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import torchvision.transforms as T
import torch
import torchvision.transforms.functional as F
from torch.nn import functional as FF
import numpy as np
import cv2
def compute_trans(height,width,ret):
angle, translate, scale, shear = ret
center = [0, 0] # [width * 0.5, height * 0.5]
matrix = F._get_inverse_affine_matrix(center, angle, translate, scale, shear)
matrix = torch.tensor(matrix).float()
matrix = matrix.reshape(2, 3)
matrix[0, 2] /= (height // 2)
matrix[1, 2] /= (width // 2)
return matrix
def compute_inv_trans(height,width,ret):
matrix = compute_trans(height,width,ret)
inv_R = torch.inverse(matrix[:, :2])
t = matrix[:, 2]
inv_matrix = torch.zeros(2, 3)
inv_matrix[:, :2] = inv_R
inv_matrix[:, 2] = -torch.mv(inv_R, t)
return inv_matrix
class RandomAffineBatch(T.RandomAffine):
def __int__(self, *args):
super(RandomAffineBatch, self).__init__(*args)
def forward(self, imgs):
channels, height, width = F.get_dimensions(imgs[0])
img_size = [width, height] # flip for keeping BC on get_params call
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
results = []
for img in imgs:
fill = self.fill
channels, height, width = F.get_dimensions(img)
if isinstance(img, torch.Tensor):
if isinstance(fill, (int, float)):
fill = [float(fill)] * channels
else:
fill = [float(f) for f in fill]
results.append(F.affine(img, *ret, interpolation=self.interpolation, fill=fill, center=self.center))
return results
def forward_with_trans(self, imgs):
channels, height, width = F.get_dimensions(imgs[0])
img_size = [width, height] # flip for keeping BC on get_params call
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
results = []
for img in imgs:
fill = self.fill
channels, height, width = F.get_dimensions(img)
if isinstance(img, torch.Tensor):
if isinstance(fill, (int, float)):
fill = [float(fill)] * channels
else:
fill = [float(f) for f in fill]
results.append(F.affine(img, *ret, interpolation=self.interpolation, fill=fill, center=self.center))
trans = compute_trans(height, width, ret)
if torch.cuda.is_available():
trans = trans.cuda()
return results, trans
def forward_with_inv_trans(self, imgs):
channels, height, width = F.get_dimensions(imgs[0])
img_size = [width, height] # flip for keeping BC on get_params call
ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img_size)
results = []
for img in imgs:
fill = self.fill
channels, height, width = F.get_dimensions(img)
if isinstance(img, torch.Tensor):
if isinstance(fill, (int, float)):
fill = [float(fill)] * channels
else:
fill = [float(f) for f in fill]
results.append(F.affine(img, *ret, interpolation=self.interpolation, fill=fill, center=self.center))
inv_trans = compute_inv_trans(height,width,ret)
if torch.cuda.is_available():
inv_trans = inv_trans.cuda()
return results, inv_trans
class RandomAffineBatchNumpy:
def __init__(self, degrees, translate=None, scale=None, shear=None):
self.degrees = degrees
self.translate = translate
self.scale = scale
self.shear = shear
def __call__(self, imgs):
if isinstance(imgs, list):
img_list=imgs
else:
img_list=[imgs,]
h,w=img_list[0].shape[:2]
assert h==w
img_size = h
random_matrix = RandomAffineMatrix(degrees=self.degrees, translate=self.translate, scale=self.scale, shear=self.shear, img_size=img_size)
trans=random_matrix()
result_list=[]
for img in img_list:
result = cv2.warpAffine(img, trans, (img_size,img_size),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0))
result_list.append(result)
if len(result_list)==1:
return result_list[0]
else:
return result_list
class RandomAffineMatrix:
def __init__(self, degrees, translate=None, scale=None, shear=None, img_size=1024):
self.degrees = degrees
self.translate = translate
self.scale = scale
self.shear = shear
self.img_size=img_size
def __call__(self):
trans=np.array([[1,0,0],[0,1,0]], dtype=np.float32)
R_hat,t_hat=self.get_random_affine_params(self.degrees, self.translate, self.scale, self.shear)
new_trans=self.deform(R_hat,t_hat,trans)
return new_trans
def batch_forward(self, trans_list):
R_hat,t_hat=self.get_random_affine_params(self.degrees, self.translate, self.scale, self.shear)
new_list =[]
for trans in trans_list:
new_trans=self.deform(R_hat,t_hat,trans)
new_list.append(new_trans)
return new_list
def deform(self,R_hat,t_hat,trans):
c = np.array([self.img_size/2, self.img_size/2])
R = trans[:, :2]
t = trans[:, 2]
R_new = np.dot(R_hat, R)
t_new = np.dot(R_hat, t - c) + t_hat + c
new_trans = np.concatenate((R_new, t_new[:, None]), axis=1)
return new_trans
def get_random_affine_params(self, degrees, translate=None, scale=None, shear=None):
# Random rotation angle
angle = np.random.uniform(-degrees, degrees)
angle_rad = np.deg2rad(angle)
img_size = self.img_size
# Random translation
if translate is not None:
max_dx = translate[0] * img_size
max_dy = translate[1] * img_size
tx = np.random.uniform(-max_dx, max_dx)
ty = np.random.uniform(-max_dy, max_dy)
else:
tx, ty = 0, 0
# Random scaling
if scale is not None:
scale_factor = np.random.uniform(scale[0], scale[1])
else:
scale_factor = 1.0
# Random shear
if shear is not None:
shear_x = np.random.uniform(shear[0], shear[1])
shear_y = np.random.uniform(shear[2], shear[3]) if len(shear) > 2 else 0
else:
shear_x, shear_y = 0, 0
# Compute the affine transformation matrix
cos_theta = np.cos(angle_rad) * scale_factor
sin_theta = np.sin(angle_rad) * scale_factor
shear_x_rad = np.deg2rad(shear_x)
shear_y_rad = np.deg2rad(shear_y)
# Create the affine transformation matrix
M = np.array([
[cos_theta + np.tan(shear_y_rad) * sin_theta, -sin_theta + np.tan(shear_y_rad) * cos_theta],
[sin_theta + np.tan(shear_x_rad) * cos_theta, cos_theta + np.tan(shear_x_rad) * sin_theta]
])
# Translation vector
t = np.array([tx, ty])
return M, t
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