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Update create_coco_tf_record.py #6

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28 changes: 16 additions & 12 deletions create_coco_tf_record.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@
FLAGS = flags.FLAGS


def load_coco_dection_dataset(imgs_dir, annotations_filepath, shuffle_img = True ):
def load_coco_dection_dataset(imgs_dir, coco, shuffle_img = True ):
"""Load data from dataset by pycocotools. This tools can be download from "http://mscoco.org/dataset/#download"
Args:
imgs_dir: directories of coco images
Expand All @@ -49,7 +49,7 @@ def load_coco_dection_dataset(imgs_dir, annotations_filepath, shuffle_img = True
Return:
coco_data: list of dictionary format information of each image
"""
coco = COCO(annotations_filepath)
# coco = COCO(annotations_filepath)
img_ids = coco.getImgIds() # totally 82783 images
cat_ids = coco.getCatIds() # totally 90 catagories, however, the number of categories is not continuous, \
# [0,12,26,29,30,45,66,68,69,71,83] are missing, this is the problem of coco dataset.
Expand Down Expand Up @@ -137,16 +137,20 @@ def main(_):
else:
raise ValueError("you must either convert train data or val data")
# load total coco data
coco_data = load_coco_dection_dataset(imgs_dir,annotations_filepath,shuffle_img=FLAGS.shuffle_imgs)
total_imgs = len(coco_data)
# write coco data to tf record
with tf.python_io.TFRecordWriter(FLAGS.output_filepath) as tfrecord_writer:
for index, img_data in enumerate(coco_data):
if index % 100 == 0:
print("Converting images: %d / %d" % (index, total_imgs))
example = dict_to_coco_example(img_data)
tfrecord_writer.write(example.SerializeToString())
coco = COCO(annotations_filepath)
tfrecord_writer = tf.python_io.TFRecordWriter(FLAGS.output_filepath)
for i in range(41):
coco_data = load_coco_dection_dataset(imgs_dir,coco,shuffle_img=FLAGS.shuffle_imgs)
total_imgs = len(coco_data)
# tfrecord_writer = tf.python_io.TFRecordWriter(FLAGS.output_filepath)
# write coco data to tf record
# with tf.python_io.TFRecordWriter(FLAGS.output_filepath) as tfrecord_writer:
for index, img_data in enumerate(coco_data):
if index % 100 == 0:
print("Converting images: %d / %d" % (index, total_imgs))
example = dict_to_coco_example(img_data)
tfrecord_writer.write(example.SerializeToString())


if __name__ == "__main__":
tf.app.run()
tf.app.run()