126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
# functions for affine transformation
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import math, torch
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import numpy as np
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import torch.nn.functional as F
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def identity2affine(full=False):
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if not full:
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parameters = torch.zeros((2,3))
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parameters[0, 0] = parameters[1, 1] = 1
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else:
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parameters = torch.zeros((3,3))
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parameters[0, 0] = parameters[1, 1] = parameters[2, 2] = 1
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return parameters
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def normalize_L(x, L):
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return -1. + 2. * x / (L-1)
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def denormalize_L(x, L):
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return (x + 1.0) / 2.0 * (L-1)
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def crop2affine(crop_box, W, H):
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assert len(crop_box) == 4, 'Invalid crop-box : {:}'.format(crop_box)
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parameters = torch.zeros(3,3)
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x1, y1 = normalize_L(crop_box[0], W), normalize_L(crop_box[1], H)
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x2, y2 = normalize_L(crop_box[2], W), normalize_L(crop_box[3], H)
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parameters[0,0] = (x2-x1)/2
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parameters[0,2] = (x2+x1)/2
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parameters[1,1] = (y2-y1)/2
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parameters[1,2] = (y2+y1)/2
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parameters[2,2] = 1
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return parameters
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def scale2affine(scalex, scaley):
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parameters = torch.zeros(3,3)
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parameters[0,0] = scalex
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parameters[1,1] = scaley
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parameters[2,2] = 1
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return parameters
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def offset2affine(offx, offy):
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parameters = torch.zeros(3,3)
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parameters[0,0] = parameters[1,1] = parameters[2,2] = 1
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parameters[0,2] = offx
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parameters[1,2] = offy
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return parameters
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def horizontalmirror2affine():
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parameters = torch.zeros(3,3)
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parameters[0,0] = -1
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parameters[1,1] = parameters[2,2] = 1
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return parameters
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# clockwise rotate image = counterclockwise rotate the rectangle
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# degree is between [0, 360]
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def rotate2affine(degree):
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assert degree >= 0 and degree <= 360, 'Invalid degree : {:}'.format(degree)
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degree = degree / 180 * math.pi
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parameters = torch.zeros(3,3)
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parameters[0,0] = math.cos(-degree)
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parameters[0,1] = -math.sin(-degree)
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parameters[1,0] = math.sin(-degree)
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parameters[1,1] = math.cos(-degree)
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parameters[2,2] = 1
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return parameters
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# shape is a tuple [H, W]
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def normalize_points(shape, points):
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assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)
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assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), 'points are wrong : {:}'.format(points.shape)
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(H, W), points = shape, points.clone()
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points[0, :] = normalize_L(points[0,:], W)
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points[1, :] = normalize_L(points[1,:], H)
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return points
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# shape is a tuple [H, W]
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def normalize_points_batch(shape, points):
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assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)
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assert isinstance(points, torch.Tensor) and (points.size(-1) == 2), 'points are wrong : {:}'.format(points.shape)
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(H, W), points = shape, points.clone()
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x = normalize_L(points[...,0], W)
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y = normalize_L(points[...,1], H)
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return torch.stack((x,y), dim=-1)
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# shape is a tuple [H, W]
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def denormalize_points(shape, points):
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assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)
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assert isinstance(points, torch.Tensor) and (points.shape[0] == 2), 'points are wrong : {:}'.format(points.shape)
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(H, W), points = shape, points.clone()
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points[0, :] = denormalize_L(points[0,:], W)
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points[1, :] = denormalize_L(points[1,:], H)
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return points
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# shape is a tuple [H, W]
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def denormalize_points_batch(shape, points):
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assert (isinstance(shape, tuple) or isinstance(shape, list)) and len(shape) == 2, 'invalid shape : {:}'.format(shape)
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assert isinstance(points, torch.Tensor) and (points.shape[-1] == 2), 'points are wrong : {:}'.format(points.shape)
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(H, W), points = shape, points.clone()
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x = denormalize_L(points[...,0], W)
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y = denormalize_L(points[...,1], H)
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return torch.stack((x,y), dim=-1)
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# make target * theta = source
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def solve2theta(source, target):
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source, target = source.clone(), target.clone()
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oks = source[2, :] == 1
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assert torch.sum(oks).item() >= 3, 'valid points : {:} is short'.format(oks)
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if target.size(0) == 2: target = torch.cat((target, oks.unsqueeze(0).float()), dim=0)
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source, target = source[:, oks], target[:, oks]
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source, target = source.transpose(1,0), target.transpose(1,0)
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assert source.size(1) == target.size(1) == 3
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#X, residual, rank, s = np.linalg.lstsq(target.numpy(), source.numpy())
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#theta = torch.Tensor(X.T[:2, :])
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X_, qr = torch.gels(source, target)
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theta = X_[:3, :2].transpose(1, 0)
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return theta
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# shape = [H,W]
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def affine2image(image, theta, shape):
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C, H, W = image.size()
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theta = theta[:2, :].unsqueeze(0)
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grid_size = torch.Size([1, C, shape[0], shape[1]])
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grid = F.affine_grid(theta, grid_size)
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affI = F.grid_sample(image.unsqueeze(0), grid, mode='bilinear', padding_mode='border')
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return affI.squeeze(0)
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