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Converted category ids to classes for SSD COCO case

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-07-24 11:59:09 +02:00
parent 3330ce69e7
commit 5cba4046a5
1 changed files with 20 additions and 4 deletions
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24
src/twomartens/masterthesis/data.py
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@ -23,8 +23,13 @@ Functions:
load_scenenet_data(...): loads the SceneNet RGB-D data into a Tensorflow data set load_scenenet_data(...): loads the SceneNet RGB-D data into a Tensorflow data set
prepare_scenenet_data(...): prepares the SceneNet RGB-D data and returns it in Python format prepare_scenenet_data(...): prepares the SceneNet RGB-D data and returns it in Python format
""" """
from typing import Callable, List, Mapping, Tuple, Optional, Generator from typing import Callable
from typing import Generator
from typing import List
from typing import Mapping
from typing import Optional
from typing import Sequence from typing import Sequence
from typing import Tuple
import numpy as np import numpy as np
import scipy import scipy
@ -32,8 +37,10 @@ import tensorflow as tf
import tqdm import tqdm
from scipy import ndimage from scipy import ndimage
from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_data_generator, \ from twomartens.masterthesis.ssd_keras.data_generator import data_augmentation_chain_original_ssd
data_augmentation_chain_original_ssd, object_detection_2d_photometric_ops, object_detection_2d_geometric_ops from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_data_generator
from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_geometric_ops
from twomartens.masterthesis.ssd_keras.data_generator import object_detection_2d_photometric_ops
from twomartens.masterthesis.ssd_keras.ssd_encoder_decoder import ssd_input_encoder from twomartens.masterthesis.ssd_keras.ssd_encoder_decoder import ssd_input_encoder
@ -299,7 +306,16 @@ def load_coco_val_ssd(clean_dataset: callable,
cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train) cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
checked_image_paths, checked_bboxes = clean_dataset(annotations, file_names, ids_to_images) checked_image_paths, checked_bboxes = clean_dataset(annotations, file_names, ids_to_images)
final_image_paths, final_labels = group_bboxes_to_images(checked_image_paths, checked_bboxes) bboxes_with_converted_cat_ids = []
for bbox in checked_bboxes:
bboxes_with_converted_cat_ids.append([
cats_to_classes[bbox[0]],
bbox[1],
bbox[2],
bbox[3],
bbox[4]
])
final_image_paths, final_labels = group_bboxes_to_images(checked_image_paths, bboxes_with_converted_cat_ids)
data_generator = object_detection_2d_data_generator.DataGenerator( data_generator = object_detection_2d_data_generator.DataGenerator(
filenames=final_image_paths, filenames=final_image_paths,