# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np
from ..utils import is_tuple_of
from .colorspace import bgr2gray, gray2bgr
def imnormalize(img, mean, std, to_rgb=True):
"""Normalize an image with mean and std.
Args:
img (ndarray): Image to be normalized.
mean (ndarray): The mean to be used for normalize.
std (ndarray): The std to be used for normalize.
to_rgb (bool): Whether to convert to rgb.
Returns:
ndarray: The normalized image.
"""
img = img.copy().astype(np.float32)
return imnormalize_(img, mean, std, to_rgb)
def imnormalize_(img, mean, std, to_rgb=True):
"""Inplace normalize an image with mean and std.
Args:
img (ndarray): Image to be normalized.
mean (ndarray): The mean to be used for normalize.
std (ndarray): The std to be used for normalize.
to_rgb (bool): Whether to convert to rgb.
Returns:
ndarray: The normalized image.
"""
# cv2 inplace normalization does not accept uint8
assert img.dtype != np.uint8
mean = np.float64(mean.reshape(1, -1))
stdinv = 1 / np.float64(std.reshape(1, -1))
if to_rgb:
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
cv2.subtract(img, mean, img) # inplace
cv2.multiply(img, stdinv, img) # inplace
return img
def imdenormalize(img, mean, std, to_bgr=True):
assert img.dtype != np.uint8
mean = mean.reshape(1, -1).astype(np.float64)
std = std.reshape(1, -1).astype(np.float64)
img = cv2.multiply(img, std) # make a copy
cv2.add(img, mean, img) # inplace
if to_bgr:
cv2.cvtColor(img, cv2.COLOR_RGB2BGR, img) # inplace
return img
def iminvert(img):
"""Invert (negate) an image.
Args:
img (ndarray): Image to be inverted.
Returns:
ndarray: The inverted image.
"""
return np.full_like(img, 255) - img
def solarize(img, thr=128):
"""Solarize an image (invert all pixel values above a threshold)
Args:
img (ndarray): Image to be solarized.
thr (int): Threshold for solarizing (0 - 255).
Returns:
ndarray: The solarized image.
"""
img = np.where(img < thr, img, 255 - img)
return img
def posterize(img, bits):
"""Posterize an image (reduce the number of bits for each color channel)
Args:
img (ndarray): Image to be posterized.
bits (int): Number of bits (1 to 8) to use for posterizing.
Returns:
ndarray: The posterized image.
"""
shift = 8 - bits
img = np.left_shift(np.right_shift(img, shift), shift)
return img
def adjust_color(img, alpha=1, beta=None, gamma=0):
r"""It blends the source image and its gray image:
.. math::
output = img * alpha + gray\_img * beta + gamma
Args:
img (ndarray): The input source image.
alpha (int | float): Weight for the source image. Default 1.
beta (int | float): Weight for the converted gray image.
If None, it's assigned the value (1 - `alpha`).
gamma (int | float): Scalar added to each sum.
Same as :func:`cv2.addWeighted`. Default 0.
Returns:
ndarray: Colored image which has the same size and dtype as input.
"""
gray_img = bgr2gray(img)
gray_img = np.tile(gray_img[..., None], [1, 1, 3])
if beta is None:
beta = 1 - alpha
colored_img = cv2.addWeighted(img, alpha, gray_img, beta, gamma)
if not colored_img.dtype == np.uint8:
# Note when the dtype of `img` is not the default `np.uint8`
# (e.g. np.float32), the value in `colored_img` got from cv2
# is not guaranteed to be in range [0, 255], so here clip
# is needed.
colored_img = np.clip(colored_img, 0, 255)
return colored_img
def imequalize(img):
"""Equalize the image histogram.
This function applies a non-linear mapping to the input image,
in order to create a uniform distribution of grayscale values
in the output image.
Args:
img (ndarray): Image to be equalized.
Returns:
ndarray: The equalized image.
"""
def _scale_channel(im, c):
"""Scale the data in the corresponding channel."""
im = im[:, :, c]
# Compute the histogram of the image channel.
histo = np.histogram(im, 256, (0, 255))[0]
# For computing the step, filter out the nonzeros.
nonzero_histo = histo[histo > 0]
step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255
if not step:
lut = np.array(range(256))
else:
# Compute the cumulative sum, shifted by step // 2
# and then normalized by step.
lut = (np.cumsum(histo) + (step // 2)) // step
# Shift lut, prepending with 0.
lut = np.concatenate([[0], lut[:-1]], 0)
# handle potential integer overflow
lut[lut > 255] = 255
# If step is zero, return the original image.
# Otherwise, index from lut.
return np.where(np.equal(step, 0), im, lut[im])
# Scales each channel independently and then stacks
# the result.
s1 = _scale_channel(img, 0)
s2 = _scale_channel(img, 1)
s3 = _scale_channel(img, 2)
equalized_img = np.stack([s1, s2, s3], axis=-1)
return equalized_img.astype(img.dtype)
def adjust_brightness(img, factor=1.):
"""Adjust image brightness.
This function controls the brightness of an image. An
enhancement factor of 0.0 gives a black image.
A factor of 1.0 gives the original image. This function
blends the source image and the degenerated black image:
.. math::
output = img * factor + degenerated * (1 - factor)
Args:
img (ndarray): Image to be brightened.
factor (float): A value controls the enhancement.
Factor 1.0 returns the original image, lower
factors mean less color (brightness, contrast,
etc), and higher values more. Default 1.
Returns:
ndarray: The brightened image.
"""
degenerated = np.zeros_like(img)
# Note manually convert the dtype to np.float32, to
# achieve as close results as PIL.ImageEnhance.Brightness.
# Set beta=1-factor, and gamma=0
brightened_img = cv2.addWeighted(
img.astype(np.float32), factor, degenerated.astype(np.float32),
1 - factor, 0)
brightened_img = np.clip(brightened_img, 0, 255)
return brightened_img.astype(img.dtype)
def adjust_contrast(img, factor=1.):
"""Adjust image contrast.
This function controls the contrast of an image. An
enhancement factor of 0.0 gives a solid grey
image. A factor of 1.0 gives the original image. It
blends the source image and the degenerated mean image:
.. math::
output = img * factor + degenerated * (1 - factor)
Args:
img (ndarray): Image to be contrasted. BGR order.
factor (float): Same as :func:`mmcv.adjust_brightness`.
Returns:
ndarray: The contrasted image.
"""
gray_img = bgr2gray(img)
hist = np.histogram(gray_img, 256, (0, 255))[0]
mean = round(np.sum(gray_img) / np.sum(hist))
degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype)
degenerated = gray2bgr(degenerated)
contrasted_img = cv2.addWeighted(
img.astype(np.float32), factor, degenerated.astype(np.float32),
1 - factor, 0)
contrasted_img = np.clip(contrasted_img, 0, 255)
return contrasted_img.astype(img.dtype)
def auto_contrast(img, cutoff=0):
"""Auto adjust image contrast.
This function maximize (normalize) image contrast by first removing cutoff
percent of the lightest and darkest pixels from the histogram and remapping
the image so that the darkest pixel becomes black (0), and the lightest
becomes white (255).
Args:
img (ndarray): Image to be contrasted. BGR order.
cutoff (int | float | tuple): The cutoff percent of the lightest and
darkest pixels to be removed. If given as tuple, it shall be
(low, high). Otherwise, the single value will be used for both.
Defaults to 0.
Returns:
ndarray: The contrasted image.
"""
def _auto_contrast_channel(im, c, cutoff):
im = im[:, :, c]
# Compute the histogram of the image channel.
histo = np.histogram(im, 256, (0, 255))[0]
# Remove cut-off percent pixels from histo
histo_sum = np.cumsum(histo)
cut_low = histo_sum[-1] * cutoff[0] // 100
cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100
histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low
histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0)
# Compute mapping
low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1]
# If all the values have been cut off, return the origin img
if low >= high:
return im
scale = 255.0 / (high - low)
offset = -low * scale
lut = np.array(range(256))
lut = lut * scale + offset
lut = np.clip(lut, 0, 255)
return lut[im]
if isinstance(cutoff, (int, float)):
cutoff = (cutoff, cutoff)
else:
assert isinstance(cutoff, tuple), 'cutoff must be of type int, ' \
f'float or tuple, but got {type(cutoff)} instead.'
# Auto adjusts contrast for each channel independently and then stacks
# the result.
s1 = _auto_contrast_channel(img, 0, cutoff)
s2 = _auto_contrast_channel(img, 1, cutoff)
s3 = _auto_contrast_channel(img, 2, cutoff)
contrasted_img = np.stack([s1, s2, s3], axis=-1)
return contrasted_img.astype(img.dtype)
def adjust_sharpness(img, factor=1., kernel=None):
"""Adjust image sharpness.
This function controls the sharpness of an image. An
enhancement factor of 0.0 gives a blurred image. A
factor of 1.0 gives the original image. And a factor
of 2.0 gives a sharpened image. It blends the source
image and the degenerated mean image:
.. math::
output = img * factor + degenerated * (1 - factor)
Args:
img (ndarray): Image to be sharpened. BGR order.
factor (float): Same as :func:`mmcv.adjust_brightness`.
kernel (np.ndarray, optional): Filter kernel to be applied on the img
to obtain the degenerated img. Defaults to None.
Note:
No value sanity check is enforced on the kernel set by users. So with
an inappropriate kernel, the ``adjust_sharpness`` may fail to perform
the function its name indicates but end up performing whatever
transform determined by the kernel.
Returns:
ndarray: The sharpened image.
"""
if kernel is None:
# adopted from PIL.ImageFilter.SMOOTH
kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13
assert isinstance(kernel, np.ndarray), \
f'kernel must be of type np.ndarray, but got {type(kernel)} instead.'
assert kernel.ndim == 2, \
f'kernel must have a dimension of 2, but got {kernel.ndim} instead.'
degenerated = cv2.filter2D(img, -1, kernel)
sharpened_img = cv2.addWeighted(
img.astype(np.float32), factor, degenerated.astype(np.float32),
1 - factor, 0)
sharpened_img = np.clip(sharpened_img, 0, 255)
return sharpened_img.astype(img.dtype)
def adjust_lighting(img, eigval, eigvec, alphastd=0.1, to_rgb=True):
"""AlexNet-style PCA jitter.
This data augmentation is proposed in `ImageNet Classification with Deep
Convolutional Neural Networks
<https://dl.acm.org/doi/pdf/10.1145/3065386>`_.
Args:
img (ndarray): Image to be adjusted lighting. BGR order.
eigval (ndarray): the eigenvalue of the convariance matrix of pixel
values, respectively.
eigvec (ndarray): the eigenvector of the convariance matrix of pixel
values, respectively.
alphastd (float): The standard deviation for distribution of alpha.
Defaults to 0.1
to_rgb (bool): Whether to convert img to rgb.
Returns:
ndarray: The adjusted image.
"""
assert isinstance(eigval, np.ndarray) and isinstance(eigvec, np.ndarray), \
f'eigval and eigvec should both be of type np.ndarray, got ' \
f'{type(eigval)} and {type(eigvec)} instead.'
assert eigval.ndim == 1 and eigvec.ndim == 2
assert eigvec.shape == (3, eigval.shape[0])
n_eigval = eigval.shape[0]
assert isinstance(alphastd, float), 'alphastd should be of type float, ' \
f'got {type(alphastd)} instead.'
img = img.copy().astype(np.float32)
if to_rgb:
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
alpha = np.random.normal(0, alphastd, n_eigval)
alter = eigvec \
* np.broadcast_to(alpha.reshape(1, n_eigval), (3, n_eigval)) \
* np.broadcast_to(eigval.reshape(1, n_eigval), (3, n_eigval))
alter = np.broadcast_to(alter.sum(axis=1).reshape(1, 1, 3), img.shape)
img_adjusted = img + alter
return img_adjusted
def lut_transform(img, lut_table):
"""Transform array by look-up table.
The function lut_transform fills the output array with values from the
look-up table. Indices of the entries are taken from the input array.
Args:
img (ndarray): Image to be transformed.
lut_table (ndarray): look-up table of 256 elements; in case of
multi-channel input array, the table should either have a single
channel (in this case the same table is used for all channels) or
the same number of channels as in the input array.
Returns:
ndarray: The transformed image.
"""
assert isinstance(img, np.ndarray)
assert 0 <= np.min(img) and np.max(img) <= 255
assert isinstance(lut_table, np.ndarray)
assert lut_table.shape == (256, )
return cv2.LUT(np.array(img, dtype=np.uint8), lut_table)
def clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
"""Use CLAHE method to process the image.
See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
Graphics Gems, 1994:474-485.` for more information.
Args:
img (ndarray): Image to be processed.
clip_limit (float): Threshold for contrast limiting. Default: 40.0.
tile_grid_size (tuple[int]): Size of grid for histogram equalization.
Input image will be divided into equally sized rectangular tiles.
It defines the number of tiles in row and column. Default: (8, 8).
Returns:
ndarray: The processed image.
"""
assert isinstance(img, np.ndarray)
assert img.ndim == 2
assert isinstance(clip_limit, (float, int))
assert is_tuple_of(tile_grid_size, int)
assert len(tile_grid_size) == 2
clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
return clahe.apply(np.array(img, dtype=np.uint8))