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208 lines
6.9 KiB
Python
208 lines
6.9 KiB
Python
"""Functions for image tiling and reconstruction"""
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# inspired by https://github.com/ProGamerGov/neural-dream/blob/master/neural_dream/dream_tile.py
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# but with all the bugs fixed and lots of simplifications
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# MIT License
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import math
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import torch
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def mask_tile(tile, overlap, std_overlap, side="bottom"):
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b, c, h, w = tile.shape
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top_overlap, bottom_overlap, right_overlap, left_overlap = overlap
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(
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std_top_overlap,
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std_bottom_overlap,
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std_right_overlap,
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std_left_overlap,
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) = std_overlap
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if "left" in side:
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lin_mask_left = torch.linspace(0, 1, std_left_overlap, device=tile.device)
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if left_overlap > std_left_overlap:
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zeros_mask = torch.zeros(
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left_overlap - std_left_overlap, device=tile.device
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)
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lin_mask_left = (
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torch.cat([zeros_mask, lin_mask_left], 0)
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.repeat(h, 1)
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.repeat(c, 1, 1)
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.unsqueeze(0)
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)
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if "right" in side:
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lin_mask_right = (
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torch.linspace(1, 0, right_overlap, device=tile.device)
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.repeat(h, 1)
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.repeat(c, 1, 1)
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.unsqueeze(0)
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)
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if "top" in side:
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lin_mask_top = torch.linspace(0, 1, std_top_overlap, device=tile.device)
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if top_overlap > std_top_overlap:
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zeros_mask = torch.zeros(top_overlap - std_top_overlap, device=tile.device)
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lin_mask_top = torch.cat([zeros_mask, lin_mask_top], 0)
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lin_mask_top = lin_mask_top.repeat(w, 1).rot90(3).repeat(c, 1, 1).unsqueeze(0)
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if "bottom" in side:
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lin_mask_bottom = (
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torch.linspace(1, 0, std_bottom_overlap, device=tile.device)
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.repeat(w, 1)
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.rot90(3)
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.repeat(c, 1, 1)
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.unsqueeze(0)
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)
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base_mask = torch.ones_like(tile)
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if "right" in side:
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base_mask[:, :, :, w - right_overlap :] = (
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base_mask[:, :, :, w - right_overlap :] * lin_mask_right
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)
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if "left" in side:
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base_mask[:, :, :, :left_overlap] = (
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base_mask[:, :, :, :left_overlap] * lin_mask_left
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)
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if "bottom" in side:
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base_mask[:, :, h - bottom_overlap :, :] = (
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base_mask[:, :, h - bottom_overlap :, :] * lin_mask_bottom
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)
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if "top" in side:
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base_mask[:, :, :top_overlap, :] = (
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base_mask[:, :, :top_overlap, :] * lin_mask_top
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)
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return tile * base_mask
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def get_tile_coords(d, tile_dim, overlap=0):
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move = int(math.ceil(round(tile_dim * (1 - overlap), 10)))
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c, tile_start, coords = 1, 0, [0]
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while tile_start + tile_dim < d:
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tile_start = move * c
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if tile_start + tile_dim >= d:
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coords.append(d - tile_dim)
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else:
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coords.append(tile_start)
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c += 1
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return coords
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def get_tiles(img, tile_coords, tile_size):
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tile_list = []
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for y in tile_coords[0]:
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for x in tile_coords[1]:
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tile = img[:, :, y : y + tile_size[0], x : x + tile_size[1]]
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tile_list.append(tile)
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return tile_list
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def final_overlap(tile_coords, tile_size):
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last_row, last_col = len(tile_coords[0]) - 1, len(tile_coords[1]) - 1
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f_ovlp = [
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(tile_coords[0][last_row - 1] + tile_size[0]) - (tile_coords[0][last_row]),
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(tile_coords[1][last_col - 1] + tile_size[1]) - (tile_coords[1][last_col]),
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]
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return f_ovlp
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def add_tiles(tiles, base_img, tile_coords, tile_size, overlap):
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f_ovlp = final_overlap(tile_coords, tile_size)
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h, w = tiles[0].size(2), tiles[0].size(3)
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if f_ovlp[0] == h:
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f_ovlp[0] = 0
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if f_ovlp[1] == w:
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f_ovlp[1] = 0
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t = 0
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(column, row) = (0, 0)
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for y in tile_coords[0]:
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for x in tile_coords[1]:
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mask_sides = ""
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c_overlap = overlap.copy()
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if row == 0:
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mask_sides += "bottom"
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elif 0 < row < len(tile_coords[0]) - 2:
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mask_sides += "bottom,top"
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elif row == len(tile_coords[0]) - 2:
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mask_sides += "bottom,top"
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elif row == len(tile_coords[0]) - 1:
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mask_sides += "top"
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if f_ovlp[0] > 0:
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c_overlap[0] = f_ovlp[0] # Change top overlap
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if column == 0:
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mask_sides += ",right"
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elif 0 < column < len(tile_coords[1]) - 2:
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mask_sides += ",right,left"
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elif column == len(tile_coords[1]) - 2:
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mask_sides += ",right,left"
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elif column == len(tile_coords[1]) - 1:
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mask_sides += ",left"
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if f_ovlp[1] > 0:
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c_overlap[3] = f_ovlp[1] # Change left overlap
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# print(f"num-tiles={len(tiles)} t={t}")
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# print(
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# f"mask_tile: tile.shape={tiles[t].shape}, overlap={c_overlap}, side={mask_sides} col={column}, row={row}"
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# )
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tile = mask_tile(tiles[t], c_overlap, std_overlap=overlap, side=mask_sides)
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# torch_img_to_pillow_img(tile).show()
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base_img[:, :, y : y + tile_size[0], x : x + tile_size[1]] = (
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base_img[:, :, y : y + tile_size[0], x : x + tile_size[1]] + tile
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)
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# torch_img_to_pillow_img(base_img).show()
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t += 1
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column += 1
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row += 1 # noqa
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# if row >= 2:
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# exit()
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column = 0
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return base_img
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def tile_setup(tile_size, overlap_percent, base_size):
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if not isinstance(tile_size, (tuple, list)):
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tile_size = (tile_size, tile_size)
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if not isinstance(overlap_percent, (tuple, list)):
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overlap_percent = (overlap_percent, overlap_percent)
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if min(tile_size) < 1:
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raise ValueError("tile_size must be at least 1")
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if max(overlap_percent) > 0.5:
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raise ValueError("overlap_percent must not be greater than 0.5")
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x_coords = get_tile_coords(base_size[1], tile_size[1], overlap_percent[1])
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y_coords = get_tile_coords(base_size[0], tile_size[0], overlap_percent[0])
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y_ovlp = int(math.floor(round(tile_size[0] * overlap_percent[0], 10)))
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x_ovlp = int(math.floor(round(tile_size[1] * overlap_percent[1], 10)))
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if len(x_coords) == 1:
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x_ovlp = 0
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if len(y_coords) == 1:
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y_ovlp = 0
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return (y_coords, x_coords), tile_size, [y_ovlp, y_ovlp, x_ovlp, x_ovlp]
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def tile_image(img, tile_size, overlap_percent):
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tile_coords, tile_size, _ = tile_setup(
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tile_size, overlap_percent, (img.size(2), img.size(3))
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)
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return get_tiles(img, tile_coords, tile_size)
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def rebuild_image(tiles, base_img, tile_size, overlap_percent):
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if len(tiles) == 1:
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return tiles[0]
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base_img = torch.zeros_like(base_img)
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tile_coords, tile_size, overlap = tile_setup(
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tile_size, overlap_percent, (base_img.size(2), base_img.size(3))
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)
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return add_tiles(tiles, base_img, tile_coords, tile_size, overlap)
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