apextoaster
/
onnx-web
1
0
Fork 0
Code Actions

feat(api): histogram equalization and blending for highres tiling

This commit is contained in:
Sean Sube 2023-06-02 01:14:40 -05:00
parent 4dc192a2c7
commit 6317094c5f
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
1 changed files with 27 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

34
api/onnx_web/chain/utils.py
View File

@ -2,7 +2,9 @@ from logging import getLogger
from math import ceil
from typing import List, Protocol, Tuple
import numpy as np
from PIL import Image
from skimage.exposure import match_histograms
from ..params import TileOrder
@ -38,20 +40,23 @@ def process_tile_grid(
tile: int,
scale: int,
filters: List[TileCallback],
overlap: float = 0.5,
**kwargs,
) -> Image.Image:
width, height = source.size
image = Image.new(source.mode, (width * scale, height * scale))
tiles_x = ceil(width / tile)
tiles_y = ceil(height / tile)
adj_tile = tile * overlap
tiles_x = ceil(width / adj_tile)
tiles_y = ceil(height / adj_tile)
total = tiles_x * tiles_y
tiles: List[Tuple[int, int, Image.Image]] = []
for y in range(tiles_y):
for x in range(tiles_x):
idx = (y * tiles_x) + x
left = x * tile
top = y * tile
left = x * adj_tile
top = y * adj_tile
logger.debug("processing tile %s of %s, %s.%s", idx + 1, total, y, x)
tile_image = source.crop((left, top, left + tile, top + tile))
@ -60,9 +65,24 @@ def process_tile_grid(
for filter in filters:
tile_image = filter(tile_image, (left, top, tile))
image.paste(tile_image, (left * scale, top * scale))
tiles.append((left, top, tile_image))
return image
scaled_size = (width * scale, height * scale, 3)
count = np.zeros_like(scaled_size)
value = np.zeros_like(scaled_size)
ref = tiles[0][2]
for left, top, tile_image in tiles:
equalized = match_histograms(tile_image, ref, channel_axis=-1)
value[
top * scale : (top + tile) * scale, left * scale : (left + tile) * scale, :
] += np.array(equalized)
count[
top * scale : (top + tile) * scale, left * scale : (left + tile) * scale, :
] += 1
pixels = np.where(count > 0, value / count, value)
return Image.fromarray(pixels)
def process_tile_spiral(