xing_loss.py

import torch
import numpy as np


def area(a, b, c):
    return (c[1] - a[1]) * (b[0] - a[0]) - (b[1] - a[1]) * (c[0] - a[0])


def triangle_area(A, B, C):
    out = (C - A).flip([-1]) * (B - A)
    out = out[..., 1] - out[..., 0]
    return out

def compute_sine_theta(s1, s2):  #s1 and s2 aret two segments to be uswed
    #s1, s2 (2, 2)
    v1 = s1[1,:] - s1[0, :]
    v2 = s2[1,:] - s2[0, :]
    #print(v1, v2)
    sine_theta = ( v1[0] * v2[1] - v1[1] * v2[0] ) / (torch.norm(v1) * torch.norm(v2))
    return sine_theta

def xing_loss(x_list, scale=1e-3):  # x[ npoints,2]
    loss = 0.
    #print(len(x_list))
    for x in x_list:
        #print(x)
        seg_loss = 0.
        N = x.size()[0]
        x = torch.cat([x,x[0,:].unsqueeze(0)], dim=0)  #(N+1,2)
        segments =  torch.cat([x[:-1,:].unsqueeze(1), x[1:,:].unsqueeze(1)], dim=1)  #(N, start/end, 2)
        assert N % 3 == 0, 'The segment number is not correct!'
        segment_num = int(N / 3)
        for i in range(segment_num):
            cs1 = segments[i*3, :, :]  #start control segs
            cs2 = segments[i*3 + 1, :, :] #middle control segs
            cs3 = segments[i*3 + 2, :, :]   #end control segs
            #print('the direction of the vectors:')
            #print(compute_sine_theta(cs1, cs2))
            direct = (compute_sine_theta(cs1, cs2) >= 0).float()
            opst = 1 - direct  #another direction
            sina = compute_sine_theta(cs1, cs3)  #the angle between cs1 and cs3
            seg_loss += direct * torch.relu( - sina) + opst * torch.relu(sina)
            # print(direct, opst, sina)
        seg_loss /= segment_num


        templ = seg_loss
        loss += templ * scale #area_loss * scale

    return loss / (len(x_list))


if __name__ == "__main__":
    #x = torch.rand([6, 2])
    #x = torch.tensor([[0,0], [1,1], [2,1], [1.5,0]])
    x = torch.tensor([[0,0], [1,1], [2,1], [0.5,0]])
    #x = torch.tensor([[1,0], [2,1], [0,1], [2,0]])
    scale = 1 #0.5
    y = xing_loss([x], scale)
    print(y)

    x = torch.tensor([[0,0], [1,1], [2,1], [2.,0]])
    #x = torch.tensor([[1,0], [2,1], [0,1], [2,0]])
    scale = 1 #0.5
    y = xing_loss([x], scale)
    print(y)