244 lines
8.9 KiB
Python
244 lines
8.9 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 os
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import time
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import json
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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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import folder_paths
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try:
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from server import PromptServer
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from aiohttp import web
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SERVER_AVAILABLE = True
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except ImportError:
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SERVER_AVAILABLE = False
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PENDING_DECISIONS = {}
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class RSCropImage:
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@classmethod
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def INPUT_TYPES(cls):
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inputs = {
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"required": {
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"image": ("IMAGE",),
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"multiple_mode": ("BOOLEAN", {
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"default": False,
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"label_on": "ON",
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"label_off": "OFF",
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}),
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"multiple_of": (["4", "8", "16", "32", "64"], {
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"default": "8",
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}),
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},
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"optional": {
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"mask": ("MASK",),
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"crop_data": ("STRING", {"multiline": False, "default": "{}"}),
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}
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}
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if SERVER_AVAILABLE:
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inputs["hidden"] = {
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"unique_id": "UNIQUE_ID",
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"prompt_id": "PROMPT_ID",
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}
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return inputs
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("CROPPED_IMAGE", "CROP_DATA")
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FUNCTION = "crop_image"
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CATEGORY = "🦊 RaykoStudio"
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OUTPUT_NODE = True
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DESCRIPTION = "An interactive node that allows you to visually crop an image directly within the node interface"
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def crop_image(self, image, multiple_mode=False, multiple_of="8",
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mask=None, crop_data="{}", unique_id=None, prompt_id=None):
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unique_id = str(unique_id) if unique_id else "unknown"
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crop_dict = {}
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try:
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crop_dict = json.loads(crop_data.replace("'", '"'))
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except Exception:
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crop_dict = {}
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# Логика вычисления координат по маске
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if mask is not None and len(mask) > 0:
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try:
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m = mask[0]
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coords = torch.nonzero(m > 0.5)
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if coords.numel() > 0:
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min_y, min_x = coords.min(dim=0)[0].tolist()
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max_y, max_x = coords.max(dim=0)[0].tolist()
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crop_dict["x"] = int(min_x)
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crop_dict["y"] = int(min_y)
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crop_dict["width"] = int(max_x - min_x + 1)
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crop_dict["height"] = int(max_y - min_y + 1)
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except Exception as e:
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print(f"[RS Crop 🦊] Error processing mask: {e}")
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updated_crop_data = json.dumps(crop_dict)
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if SERVER_AVAILABLE and unique_id:
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PENDING_DECISIONS[unique_id] = {
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"status": "pending",
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"crop_data": updated_crop_data
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}
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if prompt_id is None:
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import uuid
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prompt_id = str(uuid.uuid4())
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try:
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img_tensor = image[0]
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i = 255. * img_tensor.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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filename = f"rscrop_{unique_id}_{prompt_id}.png"
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subfolder = "rscrop"
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full_output_folder = os.path.join(folder_paths.get_temp_directory(), subfolder)
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os.makedirs(full_output_folder, exist_ok=True)
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img.save(os.path.join(full_output_folder, filename))
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PromptServer.instance.send_sync("rayko.rscrop.show", {
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"node_id": unique_id,
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"image_url": f"/view?filename={filename}&type=temp&subfolder={subfolder}",
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"image_width": img.width,
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"image_height": img.height,
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"crop_data": updated_crop_data
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})
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except Exception as e:
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print(f"[RS Crop 🦊] Error saving image: {e}")
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while True:
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state = PENDING_DECISIONS.get(unique_id, {})
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current_status = state.get("status", "pending")
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current_crop_data = state.get("crop_data", updated_crop_data)
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if current_status == "approved":
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crop_data = current_crop_data
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if unique_id in PENDING_DECISIONS:
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del PENDING_DECISIONS[unique_id]
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break
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if current_status == "cancelled":
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if unique_id in PENDING_DECISIONS:
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del PENDING_DECISIONS[unique_id]
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return (image, "{}")
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if current_status == "removed":
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if unique_id in PENDING_DECISIONS:
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del PENDING_DECISIONS[unique_id]
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return (image, "{}")
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if current_status == "rejected":
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PENDING_DECISIONS[unique_id]["status"] = "pending"
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time.sleep(0.3)
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try:
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final_crop_dict = json.loads(crop_data.replace("'", '"'))
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except Exception:
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final_crop_dict = {}
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x = round(final_crop_dict.get("x", 0))
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y = round(final_crop_dict.get("y", 0))
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width = round(final_crop_dict.get("width", 512))
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height = round(final_crop_dict.get("height", 512))
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if image is None or len(image) == 0:
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h, w = 512, 512
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crop_output = {
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"source_width": w,
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"source_height": h,
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"x": 0,
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"y": 0,
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"width": 512,
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"height": 512
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}
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return (torch.zeros((1, h, w, 3)), json.dumps(crop_output))
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img_tensor = 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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x = max(0, min(x, img_width - 1))
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y = max(0, min(y, img_height - 1))
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x2 = min(x + width, img_width)
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y2 = min(y + height, img_height)
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actual_width = max(1, x2 - x)
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actual_height = max(1, y2 - y)
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if len(img_tensor.shape) == 3:
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cropped = img_tensor[y:y2, x:x2, :].unsqueeze(0)
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else:
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cropped = img_tensor[:, y:y2, x:x2, :]
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crop_output = {
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"source_width": img_width,
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"source_height": img_height,
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"x": x,
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"y": y,
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"width": actual_width,
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"height": actual_height
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}
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return (cropped, json.dumps(crop_output))
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if SERVER_AVAILABLE:
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@PromptServer.instance.routes.post("/rayko/rscrop/decision")
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async def rscrop_decision(request):
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try:
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data = await request.json()
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node_id = str(data.get("node_id"))
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decision = data.get("decision")
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crop_data = data.get("crop_data")
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if node_id in PENDING_DECISIONS:
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if decision == "approve":
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PENDING_DECISIONS[node_id]["status"] = "approved"
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if crop_data:
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PENDING_DECISIONS[node_id]["crop_data"] = crop_data
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elif decision == "reject":
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PENDING_DECISIONS[node_id]["status"] = "rejected"
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if crop_data is not None:
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PENDING_DECISIONS[node_id]["crop_data"] = crop_data
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elif decision == "cancel":
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PENDING_DECISIONS[node_id]["status"] = "cancelled"
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return web.Response(status=200, text="Decision recorded")
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else:
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return web.Response(status=404, text=f"Node {node_id} not waiting")
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except Exception as e:
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return web.Response(status=500, text=str(e))
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@PromptServer.instance.routes.post("/rayko/rscrop/cleanup")
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async def rscrop_cleanup(request):
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try:
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data = await request.json()
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node_id = str(data.get("node_id"))
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if node_id in PENDING_DECISIONS:
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PENDING_DECISIONS[node_id]["status"] = "removed"
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return web.Response(status=200, text="Cleanup recorded")
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else:
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return web.Response(status=200, text="Node not found")
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except Exception as e:
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return web.Response(status=500, text=str(e))
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NODE_CLASS_MAPPINGS = {"RSCropImage": RSCropImage}
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NODE_DISPLAY_NAME_MAPPINGS = {"RSCropImage": "🦊 RS Crop Image"} |