offset=offset)
ref_list_len = len(ref_id_list)
if count == -1:
count = ref_list_len
#pose_estimate_list_loaded_len = len(pose_estimate_list_loaded)
for i in range(start_count, count):
ref_id = ref_id_list[i]
target_id = target_id_list[i]
SE3_ref_target = Parser.generate_ground_truth_se3(groundtruth_dict,
image_groundtruth_dict,
ref_id,
target_id,
post_process_object=None)
im_greyscale_reference, im_depth_reference = Parser.generate_image_depth_pair_match(
dataset_root, rgb_text, depth_text, match_text, ref_id)
im_greyscale_target, im_depth_target = Parser.generate_image_depth_pair_match(
dataset_root, rgb_text, depth_text, match_text, ref_id)
post_process_gt.post_process_in_mem(SE3_ref_target)
ground_truth_acc = np.matmul(ground_truth_acc, SE3_ref_target)
ground_truth_list.append(ground_truth_acc)
ref_image_list.append((im_greyscale_reference, im_depth_reference))
target_image_list.append((im_greyscale_target, im_depth_target))
image_groundtruth_dict = dict(associate.match(rgb_text, groundtruth_text))
#se3_ground_truth_prior = np.transpose(SE3.quaternion_to_s03(0.6132, 0.5962, -0.3311, -0.3986))
se3_ground_truth_prior = SE3.makeS03(0,0,pi)
se3_ground_truth_prior = np.append(se3_ground_truth_prior,np.zeros((3,1),dtype=Utils.matrix_data_type),axis=1)
se3_ground_truth_prior = SE3.append_homogeneous_along_y(se3_ground_truth_prior)
#se3_ground_truth_prior = SE3.invert(se3_ground_truth_prior)
se3_ground_truth_prior[0:3,3] = 0
for i in range(0, len(ref_id_list)):
ref_id = ref_id_list[i]
target_id = target_id_list[i]
SE3_ref_target = Parser.generate_ground_truth_se3(groundtruth_text,image_groundtruth_dict,ref_id,target_id,None)
im_greyscale_reference, im_depth_reference = Parser.generate_image_depth_pair(dataset_root,rgb_text,depth_text,match_text,ref_id)
im_greyscale_target, im_depth_target = Parser.generate_image_depth_pair(dataset_root,rgb_text,depth_text,match_text,target_id)
ground_truth_acc = np.matmul(SE3_ref_target,ground_truth_acc)
ground_truth_list.append(ground_truth_acc)
ref_image_list.append((im_greyscale_reference, im_depth_reference))
target_image_list.append((im_greyscale_target, im_depth_target))
im_greyscale_reference_1, im_depth_reference_1 = ref_image_list[0]
(image_height, image_width) = im_greyscale_reference_1.shape
se3_identity = np.identity(4, dtype=Utils.matrix_data_type)
# image gradient induces a coordiante system where y is flipped i.e have to flip it here
intrinsic_identity = Intrinsic.Intrinsic(-517.3, -516.5, 318.6, 239.5) # freiburg_1