      """
      # https://stackoverflow.com/questions/2017843/fetch-frame-count-with-ffmpeg
  
      logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
      assert isinstance(path, str), "Video path must be a string"
      exe = im_ffm.get_ffmpeg_exe()
      iswin = sys.platform.startswith("win")
      logger.debug("iswin: '%s'", iswin)
      cmd = [exe, "-i", path, "-map", "0:v:0", "-c", "copy", "-f", "null", "-"]
      logger.debug("FFMPEG Command: '%s'", " ".join(cmd))
      attempts = 3
      for attempt in range(attempts):
          try:
              logger.debug("attempt: %s of %s", attempt + 1, attempts)
              out = subprocess.check_output(cmd,
                                            stderr=subprocess.STDOUT,
                                            shell=iswin,
                                            timeout=timeout)
              logger.debug("Succesfully communicated with FFMPEG")
              break
          except subprocess.CalledProcessError as err:
              out = err.output.decode(errors="ignore")
              raise RuntimeError("FFMEG call failed with {}:\n{}".format(err.returncode, out))
          except subprocess.TimeoutExpired as err:
 
</s> remove         except subprocess.CalledProcessError as err:
            out = err.output.decode(errors="ignore")
            raise RuntimeError("FFMEG call failed with {}:\n{}".format(err.returncode, out))
        except subprocess.TimeoutExpired as err:
            this_attempt = attempt + 1
            if this_attempt == attempts:
                msg = ("FFMPEG hung while attempting to obtain the frame count. "
                       "Sometimes this issue resolves itself, so you can try running again. "
                       "Otherwise use the Effmpeg Tool to extract the frames from your video into "
                       "a folder, and then run the requested Faceswap process on that folder.")
                raise FaceswapError(msg) from err
            logger.warning("FFMPEG hung while attempting to obtain the frame count. "
                           "Retrying %s of %s", this_attempt + 1, attempts)
            continue

    # Note that other than with the subprocess calls below, ffmpeg wont hang here.
    # Worst case Python will stop/crash and ffmpeg will continue running until done.

    nframes = nsecs = None
    for line in reversed(out.splitlines()):
        if not line.startswith(b"frame="):
            continue
        line = line.decode(errors="ignore")
        logger.debug("frame line: '%s'", line)
        idx = line.find("frame=")
        if idx >= 0:
            splitframes = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
            nframes = int(splitframes)
        idx = line.find("time=")
        if idx >= 0:
            splittime = line[idx:].split("=", 1)[-1].lstrip().split(" ", 1)[0].strip()
            nsecs = convert_to_secs(*splittime.split(":"))
        logger.debug("nframes: %s, nsecs: %s", nframes, nsecs)
        return nframes, nsecs

    raise RuntimeError("Could not get number of frames")  # pragma: no cover
 </s> add         elif parts[1] == path:  # sentinel for relative paths
            allparts.insert(0, parts[1])
            break
        else:
            path = parts[0]
            allparts.insert(0, parts[1])
    logger.trace("path: %s, allparts: %s", path, allparts)
    return allparts </s> remove def count_frames_and_secs(path, timeout=60):
    """
    Adapted From ffmpeg_imageio, to handle occasional hanging issue:
    https://github.com/imageio/imageio-ffmpeg

    Get the number of frames and number of seconds for the given video
    file. Note that this operation can be quite slow for large files.

    Disclaimer: I've seen this produce different results from actually reading
    the frames with older versions of ffmpeg (2.x). Therefore I cannot say
    with 100% certainty that the returned values are always exact.
    """
    # https://stackoverflow.com/questions/2017843/fetch-frame-count-with-ffmpeg

 </s> add def full_path_split(path):
    """ Split a given path into all of it's separate components """ </s> remove def full_path_split(path):
    """ Split a given path into all of it's separate components """
    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
            break
        elif parts[1] == path:  # sentinel for relative paths
            allparts.insert(0, parts[1])
            break
        else:
            path = parts[0]
            allparts.insert(0, parts[1])
    logger.trace("path: %s, allparts: %s", path, allparts)
    return allparts


def cv2_read_img(filename, raise_error=False):
    """ Read an image with cv2 and check that an image was actually loaded.
        Logs an error if the image returned is None. or an error has occured.

        Pass raise_error=True if error should be raised """
    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
    logger.trace("Requested image: '%s'", filename)
    success = True
    image = None
    try:
        image = cv2.imread(filename)  # pylint:disable=no-member,c-extension-no-member
        if image is None:
            raise ValueError
    except TypeError:
        success = False
        msg = "Error while reading image (TypeError): '{}'".format(filename)
        logger.error(msg)
        if raise_error:
            raise Exception(msg)
    except ValueError:
        success = False
        msg = ("Error while reading image. This is most likely caused by special characters in "
               "the filename: '{}'".format(filename))
        logger.error(msg)
        if raise_error:
            raise Exception(msg)
    except Exception as err:  # pylint:disable=broad-except
        success = False
        msg = "Failed to load image '{}'. Original Error: {}".format(filename, str(err))
        logger.error(msg)
        if raise_error:
            raise Exception(msg)
    logger.trace("Loaded image: '%s'. Success: %s", filename, success)
    return image


def hash_image_file(filename):
    """ Return an image file's sha1 hash """
    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
    img = cv2_read_img(filename, raise_error=True)
    img_hash = sha1(img).hexdigest()
    logger.trace("filename: '%s', hash: %s", filename, img_hash)
    return img_hash


def hash_encode_image(image, extension):
    """ Encode the image, get the hash and return the hash with
        encoded image """
    img = cv2.imencode(extension, image)[1]  # pylint:disable=no-member,c-extension-no-member
    f_hash = sha1(
        cv2.imdecode(  # pylint:disable=no-member,c-extension-no-member
            img,
            cv2.IMREAD_UNCHANGED)).hexdigest()  # pylint:disable=no-member,c-extension-no-member
    return f_hash, img


 </s> add  </s> remove     def random_warp(self, image):
        """ get pair of random warped images from aligned face image """
        logger.trace("Randomly warping image")
        height, width = image.shape[0:2]
        coverage = self.get_coverage(image) // 2
        try:
            assert height == width and height % 2 == 0
        except AssertionError as err:
            msg = ("Training images should be square with an even number of pixels across each "
                   "side. An image was found with width: {}, height: {}."
                   "\nMost likely this is a frame rather than a face within your training set. "
                   "\nMake sure that the only images within your training set are faces generated "
                   "from the Extract process.".format(width, height))
            raise FaceswapError(msg) from err

        range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32')
        mapx = np.broadcast_to(range_, (5, 5)).copy()
        mapy = mapx.T
        # mapx, mapy = np.float32(np.meshgrid(range_,range_)) # instead of broadcast

        pad = int(1.25 * self.input_size)
        slices = slice(pad // 10, -pad // 10)
        dst_slices = [slice(0, (size + 1), (size // 4)) for size in self.output_sizes]
        interp = np.empty((2, self.input_size, self.input_size), dtype='float32')
 </s> add     # <<< COLOR AUGMENTATION >>> #
    def color_adjust(self, batch):
        """ Perform color augmentation on the passed in batch. </s> remove             assert length >= self.batchsize, msg
 </s> add             assert length >= self._batchsize, msg
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
      """
      # https://stackoverflow.com/questions/2017843/fetch-frame-count-with-ffmpeg
  
      logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
    allparts = list()
    while True:
        parts = os.path.split(path)
        if parts[0] == path:   # sentinel for absolute paths
            allparts.insert(0, parts[0])
              break
          except subprocess.CalledProcessError as err:
              out = err.output.decode(errors="ignore")
              raise RuntimeError("FFMEG call failed with {}:\n{}".format(err.returncode, out))
          except subprocess.TimeoutExpired as err:
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Optimize Data Augmentation (#881)

* Move image utils to lib.image
* Add .pylintrc file
* Remove some cv2 pylint ignores
* TrainingData: Load images from disk in batches
* TrainingData: get_landmarks to batch
* TrainingData: transform and flip to batches
* TrainingData: Optimize color augmentation
* TrainingData: Optimize target and random_warp
* TrainingData - Convert _get_closest_match for batching
* TrainingData: Warp To Landmarks optimized
* Save models to threadpoolexecutor
* Move stack_images, Rename ImageManipulation. ImageAugmentation Docstrings
* Masks: Set dtype and threshold for lib.masks based on input face
* Docstrings and Documentation 
lib/utils.py