[Tensorflow] Object Detection API - build your training environment

一、前期准备

• Prepare protoc

Download Protocol Buffers

Create folder: protoc and unzip it.

unsw@unsw-UX303UB$ ls
models  Others  protoc  train_data

unsw@unsw-UX303UB$ ls protoc/
bin  include  readme.txt

unsw@unsw-UX303UB$ ls protoc/bin/
protoc

  

• Prepare model

Download model folder from tensorflow github. 

unsw@unsw-UX303UB$ git clone https://github.com/tensorflow/models.git
Cloning into 'models'...
remote: Counting objects: 7518, done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 7518 (delta 0), reused 1 (delta 0), pack-reused 7513
Receiving objects: 100% (7518/7518), 157.87 MiB | 1.17 MiB/s, done.
Resolving deltas: 100% (4053/4053), done.
Checking connectivity... done.

unsw@unsw-UX303UB$ ls
annotations  images  models  Others  raccoon_labels.csv  xml_to_csv.py

unsw@unsw-UX303UB$ ls models/
AUTHORS     CONTRIBUTING.md    LICENSE   README.md  tutorials
CODEOWNERS  ISSUE_TEMPLATE.md  official  research   WORKSPACE

Enter: models/research/

# Set python env.
$ export PYTHONPATH=/home/unsw/Dropbox/Programmer/1-python/Tensorflow/ssd_proj/models/research/slim::pwd:pwd/slim:$PYTHONPATH
$ python object_detection/builders/model_builder_test.py
.......
----------------------------------------------------------------------
Ran 7 tests in 0.022s

OK

  

• Prepare train.record

Download: https://github.com/datitran/raccoon_dataset/blob/master/generate_tfrecord.py

"""
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 = flags.FLAGS


# TO-DO replace this with label map
def class_text_to_int(row_label):
    if row_label == 'raccoon':
        return 1
    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(os.getcwd(), 'images')
    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()

generate_tfrecord.py