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from PIL import Image
import numpy as np
import torch
from .view_base import BaseView
class NegateView(BaseView):
def __init__(self):
pass
def view(self, im):
return -im
def inverse_view(self, noise):
'''
Negating the variance estimate is "weird" so just don't do it.
This hack seems to work just fine
'''
invert_mask = torch.ones_like(noise)
invert_mask[:3] = -1
return noise * invert_mask
def make_frame(self, im, t):
im_size = im.size[0]
frame_size = int(im_size * 1.5)
# map t from [0, 1] -> [1, -1]
t = 1 - t
t = t * 2 - 1
# Interpolate from pixels from [0, 1] to [1, 0]
im = np.array(im) / 255.
im = ((2 * im - 1) * t + 1) / 2.
im = Image.fromarray((im * 255.).astype(np.uint8))
# Paste on to canvas
frame = Image.new('RGB', (frame_size, frame_size), (255, 255, 255))
frame.paste(im, ((frame_size - im_size) // 2, (frame_size - im_size) // 2))
return frame
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