def fade_image(context, block_size, frame_base: Frame, list_correction: list):
logger = logging.getLogger(__name__)
# load context
scale_factor = int(context.scale_factor)
fade_list = []
out_image = Frame()
out_image.create_new(frame_base.width, frame_base.height)
out_image.copy_image(frame_base)
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
from wrappers.frame import Frame
# f1 = Frame()
# f1.load_from_string("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\100\\frame20.png")
#
# f2 = Frame()
# f2.load_from_string("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\100\\frame21.png")
#
f1 = Frame()
f1.load_from_string(
"C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\inputs\\frame30.jpg")
f1.fade_block(0, 0, 100, -100)
f1.save_image("C:\\Users\\windwoz\\Desktop\\workspace\\violetfade\\lmfao.jpg")