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vision_transforms.py
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214 lines (170 loc) · 9.1 KB
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import math
import copy
import torch
import torch.nn.functional as F
def vec_to_perpective_matrix(vec):
# vec rep of the perspective transform has 8 dof; so add 1 for the bottom right of the perspective matrix;
# note network is initialized to transformer layer bias = [1, 0, 0, 0, 1, 0] so no need to add an identity matrix here
out = torch.cat((vec, torch.ones((vec.shape[0],1), dtype=vec.dtype, device=vec.device)), dim=1).reshape(vec.shape[0], -1)
return out.view(-1,3,3)
def gen_random_perspective_transform(params):
""" generate a batch of 3x3 homography matrices by composing rotation, translation, shear, and projection matrices,
where each samples components from a uniform(-1,1) * multiplicative_factor
"""
batch_size = params.batch_size
# debugging
if params.dict.get('identity_transform_only'):
return torch.eye(3).repeat(batch_size, 1, 1).to(params.device)
I = torch.eye(3).repeat(batch_size, 1, 1)
uniform = torch.distributions.Uniform(-1,1)
factor = 0.25
c = copy.deepcopy
# rotation component
a = math.pi / 6 * uniform.sample((batch_size,))
R = c(I)
R[:, 0, 0] = torch.cos(a)
R[:, 0, 1] = - torch.sin(a)
R[:, 1, 0] = torch.sin(a)
R[:, 1, 1] = torch.cos(a)
R.to(params.device)
# translation component
tx = factor * uniform.sample((batch_size,))
ty = factor * uniform.sample((batch_size,))
T = c(I)
T[:, 0, 2] = tx
T[:, 1, 2] = ty
T.to(params.device)
# shear component
sx = factor * uniform.sample((batch_size,))
sy = factor * uniform.sample((batch_size,))
A = c(I)
A[:, 0, 1] = sx
A[:, 1, 0] = sy
A.to(params.device)
# projective component
px = uniform.sample((batch_size,))
py = uniform.sample((batch_size,))
P = c(I)
P[:, 2, 0] = px
P[:, 2, 1] = py
P.to(params.device)
# compose the homography
H = R @ T @ P @ A
return H
def apply_transform_to_batch(im_batch_tensor, transform_tensor):
""" apply a geometric transform to a batch of image tensors
args
im_batch_tensor -- torch float tensor of shape (N, C, H, W)
transform_tensor -- torch float tensor of shape (1, 3, 3)
returns
transformed_batch_tensor -- torch float tensor of shape (N, C, H, W)
"""
N, C, H, W = im_batch_tensor.shape
device = im_batch_tensor.device
# torch.nn.functional.grid_sample takes a grid in [-1,1] and interpolates;
# construct grid in homogeneous coordinates
x, y = torch.meshgrid([torch.linspace(-1, 1, H), torch.linspace(-1, 1, W)])
x, y = x.flatten(), y.flatten()
xy_hom = torch.stack([x, y, torch.ones(x.shape[0])], dim=0).unsqueeze(0).to(device)
# tansform the [-1,1] homogeneous coords
xy_transformed = transform_tensor.matmul(xy_hom) # (N, 3, 3) matmul (N, 3, H*W) > (N, 3, H*W)
# convert to inhomogeneous coords -- cf Szeliski eq. 2.21
grid = xy_transformed[:,:2,:] / (xy_transformed[:,2,:].unsqueeze(1) + 1e-9)
grid = grid.permute(0,2,1).reshape(-1, H, W, 2) # (N, H, W, 2); cf torch.functional.grid_sample
grid = grid.expand(N, *grid.shape[1:]) # expand to minibatch
transformed_batch = F.grid_sample(im_batch_tensor, grid, mode='bilinear')
transformed_batch.transpose_(3,2)
return transformed_batch
# --------------------
# Test
# --------------------
def test_get_random_perspective_transform():
import matplotlib
matplotlib.use('TkAgg')
import numpy as np
import matplotlib.pyplot as plt
from unittest.mock import Mock
np.random.seed(6)
im = np.zeros((30,30))
im[10:20,10:20] = 1
im[20,20] = 1
imt = np.array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18,
18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253,
253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253,
253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253,
198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205,
11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253,
70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241,
225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81,
240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148,
229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253,
253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253,
253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195,
80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
# get transform
params = Mock()
params.batch_size = 1
params.dict = {'identity_transform_only': False}
params.device = torch.device('cpu')
H = gen_random_perspective_transform(params)
im = im[np.newaxis, np.newaxis, ...]
im = torch.FloatTensor(im)
im_transformed = apply_transform_to_batch(im, H)
imt = imt[np.newaxis, np.newaxis, ...]
imt = torch.FloatTensor(imt)
imt_transformed = apply_transform_to_batch(imt, H)
fig, axs = plt.subplots(2,2)
axs[0,0].imshow(im.squeeze().numpy(), cmap='gray')
axs[0,1].imshow(im_transformed.squeeze().numpy(), cmap='gray')
axs[1,0].imshow(imt.squeeze().numpy(), cmap='gray')
axs[1,1].imshow(imt_transformed.squeeze().numpy(), cmap='gray')
for ax in plt.gcf().axes:
ax.axis('off')
plt.tight_layout()
plt.savefig('images/transform_test.png')
plt.close()
if __name__ == '__main__':
test_get_random_perspective_transform()