4.16团队开发报告
1.昨天开发遇到的问题
昨天的多人脸识别算法非常不稳定，或者说效果很不好，最多识别两个人，且这两个人距离必须靠的足够远才可以。
2.今天的目标
今天需要对多人脸识别算法进行改进。
3.代码
# -*- coding: utf-8 -*-
# @Time : 20-6-4 下午6:18
# @Author : zhuying
# @Company : Minivision
# @File : functional.py
# @Software : PyCharm
from __future__ import division
import torch
from PIL import Image, ImageOps, ImageEnhance
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
import collections
import warnings
def _is_pil_image(img):
if accimage is not None:
return isinstance(img, (Image.Image, accimage.Image))
else:
return isinstance(img, Image.Image)
def _is_tensor_image(img):
return torch.is_tensor(img) and img.ndimension() == 3
def _is_numpy_image(img):
return isinstance(img, np.ndarray) and (img.ndim in {2, 3})
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not(_is_pil_image(pic) or _is_numpy_image(pic)):
raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
if isinstance(pic, np.ndarray):
# handle numpy array
# IR image channel=1: modify by lzc --> 20190730
if pic.ndim == 2:
pic = pic.reshape((pic.shape[0], pic.shape[1], 1))
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
# return img.float().div(255) modify by zkx
return img.float()
if accimage is not None and isinstance(pic, accimage.Image):
nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
pic.copyto(nppic)
return torch.from_numpy(nppic)
# handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
# put it from HWC to CHW format
# yikes, this transpose takes 80% of the loading time/CPU
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
# return img.float().div(255) #modified by zkx
return img.float()
else:
return img
def to_pil_image(pic, mode=None):
"""Convert a tensor or an ndarray to PIL Image.
See :class:`~torchvision.transforms.ToPIlImage` for more details.
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
.. _PIL.Image mode: http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#modes
Returns:
PIL Image: Image converted to PIL Image.
"""
if not(_is_numpy_image(pic) or _is_tensor_image(pic)):
raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))
npimg = pic
if isinstance(pic, torch.FloatTensor):
pic = pic.mul(255).byte()
if torch.is_tensor(pic):
npimg = np.transpose(pic.numpy(), (1, 2, 0))
if not isinstance(npimg, np.ndarray):
raise TypeError('Input pic must be a torch.Tensor or NumPy ndarray, ' +
'not {}'.format(type(npimg)))
if npimg.shape[2] == 1:
expected_mode = None
npimg = npimg[:, :, 0]
if npimg.dtype == np.uint8:
expected_mode = 'L'
if npimg.dtype == np.int16:
expected_mode = 'I;16'
if npimg.dtype == np.int32:
expected_mode = 'I'
elif npimg.dtype == np.float32:
expected_mode = 'F'
if mode is not None and mode != expected_mode:
raise ValueError("Incorrect mode ({}) supplied for input type {}. Should be {}"
.format(mode, np.dtype, expected_mode))
mode = expected_mode
elif npimg.shape[2] == 4:
permitted_4_channel_modes = ['RGBA', 'CMYK']
if mode is not None and mode not in permitted_4_channel_modes:
raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))
if mode is None and npimg.dtype == np.uint8:
mode = 'RGBA'
else:
permitted_3_channel_modes = ['RGB', 'YCbCr', 'HSV']
if mode is not None and mode not in permitted_3_channel_modes:
raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
if mode is None and npimg.dtype == np.uint8:
mode = 'RGB'
if mode is None:
raise TypeError('Input type {} is not supported'.format(npimg.dtype))
return Image.fromarray(npimg, mode=mode)
def normalize(tensor, mean, std):
"""Normalize a tensor image with mean and standard deviation.
See ``Normalize`` for more details.
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channely.
Returns:
Tensor: Normalized Tensor image.
"""
if not _is_tensor_image(tensor):
raise TypeError('tensor is not a torch image.')
for t, m, s in zip(tensor, mean, std):
t.sub_(m).div_(s)
return tensor
def resize(img, size, interpolation=Image.BILINEAR):
"""Resize the input PIL Image to the given size.
Args:
img (PIL Image): Image to be resized.
size (sequence or int): Desired output size. If size is a sequence like
(h, w), the output size will be matched to this. If size is an int,
the smaller edge of the image will be matched to this number maintaing
the aspect ratio. i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
Returns:
PIL Image: Resized image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
raise TypeError('Got inappropriate size arg: {}'.format(size))
if isinstance(size, int):
w, h = img.size
if (w <= h and w == size) or (h <= w and h == size):
return img
if w < h:
ow = size
oh = int(size * h / w)
return img.resize((ow, oh), interpolation)
else:
oh = size
ow = int(size * w / h)
return img.resize((ow, oh), interpolation)
else:
return img.resize(size[::-1], interpolation)
def scale(*args, **kwargs):
warnings.warn("The use of the transforms.Scale transform is deprecated, " +
"please use transforms.Resize instead.")
return resize(*args, **kwargs)
def pad(img, padding, fill=0):
"""Pad the given PIL Image on all sides with the given "pad" value.
Args:
img (PIL Image): Image to be padded.
padding (int or tuple): Padding on each border. If a single int is provided this
is used to pad all borders. If tuple of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a tuple of length 4 is provided
this is the padding for the left, top, right and bottom borders
respectively.
fill: Pixel fill value. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
Returns:
PIL Image: Padded image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if not isinstance(padding, (numbers.Number, tuple)):
raise TypeError('Got inappropriate padding arg')
if not isinstance(fill, (numbers.Number, str, tuple)):
raise TypeError('Got inappropriate fill arg')
if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]:
raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
"{} element tuple".format(len(padding)))
return ImageOps.expand(img, border=padding, fill=fill)
def crop(img, i, j, h, w):
"""Crop the given PIL Image.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
Returns:
PIL Image: Cropped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.crop((j, i, j + w, i + h))
def center_crop(img, output_size):
if isinstance(output_size, numbers.Number):
output_size = (int(output_size), int(output_size))
w, h = img.size
th, tw = output_size
i = int(round((h - th) / 2.))
j = int(round((w - tw) / 2.))
return crop(img, i, j, th, tw)
def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
"""Crop the given PIL Image and resize it to desired size.
Notably used in RandomResizedCrop.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
size (sequence or int): Desired output size. Same semantics as ``scale``.
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``.
Returns:
PIL Image: Cropped image.
"""
assert _is_pil_image(img), 'img should be PIL Image'
img = crop(img, i, j, h, w)
img = resize(img, size, interpolation)
return img
def hflip(img):
"""Horizontally flip the given PIL Image.
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Horizontall flipped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.transpose(Image.FLIP_LEFT_RIGHT)
def vflip(img):
"""Vertically flip the given PIL Image.
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Vertically flipped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.transpose(Image.FLIP_TOP_BOTTOM)
def five_crop(img, size):
"""Crop the given PIL Image into four corners and the central crop.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center) corresponding top left,
top right, bottom left, bottom right and center crop.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
tl = img.crop((0, 0, crop_w, crop_h))
tr = img.crop((w - crop_w, 0, w, crop_h))
bl = img.crop((0, h - crop_h, crop_w, h))
br = img.crop((w - crop_w, h - crop_h, w, h))
center = center_crop(img, (crop_h, crop_w))
return (tl, tr, bl, br, center)
def ten_crop(img, size, vertical_flip=False):
"""Crop the given PIL Image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
vertical_flip (bool): Use vertical flipping instead of horizontal
Returns:
tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip,
br_flip, center_flip) corresponding top left, top right,
bottom left, bottom right and center crop and same for the
flipped image.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
first_five = five_crop(img, size)
if vertical_flip:
img = vflip(img)
else:
img = hflip(img)
second_five = five_crop(img, size)
return first_five + second_five
def adjust_brightness(img, brightness_factor):
"""Adjust brightness of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
brightness_factor (float): How much to adjust the brightness. Can be
any non negative number. 0 gives a black image, 1 gives the
original image while 2 increases the brightness by a factor of 2.
Returns:
PIL Image: Brightness adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(brightness_factor)
return img
def adjust_contrast(img, contrast_factor):
"""Adjust contrast of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
contrast_factor (float): How much to adjust the contrast. Can be any
non negative number. 0 gives a solid gray image, 1 gives the
original image while 2 increases the contrast by a factor of 2.
Returns:
PIL Image: Contrast adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(contrast_factor)
return img
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (PIL Image): PIL Image to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
PIL Image: Saturation adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
return img
def adjust_hue(img, hue_factor):
"""Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and
cyclically shifting the intensities in the hue channel (H).
The image is then converted back to original image mode.
`hue_factor` is the amount of shift in H channel and must be in the
interval `[-0.5, 0.5]`.
See https://en.wikipedia.org/wiki/Hue for more details on Hue.
Args:
img (PIL Image): PIL Image to be adjusted.
hue_factor (float): How much to shift the hue channel. Should be in
[-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in
HSV space in positive and negative direction respectively.
0 means no shift. Therefore, both -0.5 and 0.5 will give an image
with complementary colors while 0 gives the original image.
Returns:
PIL Image: Hue adjusted image.
"""
if not(-0.5 <= hue_factor <= 0.5):
raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
input_mode = img.mode
if input_mode in {'L', '1', 'I', 'F'}:
return img
h, s, v = img.convert('HSV').split()
np_h = np.array(h, dtype=np.uint8)
# uint8 addition take cares of rotation across boundaries
with np.errstate(over='ignore'):
np_h += np.uint8(hue_factor * 255)
h = Image.fromarray(np_h, 'L')
img = Image.merge('HSV', (h, s, v)).convert(input_mode)
return img
def adjust_gamma(img, gamma, gain=1):
"""Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted
based on the following equation:
I_out = 255 * gain * ((I_in / 255) ** gamma)
See https://en.wikipedia.org/wiki/Gamma_correction for more details.
Args:
img (PIL Image): PIL Image to be adjusted.
gamma (float): Non negative real number. gamma larger than 1 make the
shadows darker, while gamma smaller than 1 make dark regions
lighter.
gain (float): The constant multiplier.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if gamma < 0:
raise ValueError('Gamma should be a non-negative real number')
input_mode = img.mode
img = img.convert('RGB')
np_img = np.array(img, dtype=np.float32)
np_img = 255 * gain * ((np_img / 255) ** gamma)
np_img = np.uint8(np.clip(np_img, 0, 255))
img = Image.fromarray(np_img, 'RGB').convert(input_mode)
return img
def rotate(img, angle, resample=False, expand=False, center=None):
"""Rotate the image by angle and then (optionally) translate it by (n_columns, n_rows)
Args:
img (PIL Image): PIL Image to be rotated.
angle ({float, int}): In degrees degrees counter clockwise order.
resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
An optional resampling filter.
See http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#filters
If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
Note that the expand flag assumes rotation around the center and no translation.
center (2-tuple, optional): Optional center of rotation.
Origin is the upper left corner.
Default is the center of the image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.rotate(angle, resample, expand, center)
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (PIL Image): Image to be converted to grayscale.
Returns:
PIL Image: Grayscale version of the image.
if num_output_channels == 1 : returned image is single channel
if num_output_channels == 3 : returned image is 3 channel with r == g == b
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if num_output_channels == 1:
img = img.convert('L')
elif num_output_channels == 3:
img = img.convert('L')
np_img = np.array(img, dtype=np.uint8)
np_img = np.dstack([np_img, np_img, np_img])
img = Image.fromarray(np_img, 'RGB')
else:
raise ValueError('num_output_channels should be either 1 or 3')
return img
# @Time : 20-6-4 下午6:18
# @Author : zhuying
# @Company : Minivision
# @File : functional.py
# @Software : PyCharm
from __future__ import division
import torch
from PIL import Image, ImageOps, ImageEnhance
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
import collections
import warnings
def _is_pil_image(img):
if accimage is not None:
return isinstance(img, (Image.Image, accimage.Image))
else:
return isinstance(img, Image.Image)
def _is_tensor_image(img):
return torch.is_tensor(img) and img.ndimension() == 3
def _is_numpy_image(img):
return isinstance(img, np.ndarray) and (img.ndim in {2, 3})
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not(_is_pil_image(pic) or _is_numpy_image(pic)):
raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
if isinstance(pic, np.ndarray):
# handle numpy array
# IR image channel=1: modify by lzc --> 20190730
if pic.ndim == 2:
pic = pic.reshape((pic.shape[0], pic.shape[1], 1))
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
# return img.float().div(255) modify by zkx
return img.float()
if accimage is not None and isinstance(pic, accimage.Image):
nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
pic.copyto(nppic)
return torch.from_numpy(nppic)
# handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
# put it from HWC to CHW format
# yikes, this transpose takes 80% of the loading time/CPU
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
# return img.float().div(255) #modified by zkx
return img.float()
else:
return img
def to_pil_image(pic, mode=None):
"""Convert a tensor or an ndarray to PIL Image.
See :class:`~torchvision.transforms.ToPIlImage` for more details.
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).
.. _PIL.Image mode: http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#modes
Returns:
PIL Image: Image converted to PIL Image.
"""
if not(_is_numpy_image(pic) or _is_tensor_image(pic)):
raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))
npimg = pic
if isinstance(pic, torch.FloatTensor):
pic = pic.mul(255).byte()
if torch.is_tensor(pic):
npimg = np.transpose(pic.numpy(), (1, 2, 0))
if not isinstance(npimg, np.ndarray):
raise TypeError('Input pic must be a torch.Tensor or NumPy ndarray, ' +
'not {}'.format(type(npimg)))
if npimg.shape[2] == 1:
expected_mode = None
npimg = npimg[:, :, 0]
if npimg.dtype == np.uint8:
expected_mode = 'L'
if npimg.dtype == np.int16:
expected_mode = 'I;16'
if npimg.dtype == np.int32:
expected_mode = 'I'
elif npimg.dtype == np.float32:
expected_mode = 'F'
if mode is not None and mode != expected_mode:
raise ValueError("Incorrect mode ({}) supplied for input type {}. Should be {}"
.format(mode, np.dtype, expected_mode))
mode = expected_mode
elif npimg.shape[2] == 4:
permitted_4_channel_modes = ['RGBA', 'CMYK']
if mode is not None and mode not in permitted_4_channel_modes:
raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))
if mode is None and npimg.dtype == np.uint8:
mode = 'RGBA'
else:
permitted_3_channel_modes = ['RGB', 'YCbCr', 'HSV']
if mode is not None and mode not in permitted_3_channel_modes:
raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
if mode is None and npimg.dtype == np.uint8:
mode = 'RGB'
if mode is None:
raise TypeError('Input type {} is not supported'.format(npimg.dtype))
return Image.fromarray(npimg, mode=mode)
def normalize(tensor, mean, std):
"""Normalize a tensor image with mean and standard deviation.
See ``Normalize`` for more details.
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channely.
Returns:
Tensor: Normalized Tensor image.
"""
if not _is_tensor_image(tensor):
raise TypeError('tensor is not a torch image.')
for t, m, s in zip(tensor, mean, std):
t.sub_(m).div_(s)
return tensor
def resize(img, size, interpolation=Image.BILINEAR):
"""Resize the input PIL Image to the given size.
Args:
img (PIL Image): Image to be resized.
size (sequence or int): Desired output size. If size is a sequence like
(h, w), the output size will be matched to this. If size is an int,
the smaller edge of the image will be matched to this number maintaing
the aspect ratio. i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
Returns:
PIL Image: Resized image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
raise TypeError('Got inappropriate size arg: {}'.format(size))
if isinstance(size, int):
w, h = img.size
if (w <= h and w == size) or (h <= w and h == size):
return img
if w < h:
ow = size
oh = int(size * h / w)
return img.resize((ow, oh), interpolation)
else:
oh = size
ow = int(size * w / h)
return img.resize((ow, oh), interpolation)
else:
return img.resize(size[::-1], interpolation)
def scale(*args, **kwargs):
warnings.warn("The use of the transforms.Scale transform is deprecated, " +
"please use transforms.Resize instead.")
return resize(*args, **kwargs)
def pad(img, padding, fill=0):
"""Pad the given PIL Image on all sides with the given "pad" value.
Args:
img (PIL Image): Image to be padded.
padding (int or tuple): Padding on each border. If a single int is provided this
is used to pad all borders. If tuple of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a tuple of length 4 is provided
this is the padding for the left, top, right and bottom borders
respectively.
fill: Pixel fill value. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
Returns:
PIL Image: Padded image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if not isinstance(padding, (numbers.Number, tuple)):
raise TypeError('Got inappropriate padding arg')
if not isinstance(fill, (numbers.Number, str, tuple)):
raise TypeError('Got inappropriate fill arg')
if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]:
raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
"{} element tuple".format(len(padding)))
return ImageOps.expand(img, border=padding, fill=fill)
def crop(img, i, j, h, w):
"""Crop the given PIL Image.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
Returns:
PIL Image: Cropped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.crop((j, i, j + w, i + h))
def center_crop(img, output_size):
if isinstance(output_size, numbers.Number):
output_size = (int(output_size), int(output_size))
w, h = img.size
th, tw = output_size
i = int(round((h - th) / 2.))
j = int(round((w - tw) / 2.))
return crop(img, i, j, th, tw)
def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
"""Crop the given PIL Image and resize it to desired size.
Notably used in RandomResizedCrop.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
size (sequence or int): Desired output size. Same semantics as ``scale``.
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``.
Returns:
PIL Image: Cropped image.
"""
assert _is_pil_image(img), 'img should be PIL Image'
img = crop(img, i, j, h, w)
img = resize(img, size, interpolation)
return img
def hflip(img):
"""Horizontally flip the given PIL Image.
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Horizontall flipped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.transpose(Image.FLIP_LEFT_RIGHT)
def vflip(img):
"""Vertically flip the given PIL Image.
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Vertically flipped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.transpose(Image.FLIP_TOP_BOTTOM)
def five_crop(img, size):
"""Crop the given PIL Image into four corners and the central crop.
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
Returns:
tuple: tuple (tl, tr, bl, br, center) corresponding top left,
top right, bottom left, bottom right and center crop.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
w, h = img.size
crop_h, crop_w = size
if crop_w > w or crop_h > h:
raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
(h, w)))
tl = img.crop((0, 0, crop_w, crop_h))
tr = img.crop((w - crop_w, 0, w, crop_h))
bl = img.crop((0, h - crop_h, crop_w, h))
br = img.crop((w - crop_w, h - crop_h, w, h))
center = center_crop(img, (crop_h, crop_w))
return (tl, tr, bl, br, center)
def ten_crop(img, size, vertical_flip=False):
"""Crop the given PIL Image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inputs and targets your ``Dataset`` returns.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
vertical_flip (bool): Use vertical flipping instead of horizontal
Returns:
tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip,
br_flip, center_flip) corresponding top left, top right,
bottom left, bottom right and center crop and same for the
flipped image.
"""
if isinstance(size, numbers.Number):
size = (int(size), int(size))
else:
assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
first_five = five_crop(img, size)
if vertical_flip:
img = vflip(img)
else:
img = hflip(img)
second_five = five_crop(img, size)
return first_five + second_five
def adjust_brightness(img, brightness_factor):
"""Adjust brightness of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
brightness_factor (float): How much to adjust the brightness. Can be
any non negative number. 0 gives a black image, 1 gives the
original image while 2 increases the brightness by a factor of 2.
Returns:
PIL Image: Brightness adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(brightness_factor)
return img
def adjust_contrast(img, contrast_factor):
"""Adjust contrast of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
contrast_factor (float): How much to adjust the contrast. Can be any
non negative number. 0 gives a solid gray image, 1 gives the
original image while 2 increases the contrast by a factor of 2.
Returns:
PIL Image: Contrast adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(contrast_factor)
return img
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (PIL Image): PIL Image to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
PIL Image: Saturation adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
return img
def adjust_hue(img, hue_factor):
"""Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and
cyclically shifting the intensities in the hue channel (H).
The image is then converted back to original image mode.
`hue_factor` is the amount of shift in H channel and must be in the
interval `[-0.5, 0.5]`.
See https://en.wikipedia.org/wiki/Hue for more details on Hue.
Args:
img (PIL Image): PIL Image to be adjusted.
hue_factor (float): How much to shift the hue channel. Should be in
[-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in
HSV space in positive and negative direction respectively.
0 means no shift. Therefore, both -0.5 and 0.5 will give an image
with complementary colors while 0 gives the original image.
Returns:
PIL Image: Hue adjusted image.
"""
if not(-0.5 <= hue_factor <= 0.5):
raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
input_mode = img.mode
if input_mode in {'L', '1', 'I', 'F'}:
return img
h, s, v = img.convert('HSV').split()
np_h = np.array(h, dtype=np.uint8)
# uint8 addition take cares of rotation across boundaries
with np.errstate(over='ignore'):
np_h += np.uint8(hue_factor * 255)
h = Image.fromarray(np_h, 'L')
img = Image.merge('HSV', (h, s, v)).convert(input_mode)
return img
def adjust_gamma(img, gamma, gain=1):
"""Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted
based on the following equation:
I_out = 255 * gain * ((I_in / 255) ** gamma)
See https://en.wikipedia.org/wiki/Gamma_correction for more details.
Args:
img (PIL Image): PIL Image to be adjusted.
gamma (float): Non negative real number. gamma larger than 1 make the
shadows darker, while gamma smaller than 1 make dark regions
lighter.
gain (float): The constant multiplier.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if gamma < 0:
raise ValueError('Gamma should be a non-negative real number')
input_mode = img.mode
img = img.convert('RGB')
np_img = np.array(img, dtype=np.float32)
np_img = 255 * gain * ((np_img / 255) ** gamma)
np_img = np.uint8(np.clip(np_img, 0, 255))
img = Image.fromarray(np_img, 'RGB').convert(input_mode)
return img
def rotate(img, angle, resample=False, expand=False, center=None):
"""Rotate the image by angle and then (optionally) translate it by (n_columns, n_rows)
Args:
img (PIL Image): PIL Image to be rotated.
angle ({float, int}): In degrees degrees counter clockwise order.
resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
An optional resampling filter.
See http://pillow.readthedocs.io/en/3.4.x/handbook/concepts.html#filters
If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
Note that the expand flag assumes rotation around the center and no translation.
center (2-tuple, optional): Optional center of rotation.
Origin is the upper left corner.
Default is the center of the image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.rotate(angle, resample, expand, center)
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (PIL Image): Image to be converted to grayscale.
Returns:
PIL Image: Grayscale version of the image.
if num_output_channels == 1 : returned image is single channel
if num_output_channels == 3 : returned image is 3 channel with r == g == b
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
if num_output_channels == 1:
img = img.convert('L')
elif num_output_channels == 3:
img = img.convert('L')
np_img = np.array(img, dtype=np.uint8)
np_img = np.dstack([np_img, np_img, np_img])
img = Image.fromarray(np_img, 'RGB')
else:
raise ValueError('num_output_channels should be either 1 or 3')
return img
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