new_jv = []
ori_jv = []
for i in range(len(j_list)):
# make new joint verts
for j in point_list[i]:
new_jv.append(verts[j] + joint_para[i])
ori_jv.append(verts[j])
new_jv = np.array(new_jv)
ori_jv = np.array(ori_jv)
# joint deform
fd_ja = fast_deform_dja(weight=10.0)
ja_verts = fd_ja.deform(np.asarray(verts), new_jv)
# make src_a
proj_sil_j = my_renderer.silhouette(verts=ja_verts)
src_sil_j = np.zeros((224, 224, 2))
src_a = np.zeros((200, 4, 32, 32))
# make anchor posi
anchor_verts = np.zeros((200, 3))
for i in range(achr_num):
anchor_verts[i, :] = ja_verts[achr_id[i], :]
achr_posi = get_anchor_posi(anchor_verts)
for i in range(len(achr_posi)):
crop_sil = center_crop(proj_sil_j, achr_posi[i], 32)
crop_img = center_crop(src_img, achr_posi[i], 32)
crop_img = crop_img.astype(np.int)
crop_img = crop_img - crop_img[15, 15, :]
crop_img = np.absolute(crop_img)
# compute error
avergedist_tgt, _, crsp_ind_tgt = knnsearch(verts_tgt_fit, verts_gt)
avergedist_tgt_visi, _, _ = knnsearch(verts_tgt_visi_fit, verts_gt_visi)
error_list.append(avergedist_tgt)
error_list_visi.append(avergedist_tgt_visi)
# read gt sil
sil_gt = np.array(PIL.Image.open("./eval_data/%s_set/input/%03d_sil.png" \
% (set_name, i)))
sil_gt[sil_gt < 128] = 0
sil_gt[sil_gt >= 128] = 255
# render result_sil
sil_result = my_renderer.silhouette(verts=verts_tgt)
iou = sil_iou(sil_result, sil_gt)
iou_list.append(iou)
crsp_verts_tgt = np.array(
[verts_tgt_fit[crsp_ind_tgt[j][0]] for j in range(len(crsp_ind_tgt))])
diff_array_tgt = crsp_verts_tgt - verts_gt
print("mean %f %f %f\r\n" % \
(np.mean(error_list),
np.mean(error_list_visi),
np.mean(iou_list)))
with open("./eval_report/%s_%s_report.txt" % (set_name, opt.tgt),
#verts, cam, proc_para, std_img = hmr_pred.predict(src_img)
with open ('./eval_data/%s_set/pred_save/pre_%03d.pkl' % \
(opt.set, test_ind), 'rb') as fp:
hmr_data = pickle.load(fp)
verts = hmr_data['verts']
cam = hmr_data['cam']
proc_para = hmr_data['proc_para']
std_img = hmr_data['std_img']
mesh = make_trimesh(verts, faces, compute_vn=True)
vert_norms = mesh.vertex_normals()
# get proj sil
proj_sil = renderer.silhouette(verts=verts,
cam=cam,
img_size=src_img.shape,
norm=True)
# make joint posi
joint_posi = get_joint_posi(verts, point_list)
sil_j = np.expand_dims(proj_sil, 2)
src_j = np.zeros((10, 4, 64, 64))
for i in range(len(joint_posi)):
crop_sil = center_crop(sil_j, joint_posi[i], 64)
crop_img = center_crop(src_img, joint_posi[i], 64)
crop_img = crop_img.astype(np.float)
crop_img = crop_img - crop_img[31, 31, :]
crop_img = np.absolute(crop_img)
crop_img = crop_img / 255.0
src_j[i, 0, :, :] = np.rollaxis(crop_sil, 2, 0)