def main(_):
if not os.path.exists(FLAGS.images_dir):
raise ValueError('`images_dir` is not exist.')
if not os.path.exists(FLAGS.annotations_json_dir):
raise ValueError('`annotations_json_dir` is not exist.')
if not os.path.exists(FLAGS.label_map_path):
raise ValueError('`label_map_path` is not exist.')
label_map = read_pbtxt_file.get_label_map_dict(FLAGS.label_map_path)
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
num_annotations_skiped = 0
annotations_json_path = os.path.join(FLAGS.annotations_json_dir, '*.json')
for i, annotation_file in enumerate(glob.glob(annotations_json_path)):
if i % 100 == 0:
print('On image %d', i)
annotation_dict = _get_annotation_dict(FLAGS.images_dir,
annotation_file)
if annotation_dict is None:
num_annotations_skiped += 1
continue
tf_example = create_tf_example(annotation_dict, label_map)
writer.write(tf_example.SerializeToString())
print('Successfully created TFRecord to {}.'.format(FLAGS.output_path))
def main(_):
train, validate, test = sys.argv[1:4]
#print("train, validate ,test=",(train, validate ,test))
KindPath = ['Train', "Validate", "Test"]
DirNum = [{}, {}, {}]
if not os.path.exists(FLAGS.images_dir):
raise ValueError('`images_dir` is not exist.')
if not os.path.exists(FLAGS.annotations_json_dir):
raise ValueError('`annotations_json_dir` is not exist.')
if not os.path.exists(FLAGS.label_map_path):
raise ValueError('`label_map_path` is not exist.')
label_map = read_pbtxt_file.get_label_map_dict(FLAGS.label_map_path)
TargetDir = FLAGS.output_path + os.sep + "Test"
if os.path.isdir(TargetDir): rmtree(FLAGS.output_path + os.sep + "Test")
writer = []
for i in range(2):
writer.append(
tf.io.TFRecordWriter(FLAGS.output_path + os.sep + KindPath[i] +
".record"))
num_annotations_skiped = 0
annotations_json_path = os.path.join(FLAGS.annotations_json_dir, '*.json')
annotations_json_path = []
for root, dirs, files in os.walk(FLAGS.annotations_json_dir):
for f in files:
fullpath = os.path.join(root, f)
if ".json" in fullpath:
annotations_json_path.append(fullpath)
for i, annotation_file in enumerate(annotations_json_path):
ix = np.random.choice([0, 1, 2], size=1, p=[train, validate, test])[0]
if ix == 2:
DirNum[ix] = ImgCopy(annotation_file, FLAGS.output_path,
DirNum[ix])
continue
#print(i," :",annotation_file)
#if i % 100 == 0:
# print('On image %d', i)
print("\n")
print("ix=", ix)
print("DirNum=", DirNum)
print("annotation_file=", annotation_file)
annotation_dict, DirNum[ix] = _get_annotation_dict(
annotation_file, DirNum[ix])
if annotation_dict is None:
num_annotations_skiped += 1
continue
tf_example = create_tf_example(annotation_dict, label_map)
writer[ix].write(tf_example.SerializeToString())
#writer.write(tf_example.SerializeToString())
#print('Successfully created TFRecord to {}.'.format(FLAGS.output_path))
print("DirNum=", DirNum)
def main(_):
if not os.path.exists(FLAGS.images_dir):
raise ValueError('`images_dir` is not exist.')
if not os.path.exists(FLAGS.annotations_json_dir):
raise ValueError('`annotations_json_dir` is not exist.')
if not os.path.exists(FLAGS.label_map_path):
raise ValueError('`label_map_path` is not exist.')
label_map = read_pbtxt_file.get_label_map_dict(FLAGS.label_map_path)
if not os.path.exists(FLAGS.output_path):
os.makedirs(FLAGS.output_path)
print("tfrecord文件夹不存在，已重新创建...")
writer = tf.python_io.TFRecordWriter(FLAGS.output_path + "/train.record")
num_annotations_skiped = 0
annotations_json_path = os.path.join(FLAGS.annotations_json_dir, '*.json')
print("annotations_json_path: ", annotations_json_path)
annotations_json_pathAll = glob.glob(annotations_json_path)
print("len(annotations_json_pathAll): ", len(annotations_json_pathAll))
numImages = len(annotations_json_pathAll)
randIndices = np.random.permutation(len(annotations_json_pathAll))
print("randIndices: ", randIndices)
for i in range(numImages):
annotation_file = annotations_json_pathAll[randIndices[i]]
print(i, ": ", annotation_file)
if i % 10 == 0:
print('On image %d...' % i)
annotation_dict = _get_annotation_dict(FLAGS.images_dir,
annotation_file)
if annotation_dict is None:
num_annotations_skiped += 1
continue
tf_example = create_tf_example(annotation_dict, label_map)
writer.write(tf_example.SerializeToString())
print('num_annotations_skiped: ', num_annotations_skiped)
print('Successfully created TFRecord to {}.'.format(FLAGS.output_path))