"""
This file is obtained and modified from:
- https://github.com/jbohnslav/opencv_transforms
- https://github.com/YU-Zhiyang/opencv_transforms_torchvision
"""
import os
thread = "1"
os.environ["MKL_NUM_THREADS"] = thread
os.environ["NUMEXPR_NUM_THREADS"] = thread
os.environ["OMP_NUM_THREADS"] = thread
os.environ["VECLIB_MAXIMUM_THREADS"] = thread
os.environ["OPENBLAS_NUM_THREADS"] = thread
import cv2
cv2.setNumThreads(0)
import torch
import math
import random
from PIL import Image, ImageOps, ImageEnhance, PILLOW_VERSION
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
import collections
import warnings
_cv2_pad_to_str = {'constant':cv2.BORDER_CONSTANT,
'edge':cv2.BORDER_REPLICATE,
'reflect':cv2.BORDER_REFLECT_101,
'symmetric':cv2.BORDER_REFLECT
}
_cv2_interpolation_to_str= {'nearest':cv2.INTER_NEAREST,
'bilinear':cv2.INTER_LINEAR,
'area':cv2.INTER_AREA,
'bicubic':cv2.INTER_CUBIC,
'lanczos':cv2.INTER_LANCZOS4}
_cv2_interpolation_from_str= {v:k for k,v in _cv2_interpolation_to_str.items()}
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_numpy_image(pic)):
raise TypeError('pic should be ndarray. Got {}'.format(type(pic)))
# handle numpy array
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
if isinstance(img, torch.ByteTensor) or img.dtype==torch.uint8:
return img.float()
else:
return img
def normalize(tensor, mean, std):
"""Normalize a tensor image with mean and standard deviation.
.. note::
This transform acts in-place, i.e., it mutates the input tensor.
See :class:`~torchvision.transforms.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.')
# This is faster than using broadcasting, don't change without benchmarking
for t, m, s in zip(tensor, mean, std):
t.sub_(m).div_(s)
return tensor
def resize(img, size, interpolation=cv2.INTER_LINEAR):
r"""Resize the input numpy ndarray to the given size.
Args:
img (numpy ndarray): Image to be resized.
size (sequence or int): Desired output size. If size is a sequence like
(w, h), 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
:math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`
interpolation (int, optional): Desired interpolation. Default is
``cv2.INTER_LINEAR``
Returns:
PIL Image: Resized image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy 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))
w, h, = img.shape[1], img.shape[0]
if isinstance(size, int):
if (w <= h and w == size) or (h <= w and h == size):
return img
if w < h:
ow = size
oh = int(size * h / w)
output = cv2.resize(img, dsize=(ow, oh), interpolation=interpolation)
else:
oh = size
ow = int(size * w / h)
output = cv2.resize(img, dsize=(ow, oh), interpolation=interpolation)
else:
output = cv2.resize(img, dsize=size[::-1], interpolation=interpolation)
if img.shape[2]==1:
return(output[:,:,np.newaxis])
else:
return(output)
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, padding_mode='constant'):
r"""Pad the given numpy ndarray on all sides with specified padding mode and fill value.
Args:
img (numpy ndarray): 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 for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant
padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
- constant: pads with a constant value, this value is specified with fill
- edge: pads with the last value on the edge of the image
- reflect: pads with reflection of image (without repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image (repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]
Returns:
Numpy image: padded image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy ndarray. 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 not isinstance(padding_mode, str):
raise TypeError('Got inappropriate padding_mode 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)))
assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'], \
'Padding mode should be either constant, edge, reflect or symmetric'
if isinstance(padding, int):
pad_left = pad_right = pad_top = pad_bottom = padding
if isinstance(padding, collections.Sequence) and len(padding) == 2:
pad_left = pad_right = padding[0]
pad_top = pad_bottom = padding[1]
if isinstance(padding, collections.Sequence) and len(padding) == 4:
pad_left = padding[0]
pad_top = padding[1]
pad_right = padding[2]
pad_bottom = padding[3]
if img.shape[2]==1:
return(cv2.copyMakeBorder(img, top=pad_top, bottom=pad_bottom, left=pad_left, right=pad_right,
borderType=_cv2_pad_to_str[padding_mode], value=fill)[:,:,np.newaxis])
else:
return(cv2.copyMakeBorder(img, top=pad_top, bottom=pad_bottom, left=pad_left, right=pad_right,
borderType=_cv2_pad_to_str[padding_mode], value=fill))
def crop(img, i, j, h, w):
"""Crop the given PIL Image.
Args:
img (numpy ndarray): 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:
numpy ndarray: Cropped image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy image. Got {}'.format(type(img)))
return img[i:i+h, j:j+w, :]
def center_crop(img, output_size):
if isinstance(output_size, numbers.Number):
output_size = (int(output_size), int(output_size))
h,w = img.shape[0:2]
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=cv2.INTER_LINEAR):
"""Crop the given numpy ndarray and resize it to desired size.
Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
Args:
img (numpy ndarray): 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
``cv2.INTER_LINEAR``.
Returns:
PIL Image: Cropped image.
"""
assert _is_numpy_image(img), 'img should be numpy image'
img = crop(img, i, j, h, w)
img = resize(img, size, interpolation=interpolation)
return img
def hflip(img):
"""Horizontally flip the given numpy ndarray.
Args:
img (numpy ndarray): image to be flipped.
Returns:
numpy ndarray: Horizontally flipped image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy image. Got {}'.format(type(img)))
# img[:,::-1] is much faster, but doesn't work with torch.from_numpy()!
if img.shape[2]==1:
return cv2.flip(img,1)[:,:,np.newaxis]
else:
return cv2.flip(img, 1)
def vflip(img):
"""Vertically flip the given numpy ndarray.
Args:
img (numpy ndarray): Image to be flipped.
Returns:
numpy ndarray: Vertically flipped image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
if img.shape[2]==1:
return cv2.flip(img, 0)[:,:,np.newaxis]
else:
return cv2.flip(img, 0)
# img[::-1] is much faster, but doesn't work with torch.from_numpy()!
def five_crop(img, size):
"""Crop the given numpy ndarray 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.shape[0:2]
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 = crop(img, 0, 0, crop_h, crop_w)
tr = crop(img, w - crop_w, 0, crop_h, w)
bl = crop(img, 0, h - crop_h, crop_w, h)
br = crop(img, w - crop_w, h - crop_h, h,w)
center = center_crop(img, (crop_h, crop_w))
return (tl, tr, bl, br, center)
def ten_crop(img, size, vertical_flip=False):
r"""Crop the given numpy ndarray 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 (numpy ndarray): numpy ndarray 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:
numpy ndarray: Brightness adjusted image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
table = np.array([ i*brightness_factor for i in range (0,256)]).clip(0,255).astype('uint8')
# same thing but a bit slower
# cv2.convertScaleAbs(img, alpha=brightness_factor, beta=0)
if img.shape[2] == 1:
return cv2.LUT(img, table)[:,:,np.newaxis]
else:
return cv2.LUT(img, table)
def adjust_contrast(img, contrast_factor):
"""Adjust contrast of an mage.
Args:
img (numpy ndarray): numpy ndarray 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:
numpy ndarray: Contrast adjusted image.
"""
# much faster to use the LUT construction than anything else I've tried
# it's because you have to change dtypes multiple times
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
table = np.array([ (i-74)*contrast_factor+74 for i in range (0,256)]).clip(0,255).astype('uint8')
# enhancer = ImageEnhance.Contrast(img)
# img = enhancer.enhance(contrast_factor)
if img.shape[2] == 1:
return cv2.LUT(img, table)[:,:,np.newaxis]
else:
return cv2.LUT(img, table)
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (numpy ndarray): numpy ndarray 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:
numpy ndarray: Saturation adjusted image.
"""
# ~10ms slower than PIL!
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
img = Image.fromarray(img)
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
return np.array(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 `Hue`_ for more details.
.. _Hue: https://en.wikipedia.org/wiki/Hue
Args:
img (numpy ndarray): numpy ndarray 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:
numpy ndarray: Hue adjusted image.
"""
# After testing, found that OpenCV calculates the Hue in a call to
# cv2.cvtColor(..., cv2.COLOR_BGR2HSV) differently from PIL
# This function takes 160ms! should be avoided
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_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
img = Image.fromarray(img)
input_mode = img.mode
if input_mode in {'L', '1', 'I', 'F'}:
return np.array(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 np.array(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:
.. math::
I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}
See `Gamma Correction`_ for more details.
.. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction
Args:
img (numpy ndarray): numpy ndarray to be adjusted.
gamma (float): Non negative real number, same as :math:`\gamma` in the equation.
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_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
if gamma < 0:
raise ValueError('Gamma should be a non-negative real number')
# from here
# https://stackoverflow.com/questions/33322488/how-to-change-image-illumination-in-opencv-python/41061351
table = np.array([((i / 255.0) ** gamma) * 255 * gain
for i in np.arange(0, 256)]).astype('uint8')
if img.shape[2]==1:
return cv2.LUT(img, table)[:,:,np.newaxis]
else:
return cv2.LUT(img, table)
def rotate(img, angle, resample=False, expand=False, center=None):
"""Rotate the image by angle.
Args:
img (numpy ndarray): numpy ndarray to be rotated.
angle (float or int): In degrees degrees counter clockwise order.
resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
An optional resampling filter. See `filters`_ for more information.
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.
.. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
rows,cols = img.shape[0:2]
if center is None:
center = (cols/2, rows/2)
M = cv2.getRotationMatrix2D(center,angle,1)
if img.shape[2]==1:
return cv2.warpAffine(img,M,(cols,rows))[:,:,np.newaxis]
else:
return cv2.warpAffine(img,M,(cols,rows))
def _get_affine_matrix(center, angle, translate, scale, shear):
# Helper method to compute matrix for affine transformation
# We need compute affine transformation matrix: M = T * C * RSS * C^-1
# where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1]
# C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1]
# RSS is rotation with scale and shear matrix
# RSS(a, scale, shear) = [ cos(a)*scale -sin(a + shear)*scale 0]
# [ sin(a)*scale cos(a + shear)*scale 0]
# [ 0 0 1]
angle = math.radians(angle)
shear = math.radians(shear)
# scale = 1.0 / scale
T = np.array([[1, 0, translate[0]], [0, 1, translate[1]], [0,0,1]])
C = np.array([[1, 0, center[0]], [0, 1, center[1]], [0,0,1]])
RSS = np.array([[math.cos(angle)*scale, -math.sin(angle+shear)*scale, 0],
[math.sin(angle)*scale, math.cos(angle+shear)*scale, 0],
[0,0,1]])
matrix = T @ C @ RSS @ np.linalg.inv(C)
return matrix[:2,:]
def affine(img, angle, translate, scale, shear, interpolation=cv2.INTER_LINEAR, mode=cv2.BORDER_CONSTANT, fillcolor=0):
"""Apply affine transformation on the image keeping image center invariant
Args:
img (numpy ndarray): numpy ndarray to be transformed.
angle (float or int): rotation angle in degrees between -180 and 180, clockwise direction.
translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
scale (float): overall scale
shear (float): shear angle value in degrees between -180 to 180, clockwise direction.
interpolation (``cv2.INTER_NEAREST` or ``cv2.INTER_LINEAR`` or ``cv2.INTER_AREA``, ``cv2.INTER_CUBIC``):
An optional resampling filter.
See `filters`_ for more information.
If omitted, it is set to ``cv2.INTER_LINEAR``, for bilinear interpolation.
mode (``cv2.BORDER_CONSTANT`` or ``cv2.BORDER_REPLICATE`` or ``cv2.BORDER_REFLECT`` or ``cv2.BORDER_REFLECT_101``)
Method for filling in border regions.
Defaults to cv2.BORDER_CONSTANT, meaning areas outside the image are filled with a value (val, default 0)
val (int): Optional fill color for the area outside the transform in the output image. Default: 0
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"Argument translate should be a list or tuple of length 2"
assert scale > 0.0, "Argument scale should be positive"
output_size = img.shape[0:2]
center = (img.shape[1] * 0.5 + 0.5, img.shape[0] * 0.5 + 0.5)
matrix = _get_affine_matrix(center, angle, translate, scale, shear)
if img.shape[2]==1:
return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor)[:,:,np.newaxis]
else:
return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor)
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (numpy ndarray): Image to be converted to grayscale.
Returns:
numpy ndarray: 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_numpy_image(img):
raise TypeError('img should be numpy ndarray. Got {}'.format(type(img)))
if num_output_channels==1:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis]
elif num_output_channels==3:
# much faster than doing cvtColor to go back to gray
img = np.broadcast_to(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis], img.shape)
return img
def perspective(img, fov=45, anglex=0, angley=0, anglez=0, shear=0,
translate=(0, 0), scale=(1, 1), resample='bilinear', fillcolor=(0, 0, 0)):
"""
This function is partly referred to https://blog.csdn.net/dcrmg/article/details/80273818
"""
imgtype = img.dtype
gray_scale = False
if len(img.shape) == 2:
gray_scale = True
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
h, w, _ = img.shape
centery = h * 0.5
centerx = w * 0.5
alpha = math.radians(shear)
beta = math.radians(anglez)
lambda1 = scale[0]
lambda2 = scale[1]
tx = translate[0]
ty = translate[1]
sina = math.sin(alpha)
cosa = math.cos(alpha)
sinb = math.sin(beta)
cosb = math.cos(beta)
M00 = cosb * (lambda1 * cosa ** 2 + lambda2 * sina ** 2) - sinb * (lambda2 - lambda1) * sina * cosa
M01 = - sinb * (lambda1 * sina ** 2 + lambda2 * cosa ** 2) + cosb * (lambda2 - lambda1) * sina * cosa
M10 = sinb * (lambda1 * cosa ** 2 + lambda2 * sina ** 2) + cosb * (lambda2 - lambda1) * sina * cosa
M11 = + cosb * (lambda1 * sina ** 2 + lambda2 * cosa ** 2) + sinb * (lambda2 - lambda1) * sina * cosa
M02 = centerx - M00 * centerx - M01 * centery + tx
M12 = centery - M10 * centerx - M11 * centery + ty
affine_matrix = np.array([[M00, M01, M02], [M10, M11, M12], [0, 0, 1]], dtype=np.float32)
# -------------------------------------------------------------------------------
z = np.sqrt(w ** 2 + h ** 2) / 2 / np.tan(math.radians(fov / 2))
radx = math.radians(anglex)
rady = math.radians(angley)
sinx = math.sin(radx)
cosx = math.cos(radx)
siny = math.sin(rady)
cosy = math.cos(rady)
r = np.array([[cosy, 0, -siny, 0],
[-siny * sinx, cosx, -sinx * cosy, 0],
[cosx * siny, sinx, cosx * cosy, 0],
[0, 0, 0, 1]])
pcenter = np.array([centerx, centery, 0, 0], np.float32)
p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
p3 = np.array([0, h, 0, 0], np.float32) - pcenter
p4 = np.array([w, h, 0, 0], np.float32) - pcenter
dst1 = r.dot(p1)
dst2 = r.dot(p2)
dst3 = r.dot(p3)
dst4 = r.dot(p4)
list_dst = [dst1, dst2, dst3, dst4]
org = np.array([[0, 0],
[w, 0],
[0, h],
[w, h]], np.float32)
dst = np.zeros((4, 2), np.float32)
for i in range(4):
dst[i, 0] = list_dst[i][0] * z / (z - list_dst[i][2]) + pcenter[0]
dst[i, 1] = list_dst[i][1] * z / (z - list_dst[i][2]) + pcenter[1]
perspective_matrix = cv2.getPerspectiveTransform(org, dst)
total_matrix = perspective_matrix @ affine_matrix
result_img = cv2.warpPerspective(img, total_matrix, (w, h), flags=_cv2_interpolation_to_str[resample],
borderMode=cv2.BORDER_CONSTANT, borderValue=fillcolor)
if gray_scale:
result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2GRAY)
return result_img.astype(imgtype)
