图像处理-转化为tfrecords文件

忘是亡心i 2023-10-11 12:32 11阅读 0赞

#!/usr/bin/env python
    """
    Usage:
      # From tensorflow/models/
      # Create train data:
      python generate_tfrecord.py --csv_input=data/train_labels.csv  --output_path=train.record
      # Create test data:
      python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record
    """
    from __future__ import division
    from __future__ import print_function
    from __future__ import absolute_import
    
    import os
    import io
    import pandas as pd
    import tensorflow as tf
    
    from PIL import Image
    from object_detection.utils import dataset_util
    from collections import namedtuple, OrderedDict
    
    flags = tf.app.flags
    flags.DEFINE_string('csv_input', "", 'Path to the CSV input')
    flags.DEFINE_string('output_path', "", 'Path to output TFRecord')
    flags.DEFINE_string('image_dir', "", 'Path to images')
    FLAGS = flags.FLAGS
    
    
    # TO-DO replace this with label map
    def class_text_to_int(row_label):
        if row_label == 'jeans':
            return 1
        if row_label == 'sunglasses':
            return 2
        else:
            None
    
    def split(df, group):
        data = namedtuple('data', ['filename', 'object'])
        gb = df.groupby(group)
        return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
    
    
    def create_tf_example(group, path):
        with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
            encoded_jpg = fid.read()
        encoded_jpg_io = io.BytesIO(encoded_jpg)
        image = Image.open(encoded_jpg_io)
        width, height = image.size
    
        filename = group.filename.encode('utf8')
        image_format = b'jpg'
        xmins = []
        xmaxs = []
        ymins = []
        ymaxs = []
        classes_text = []
        classes = []
    
        for index, row in group.object.iterrows():
            xmins.append(row['xmin'] / width)
            xmaxs.append(row['xmax'] / width)
            ymins.append(row['ymin'] / height)
            ymaxs.append(row['ymax'] / height)
            classes_text.append(row['class'].encode('utf8'))
            classes.append(class_text_to_int(row['class']))
    
        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/encoded': dataset_util.bytes_feature(encoded_jpg),
            '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(classes_text),
            'image/object/class/label': dataset_util.int64_list_feature(classes),
        }))
        return tf_example
    
    
    def main(_):
        writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
        path = os.path.join(FLAGS.image_dir)
        examples = pd.read_csv(FLAGS.csv_input)
        grouped = split(examples, 'filename')
        for group in grouped:
            tf_example = create_tf_example(group, path)
            writer.write(tf_example.SerializeToString())
    
        writer.close()
        output_path = os.path.join(os.getcwd(), FLAGS.output_path)
        print('Successfully created the TFRecords: {}'.format(output_path))
    
    
    if __name__ == '__main__':
        tf.app.run()