pip install glob2
pip install opencv-python

# FROM models/research/object_detection
wget http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz
tar -zxvf faster_rcnn_inception_v2_coco_2018_01_28.tar.gz

透過 TFRecordWriter 產生 train.record / eval.record

產生的方式可以參考

def create_tf_example(ex):


  height = ex['height']
  width = ex['width']
  filename = ex['filename']
  image_format = ex['image_format']
  xmins = ex['xmins']
  xmaxs = ex['xmaxs']
  ymins = ex['ymins']
  ymaxs = ex['ymaxs']
  classes_text = ex['classes_text']
  classes = ex['classes']

  print(classes_text)
  print(classes)

  tf_example = tf.train.Example(features=tf.train.Features(feature={
    'image/height': dataset_util.int64_feature(height),
    'image/width': dataset_util.int64_feature(width),
    'image/filename': dataset_util.bytes_feature(filename),
    'image/source_id': dataset_util.bytes_feature(filename),
    'image/format': dataset_util.bytes_feature(image_format),
    'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
    'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
    'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
    'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
    'image/object/class/text': dataset_util.bytes_list_feature(str(classes_text)),
    'image/object/class/label': dataset_util.int64_list_feature(classes),
  }))


  return tf_example