# 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)