the number of channels. Returns: int, the number of input image channels. Raises: RuntimeError: If the dimension of `image` is less than 2. TypeError: If `image` is not of type <class 'numpy.ndarray'> or <class 'PIL.Image.Image'>. Examples: >>> import mindspore.dataset.vision as vision >>> from PIL import Image >>> image = Image.open("/path/to/image_file") >>> num_channels = vision.get_image_num_channels(image) """ if isinstance(image, np.ndarray): return cde.get_image_num_channels(cde.Tensor(image)) if isinstance(image, Image.Image): if hasattr(image, "getbands"): return len(image.getbands()) return image.channels raise TypeError("Input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, Image.Image, type(image)))
[docs]def get_image_size(image): """ Get the size of input image as [height, width]. Args: image (Union[numpy.ndarray, PIL.Image.Image]): The image to get size. Returns: list[int, int], the image size. Raises: RuntimeError: If the dimension of `image` is less than 2. TypeError: If `image` is not of type <class 'numpy.ndarray'> or <class 'PIL.Image.Image'>. Examples: >>> import mindspore.dataset.vision as vision >>> from PIL import Image >>> image = Image.open("/path/to/image_file") >>> image_size = vision.get_image_size(image) """ if isinstance(image, np.ndarray): return cde.get_image_size(cde.Tensor(image)) if isinstance(image, Image.Image): size_list = list(image.size) size_list[0], size_list[1] = size_list[1], size_list[0] return size_list raise TypeError("Input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, Image.Image, type(image)))
def parse_padding(padding): """ Parses and prepares the padding tuple""" if isinstance(padding, numbers.Number): padding = [padding] * 4 if len(padding) == 2: left = right = padding[0] top = bottom = padding[1] padding = (left, top, right, bottom,) if isinstance(padding, list): padding = tuple(padding) return padding
[docs]def read_file(filename): """ Read a file in binary mode. Args: filename(str): The path to the file to be read. Returns: numpy.ndarray, the one dimension uint8 data. Raises: TypeError: If `filename` is not of type str. RuntimeError: If `filename` does not exist or is not a common file. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> output = vision.read_file("/path/to/file") """ if isinstance(filename, str): return cde.read_file(filename).as_array() raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename)))
[docs]def read_image(filename, mode=ImageReadMode.UNCHANGED): """ Read a image file and decode it into one channel grayscale data or RGB color data. Supported file types are JPEG, PNG, BMP, TIFF. Args: filename(str): The path to the image file to be read. mode(ImageReadMode, optional): The mode used for decoding the image. It can be ``ImageReadMode.UNCHANGED``, ``ImageReadMode.GRAYSCALE``, ``IMageReadMode.COLOR``. Default: ``ImageReadMode.UNCHANGED``. - ImageReadMode.UNCHANGED, remain the output in the original format. - ImageReadMode.GRAYSCALE, convert the output into one channel grayscale data. - IMageReadMode.COLOR, convert the output into three channels RGB color data. Returns: numpy.ndarray, three dimensions uint8 data in the shape of (Height, Width, Channels). Raises: TypeError: If `filename` is not of type str. TypeError: If `mode` is not of type :class:`mindspore.dataset.vision.ImageReadMode` . RuntimeError: If `filename` does not exist, or not a regular file, or not a supported image file. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> from mindspore.dataset.vision import ImageReadMode >>> output = vision.read_image("/path/to/image_file", ImageReadMode.UNCHANGED) """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if not isinstance(mode, ImageReadMode): raise TypeError("Input mode is not of type {0}, but got: {1}.".format(ImageReadMode, type(mode))) return cde.read_image(filename, ImageReadMode.to_c_type(mode)).as_array()
[docs]def read_video(filename, start_pts=0, end_pts=None, pts_unit="pts"): """ Read the video, audio, metadata from a video file. It supports AVI, H264, H265, MOV, MP4, WMV file formats. Args: filename(str): The path to the video file to be read. start_pts(Union[float, Fraction, int], optional): The start presentation timestamp of the video. Default: 0. end_pts(Union[float, Fraction, int], optional): The end presentation timestamp of the video. Default: None. The None is represented by 2147483647. pts_unit(str, optional): The unit of the timestamps. It can be any of ["pts", "sec"]. Default: "pts". Returns: - numpy.ndarray, four dimensions uint8 data for video. The format is [T, H, W, C]. `T` is the number of frames, `H` is the height, `W` is the width, `C` is the channel for RGB. - numpy.ndarray, two dimensions float for audio. The format is [C, L]. `C` is the number of channels. `L` is the length of the points in one channel. - dict, metadata for the video and audio. It contains video_fps data of type float and audio_fps data of type int. Raises: TypeError: If `filename` is not of type str. TypeError: If `start_pts` is not of type [float, Fraction, int]. TypeError: If `end_pts` is not of type [float, Fraction, int]. TypeError: If `pts_unit` is not of type str. RuntimeError: If `filename` does not exist, or not a regular file, or not a supported video file. ValueError: If `start_pts` is less than 0. ValueError: If `end_pts` is less than `start_pts`. ValueError: If `pts_unit` is not in ["pts", "sec"]. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> video_output, audio_output, metadata_output = vision.read_video("/path/to/file") """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if not isinstance(start_pts, (float, Fraction, int)): raise TypeError("Input start_pts is not of type [{0}, {1}, {2}], but got: {3}".format(float, Fraction, int, type(start_pts))) if start_pts < 0.0: err_msg = "Not supported start_pts for " + str(start_pts) + ". The start_pts should be >= 0." raise ValueError(err_msg) if end_pts is None: end_pts = 2147483647.0 if not isinstance(end_pts, (float, Fraction, int)): raise TypeError("Input end_pts is not of type [{0}, {1}, {2}], but got: {3}".format(float, Fraction, int, type(end_pts))) if end_pts < start_pts: err_msg = "Not supported end_pts for " + str(end_pts) + ". start_pts = " + str(start_pts) + "." err_msg += " The end_pts should be >= start_pts." raise ValueError(err_msg) if not isinstance(pts_unit, str): raise TypeError("Input pts_unit is not of type {0}, but got: {1}.".format(str, type(pts_unit))) if pts_unit not in ["pts", "sec"]: raise ValueError("Not supported pts_unit for " + pts_unit) video_output, audio_output, raw_metadata = cde.read_video(filename, float(start_pts), float(end_pts), pts_unit) if video_output is not None: video_output = video_output.as_array() if audio_output is not None: audio_output = audio_output.as_array() metadata_output = {} for key in raw_metadata: if key == "video_fps": metadata_output[key] = float(raw_metadata[key]) continue if key == "audio_fps": metadata_output[key] = int(raw_metadata[key]) continue metadata_output[key] = raw_metadata[key] return video_output, audio_output, metadata_output
[docs]def read_video_timestamps(filename, pts_unit="pts"): """ Read the timestamps and frames per second of a video file. It supports AVI, H264, H265, MOV, MP4, WMV files. Args: filename(str): The path to the video file to be read. pts_unit(str, optional): The unit of the timestamps. It can be any of ["pts", "sec"]. Default: "pts". Returns: - list, when `pts_unit` is set to "pts", list[int] is returned, when `pts_unit` is set to "sec", list[float] is returned. - float, the frames per second of the video file. Raises: TypeError: If `filename` is not of type str. TypeError: If `pts_unit` is not of type str. RuntimeError: If `filename` does not exist, or not a regular file, or not a supported video file. RuntimeError: If `pts_unit` is not in ["pts", "sec"]. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> video_timestamps, video_fps = vision.read_video_timestamps("/path/to/file") """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if not isinstance(pts_unit, str): raise TypeError("Input pts_unit is not of type {0}, but got: {1}.".format(str, type(pts_unit))) video_pts, video_fps, time_base = cde.read_video_timestamps(filename, pts_unit) if video_pts == []: return video_pts, None if pts_unit == "pts": return video_pts, video_fps return [x * time_base for x in video_pts], video_fps
[docs]def write_file(filename, data): """ Write the one dimension uint8 data into a file using binary mode. Args: filename (str): The path to the file to be written. data (Union[numpy.ndarray, mindspore.Tensor]): The one dimension uint8 data to be written. Raises: TypeError: If `filename` is not of type str. TypeError: If `data` is not of type numpy.ndarray or mindspore.Tensor. RuntimeError: If the `filename` is not a common file. RuntimeError: If the data type of `data` is not uint8. RuntimeError: If the shape of `data` is not a one-dimensional array. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> import numpy as np >>> # Generate a random data with 1024 bytes >>> data = np.random.randint(256, size=(1024), dtype=np.uint8) >>> vision.write_file("/path/to/file", data) """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if isinstance(data, np.ndarray): return cde.write_file(filename, cde.Tensor(data)) if isinstance(data, mindspore.Tensor): return cde.write_file(filename, cde.Tensor(data.asnumpy())) raise TypeError("Input data is not of type {0} or {1}, but got: {2}.".format(np.ndarray, mindspore.Tensor, type(data)))
[docs]def write_jpeg(filename, image, quality=75): """ Write the image data into a JPEG file. Args: filename (str): The path to the file to be written. image (Union[numpy.ndarray, mindspore.Tensor]): The image data to be written. quality (int, optional): Quality of the resulting JPEG file, in range of [1, 100]. Default: ``75``. Raises: TypeError: If `filename` is not of type str. TypeError: If `image` is not of type numpy.ndarray or mindspore.Tensor. TypeError: If `quality` is not of type int. RuntimeError: If the `filename` does not exist or not a common file. RuntimeError: If the data type of `image` is not uint8. RuntimeError: If the shape of `image` is not <H, W> or <H, W, 1> or <H, W, 3>. RuntimeError: If `quality` is less than 1 or greater than 100. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> import numpy as np >>> # Generate a random image with height=120, width=340, channels=3 >>> image = np.random.randint(256, size=(120, 340, 3), dtype=np.uint8) >>> vision.write_jpeg("/path/to/file", image) """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if not isinstance(quality, int): raise TypeError("Input quality is not of type {0}, but got: {1}.".format(int, type(quality))) if isinstance(image, np.ndarray): return cde.write_jpeg(filename, cde.Tensor(image), quality) if isinstance(image, mindspore.Tensor): return cde.write_jpeg(filename, cde.Tensor(image.asnumpy()), quality) raise TypeError("Input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, mindspore.Tensor, type(image)))
[docs]def write_png(filename, image, compression_level=6): """ Write the image into a PNG file. Args: filename (str): The path to the file to be written. image (Union[numpy.ndarray, mindspore.Tensor]): The image data to be written. compression_level (int, optional): Compression level for the resulting PNG file, in range of [0, 9]. Default: ``6``. Raises: TypeError: If `filename` is not of type str. TypeError: If `image` is not of type numpy.ndarray or mindspore.Tensor. TypeError: If `compression_level` is not of type int. RuntimeError: If the `filename` does not exist or not a common file. RuntimeError: If the data type of `image` is not uint8. RuntimeError: If the shape of `image` is not <H, W> or <H, W, 1> or <H, W, 3>. RuntimeError: If `compression_level` is less than 0 or greater than 9. Supported Platforms: ``CPU`` Examples: >>> import mindspore.dataset.vision as vision >>> import numpy as np >>> # Generate a random image with height=120, width=340, channels=3 >>> image = np.random.randint(256, size=(120, 340, 3), dtype=np.uint8) >>> vision.write_png("/path/to/file", image) """ if not isinstance(filename, str): raise TypeError("Input filename is not of type {0}, but got: {1}.".format(str, type(filename))) if not isinstance(compression_level, int): raise TypeError("Input compression_level is not of type {0}, but got: {1}.".format(int, type(compression_level))) if isinstance(image, np.ndarray): return cde.write_png(filename, cde.Tensor(image), compression_level) if isinstance(image, mindspore.Tensor): return cde.write_png(filename, cde.Tensor(image.asnumpy()), compression_level) raise TypeError("The input image is not of type {0} or {1}, but got: {2}.".format(np.ndarray, mindspore.Tensor, type(image)))