def fade_image(context, frame_base: Frame, list_correction: list):
"""
Apply a flat scalar to the respective blocks in the image. See "fade.cpp" in dandere2x_cpp for more in depth
documentation. Roughly
frame_next = frame_next + scalar
Although frame_residuals needs to also be transformed
Method Tasks:
- Load all the vectors and their scalars into a list
- Apply the scalar to all the vectors in the image
"""
# load context
scale_factor = int(context.scale_factor)
block_size = int(context.block_size)
fade_data_size = 3
for x in range(int(len(list_correction) / fade_data_size)):
# load vector
vector = FadeData(int(list_correction[x * fade_data_size + 0]),
int(list_correction[x * fade_data_size + 1]),
int(list_correction[x * fade_data_size + 2]))
# apply vector
frame_base.fade_block(vector.x * scale_factor, vector.y * scale_factor,
block_size * scale_factor, vector.scalar)
# out_image.frame = np.clip(out_image.frame, 0, 255)
return frame_base
def fade_image(context, out_image: Frame, list_correction: list):
# load context
scale_factor = int(context.scale_factor)
logger = logging.getLogger(__name__)
fade_list = []
block_size = int(context.block_size)
fade_data_size = 3
for x in range(int(len(list_correction) / fade_data_size)):
fade_list.append(
FadeData(int(list_correction[x * fade_data_size + 0]),
int(list_correction[x * fade_data_size + 1]),
int(list_correction[x * fade_data_size + 2])))
# copy over predictive vectors into new image
for vector in fade_list:
out_image.fade_block(vector.x * scale_factor, vector.y * scale_factor,
block_size * scale_factor, vector.scalar)
# out_image.frame = np.clip(out_image.frame, 0, 255)
return out_image
