naswot/autodl/utils/affine_utils.py

# functions for affine transformation
import math, torch
import numpy as np
import torch.nn.functional as F

def identity2affine(full=False):
  if not full:
    parameters = torch.zeros((2,3))
    parameters[0, 0] = parameters[1, 1] = 1
  else:
    parameters = torch.zeros((3,3))
    parameters[0, 0] = parameters[1, 1] = parameters[2, 2] = 1
  return parameters

def normalize_L(x, L):
  return -1. + 2. * x / (L-1)

def denormalize_L(x, L):
  return (x + 1.0) / 2.0 * (L-1)

def crop2affine(crop_box, W, H):
  assert len(crop_box) == 4, 'Invalid crop-box : {:}'.format(crop_box)
  parameters = torch.zeros(3,3)
  x1, y1 = normalize_L(crop_box[0], W), normalize_L(crop_box[1], H)
  x2, y2 = normalize_L(crop_box[2], W), normalize_L(crop_box[3], H)
  parameters[0,0] = (x2-x1)/2
  parameters[0,2] = (x2+x1)/2

  parameters[1,1] = (y2-y1)/2
  parameters[1,2] = (y2+y1)/2
  parameters[2,2] = 1
  return parameters

def scale2affine(scalex, scaley):
  parameters = torch.zeros(3,3)
  parameters[0,0] = scalex
  parameters[1,1] = scaley
  parameters[2,2] = 1
  return parameters
 
def offset2affine(offx, offy):
  parameters = torch.zeros(3,3)
  parameters[0,0] = parameters[1,1] = parameters[2,2] = 1
  parameters[0,2] = offx
  parameters[1,2] = offy
  return parameters

def horizontalmirror2affine():
  parameters = torch.zeros(3,3)
  parameters[0,0] = -1
  parameters[1,1] = parameters[2,2] = 1
  return parameters

# clockwise rotate image = counterclockwise rotate the rectangle
# degree is between [0, 360]
def rotate2affine(degree):
  assert degree >= 0 and degree <= 360, 'Invalid degree : {:}'.format(degree)
  degree = degree / 180 * math.pi
  parameters = torch.zeros(3,3)
  parameters[0,0] =  math.cos(-degree)
  parameters[0,1] = -math.sin(-degree)
  parameters[1,0] =  math.sin(-degree)
  parameters[1,1] =  math.cos(-degree)
  parameters[2,2] = 1
  return parameters

# shape is a tuple [H, W]
def normalize_points(shape, points):
  assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)  
  assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), 'points are wrong : {:}'.format(points.shape)
  (H, W), points = shape, points.clone()
  points[0, :] = normalize_L(points[0,:], W)
  points[1, :] = normalize_L(points[1,:], H)
  return points

# shape is a tuple [H, W]
def normalize_points_batch(shape, points):
  assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)  
  assert isinstance(points, torch.Tensor) and (points.size(-1) == 2), 'points are wrong : {:}'.format(points.shape)
  (H, W), points = shape, points.clone()
  x = normalize_L(points[...,0], W)
  y = normalize_L(points[...,1], H)
  return torch.stack((x,y), dim=-1)

# shape is a tuple [H, W]
def denormalize_points(shape, points):
  assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)  
  assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), 'points are wrong : {:}'.format(points.shape)
  (H, W), points = shape, points.clone()
  points[0, :] = denormalize_L(points[0,:], W)
  points[1, :] = denormalize_L(points[1,:], H)
  return points

# shape is a tuple [H, W]
def denormalize_points_batch(shape, points):
  assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)  
  assert isinstance(points, torch.Tensor) and (points.shape[-1] == 2), 'points are wrong : {:}'.format(points.shape)
  (H, W), points = shape, points.clone()
  x = denormalize_L(points[...,0], W)
  y = denormalize_L(points[...,1], H)
  return torch.stack((x,y), dim=-1)

# make target * theta = source
def solve2theta(source, target):
  source, target = source.clone(), target.clone()
  oks = source[2, :] == 1
  assert torch.sum(oks).item() >= 3, 'valid points : {:} is short'.format(oks)
  if target.size(0) == 2: target = torch.cat((target, oks.unsqueeze(0).float()), dim=0)
  source, target = source[:, oks], target[:, oks]
  source, target = source.transpose(1,0), target.transpose(1,0)
  assert source.size(1) == target.size(1) == 3
  #X, residual, rank, s = np.linalg.lstsq(target.numpy(), source.numpy())
  #theta = torch.Tensor(X.T[:2, :])
  X_, qr = torch.gels(source, target)
  theta = X_[:3, :2].transpose(1, 0)
  return theta

# shape = [H,W]
def affine2image(image, theta, shape):
  C, H, W = image.size()
  theta = theta[:2, :].unsqueeze(0)
  grid_size = torch.Size([1, C, shape[0], shape[1]])
  grid  = F.affine_grid(theta, grid_size)
  affI  = F.grid_sample(image.unsqueeze(0), grid, mode='bilinear', padding_mode='border')
  return affI.squeeze(0)