Initial commit

Author: halhwadi

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

cmd_comands.txt

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+creating coco json files:
+python labelme2coco.py images\train train --labels labels.txt
+python labelme2coco.py images\test test --labels labels.txt
+
+
+Creating tefreocrd:
+python create_coco_tf_record.py --logtostderr --train_image_dir=images/train --test_image_dir=images/test --train_annotations_file=train/annotations.json --test_annotations_file=test/annotations.json --output_dir=tfrecords --include_masks
+
+training the model
+python model_main_tf2.py --model_dir=model --pipeline_config_path=model\pipeline.config --num_train_steps=3000
+
+Running tensorboard:
+tensorboard --logdir=model\train
+
+
+
+
+

create_coco_tf_record.py

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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+r"""Convert raw COCO dataset to TFRecord for object_detection.
+
+Please note that this tool creates sharded output files.
+
+Example usage:
+    python create_coco_tf_record.py --logtostderr \
+      --train_image_dir="${TRAIN_IMAGE_DIR}" \
+      --test_image_dir="${TEST_IMAGE_DIR}" \
+      --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \
+      --test_annotations_file="${TEST_ANNOTATIONS_FILE}" \
+      --output_dir="${OUTPUT_DIR}"
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import hashlib
+import io
+import json
+import os
+import contextlib2
+import numpy as np
+import PIL.Image
+
+from pycocotools import mask
+
+from tensorflow.python.framework.versions import VERSION
+if VERSION >= "2.0.0a0":
+    import tensorflow.compat.v1 as tf
+else:
+    import tensorflow as tf
+
+from object_detection.dataset_tools import tf_record_creation_util
+from object_detection.utils import dataset_util
+from object_detection.utils import label_map_util
+
+
+flags = tf.app.flags
+tf.flags.DEFINE_boolean('include_masks', True,
+                        'Whether to include instance segmentations masks '
+                        '(PNG encoded) in the result. default: True.')
+tf.flags.DEFINE_string('train_image_dir', '',
+                       'Training image directory.')
+tf.flags.DEFINE_string('test_image_dir', '',
+                       'Test image directory.')
+tf.flags.DEFINE_string('train_annotations_file', '',
+                       'Training annotations JSON file.')
+tf.flags.DEFINE_string('test_annotations_file', '',
+                       'Test-dev annotations JSON file.')
+tf.flags.DEFINE_string('output_dir', '/tmp/', 'Output data directory.')
+
+FLAGS = flags.FLAGS
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+
+def create_tf_example(image,
+                      annotations_list,
+                      image_dir,
+                      category_index,
+                      include_masks=False):
+  """Converts image and annotations to a tf.Example proto.
+
+  Args:
+    image: dict with keys:
+      [u'license', u'file_name', u'coco_url', u'height', u'width',
+      u'date_captured', u'flickr_url', u'id']
+    annotations_list:
+      list of dicts with keys:
+      [u'segmentation', u'area', u'iscrowd', u'image_id',
+      u'bbox', u'category_id', u'id']
+      Notice that bounding box coordinates in the official COCO dataset are
+      given as [x, y, width, height] tuples using absolute coordinates where
+      x, y represent the top-left (0-indexed) corner.  This function converts
+      to the format expected by the Tensorflow Object Detection API (which is
+      which is [ymin, xmin, ymax, xmax] with coordinates normalized relative
+      to image size).
+    image_dir: directory containing the image files.
+    category_index: a dict containing COCO category information keyed
+      by the 'id' field of each category.  See the
+      label_map_util.create_category_index function.
+    include_masks: Whether to include instance segmentations masks
+      (PNG encoded) in the result. default: False.
+  Returns:
+    example: The converted tf.Example
+    num_annotations_skipped: Number of (invalid) annotations that were ignored.
+
+  Raises:
+    ValueError: if the image pointed to by data['filename'] is not a valid JPEG
+  """
+  image_height = image['height']
+  image_width = image['width']
+  filename = image['file_name']
+  image_id = image['id']
+
+  full_path = os.path.join(image_dir, filename)
+  with tf.gfile.GFile(full_path, 'rb') as fid:
+    encoded_jpg = fid.read()
+  encoded_jpg_io = io.BytesIO(encoded_jpg)
+  image = PIL.Image.open(encoded_jpg_io)
+  key = hashlib.sha256(encoded_jpg).hexdigest()
+
+  xmin = []
+  xmax = []
+  ymin = []
+  ymax = []
+  is_crowd = []
+  category_names = []
+  category_ids = []
+  area = []
+  encoded_mask_png = []
+  num_annotations_skipped = 0
+  for object_annotations in annotations_list:
+    (x, y, width, height) = tuple(object_annotations['bbox'])
+    if width <= 0 or height <= 0:
+      num_annotations_skipped += 1
+      continue
+    if x + width > image_width or y + height > image_height:
+      num_annotations_skipped += 1
+      continue
+    xmin.append(float(x) / image_width)
+    xmax.append(float(x + width) / image_width)
+    ymin.append(float(y) / image_height)
+    ymax.append(float(y + height) / image_height)
+    is_crowd.append(object_annotations['iscrowd'])
+    category_id = int(object_annotations['category_id'])
+    category_ids.append(category_id)
+    category_names.append(category_index[category_id]['name'].encode('utf8'))
+    area.append(object_annotations['area'])
+
+    if include_masks:
+      run_len_encoding = mask.frPyObjects(object_annotations['segmentation'],
+                                          image_height, image_width)
+      binary_mask = mask.decode(run_len_encoding)
+      if not object_annotations['iscrowd']:
+        binary_mask = np.amax(binary_mask, axis=2)
+      pil_image = PIL.Image.fromarray(binary_mask)
+      output_io = io.BytesIO()
+      pil_image.save(output_io, format='PNG')
+      encoded_mask_png.append(output_io.getvalue())
+  feature_dict = {
+      'image/height':
+          dataset_util.int64_feature(image_height),
+      'image/width':
+          dataset_util.int64_feature(image_width),
+      'image/filename':
+          dataset_util.bytes_feature(filename.encode('utf8')),
+      'image/source_id':
+          dataset_util.bytes_feature(str(image_id).encode('utf8')),
+      'image/key/sha256':
+          dataset_util.bytes_feature(key.encode('utf8')),
+      'image/encoded':
+          dataset_util.bytes_feature(encoded_jpg),
+      'image/format':
+          dataset_util.bytes_feature('jpeg'.encode('utf8')),
+      'image/object/bbox/xmin':
+          dataset_util.float_list_feature(xmin),
+      'image/object/bbox/xmax':
+          dataset_util.float_list_feature(xmax),
+      'image/object/bbox/ymin':
+          dataset_util.float_list_feature(ymin),
+      'image/object/bbox/ymax':
+          dataset_util.float_list_feature(ymax),
+      'image/object/bbox/class/label':
+      dataset_util.int64_list_feature(category_ids),
+      'image/object/class/text':
+          dataset_util.bytes_list_feature(category_names),
+      'image/object/class/label':
+      dataset_util.int64_list_feature(category_ids),
+      'image/object/is_crowd':
+          dataset_util.int64_list_feature(is_crowd),
+      'image/object/area':
+          dataset_util.float_list_feature(area),
+  }
+  if include_masks:
+    feature_dict['image/object/mask'] = (
+        dataset_util.bytes_list_feature(encoded_mask_png))
+  example = tf.train.Example(features=tf.train.Features(feature=feature_dict))
+  return key, example, num_annotations_skipped
+
+
+def _create_tf_record_from_coco_annotations(
+    annotations_file, image_dir, output_path, include_masks):
+  """Loads COCO annotation json files and converts to tf.Record format.
+
+  Args:
+    annotations_file: JSON file containing bounding box annotations.
+    image_dir: Directory containing the image files.
+    output_path: Path to output tf.Record file.
+    include_masks: Whether to include instance segmentations masks
+      (PNG encoded) in the result. default: False.
+  """
+  with tf.gfile.GFile(annotations_file, 'r') as fid:
+    output_tfrecords = tf.python_io.TFRecordWriter(output_path)
+    groundtruth_data = json.load(fid)
+    images = groundtruth_data['images']
+    category_index = label_map_util.create_category_index(
+        groundtruth_data['categories'])
+
+    annotations_index = {}
+    if 'annotations' in groundtruth_data:
+      tf.logging.info(
+          'Found groundtruth annotations. Building annotations index.')
+      for annotation in groundtruth_data['annotations']:
+        image_id = annotation['image_id']
+        if image_id not in annotations_index:
+          annotations_index[image_id] = []
+        annotations_index[image_id].append(annotation)
+    missing_annotation_count = 0
+    for image in images:
+      image_id = image['id']
+      if image_id not in annotations_index:
+        missing_annotation_count += 1
+        annotations_index[image_id] = []
+    tf.logging.info('%d images are missing annotations.',
+                    missing_annotation_count)
+
+    total_num_annotations_skipped = 0
+    for idx, image in enumerate(images):
+      if idx % 100 == 0:
+        tf.logging.info('On image %d of %d', idx, len(images))
+      annotations_list = annotations_index[image['id']]
+      _, tf_example, num_annotations_skipped = create_tf_example(
+          image, annotations_list, image_dir, category_index, include_masks)
+      total_num_annotations_skipped += num_annotations_skipped
+      output_tfrecords.write(tf_example.SerializeToString())
+    tf.logging.info('Finished writing, skipped %d annotations.',
+                    total_num_annotations_skipped)
+
+
+def main(_):
+  assert FLAGS.train_image_dir, '`train_image_dir` missing.'
+  assert FLAGS.test_image_dir, '`test_image_dir` missing.'
+  assert FLAGS.train_annotations_file, '`train_annotations_file` missing.'
+  assert FLAGS.test_annotations_file, '`test_annotations_file` missing.'
+
+  if not tf.gfile.IsDirectory(FLAGS.output_dir):
+    tf.gfile.MakeDirs(FLAGS.output_dir)
+  train_output_path = os.path.join(FLAGS.output_dir, 'train.record')
+  testdev_output_path = os.path.join(FLAGS.output_dir, 'test.record')
+
+  _create_tf_record_from_coco_annotations(
+      FLAGS.train_annotations_file,
+      FLAGS.train_image_dir,
+      train_output_path,
+      FLAGS.include_masks)
+  _create_tf_record_from_coco_annotations(
+      FLAGS.test_annotations_file,
+      FLAGS.test_image_dir,
+      testdev_output_path,
+      FLAGS.include_masks)
+
+
+if __name__ == '__main__':
+  tf.app.run()