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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
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34
api/onnx_web/chain/utils.py
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@ -2,7 +2,9 @@ from logging import getLogger
from math import ceil from math import ceil
from typing import List, Protocol, Tuple from typing import List, Protocol, Tuple
import numpy as np
from PIL import Image from PIL import Image
from skimage.exposure import match_histograms
from ..params import TileOrder from ..params import TileOrder
@ -38,20 +40,23 @@ def process_tile_grid(
tile: int, tile: int,
scale: int, scale: int,
filters: List[TileCallback], filters: List[TileCallback],
overlap: float = 0.5,
**kwargs, **kwargs,
) -> Image.Image: ) -> Image.Image:
width, height = source.size width, height = source.size
image = Image.new(source.mode, (width * scale, height * scale))
tiles_x = ceil(width / tile) adj_tile = tile * overlap
tiles_y = ceil(height / tile) tiles_x = ceil(width / adj_tile)
tiles_y = ceil(height / adj_tile)
total = tiles_x * tiles_y total = tiles_x * tiles_y
tiles: List[Tuple[int, int, Image.Image]] = []
for y in range(tiles_y): for y in range(tiles_y):
for x in range(tiles_x): for x in range(tiles_x):
idx = (y * tiles_x) + x idx = (y * tiles_x) + x
left = x * tile left = x * adj_tile
top = y * tile top = y * adj_tile
logger.debug("processing tile %s of %s, %s.%s", idx + 1, total, y, x) logger.debug("processing tile %s of %s, %s.%s", idx + 1, total, y, x)
tile_image = source.crop((left, top, left + tile, top + tile)) tile_image = source.crop((left, top, left + tile, top + tile))
@ -60,9 +65,24 @@ def process_tile_grid(
for filter in filters: for filter in filters:
tile_image = filter(tile_image, (left, top, tile)) 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( def process_tile_spiral(