ordinal_model.result],
feed_dict={input_images: batch_images_np, input_relation_table: batch_relation_table_np, input_loss_table_log: batch_loss_table_log_np, input_loss_table_pow: batch_loss_table_pow_np, input_batch_size: configs.batch_size})
print("Iter: {:07d}. Loss : {:07f}\n\n".format(
data_index.val, loss))
print((len(img_path_for_show) * "{}\n").format(
*zip(img_path_for_show, label_path_for_show)))
# multiply the scale
depth = depth - depth[:, 0]
depth = cur_depth_scale * depth
depth_eval.add(np.array(gt_depth_arr), depth)
depth_eval.printMean()
############# evaluate the coords recovered from the gt 2d and gt root depth
for b in range(configs.batch_size):
c_j_2d, c_j_3d, _ = volume_utils.local_to_global(
depth[b], depth_root_arr[b], crop_joints_2d_arr[b],
source_txt_arr[b], center_arr[b], scale_arr[b])
coords_eval.add(gt_joints_3d_arr[b], c_j_3d)
coords_eval.printMean()
print("\n\n")
depth_eval.save("../eval_result/ord_3_1/depth_eval_{}w.npy".format(
cur_model_iterations / 10000))
coords_eval.save(
"../eval_result/ord_3_1/coord_eval_{}w.npy".format(
cur_model_iterations / 10000))
sys.stdout.write("Mean: ")
mean_depth_eval.printMean()
##### The raw results
raw_depth = cur_depth_scale * (raw_depth -
raw_depth[:, 0][:, np.newaxis])
raw_joints_2d = raw_joints_2d * configs.coords_2d_scale
raw_depth_eval.add(np.array(gt_depth_arr), raw_depth)
sys.stdout.write("raw: ")
raw_depth_eval.printMean()
############# evaluate the coords recovered from the pd 2d and gt root depth
for b in range(configs.batch_size):
mean_c_j_2d, mean_c_j_3d, _ = volume_utils.local_to_global(
mean_depth[b], depth_root_arr[b], mean_joints_2d[b],
source_txt_arr[b], center_arr[b], scale_arr[b])
mean_coords_eval.add(
gt_joints_3d_arr[b] - gt_joints_3d_arr[b][0],
mean_c_j_3d - mean_c_j_3d[0])
raw_c_j_2d, raw_c_j_3d, _ = volume_utils.local_to_global(
raw_depth[b], depth_root_arr[b], raw_joints_2d[b],
source_txt_arr[b], center_arr[b], scale_arr[b])
raw_coords_eval.add(
gt_joints_3d_arr[b] - gt_joints_3d_arr[b][0],
raw_c_j_3d - raw_c_j_3d[0])
sys.stdout.write("Mean: ")
mean_coords_eval.printMean()
sys.stdout.write("Raw: ")
map(lambda x: volume_utils.voxel_z_centers[x],
mean_vol_joints[:, :, 2].tolist()))
mean_pd_coords_2d = mean_vol_joints[:, :, 0:
2] * configs.coords_2d_scale
raw_vol_joints = raw_vol_joints.astype(np.int32)
raw_pd_depth = np.array(
map(lambda x: volume_utils.voxel_z_centers[x],
raw_vol_joints[:, :, 2].tolist()))
raw_pd_coords_2d = raw_vol_joints[:, :, 0:
2] * configs.coords_2d_scale
# ############# evaluate the coords recovered from the gt 2d and gt root depth
for b in range(configs.batch_size):
mean_c_j_2d_pd, mean_c_j_3d_pd, _ = volume_utils.local_to_global(
mean_pd_depth[b], depth_root_arr[b],
mean_pd_coords_2d[b], source_txt_arr[b], center_arr[b],
scale_arr[b])
raw_c_j_2d_pd, raw_c_j_3d_pd, _ = volume_utils.local_to_global(
raw_pd_depth[b], depth_root_arr[b],
raw_pd_coords_2d[b], source_txt_arr[b], center_arr[b],
scale_arr[b])
#### Use the mean skeleton to evaluate
opt_mean_c_j_3d_pd = np.reshape(
skeleton_opt.opt(
volume_utils.recover_2d(
mean_pd_coords_2d[b],
scale=scale_arr[b],
center=center_arr[b]).flatten().tolist(),
mean_pd_depth[b].flatten().tolist(),
cam_matrix_arr[b].flatten().tolist()), [-1, 3])
m_btn_callback.reset()
# cur_index = data_index.val
cur_index = 628
cropped_img = cv2.imread(images_file_fn(cur_index))
cur_label = np.load(annots_file_fn(cur_index)).tolist()
joints_3d = cur_label["joints_3d"]
joints_2d = cur_label["joints_2d"]
cur_depth = joints_3d[:, 2] - joints_3d[0, 2]
root_depth = joints_3d[0, 2]
joints_2d, joints_3d, proj_mat = volume_utils.local_to_global(
cur_depth, root_depth, joints_2d, cur_label["source"],
cur_label["center"], cur_label["scale"])
visualBox.setProjMat(proj_mat)
cur_img = volume_utils.put_cropped_back(cropped_img,
cur_label["center"],
cur_label["scale"])
cur_img = display_utils.drawLines(cur_img,
joints_2d,
indices=pose_defs.h36m_pose)
cur_img = display_utils.drawPoints(cur_img, joints_2d)
# print(joints_3d - joints_3d[0])
# joints_3d -= joints_3d[0] # minus the root