'target_image': tgt
})
else:
corr4d = model({'source_image': src, 'target_image': tgt})
delta4d = None
if args.benchmark:
mid.record()
# reshape corr tensor and get matches for each point in image B
batch_size, ch, fs1, fs2, fs3, fs4 = corr4d.size()
# pad image and plot
if plot:
h_max = int(np.max([src.shape[2], tgt.shape[2]]))
im = plot_image(torch.cat(
(padim(src, h_max), padim(tgt, h_max)), dim=3),
return_im=True)
plt.imshow(im)
if matching_both_directions:
(xA_, yA_, xB_, yB_,
score_) = corr_to_matches(corr4d,
scale='positive',
do_softmax=do_softmax,
delta4d=delta4d,
k_size=k_size)
(xA2_, yA2_, xB2_, yB2_,
score2_) = corr_to_matches(corr4d,
scale='positive',
do_softmax=do_softmax,
delta4d=delta4d,
yb = float(rowA[0][i])
c = np.random.rand(3)
# plt.gca().add_artist(plt.Circle((xa, ya), radius=3, color=c))
# plt.gca().add_artist(plt.Circle((xb, yb), radius=3, color=c))
# plt.plot([xa, xb], [ya, yb], c=c, linestyle='-', linewidth=1.5)
# plt.axis('off')
# plt.title('gridCellMatching')
# plt.savefig('cell.png', dpi=300)
# plt.show()
###############################################MatchingGridCellPoints##############################################
# display result
im = plot_image(torch.cat(
(batch_tnf['target_image'], batch_tnf['source_image']), 3),
0,
return_im=True)
tgt_pts[0, 0, :] = unnormalize_axis(tgt_pts[0, 0, :],
tgt.shape[2]) # B points
tgt_pts[0, 1, :] = unnormalize_axis(tgt_pts[0, 1, :], tgt.shape[1])
src_pts[0, 0, :] = unnormalize_axis(src_pts[0, 0, :],
src.shape[2]) # A points
src_pts[0, 1, :] = unnormalize_axis(src_pts[0, 1, :], src.shape[1])
warped_points[0, 0, :] = unnormalize_axis(warped_points[0, 0, :],
src.shape[2])
warped_points[0, 1, :] = unnormalize_axis(warped_points[0, 1, :],
src.shape[1]) # 变形后的点坐标反归一化
# cat image(B+A:tar+src) # tranform B points to A
# plt.imshow(im)
# for i in range(tgt_pts.shape[2]):
# xa = float(tgt_pts[0, 0, i])