263 lines
7.2 KiB
Python
263 lines
7.2 KiB
Python
from logging import getLogger
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from math import ceil
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from typing import List, Protocol, Tuple
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import numpy as np
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from PIL import Image
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from skimage.exposure import match_histograms
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from ..params import TileOrder
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logger = getLogger(__name__)
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class TileCallback(Protocol):
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"""
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Definition for a tile job function.
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"""
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def __call__(self, image: Image.Image, dims: Tuple[int, int, int]) -> Image.Image:
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"""
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Run this stage against a single tile.
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"""
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pass
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def complete_tile(
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source: Image.Image,
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tile: int,
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) -> Image.Image:
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if source.width < tile or source.height < tile:
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full_source = Image.new(source.mode, (tile, tile))
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full_source.paste(source)
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return full_source
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return source
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def get_tile_grads(
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left: int,
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top: int,
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tile: int,
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width: int,
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height: int,
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) -> Tuple[Tuple[float, float, float, float], Tuple[float, float, float, float]]:
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grad_x = [0, 1, 1, 0]
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grad_y = [0, 1, 1, 0]
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if left <= 0:
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grad_x[0] = 1
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if top <= 0:
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grad_y[0] = 1
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if (left + tile) >= width:
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grad_x[3] = 1
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if (top + tile) >= height:
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grad_y[3] = 1
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return (grad_x, grad_y)
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def process_tile_grid(
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source: Image.Image,
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tile: int,
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scale: int,
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filters: List[TileCallback],
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overlap: float = 0.5,
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**kwargs,
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) -> Image.Image:
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width, height = source.size
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adj_tile = int(float(tile) * overlap)
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tiles_x = ceil(width / adj_tile)
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tiles_y = ceil(height / adj_tile)
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total = tiles_x * tiles_y
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tiles: List[Tuple[int, int, Image.Image]] = []
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for y in range(tiles_y):
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for x in range(tiles_x):
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idx = (y * tiles_x) + x
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left = x * adj_tile
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top = y * adj_tile
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logger.debug("processing tile %s of %s, %s.%s", idx + 1, total, y, x)
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tile_image = source.crop((left, top, left + tile, top + tile))
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tile_image = complete_tile(tile_image, tile)
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for filter in filters:
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tile_image = filter(tile_image, (left, top, tile))
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tiles.append((left, top, tile_image))
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scaled_size = (width * scale, height * scale, 3)
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count = np.zeros(scaled_size)
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value = np.zeros(scaled_size)
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ref = np.array(tiles[0][2])
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for left, top, tile_image in tiles:
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# histogram equalization
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equalized = np.array(tile_image)
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equalized = match_histograms(equalized, ref, channel_axis=-1)
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# gradient blending
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points = [0, adj_tile * scale, (tile - adj_tile) * scale, (tile * scale) - 1]
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grad_x, grad_y = get_tile_grads(left, top, adj_tile, width, height)
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mult_x = [np.interp(i, points, grad_x) for i in range(tile * scale)]
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mult_y = [np.interp(i, points, grad_y) for i in range(tile * scale)]
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mask = np.ones_like(equalized[:, :, 0]) * mult_x
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mask = (mask.T * mult_y).T
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for c in range(3):
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equalized[:, :, c] = (equalized[:, :, c] * mask).astype(np.uint8)
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# accumulation
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value[
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top * scale : (top * scale) + equalized.shape[0], left * scale : (left * scale) + equalized.shape[1], :
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] += equalized
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count[
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top * scale : (top * scale) + equalized.shape[0], left * scale : (left * scale) + equalized.shape[1], :
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] += np.repeat(mask, 3, axis=2)
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pixels = np.where(count > 0, value / count, value)
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return Image.fromarray(pixels)
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def process_tile_spiral(
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source: Image.Image,
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tile: int,
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scale: int,
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filters: List[TileCallback],
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overlap: float = 0.5,
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**kwargs,
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) -> Image.Image:
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if scale != 1:
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raise ValueError("unsupported scale")
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width, height = source.size
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image = Image.new("RGB", (width * scale, height * scale))
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image.paste(source, (0, 0, width, height))
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# tile tuples is source, multiply by scale for dest
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counter = 0
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tiles = generate_tile_spiral(width, height, tile, overlap=overlap)
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for left, top in tiles:
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counter += 1
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logger.debug("processing tile %s of %s, %sx%s", counter, len(tiles), left, top)
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tile_image = image.crop((left, top, left + tile, top + tile))
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tile_image = complete_tile(tile_image, tile)
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for filter in filters:
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tile_image = filter(tile_image, (left, top, tile))
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image.paste(tile_image, (left * scale, top * scale))
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return image
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def process_tile_order(
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order: TileOrder,
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source: Image.Image,
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tile: int,
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scale: int,
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filters: List[TileCallback],
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**kwargs,
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) -> Image.Image:
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if order == TileOrder.grid:
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logger.debug("using grid tile order with tile size: %s", tile)
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return process_tile_grid(source, tile, scale, filters, **kwargs)
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elif order == TileOrder.kernel:
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logger.debug("using kernel tile order with tile size: %s", tile)
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raise NotImplementedError()
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elif order == TileOrder.spiral:
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logger.debug("using spiral tile order with tile size: %s", tile)
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return process_tile_spiral(source, tile, scale, filters, **kwargs)
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else:
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logger.warn("unknown tile order: %s", order)
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raise ValueError()
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def generate_tile_spiral(
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width: int,
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height: int,
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tile: int,
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overlap: float = 0.0,
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) -> List[Tuple[int, int]]:
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spacing = 1.0 - overlap
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# round dims up to nearest tiles
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tile_width = ceil(width / tile)
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tile_height = ceil(height / tile)
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# start walking from the north-west corner, heading east
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dir_height = 0
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dir_width = 1
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walk_height = tile_height
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walk_width = tile_width
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accum_height = 0
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accum_width = 0
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tile_top = 0
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tile_left = 0
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tile_coords = []
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while walk_width > 0 and walk_height > 0:
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# exhaust the current direction, then turn
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while accum_width < walk_width and accum_height < walk_height:
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# add a tile
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logger.trace(
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"adding tile at %s:%s, %s:%s, %s:%s, %s",
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tile_left,
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tile_top,
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accum_width,
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accum_height,
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walk_width,
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walk_height,
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spacing,
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)
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tile_coords.append((int(tile_left), int(tile_top)))
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# move to the next
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tile_top += dir_height * spacing * tile
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tile_left += dir_width * spacing * tile
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accum_height += abs(dir_height * spacing)
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accum_width += abs(dir_width * spacing)
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# reset for the next direction
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accum_height = 0
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accum_width = 0
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# why tho
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tile_top -= dir_height
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tile_left -= dir_width
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# turn right
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if dir_width == 1 and dir_height == 0:
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dir_width = 0
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dir_height = 1
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elif dir_width == 0 and dir_height == 1:
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dir_width = -1
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dir_height = 0
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elif dir_width == -1 and dir_height == 0:
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dir_width = 0
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dir_height = -1
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elif dir_width == 0 and dir_height == -1:
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dir_width = 1
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dir_height = 0
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# step to the next tile as part of the turn
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tile_top += dir_height
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tile_left += dir_width
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# shrink the last direction
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walk_height -= abs(dir_height)
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walk_width -= abs(dir_width)
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return tile_coords
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