Python preprocessing.cv2resizeminedge() Examples

The following are 6 code examples of preprocessing.cv2resizeminedge(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module preprocessing , or try the search function .
Example #1
Source File: videos_to_tfrecords.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None
Example #2
Source File: videos_to_tfrecords.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None
Example #3
Source File: videos_to_tfrecords.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None
Example #4
Source File: videos_to_tfrecords.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None
Example #5
Source File: videos_to_tfrecords.py    From models with Apache License 2.0 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None
Example #6
Source File: videos_to_tfrecords.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def ParallelPreprocessing(args):
  """Parallel preprocessing: rotation, resize and jpeg encoding to string."""
  (vid_path, timestep, num_timesteps, view) = args
  try:
    image = GetSpecificFrame(vid_path, timestep)

    # Resizing.
    resize_str = ''
    if FLAGS.resize_min_edge > 0:
      resize_str += ', resize ' + shapestring(image)
      image = cv2resizeminedge(image, FLAGS.resize_min_edge)
      resize_str += ' => ' + shapestring(image)

    # Rotating.
    rotate = None
    if FLAGS.rotate:
      rotate = FLAGS.rotate
      if FLAGS.rotate_if_matching is not None:
        rotate = None
        patt = re.compile(FLAGS.rotate_if_matching)
        if patt.match(vid_path) is not None:
          rotate = FLAGS.rotate
      if rotate is not None:
        image = cv2rotateimage(image, FLAGS.rotate)

    # Jpeg encoding.
    image = Image.fromarray(image)
    im_string = bytes_feature([JpegString(image)])

    if timestep % FLAGS.log_frequency == 0:
      tf.logging.info('Loaded frame %d / %d for %s (rotation %s%s) from %s' %
                      (timestep, num_timesteps, view, str(rotate), resize_str,
                       vid_path))
    return im_string
  except cv2.error as e:
    tf.logging.error('Error while loading frame %d of %s: %s' %
                     (timestep, vid_path, str(e)))
    return None