| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758 |
- from fastai.core import *
- from fastai.vision import *
- from matplotlib.axes import Axes
- from matplotlib.figure import Figure
- from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
- from .filters import IFilter
- from IPython.display import display
- from tensorboardX import SummaryWriter
- from scipy import misc
- from PIL import Image
- 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)
- def _open_pil_image(self, path:Path)->Image:
- return PIL.Image.open(path).convert('RGB')
- def plot_transformed_image(self, path:str, figsize:(int,int)=(20,20), render_factor:int=None)->Image:
- path = Path(path)
- result = self.get_transformed_image(path, render_factor)
- orig = self._open_pil_image(path)
- fig,axes = plt.subplots(1, 2, figsize=figsize)
- self._plot_image(orig, axes=axes[0], figsize=figsize)
- self._plot_image(result, axes=axes[1], figsize=figsize)
- if self.results_dir is not None:
- self._save_result_image(path, result)
- def _save_result_image(self, source_path:Path, image:Image):
- result_path = self.results_dir/source_path.name
- image.save(result_path)
- def get_transformed_image(self, path:Path, render_factor:int=None)->Image:
- orig_image = self._open_pil_image(path)
- filtered_image = self.filter.filter(orig_image, orig_image, render_factor=render_factor)
- return filtered_image
- def _plot_image(self, image:Image, axes:Axes=None, figsize=(20,20)):
- if axes is None:
- _,axes = plt.subplots(figsize=figsize)
- axes.imshow(np.asarray(image)/255)
- axes.axis('off')
- def _get_num_rows_columns(self, num_images:int, max_columns:int)->(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
|
