def _original_format(self, image, chw, normalized, gray3dim):
""" Return transformed image with original format. """
if not is_numpy(image):
image = np.array(image)
if chw:
image = hwc_to_chw(image)
if normalized:
image = image / 255
if gray3dim:
image = np.expand_dims(image, 0)
return image
def chw_to_hwc(img):
"""
Transpose the input image; shape (C, H, W) to shape (H, W, C).
Args:
img (numpy.ndarray): Image to be converted.
Returns:
img (numpy.ndarray), Converted image.
"""
if is_numpy(img):
return img.transpose(1, 2, 0).copy()
raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
def is_chw(img):
"""
Check if the input image is shape (H, W, C).
Args:
img (numpy.ndarray): Image to be checked.
Returns:
Bool, True if input is shape (H, W, C).
"""
if is_numpy(img):
img_shape = np.shape(img)
if img_shape[0] == 3 and img_shape[1] > 3 and img_shape[2] > 3:
return True
return False
raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
def is_rgb(img):
"""
Check if the input image is RGB.
Args:
img (numpy.ndarray): Image to be checked.
Returns:
Bool, True if input is RGB.
"""
if is_numpy(img):
img_shape = np.shape(img)
if len(np.shape(img)) == 3 and (img_shape[0] == 3 or img_shape[2] == 3):
return True
return False
raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
def is_normalized(img):
"""
Check if the input image is normalized between 0 to 1.
Args:
img (numpy.ndarray): Image to be checked.
Returns:
Bool, True if input is normalized between 0 to 1.
"""
if is_numpy(img):
minimal = np.min(img)
maximun = np.max(img)
if minimal >= 0 and maximun <= 1:
return True
return False
raise TypeError('img should be Numpy array. Got {}'.format(type(img)))