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# Copyright 2022 Dakewe Biotech Corporation. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import math
import random
from typing import Any
import cv2
import numpy as np
import torch
from numpy import ndarray
from torch import Tensor
__all__ = [
"image_to_tensor", "tensor_to_image",
"image_resize", "preprocess_one_image",
"expand_y", "rgb_to_ycbcr", "bgr_to_ycbcr", "ycbcr_to_bgr", "ycbcr_to_rgb",
"rgb_to_ycbcr_torch", "bgr_to_ycbcr_torch",
"center_crop", "random_crop", "random_rotate", "random_vertically_flip", "random_horizontally_flip",
]
# Code reference `https://github.com/xinntao/BasicSR/blob/master/basicsr/utils/matlab_functions.py`
def _cubic(x: Any) -> Any:
"""Implementation of `cubic` function in Matlab under Python language.
Args:
x: Element vector.
Returns:
Bicubic interpolation
"""
absx = torch.abs(x)
absx2 = absx ** 2
absx3 = absx ** 3
return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (
((absx > 1) * (absx <= 2)).type_as(absx))
# Code reference `https://github.com/xinntao/BasicSR/blob/master/basicsr/utils/matlab_functions.py`
def _calculate_weights_indices(in_length: int,
out_length: int,
scale: float,
kernel_width: int,
antialiasing: bool) -> [np.ndarray, np.ndarray, int, int]:
"""Implementation of `calculate_weights_indices` function in Matlab under Python language.
Args:
in_length (int): Input length.
out_length (int): Output length.
scale (float): Scale factor.
kernel_width (int): Kernel width.
antialiasing (bool): Whether to apply antialiasing when down-sampling operations.
Caution: Bicubic down-sampling in PIL uses antialiasing by default.
Returns:
weights, indices, sym_len_s, sym_len_e
"""
if (scale < 1) and antialiasing:
# Use a modified kernel (larger kernel width) to simultaneously
# interpolate and antialiasing
kernel_width = kernel_width / scale
# Output-space coordinates
x = torch.linspace(1, out_length, out_length)
# Input-space coordinates. Calculate the inverse mapping such that 0.5
# in output space maps to 0.5 in input space, and 0.5 + scale in output
# space maps to 1.5 in input space.
u = x / scale + 0.5 * (1 - 1 / scale)
# What is the left-most pixel that can be involved in the computation?
left = torch.floor(u - kernel_width / 2)
# What is the maximum number of pixels that can be involved in the
# computation? Note: it's OK to use an extra pixel here; if the
# corresponding weights are all zero, it will be eliminated at the end
# of this function.
p = math.ceil(kernel_width) + 2
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand(
out_length, p)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices
# apply cubic kernel
if (scale < 1) and antialiasing:
weights = scale * _cubic(distance_to_center * scale)
else:
weights = _cubic(distance_to_center)
# Normalize the weights matrix so that each row sums to 1.
weights_sum = torch.sum(weights, 1).view(out_length, 1)
weights = weights / weights_sum.expand(out_length, p)
# If a column in weights is all zero, get rid of it. only consider the
# first and last column.
weights_zero_tmp = torch.sum((weights == 0), 0)
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
indices = indices.narrow(1, 1, p - 2)
weights = weights.narrow(1, 1, p - 2)
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
indices = indices.narrow(1, 0, p - 2)
weights = weights.narrow(1, 0, p - 2)
weights = weights.contiguous()
indices = indices.contiguous()
sym_len_s = -indices.min() + 1
sym_len_e = indices.max() - in_length
indices = indices + sym_len_s - 1
return weights, indices, int(sym_len_s), int(sym_len_e)
def image_to_tensor(image: ndarray, range_norm: bool, half: bool) -> Tensor:
"""Convert the image data type to the Tensor (NCWH) data type supported by PyTorch
Args:
image (np.ndarray): The image data read by ``OpenCV.imread``, the data range is [0,255] or [0, 1]
range_norm (bool): Scale [0, 1] data to between [-1, 1]
half (bool): Whether to convert torch.float32 similarly to torch.half type
Returns:
tensor (Tensor): Data types supported by PyTorch
Examples:
>>> example_image = cv2.imread("lr_image.bmp")
>>> example_tensor = image_to_tensor(example_image, range_norm=True, half=False)
"""
# Convert image data type to Tensor data type
tensor = torch.from_numpy(np.ascontiguousarray(image)).permute(2, 0, 1).float()
# Scale the image data from [0, 1] to [-1, 1]
if range_norm:
tensor = tensor.mul(2.0).sub(1.0)
# Convert torch.float32 image data type to torch.half image data type
if half:
tensor = tensor.half()
return tensor
def tensor_to_image(tensor: Tensor, range_norm: bool, half: bool) -> Any:
"""Convert the Tensor(NCWH) data type supported by PyTorch to the np.ndarray(WHC) image data type
Args:
tensor (Tensor): Data types supported by PyTorch (NCHW), the data range is [0, 1]
range_norm (bool): Scale [-1, 1] data to between [0, 1]
half (bool): Whether to convert torch.float32 similarly to torch.half type.
Returns:
image (np.ndarray): Data types supported by PIL or OpenCV
Examples:
>>> example_image = cv2.imread("lr_image.bmp")
>>> example_tensor = image_to_tensor(example_image, range_norm=False, half=False)
"""
if range_norm:
tensor = tensor.add(1.0).div(2.0)
if half:
tensor = tensor.half()
image = tensor.squeeze(0).permute(1, 2, 0).mul(255).clamp(0, 255).cpu().numpy().astype("uint8")
return image
def preprocess_one_image(image_path: str, device: torch.device) -> Tensor:
image = cv2.imread(image_path).astype(np.float32) / 255.0
# BGR to RGB
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Convert image data to pytorch format data
tensor = image_to_tensor(image, False, False).unsqueeze_(0)
# Transfer tensor channel image format data to CUDA device
tensor = tensor.to(device=device, memory_format=torch.channels_last, non_blocking=True)
return tensor
# Code reference `https://github.com/xinntao/BasicSR/blob/master/basicsr/utils/matlab_functions.py`
def image_resize(image: Any, scale_factor: float, antialiasing: bool = True) -> Any:
"""Implementation of `imresize` function in Matlab under Python language.
Args:
image: The input image.
scale_factor (float): Scale factor. The same scale applies for both height and width.
antialiasing (bool): Whether to apply antialiasing when down-sampling operations.
Caution: Bicubic down-sampling in `PIL` uses antialiasing by default. Default: ``True``.
Returns:
out_2 (np.ndarray): Output image with shape (c, h, w), [0, 1] range, w/o round
"""
squeeze_flag = False
if type(image).__module__ == np.__name__: # numpy type
numpy_type = True
if image.ndim == 2:
image = image[:, :, None]
squeeze_flag = True
image = torch.from_numpy(image.transpose(2, 0, 1)).float()
else:
numpy_type = False
if image.ndim == 2:
image = image.unsqueeze(0)
squeeze_flag = True
in_c, in_h, in_w = image.size()
out_h, out_w = math.ceil(in_h * scale_factor), math.ceil(in_w * scale_factor)
kernel_width = 4
# get weights and indices
weights_h, indices_h, sym_len_hs, sym_len_he = _calculate_weights_indices(in_h, out_h, scale_factor, kernel_width,
antialiasing)
weights_w, indices_w, sym_len_ws, sym_len_we = _calculate_weights_indices(in_w, out_w, scale_factor, kernel_width,
antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
img_aug.narrow(1, sym_len_hs, in_h).copy_(image)
sym_patch = image[:, :sym_len_hs, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)
sym_patch = image[:, -sym_len_he:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(in_c, out_h, in_w)
kernel_width = weights_h.size(1)
for i in range(out_h):
idx = int(indices_h[i][0])
for j in range(in_c):
out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)
sym_patch = out_1[:, :, :sym_len_ws]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)
sym_patch = out_1[:, :, -sym_len_we:]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(in_c, out_h, out_w)
kernel_width = weights_w.size(1)
for i in range(out_w):
idx = int(indices_w[i][0])
for j in range(in_c):
out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i])
if squeeze_flag:
out_2 = out_2.squeeze(0)
if numpy_type:
out_2 = out_2.numpy()
if not squeeze_flag:
out_2 = out_2.transpose(1, 2, 0)
return out_2
def expand_y(image: np.ndarray) -> np.ndarray:
"""Convert BGR channel to YCbCr format,
and expand Y channel data in YCbCr, from HW to HWC
Args:
image (np.ndarray): Y channel image data
Returns:
y_image (np.ndarray): Y-channel image data in HWC form
"""
# Normalize image data to [0, 1]
image = image.astype(np.float32) / 255.
# Convert BGR to YCbCr, and extract only Y channel
y_image = bgr_to_ycbcr(image, only_use_y_channel=True)
# Expand Y channel
y_image = y_image[..., None]
# Normalize the image data to [0, 255]
y_image = y_image.astype(np.float64) * 255.0
return y_image
def rgb_to_ycbcr(image: np.ndarray, only_use_y_channel: bool) -> np.ndarray:
"""Implementation of rgb2ycbcr function in Matlab under Python language
Args:
image (np.ndarray): Image input in RGB format.
only_use_y_channel (bool): Extract Y channel separately
Returns:
image (np.ndarray): YCbCr image array data
"""
if only_use_y_channel:
image = np.dot(image, [65.481, 128.553, 24.966]) + 16.0
else:
image = np.matmul(image, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [
16, 128, 128]
image /= 255.
image = image.astype(np.float32)
return image
def bgr_to_ycbcr(image: np.ndarray, only_use_y_channel: bool) -> np.ndarray:
"""Implementation of bgr2ycbcr function in Matlab under Python language.
Args:
image (np.ndarray): Image input in BGR format
only_use_y_channel (bool): Extract Y channel separately
Returns:
image (np.ndarray): YCbCr image array data
"""
if only_use_y_channel:
image = np.dot(image, [24.966, 128.553, 65.481]) + 16.0
else:
image = np.matmul(image, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [
16, 128, 128]
image /= 255.
image = image.astype(np.float32)
return image
def ycbcr_to_rgb(image: np.ndarray) -> np.ndarray:
"""Implementation of ycbcr2rgb function in Matlab under Python language.
Args:
image (np.ndarray): Image input in YCbCr format.
Returns:
image (np.ndarray): RGB image array data
"""
image_dtype = image.dtype
image *= 255.
image = np.matmul(image, [[0.00456621, 0.00456621, 0.00456621],
[0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
image /= 255.
image = image.astype(image_dtype)
return image
def ycbcr_to_bgr(image: np.ndarray) -> np.ndarray:
"""Implementation of ycbcr2bgr function in Matlab under Python language.
Args:
image (np.ndarray): Image input in YCbCr format.
Returns:
image (np.ndarray): BGR image array data
"""
image_dtype = image.dtype
image *= 255.
image = np.matmul(image, [[0.00456621, 0.00456621, 0.00456621],
[0.00791071, -0.00153632, 0],
[0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921]
image /= 255.
image = image.astype(image_dtype)
return image
def rgb_to_ycbcr_torch(tensor: Tensor, only_use_y_channel: bool) -> Tensor:
"""Implementation of rgb2ycbcr function in Matlab under PyTorch
References from：`https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion`
Args:
tensor (Tensor): Image data in PyTorch format
only_use_y_channel (bool): Extract only Y channel
Returns:
tensor (Tensor): YCbCr image data in PyTorch format
"""
if only_use_y_channel:
weight = Tensor([[65.481], [128.553], [24.966]]).to(tensor)
tensor = torch.matmul(tensor.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0
else:
weight = Tensor([[65.481, -37.797, 112.0],
[128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]).to(tensor)
bias = Tensor([16, 128, 128]).view(1, 3, 1, 1).to(tensor)
tensor = torch.matmul(tensor.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias
tensor /= 255.
return tensor
def bgr_to_ycbcr_torch(tensor: Tensor, only_use_y_channel: bool) -> Tensor:
"""Implementation of bgr2ycbcr function in Matlab under PyTorch
References from：`https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion`
Args:
tensor (Tensor): Image data in PyTorch format
only_use_y_channel (bool): Extract only Y channel
Returns:
tensor (Tensor): YCbCr image data in PyTorch format
"""
if only_use_y_channel:
weight = Tensor([[24.966], [128.553], [65.481]]).to(tensor)
tensor = torch.matmul(tensor.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0
else:
weight = Tensor([[24.966, 112.0, -18.214],
[128.553, -74.203, -93.786],
[65.481, -37.797, 112.0]]).to(tensor)
bias = Tensor([16, 128, 128]).view(1, 3, 1, 1).to(tensor)
tensor = torch.matmul(tensor.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias
tensor /= 255.
return tensor
def center_crop(image: np.ndarray, image_size: int) -> np.ndarray:
"""Crop small image patches from one image center area.
Args:
image (np.ndarray): The input image for `OpenCV.imread`.
image_size (int): The size of the captured image area.
Returns:
patch_image (np.ndarray): Small patch image
"""
image_height, image_width = image.shape[:2]
# Just need to find the top and left coordinates of the image
top = (image_height - image_size) // 2
left = (image_width - image_size) // 2
# Crop image patch
patch_image = image[top:top + image_size, left:left + image_size, ...]
return patch_image
def random_crop(image: np.ndarray, image_size: int) -> np.ndarray:
"""Crop small image patches from one image.
Args:
image (np.ndarray): The input image for `OpenCV.imread`.
image_size (int): The size of the captured image area.
Returns:
patch_image (np.ndarray): Small patch image
"""
image_height, image_width = image.shape[:2]
# Just need to find the top and left coordinates of the image
top = random.randint(0, image_height - image_size)
left = random.randint(0, image_width - image_size)
# Crop image patch
patch_image = image[top:top + image_size, left:left + image_size, ...]
return patch_image
def random_rotate(image,
angles: list,
center: tuple[int, int] = None,
scale_factor: float = 1.0) -> np.ndarray:
"""Rotate an image by a random angle
Args:
image (np.ndarray): Image read with OpenCV
angles (list): Rotation angle range
center (optional, tuple[int, int]): High resolution image selection center point. Default: ``None``
scale_factor (optional, float): scaling factor. Default: 1.0
Returns:
rotated_image (np.ndarray): image after rotation
"""
image_height, image_width = image.shape[:2]
if center is None:
center = (image_width // 2, image_height // 2)
# Random select specific angle
angle = random.choice(angles)
matrix = cv2.getRotationMatrix2D(center, angle, scale_factor)
rotated_image = cv2.warpAffine(image, matrix, (image_width, image_height))
return rotated_image
def random_horizontally_flip(image: np.ndarray, p: float = 0.5) -> np.ndarray:
"""Flip the image upside down randomly
Args:
image (np.ndarray): Image read with OpenCV
p (optional, float): Horizontally flip probability. Default: 0.5
Returns:
horizontally_flip_image (np.ndarray): image after horizontally flip
"""
if random.random() < p:
horizontally_flip_image = cv2.flip(image, 1)
else:
horizontally_flip_image = image
return horizontally_flip_image
def random_vertically_flip(image: np.ndarray, p: float = 0.5) -> np.ndarray:
"""Flip an image horizontally randomly
Args:
image (np.ndarray): Image read with OpenCV
p (optional, float): Vertically flip probability. Default: 0.5
Returns:
vertically_flip_image (np.ndarray): image after vertically flip
"""
if random.random() < p:
vertically_flip_image = cv2.flip(image, 0)
else:
vertically_flip_image = image
return vertically_flip_image
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