# 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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"""
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 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)))