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import cv2
import gc
import requests
from io import BytesIO
import base64
from scipy import misc
from PIL import Image
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from typing import Tuple
import torch
from fastai.core import *
from fastai.vision import *
from .filters import IFilter, MasterFilter, ColorizerFilter
from .generators import gen_inference_deep, gen_inference_wide
# class LoadedModel
class ModelImageVisualizer:
def __init__(self, filter: IFilter, results_dir: str = None):
self.filter = filter
self.results_dir = None if results_dir is None else Path(results_dir)
self.results_dir.mkdir(parents=True, exist_ok=True)
def _clean_mem(self):
torch.cuda.empty_cache()
# gc.collect()
def _open_pil_image(self, path: Path) -> Image:
return Image.open(path).convert('RGB')
def _get_image_from_url(self, url: str) -> Image:
response = requests.get(url, timeout=30, headers={'Accept': '*/*;q=0.8'})
img = Image.open(BytesIO(response.content)).convert('RGB')
return img
def plot_transformed_image_from_url(
self,
url: str,
path: str = 'test_images/image.png',
results_dir:Path = None,
figsize: Tuple[int, int] = (20, 20),
render_factor: int = None,
display_render_factor: bool = False,
compare: bool = False,
post_process: bool = True,
watermarked: bool = True,
) -> Path:
img = self._get_image_from_url(url)
img.save(path)
return self.plot_transformed_image(
path=path,
results_dir=results_dir,
figsize=figsize,
render_factor=render_factor,
display_render_factor=display_render_factor,
compare=compare,
post_process = post_process,
watermarked=watermarked,
)
def plot_transformed_image(
self,
path: str,
results_dir:Path = None,
figsize: Tuple[int, int] = (20, 20),
render_factor: int = None,
display_render_factor: bool = False,
compare: bool = False,
post_process: bool = True,
watermarked: bool = True,
) -> Path:
path = Path(path)
if results_dir is None:
results_dir = Path(self.results_dir)
result = self.get_transformed_image(
path, render_factor, post_process=post_process,watermarked=watermarked
)
orig = self._open_pil_image(path)
if compare:
self._plot_comparison(
figsize, render_factor, display_render_factor, orig, result
)
else:
self._plot_solo(figsize, render_factor, display_render_factor, result)
orig.close()
result_path = self._save_result_image(path, result, results_dir=results_dir)
result.close()
return result_path
def plot_transformed_pil_image(
self,
input_image: Image,
figsize: Tuple[int, int] = (20, 20),
render_factor: int = None,
display_render_factor: bool = False,
compare: bool = False,
post_process: bool = True,
) -> Image:
result = self.get_transformed_pil_image(
input_image, render_factor, post_process=post_process
)
if compare:
self._plot_comparison(
figsize, render_factor, display_render_factor, input_image, result
)
else:
self._plot_solo(figsize, render_factor, display_render_factor, result)
return result
def _plot_comparison(
self,
figsize: Tuple[int, int],
render_factor: int,
display_render_factor: bool,
orig: Image,
result: Image,
):
fig, axes = plt.subplots(1, 2, figsize=figsize)
self._plot_image(
orig,
axes=axes[0],
figsize=figsize,
render_factor=render_factor,
display_render_factor=False,
)
self._plot_image(
result,
axes=axes[1],
figsize=figsize,
render_factor=render_factor,
display_render_factor=display_render_factor,
)
def _plot_solo(
self,
figsize: Tuple[int, int],
render_factor: int,
display_render_factor: bool,
result: Image,
):
fig, axes = plt.subplots(1, 1, figsize=figsize)
self._plot_image(
result,
axes=axes,
figsize=figsize,
render_factor=render_factor,
display_render_factor=display_render_factor,
)
def _save_result_image(self, source_path: Path, image: Image, results_dir = None) -> Path:
if results_dir is None:
results_dir = Path(self.results_dir)
result_path = results_dir / source_path.name
image.save(result_path)
return result_path
def get_transformed_image(
self, path: Path, render_factor: int = None, post_process: bool = True,
watermarked: bool = True,
) -> Image:
self._clean_mem()
orig_image = self._open_pil_image(path)
filtered_image = self.filter.filter(
orig_image, orig_image, render_factor=render_factor,post_process=post_process
)
return filtered_image
def get_transformed_pil_image(
self, input_image: Image, render_factor: int = None, post_process: bool = True,
) -> Image:
self._clean_mem()
filtered_image = self.filter.filter(
input_image, input_image, render_factor=render_factor,post_process=post_process
)
return filtered_image
def _plot_image(
self,
image: Image,
render_factor: int,
axes: Axes = None,
figsize=(20, 20),
display_render_factor = False,
):
if axes is None:
_, axes = plt.subplots(figsize=figsize)
axes.imshow(np.asarray(image) / 255)
axes.axis('off')
if render_factor is not None and display_render_factor:
plt.text(
10,
10,
'render_factor: ' + str(render_factor),
color='white',
backgroundcolor='black',
)
def _get_num_rows_columns(self, num_images: int, max_columns: int) -> Tuple[int, int]:
columns = min(num_images, max_columns)
rows = num_images // columns
rows = rows if rows * columns == num_images else rows + 1
return rows, columns
def get_image_colorizer(
root_folder: Path = Path('./'), render_factor: int = 35, artistic: bool = True
) -> ModelImageVisualizer:
if artistic:
return get_artistic_image_colorizer(root_folder=root_folder, render_factor=render_factor)
else:
return get_stable_image_colorizer(root_folder=root_folder, render_factor=render_factor)
def get_stable_image_colorizer(
root_folder: Path = Path('./'),
weights_name: str = 'ColorizeStable_gen',
results_dir='result_images',
render_factor: int = 35
) -> ModelImageVisualizer:
learn = gen_inference_wide(root_folder=root_folder, weights_name=weights_name)
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
return vis
def get_artistic_image_colorizer(
root_folder: Path = Path('./'),
weights_name: str = 'ColorizeArtistic_gen',
results_dir='result_images',
render_factor: int = 35
) -> ModelImageVisualizer:
learn = gen_inference_deep(root_folder=root_folder, weights_name=weights_name)
filtr = MasterFilter([ColorizerFilter(learn=learn)], render_factor=render_factor)
vis = ModelImageVisualizer(filtr, results_dir=results_dir)
return vis |