mirror of
https://github.com/jantic/DeOldify.git
synced 2026-08-28 17:45:27 +08:00
Jason Antic
125 lines
4.7 KiB
Python
125 lines
4.7 KiB
Python
from fastai.basic_data import DatasetType
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from fastai.basic_train import Learner
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from abc import ABC, abstractmethod
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from fastai.core import *
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from fastai.vision import *
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from fastai.vision.image import *
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from fastai.vision.data import *
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from fastai import *
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import cv2
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from PIL import Image as PilImage
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from deoldify import device as device_settings
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import logging
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class IFilter(ABC):
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@abstractmethod
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def filter(
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self, orig_image: PilImage, filtered_image: PilImage, render_factor: int
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) -> PilImage:
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pass
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class BaseFilter(IFilter):
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def __init__(self, learn: Learner, stats: tuple = imagenet_stats):
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super().__init__()
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self.learn = learn
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if not device_settings.is_gpu():
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self.learn.model = self.learn.model.cpu()
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self.device = next(self.learn.model.parameters()).device
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self.norm, self.denorm = normalize_funcs(*stats)
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def _transform(self, image: PilImage) -> PilImage:
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return image
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def _scale_to_square(self, orig: PilImage, targ: int) -> PilImage:
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# a simple stretch to fit a square really makes a big difference in rendering quality/consistency.
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# I've tried padding to the square as well (reflect, symetric, constant, etc). Not as good!
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targ_sz = (targ, targ)
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return orig.resize(targ_sz, resample=PIL.Image.BILINEAR)
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def _get_model_ready_image(self, orig: PilImage, sz: int) -> PilImage:
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result = self._scale_to_square(orig, sz)
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result = self._transform(result)
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return result
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def _model_process(self, orig: PilImage, sz: int) -> PilImage:
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model_image = self._get_model_ready_image(orig, sz)
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x = pil2tensor(model_image, np.float32)
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x = x.to(self.device)
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x.div_(255)
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x, y = self.norm((x, x), do_x=True)
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try:
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result = self.learn.pred_batch(
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ds_type=DatasetType.Valid, batch=(x[None], y[None]), reconstruct=True
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)
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except RuntimeError as rerr:
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if 'memory' not in str(rerr):
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raise rerr
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logging.warn('Warning: render_factor was set too high, and out of memory error resulted. Returning original image.')
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return model_image
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out = result[0]
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out = self.denorm(out.px, do_x=False)
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out = image2np(out * 255).astype(np.uint8)
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return PilImage.fromarray(out)
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def _unsquare(self, image: PilImage, orig: PilImage) -> PilImage:
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targ_sz = orig.size
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image = image.resize(targ_sz, resample=PIL.Image.BILINEAR)
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return image
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class ColorizerFilter(BaseFilter):
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def __init__(self, learn: Learner, stats: tuple = imagenet_stats):
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super().__init__(learn=learn, stats=stats)
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self.render_base = 16
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def filter(
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self, orig_image: PilImage, filtered_image: PilImage, render_factor: int, post_process: bool = True) -> PilImage:
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render_sz = render_factor * self.render_base
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model_image = self._model_process(orig=filtered_image, sz=render_sz)
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raw_color = self._unsquare(model_image, orig_image)
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if post_process:
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return self._post_process(raw_color, orig_image)
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else:
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return raw_color
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def _transform(self, image: PilImage) -> PilImage:
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return image.convert('LA').convert('RGB')
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# This takes advantage of the fact that human eyes are much less sensitive to
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# imperfections in chrominance compared to luminance. This means we can
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# save a lot on memory and processing in the model, yet get a great high
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# resolution result at the end. This is primarily intended just for
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# inference
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def _post_process(self, raw_color: PilImage, orig: PilImage) -> PilImage:
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color_np = np.asarray(raw_color)
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orig_np = np.asarray(orig)
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color_yuv = cv2.cvtColor(color_np, cv2.COLOR_RGB2YUV)
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# do a black and white transform first to get better luminance values
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orig_yuv = cv2.cvtColor(orig_np, cv2.COLOR_RGB2YUV)
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hires = np.copy(orig_yuv)
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hires[:, :, 1:3] = color_yuv[:, :, 1:3]
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final = cv2.cvtColor(hires, cv2.COLOR_YUV2RGB)
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final = PilImage.fromarray(final)
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return final
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class MasterFilter(BaseFilter):
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def __init__(self, filters: List[IFilter], render_factor: int):
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self.filters = filters
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self.render_factor = render_factor
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def filter(
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self, orig_image: PilImage, filtered_image: PilImage, render_factor: int = None, post_process: bool = True) -> PilImage:
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render_factor = self.render_factor if render_factor is None else render_factor
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for filter in self.filters:
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filtered_image = filter.filter(orig_image, filtered_image, render_factor, post_process)
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return filtered_image
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