image_utils.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Image handling utilities

License_info:
# ==============================================================================
# ISC License (ISC)
# Copyright 2020 Christian Doppler Laboratory for Embedded Machine Learning
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
# REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
# INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
# LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
# OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
# PERFORMANCE OF THIS SOFTWARE.

# The following script uses several method fragments from Tensorflow
https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_pascal_tf_record.py

Tensorflow has the following licence:
# ==============================================================================
# Copyright 2020 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.
# ==============================================================================
"""

# Futures
from __future__ import print_function

# Built-in/Generic Imports
import os

# Libs
import numpy as np
from six import BytesIO
import re
import matplotlib.pyplot as plt
from PIL import Image
import tensorflow as tf

# Own modules

__author__ = 'Alexander Wendt'
__copyright__ = 'Copyright 2020, Christian Doppler Laboratory for ' \
                'Embedded Machine Learning'
__credits__ = ['']
__license__ = 'ISC'
__version__ = '0.2.0'
__maintainer__ = 'Alexander Wendt'
__email__ = 'alexander.wendt@tuwien.ac.at'
__status__ = 'Experiental'

def load_image_into_numpy_array(path):
  """Load an image from file into a numpy array.

  Puts image into numpy array to feed into tensorflow graph.
  Note that by convention we put it into a numpy array with shape
  (height, width, channels), where channels=3 for RGB.

  Args:
    path: a file path (this can be local or on colossus)

  Returns:
    uint8 numpy array with shape (img_height, img_width, 3)
  """
  img_data = tf.io.gfile.GFile(path, 'rb').read()
  image = Image.open(BytesIO(img_data))
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


def get_images_name(image_folder):
    image_folder = image_folder.replace('\\', '/')
    image_names = [f for f in os.listdir(image_folder)
                   if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(.jpg|.jpeg|.png)$', f)]

    return image_names

def show_save_figure(fig, output_dir=None, filename=None, show_image=True):
    '''
    Show and save an image

    :param
        output_dir: Directory to put image
        filename: Filename to use. No file ending necessary. Png will be used. If None, then image is not saved.
            If image filename and outputdir is is set, the image will be saved
        show_image: Show image as non blocking. Default: True


    '''
    if filename:
        if not os.path.isdir(output_dir):
            os.makedirs(output_dir)
        fig.savefig(os.path.join(output_dir, filename))
    if show_image:
        plt.show(block=False)
        plt.pause(0.1)
        plt.close()