def splice(self, image, mask, gan_out):
if mask.shape[-1] > 0:
mask = (np.sum(mask, -1, keepdims=True) < 1)
mask = 1 - mask # invert mask for blending
mask = mask.astype('uint8') * 255
mask = GaussianBlur(mask, (29, 29), 0)
# mask_img = np.zeros([mask.shape[0], mask.shape[1],3]).astype('uint8')
# for i in range(3):
# mask_img[:,:,i] = mask
mask_img = mask.astype(float) / 255
# proper blending courtesy of https://www.learnopencv.com/alpha-blending-using-opencv-cpp-python/
fg_o = gan_out.astype(float)
bg_o = image.astype(float)
fg = np.zeros([mask.shape[0], mask.shape[1], 3]).astype(float)
bg = np.zeros([mask.shape[0], mask.shape[1], 3]).astype(
float
) # create foreground and background images with proper rgb channels
cover = image
for i in range(3):
# Multiply the fg with the mask matte
fg[:, :, i] = multiply(mask_img, fg_o[:, :, i])
# Multiply the bg with ( 1 - mask_img )
bg[:, :, i] = multiply(1.0 - mask_img, bg_o[:, :, i])
# Add the masked fg and bg.
cover[:, :, i] = add(fg[:, :, i], bg[:, :, i])
else:
#error case, return image
cover = image
return cover
def field_flattening(image, filter_type="gaussian", ksize=31, sigma=101):
"""
Function that will field flatten a single channel image
"""
if filter_type == "gaussian":
blurred_image = GaussianBlur(image.astype(np.uint8),
ksize=(ksize, ksize),
sigmaX=sigma)
if filter_type == "median":
blurred_image = medianBlur(image.astype(np.uint8), ksize=ksize)
flat_image = np.divide(image.astype(float), blurred_image.astype(float))
flat_image[np.isnan(flat_image)] = 0
flat_image = (min_max_rescaling(flat_image)) * 255
return flat_image