I am trying to reproduce the project document unwarping 1. Because i dont have enoguh data. I was trying to make fake dataset. Here is my code.
def create_grid(width=100, height=100):
    mr = width
    mc = height

    xx = np.arange(mr-1, -1, -1)
    yy = np.arange(0, mc, 1)
    [Y, X] = np.meshgrid(xx, yy)
    ms = np.transpose(np.asarray([X.flatten('F'), Y.flatten('F')]), (1, 0))

    perturbed_mesh = ms
    nv = np.random.randint(20) - 1
    for k in range(nv):
        # Choosing one vertex randomly
        vidx = np.random.randint(np.shape(ms)[1])
        vtex = ms[vidx, :]
        # Vector between all vertices and the selected one
        xv = perturbed_mesh - vtex
        # Random movement
        mv = (np.random.rand(1, 2) - 0.5)*20
        hxv = np.zeros((np.shape(xv)[0], np.shape(xv)[1] + 1))
        hxv[:, :-1] = xv
        hmv = np.tile(np.append(mv, 0), (np.shape(xv)[0], 1))
        d = np.cross(hxv, hmv)
        d = np.absolute(d[:, 2])
        d = d / (np.linalg.norm(mv, ord=2))
        wt = d

        curve_type = np.random.rand(1)
        if curve_type > 0.3:
            alpha = np.random.rand(1) * 20 + 20
            wt = alpha / (wt + alpha)
        else:
            alpha = np.random.rand(1) + 1
            wt = 1 - (wt / 100)**alpha
        msmv = mv * np.expand_dims(wt, axis=1)
        perturbed_mesh = perturbed_mesh + msmv
        
    perturbed_mesh[:, 0] = np.where(perturbed_mesh[:, 0] > height, height, perturbed_mesh[:, 0])
    perturbed_mesh[:, 1] = np.where(perturbed_mesh[:, 1] > width, width, perturbed_mesh[:, 1])
    perturbed_mesh[:, 0] = np.where(perturbed_mesh[:, 0] < 0, 0, perturbed_mesh[:, 0])
    perturbed_mesh[:, 1] = np.where(perturbed_mesh[:, 1] < 0, 0, perturbed_mesh[:, 1])

#     plt.scatter(perturbed_mesh[:, 0], perturbed_mesh[:, 1], c=np.arange(0, mr*mc))
#     plt.show()

    return perturbed_mesh[:, 0], perturbed_mesh[:, 1]

    src_img = cv2.imread('source.jpg')
    src_img = cv2.flip(src_img, 0)
    scale_percent = 30 # percent of original size
    width = int(src_img.shape[1] * scale_percent / 100)
    height = int(src_img.shape[0] * scale_percent / 100)
    dim = (width, height)
    src_img = cv2.resize(src_img, dim, interpolation=cv2.INTER_AREA)

    height, width, _ = src_img.shape
    dh = height // 10
    dw = width // 10
    img = cv2.copyMakeBorder(src_img, dh, dh, dw, dw, borderType=cv2.BORDER_CONSTANT, value=(0,0,0))
    nh, nw, _ = img.shape
    xs, ys = create_grid(nh, nw)
    xs = xs.reshape(nh, nw).astype(np.float32)
    ys = ys.reshape(nh, nw).astype(np.float32)
    dst = cv2.remap(img, xs, ys, cv2.INTER_CUBIC, borderValue=0)
    cv2.imwrite('result.jpg', dst)

Source image is

source951×1750 158 KB

But my result is

result951×1750 250 KB

Expected result is

What may go wrong in my code? Or is there any pytorch equivalent to make such fake dataset? Here is the paper 1 talking about how to make fake data.