def resize_perturbation(image_array, min_factor=0.3):
factor = random.random() * (1 - min_factor) + min_factor
new_size = (int(image_array.shape[1] * factor),
int(image_array.shape[0] * factor))
original_size = image_array.shape[:2][::-1]
image = img_util.get_PIL(image_array).resize(new_size,
resample=PIL.Image.BILINEAR)
image = img_util.get_PIL(img_util.get_np(image))
image_array = img_util.get_np(
image.resize(original_size, resample=PIL.Image.BILINEAR))
#image_array = binarize(image_array)
return image_array
def median(image_array, size):
image_array = img_util.get_np(image_array)
return cv2.medianBlur(image_array, size)
def dilate(image_array, size):
image_array = img_util.get_np(image_array)
return cv2.dilate(image_array, np.ones((size, size)))
def erode(image_array, size):
image_array = img_util.get_np(image_array)
return cv2.erode(image_array, np.ones((size, size)))
def dilate_perturbation(image_array, range_max=2):
image_array = img_util.get_np(image_array)
erode_value = random.randint(1, range_max)
if erode_value > 1:
image_array = dilate(image_array, erode_value)
return image_array