137 lines
4.8 KiB
Python
137 lines
4.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import numpy as np
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from PIL import Image
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def tensor2pil(t_image):
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return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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class RSTileImage:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"width_factor_tile": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
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"height_factor_tile": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
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"overlap_rate": ("FLOAT", {"default": 0.1, "min": 0.00, "max": 0.95, "step": 0.05}),
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}
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}
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RETURN_TYPES = ("IMAGE", "LIST", "TUPLE", "TUPLE", "INT", "INT")
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RETURN_NAMES = ("IMAGES", "POSITIONS", "ORIGINAL_SIZE", "GRID_SIZE", "tile_width", "tile_height")
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FUNCTION = "process"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Calculates tile size based on factors/overlap and splits the image into a batch of tiles."
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def calculate_tile_size(self, raw_size, factor, overlap_rate):
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if overlap_rate == 0:
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if factor == 1:
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tile_size = raw_size
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else:
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tile_size = int(raw_size / factor)
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else:
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if factor == 1:
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tile_size = raw_size
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else:
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tile_size = int(raw_size / (1 + (factor - 1) * (1 - overlap_rate)))
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if tile_size % 8 != 0:
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tile_size = ((tile_size + 7) // 8) * 8
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return max(8, tile_size)
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def process(self, image, width_factor_tile, height_factor_tile, overlap_rate):
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_, raw_H, raw_W, _ = image.shape
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tile_width = self.calculate_tile_size(raw_W, width_factor_tile, overlap_rate)
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tile_height = self.calculate_tile_size(raw_H, height_factor_tile, overlap_rate)
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pil_image = tensor2pil(image[0].unsqueeze(0))
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img_width, img_height = pil_image.size
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if img_width <= tile_width and img_height <= tile_height:
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return (
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image,
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[(0, 0, img_width, img_height)],
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(img_width, img_height),
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(1, 1),
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tile_width,
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tile_height
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)
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def calculate_step(size, tile_size):
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if size <= tile_size:
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return 1, 0
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else:
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num_tiles = (size + tile_size - 1) // tile_size
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overlap = (num_tiles * tile_size - size) // (num_tiles - 1)
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step = tile_size - overlap
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return num_tiles, step
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num_cols, step_x = calculate_step(img_width, tile_width)
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num_rows, step_y = calculate_step(img_height, tile_height)
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tiles = []
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positions = []
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for y in range(num_rows):
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for x in range(num_cols):
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left = x * step_x
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upper = y * step_y
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right = min(left + tile_width, img_width)
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lower = min(upper + tile_height, img_height)
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if right - left < tile_width:
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left = max(0, img_width - tile_width)
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right = img_width
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if lower - upper < tile_height:
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upper = max(0, img_height - tile_height)
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lower = img_height
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tile = pil_image.crop((left, upper, right, lower))
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tile_tensor = pil2tensor(tile)
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tiles.append(tile_tensor)
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positions.append((left, upper, right, lower))
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if tiles:
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tiles_batch = torch.cat(tiles, dim=0)
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else:
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tiles_batch = image
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return (
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tiles_batch,
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positions,
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(img_width, img_height),
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(num_cols, num_rows),
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tile_width,
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tile_height
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)
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NODE_CLASS_MAPPINGS = {
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"RSTileImage": RSTileImage
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RSTileImage": "🦊 RS Tile Adjustments"
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} |