def postprocess_image(image: tf.Tensor, original_shape: tuple) -> np.array:
"""
Transforms created image from tf.Tensor to np. array. Image is resized to original
size, batch dimension is removed, also VGG preprocessing is reverted. Output image
is normalized to values [0, 1].
Args:
image: created image as tf.Tensor
original_shape: original shape of the image to resize to
Returns:
image: image as np.array with values in [0, 1] and size of original image
"""
image = image.numpy()
print(f'\nPostprocessing result image from {image.shape} to {original_shape}')
image = image.reshape(image.shape[1:]) # drop batch dimension
# # INVERTING VGG19 PREPROCESSING
# image[:, :, 0] += 103.939
# image[:, :, 1] += 116.779
# image[:, :, 2] += 123.68
# image = image[:, :, ::-1]
image = tf.image.resize(image, original_shape[0:-1]).numpy()
image = normalize_image(image) # normalize values to [0, 1]
return image
def imwrite(img: tf.Tensor,
filename: str,
gamma: float = 2.2,
normalize: bool = False):
"""
write img to filename
Args
====
img: tf.Tensor
filename: str
gamma: float
if the image is not an OpenEXR file, apply gamma correction
normalize:
normalize img to the range [0, 1] before writing
"""
directory = os.path.dirname(filename)
if directory != '' and not os.path.exists(directory):
os.makedirs(directory)
assert (tf.executing_eagerly())
img = img.numpy()
if normalize:
img_rng = np.max(img) - np.min(img)
if img_rng > 0:
img = (img - np.min(img)) / img_rng
if filename[-4:] == '.exr':
if len(img.shape) == 2:
img = img.reshape((img.shape[0], img.shape[1], 1))
if img.shape[2] == 1:
img = np.tile(img, (1, 1, 3))
img_r = img[:, :, 0]
img_g = img[:, :, 1]
img_b = img[:, :, 2]
pixels_r = img_r.astype(np.float16).tostring()
pixels_g = img_g.astype(np.float16).tostring()
pixels_b = img_b.astype(np.float16).tostring()
HEADER = OpenEXR.Header(img.shape[1], img.shape[0])
half_chan = Imath.Channel(Imath.PixelType(Imath.PixelType.HALF))
HEADER['channels'] = dict([(c, half_chan) for c in "RGB"])
exr = OpenEXR.OutputFile(filename, HEADER)
exr.writePixels({'R': pixels_r, 'G': pixels_g, 'B': pixels_b})
exr.close()
else:
skimage.io.imsave(filename,
(np.power(np.clip(img, 0.0, 1.0), 1.0 / gamma) *
255).astype(np.uint8))
def tensor_to_image(tensor: tf.Tensor, width: int, height: int, channels: int = 3) -> np.array:
"""
Convert tensor representation of an image into displayable array
:param tensor: tensor with image content
:param height: target image height
:param width: target image width
:param channels: number of channels in an image
:return: ND numpy array with image content
"""
tensor = tensor.reshape((height, width, channels))
# Remove zero-center by mean pixel
tensor[:, :, 0] += 103.939
tensor[:, :, 1] += 116.779
tensor[:, :, 2] += 123.68
tensor = tensor[:, :, ::-1]
return np.clip(tensor, 0, 255).astype('uint8')
