89 lines
3.1 KiB
Python
89 lines
3.1 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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import json
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class RSInsertCropImage:
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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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"original_image": ("IMAGE",),
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"cropped_image": ("IMAGE",),
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},
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"optional": {
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"crop_data": ("STRING", {"multiline": False, "default": "{}", "forceInput": True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "insert_crop"
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CATEGORY = "🦊 RaykoStudio"
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OUTPUT_NODE = True
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DESCRIPTION = "Node that allows insert a previously cut fragment back into the original image"
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def insert_crop(self, original_image, cropped_image, crop_data="{}"):
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try:
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if not crop_data.strip().startswith("{"):
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crop_data = "{" + crop_data + "}"
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crop_dict = json.loads(crop_data.replace("'", '"'))
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except Exception as e:
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print(f"[RS Insert Crop 🦊] Error parsing crop_ {e}")
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return (original_image,)
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x = int(crop_dict.get("x", 0))
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y = int(crop_dict.get("y", 0))
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crop_width = int(crop_dict.get("width", 0))
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crop_height = int(crop_dict.get("height", 0))
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if original_image is None or len(original_image) == 0:
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return (original_image,)
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if cropped_image is None or len(cropped_image) == 0:
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return (original_image,)
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img_tensor = original_image[0]
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img_height = img_tensor.shape[0]
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img_width = img_tensor.shape[1]
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crop_tensor = cropped_image[0]
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crop_h = crop_tensor.shape[0]
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crop_w = crop_tensor.shape[1]
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result = img_tensor.clone()
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x_start = max(0, x)
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y_start = max(0, y)
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x_end = min(x + crop_w, img_width)
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y_end = min(y + crop_h, img_height)
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crop_x_start = max(0, -x)
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crop_y_start = max(0, -y)
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crop_x_end = crop_x_start + (x_end - x_start)
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crop_y_end = crop_y_start + (y_end - y_start)
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if x_end > x_start and y_end > y_start and crop_x_end > crop_x_start and crop_y_end > crop_y_start:
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result[y_start:y_end, x_start:x_end, :] = crop_tensor[crop_y_start:crop_y_end, crop_x_start:crop_x_end, :]
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result = result.unsqueeze(0)
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return (result,)
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NODE_CLASS_MAPPINGS = {"RSInsertCropImage": RSInsertCropImage}
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NODE_DISPLAY_NAME_MAPPINGS = {"RSInsertCropImage": "🦊 RS Insert Crop"} |