print('Completed generating {} depth samples for sequence={}'.format( n_generated, sequence_dirpath)) output_sparse_depth_filepath = \ TRAIN_SPARSE_DEPTH_OUTPUT_FILEPATH[:-4] + '-' + seq_id + '.txt' output_validity_map_filepath = \ TRAIN_VALIDITY_MAP_OUTPUT_FILEPATH[:-4] + '-' + seq_id + '.txt' output_semi_dense_depth_filepath = \ TRAIN_SEMI_DENSE_DEPTH_OUTPUT_FILEPATH[:-4] + '-' + seq_id + '.txt' output_ground_truth_filepath = \ TRAIN_GROUND_TRUTH_OUTPUT_FILEPATH[:-4] + '-' + seq_id + '.txt' print('Storing {} sparse depth file paths into: {}'.format( len(output_sequence_sparse_depth_paths), output_sparse_depth_filepath)) data_utils.write_paths(output_sparse_depth_filepath, output_sequence_sparse_depth_paths) print('Storing {} validity map file paths into: {}'.format( len(output_sequence_validity_map_paths), output_validity_map_filepath)) data_utils.write_paths(output_validity_map_filepath, output_sequence_validity_map_paths) print('Storing {} semi dense depth file paths into: {}'.format( len(output_sequence_semi_dense_depth_paths), output_semi_dense_depth_filepath)) data_utils.write_paths(output_semi_dense_depth_filepath, output_sequence_semi_dense_depth_paths) print('Storing {} ground truth file paths into: {}'.format( len(output_sequence_ground_truth_paths), output_ground_truth_filepath)) data_utils.write_paths(output_ground_truth_filepath,
continue else: train_image_clean_output_paths.append(image_output_path) train_sparse_depth_clean_output_paths.append(sparse_depth_output_path) train_validity_map_clean_output_paths.append(validity_map_output_path) train_ground_truth_clean_output_paths.append(ground_truth_output_path) train_intrinsics_clean_output_paths.append(intrinsics_output_path) sys.stdout.write( 'Processed {}/{} examples \r'.format(idx + 1, n_sample)) sys.stdout.flush() print('Storing training image file paths into: %s' % TRAIN_IMAGE_OUTPUT_FILEPATH) data_utils.write_paths( TRAIN_IMAGE_OUTPUT_FILEPATH, train_image_output_paths) print('Storing training sparse depth file paths into: %s' % TRAIN_SPARSE_DEPTH_OUTPUT_FILEPATH) data_utils.write_paths( TRAIN_SPARSE_DEPTH_OUTPUT_FILEPATH, train_sparse_depth_output_paths) print('Storing training validity map file paths into: %s' % TRAIN_VALIDITY_MAP_OUTPUT_FILEPATH) data_utils.write_paths( TRAIN_VALIDITY_MAP_OUTPUT_FILEPATH, train_validity_map_output_paths) print('Storing training ground truth file paths into: %s' % TRAIN_GROUND_TRUTH_OUTPUT_FILEPATH) data_utils.write_paths( TRAIN_GROUND_TRUTH_OUTPUT_FILEPATH, train_ground_truth_output_paths)
assert (len(stereo_flow_image0_paths) == len(stereo_flow_disparity_paths)) # Split KITTI 2012 dataset into training and test sets stereo_flow_train_image0_paths = stereo_flow_image0_paths[0:160] stereo_flow_train_image1_paths = stereo_flow_image1_paths[0:160] stereo_flow_train_disparity_paths = stereo_flow_disparity_paths[0:160] stereo_flow_test_image0_paths = stereo_flow_image0_paths[160:] stereo_flow_test_image1_paths = stereo_flow_image1_paths[160:] stereo_flow_test_disparity_paths = stereo_flow_disparity_paths[160:] # Write all paths to disk print( 'Storing all {} stereo flow left stereo images file paths into: {}'.format( len(stereo_flow_image0_paths), STEREO_FLOW_ALL_IMAGE0_FILEPATH)) data_utils.write_paths(STEREO_FLOW_ALL_IMAGE0_FILEPATH, stereo_flow_image0_paths) print('Storing all {} stereo flow right stereo images file paths into: {}'. format(len(stereo_flow_image1_paths), STEREO_FLOW_ALL_IMAGE1_FILEPATH)) data_utils.write_paths(STEREO_FLOW_ALL_IMAGE1_FILEPATH, stereo_flow_image1_paths) print('Storing all {} stereo flow ground truth disparity file paths into: {}'. format(len(stereo_flow_disparity_paths), STEREO_FLOW_ALL_DISPARITY_FILEPATH)) data_utils.write_paths(STEREO_FLOW_ALL_DISPARITY_FILEPATH, stereo_flow_disparity_paths) # Write training paths to disk print('Storing {} training stereo flow left stereo images file paths into: {}'. format(len(stereo_flow_train_image0_paths),
output_semi_dense_depth_path, \ output_dense_depth_path, \ output_ground_truth_path = result # Collect filepaths output_sparse_depth_paths.append(output_sparse_depth_path) output_validity_map_paths.append(output_validity_map_path) output_semi_dense_depth_paths.append(output_semi_dense_depth_path) output_dense_depth_paths.append(output_dense_depth_path) output_ground_truth_paths.append(output_ground_truth_path) print('Completed generating {} depth samples for using KITTI sequence={} camera={}'.format( n_vkitti_filepaths, kitti_sequence, camera_dirpath)) print('Storing sparse depth file paths into: %s' % OUTPUT_SPARSE_DEPTH_FILEPATH) data_utils.write_paths( OUTPUT_SPARSE_DEPTH_FILEPATH, output_sparse_depth_paths) print('Storing validity map file paths into: %s' % OUTPUT_VALIDITY_MAP_FILEPATH) data_utils.write_paths( OUTPUT_VALIDITY_MAP_FILEPATH, output_validity_map_paths) print('Storing semi dense depth file paths into: %s' % OUTPUT_SEMI_DENSE_DEPTH_FILEPATH) data_utils.write_paths( OUTPUT_SEMI_DENSE_DEPTH_FILEPATH, output_semi_dense_depth_paths) print('Storing dense depth file paths into: %s' % OUTPUT_DENSE_DEPTH_FILEPATH) data_utils.write_paths( OUTPUT_DENSE_DEPTH_FILEPATH, output_dense_depth_paths) print('Storing ground-truth depth file paths into: %s' % OUTPUT_GROUND_TRUTH_FILEPATH) data_utils.write_paths(
print('Completed processing {} examples for sequence={}'.format( len(pool_input), seq_dirpath)) print('Completed processing {} examples for density={}'.format( n_sample, data_dirpath)) void_train_image_filepath, \ void_train_sparse_depth_filepath, \ void_train_validity_map_filepath, \ void_train_ground_truth_filepath, \ void_train_intrinsics_filepath = train_filepaths print('Storing training image file paths into: %s' % void_train_image_filepath) data_utils.write_paths(void_train_image_filepath, train_image_outpaths) print('Storing training sparse depth file paths into: %s' % void_train_sparse_depth_filepath) data_utils.write_paths(void_train_sparse_depth_filepath, train_sparse_depth_outpaths) print('Storing training validity map file paths into: %s' % void_train_validity_map_filepath) data_utils.write_paths(void_train_validity_map_filepath, train_validity_map_outpaths) print('Storing training groundtruth depth file paths into: %s' % void_train_ground_truth_filepath) data_utils.write_paths(void_train_ground_truth_filepath, train_ground_truth_outpaths)
output_depth_dirpath = os.path.dirname(output_depth_path) if not os.path.exists(output_depth_dirpath): os.makedirs(output_depth_dirpath) train_output_depth_paths.append(output_depth_path) data_utils.save_depth(output_depth, output_depth_path) n += 1 print('Processed {}/{} training examples \r'.format(n + 1, n_sample), end='\r') except tf.errors.OutOfRangeError: print('Currently processed {}/{} training examples'.format(n, n_sample)) break print('Storing prediction for training file paths into: %s' % args.train_output_depth_path) data_utils.write_paths(args.train_output_depth_path, train_output_depth_paths) ''' Setup for validation and testing set ''' # Load validation sparse depth and validity map paths from file val_sparse_depth_paths = data_utils.read_paths(args.val_sparse_depth_path) # Load testing sparse depth and validity map paths from file test_sparse_depth_paths = data_utils.read_paths(args.test_sparse_depth_path) val_output_depth_paths = [] test_output_depth_paths = [] modes = [ [