commit 2c11222644aacbbace037ba132f845209dc838d4 Author: yemiancheng Date: Sat Apr 12 22:55:17 2025 +0800 build(core): ini diff --git a/.github/workflows/publish_to_comfy.yml b/.github/workflows/publish_to_comfy.yml new file mode 100644 index 0000000..b278ccd --- /dev/null +++ b/.github/workflows/publish_to_comfy.yml @@ -0,0 +1,21 @@ +name: Publish to Comfy registry +on: + workflow_dispatch: + push: + branches: + - main + paths: + - "pyproject.toml" + +jobs: + publish-node: + name: Publish Custom Node to registry + runs-on: ubuntu-latest + steps: + - name: Check out code + uses: actions/checkout@v4 + - name: Publish Custom Node + uses: Comfy-Org/publish-node-action@main + with: + ## Add your own personal access token to your Github Repository secrets and reference it here. + personal_access_token: ${{ secrets.COMFY_REGISTRY_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..10a149b --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +models +__pycache__ \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..c91f0a4 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 ymc-github + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..d521fa5 --- /dev/null +++ b/README.md @@ -0,0 +1,74 @@ +
+

comfyui_node_ymc_effect_shatter

+

+ 🤖 some comfyui custom nodes to make effect shatter +

+ +
+ +## Why + +## Features + +## Install + +```bash +# cd to comfyui/custom_nodes +git clone https://github.com/ymc-github/comfyui_node_ymc_effect_shatter +``` + +- **deps will be installed automatically** if requirements in requirements.txt not installed when comfyui up + +## Usage + +- you can find it in right mouse menu : `ymc/image/effect/shatter` + +## Examples + + + + + +
+ 项目示例 + 项目示例 + 项目示例 + 项目示例 + 项目示例 +
+ +
+ 项目示例 + 项目示例 + 项目示例 + 项目示例 + 项目示例 +
+ +
+ 项目示例 + 项目示例 + 项目示例 + 项目示例 + 项目示例 +
+ +## Based-on + +- pypi package [yors_comfyui_node_setup](https://pypi.org/project/yors_comfyui_node_setup/) + +## Published to Comfy registry + +- get more details in [publish_to_comfy.yml](.github/workflows/publish_to_comfy.yml) + +- [docs for publishing to comfy registey](https://docs.comfy.org/registry/overview) + +- installed with comfy-cli ? `comfy node registry-install comfyui_node_ymc_effect_shatter` + +## Author + +ymc-github + +## License + +MIT diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..b69ec40 --- /dev/null +++ b/__init__.py @@ -0,0 +1,31 @@ +# v1 +# from yors_comfyui_node_setup import entry,node_install_requirements # global + +# # install requirements +# node_install_requirements(__file__) + +# # export comfyui node vars +# __all__,NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS,NODE_MENU_NAMES = entry(__name__,__file__) + +# v2 +from yors_comfyui_node_setup import node_install_requirements,entry_pre_import,entry_import,get_all_classs_in_sys,register_node_list_local + +# install requirements +node_install_requirements(__file__) + +# gen __all__ +__all__ = entry_pre_import(__name__,__file__) + +# import moudle with __all__ +entry_import(__name__,__all__) + +# get class after importing moudle with __all__ +this_module_all_classes = get_all_classs_in_sys(__name__) + +# register node with default category +# NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS,NODE_MENU_NAMES = register_node_list(this_module_all_classes,False) + +# addtional register node with custom category +NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS,NODE_MENU_NAMES = register_node_list_local(this_module_all_classes,True,"ymc/image/effect/shatter") +# print(NODE_CLASS_MAPPINGS,NODE_DISPLAY_NAME_MAPPINGS,NODE_MENU_NAMES) +# print("\n".join(NODE_MENU_NAMES)) \ No newline at end of file diff --git a/conf.py b/conf.py new file mode 100644 index 0000000..9fbe1ed --- /dev/null +++ b/conf.py @@ -0,0 +1,2 @@ +CURRENT_CATEGORY = "ymc/image/effect/shatter" +CURRENT_FUNCTION = "exec" diff --git a/examples/001.webp b/examples/001.webp new file mode 100644 index 0000000..c78a124 Binary files /dev/null and b/examples/001.webp differ diff --git a/examples/002.webp b/examples/002.webp new file mode 100644 index 0000000..89d0136 Binary files /dev/null and b/examples/002.webp differ diff --git a/examples/003.webp b/examples/003.webp new file mode 100644 index 0000000..2f5690c Binary files /dev/null and b/examples/003.webp differ diff --git a/examples/004.webp b/examples/004.webp new file mode 100644 index 0000000..8c8f8b6 Binary files /dev/null and b/examples/004.webp differ diff --git a/examples/005.webp b/examples/005.webp new file mode 100644 index 0000000..35d32f3 Binary files /dev/null and b/examples/005.webp differ diff --git a/examples/006.webp b/examples/006.webp new file mode 100644 index 0000000..7b07dcc Binary files /dev/null and b/examples/006.webp differ diff 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/dev/null and b/examples/013.webp differ diff --git a/examples/014.webp b/examples/014.webp new file mode 100644 index 0000000..ebe8d00 Binary files /dev/null and b/examples/014.webp differ diff --git a/examples/015.webp b/examples/015.webp new file mode 100644 index 0000000..028d119 Binary files /dev/null and b/examples/015.webp differ diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..fd6a926 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui_node_ymc_effect_shatter" +description = "some comfyui custom nodes to make effect shatter" +version = "1.0.0" + +[project.license] +file = "LICENSE" + +[project.urls] +Repository = "https://github.com/ymc-github/comfyui_node_ymc_effect_shatter" + +[tool.comfy] +PublisherId = "ymc-github" +DisplayName = "comfyui_node_ymc_effect_shatter" +Icon = "" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3b7ce3c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +yors_comfyui_node_setup +yors_pano_zero_field \ No newline at end of file diff --git a/shatter_effect.py b/shatter_effect.py new file mode 100644 index 0000000..3e657b3 --- /dev/null +++ b/shatter_effect.py @@ -0,0 +1,52 @@ + +from .conf import CURRENT_CATEGORY, CURRENT_FUNCTION +from yors_pano_zero_field import ZeroField +# from yors_pano_ansi_color import log_msg +import numpy as n +from .util import create_shatter_effect +# from ...base_util import dict_read_with_stro + +# required,optional +class ShatterEffectAdvNode: + @classmethod + def INPUT_TYPES(s): + # names='input_image,output_dir,frame_count,shard_size,speed,rotation,fade_start' + + return { + "required": { + "input_image_path": ZeroField.any(),#STRING,MASK,IMAGE + "output_folder": ("STRING", {"multiline": False, "default": "output_frames"}), + }, + "optional": { + "frame_count": ("STRING", {"multiline": False, "default": "30"}), + "shard_size": ("STRING", {"multiline": False, "default": "40"}), + "speed": ("STRING", {"multiline": False, "default": "10"}), + "rotation": ("STRING", {"multiline": False, "default": "5"}), + "fade_start": ("STRING", {"multiline": False, "default": "0"}), + "name_prefix": ("STRING", {"multiline": False, "default": "frame_"}), + "name_id_length": ("STRING", {"multiline": False, "default": "3"}), + "frame_start_index": ("STRING", {"multiline": False, "default": "1"}), + "shard_shape": ("STRING", {"multiline": False, "default": "square"}), + "shard_mask_image": ZeroField.any(), + "shards_area_mask": ZeroField.any(), + "show_image": ("STRING", {"multiline": False, "default": "False"}), + "save_image": ("STRING", {"multiline": False, "default": "True"}), + } + } + + RETURN_TYPES = ("STRING","STRING","IMAGE","MASK",) + RETURN_NAMES = ("output_folder","filepaths","images","masks",) + + FUNCTION = CURRENT_FUNCTION + CATEGORY = CURRENT_CATEGORY + NODE_DESC = "effect shatter (1.0)" + + OUTPUT_NODE = True + + def exec(self, **kwargs): + files,images,masks,has_non_empty_mask = create_shatter_effect(kwargs) + # last_tensor = get_last_frame(input_image,files) + output_folder = kwargs.get('output_folder') + return (output_folder,'\n'.join(files),images,masks) + + diff --git a/template.readme.md b/template.readme.md new file mode 100644 index 0000000..be8b60a --- /dev/null +++ b/template.readme.md @@ -0,0 +1,44 @@ +
+

{node_name}

+

+ 🤖 {node_desc} +

+ +
+ +## Why + +## Features + +## Install + +```bash +# cd to comfyui/custom_nodes +git clone https://github.com/ymc-github/{node_name} +``` + +- **deps will be installed automatically** if requirements in requirements.txt not installed when comfyui up + +## Usage + +- you can find it in right mouse menu : `{node_right_menu}` + +## Based-on + +- pypi package [yors_comfyui_node_setup](https://pypi.org/project/yors_comfyui_node_setup/) + +## Published to Comfy registry + +- get more details in [publish_to_comfy.yml](.github/workflows/publish_to_comfy.yml) + +- [docs for publishing to comfy registey](https://docs.comfy.org/registry/overview) + +- installed with comfy-cli ? `comfy node registry-install {node_name}` + +## Author + +ymc-github + +## License + +MIT diff --git a/util.py b/util.py new file mode 100644 index 0000000..a282ccb --- /dev/null +++ b/util.py @@ -0,0 +1,773 @@ +import os +import random +import math +from PIL import Image, ImageDraw,ImageOps +import torch +from typing import Union +import numpy as np +from pathlib import Path +# from torchvision import transforms + +# from .tensor_pillow_loading import pillow_image_from_path +# from .tensor_pillow_conversion import pillow_image_from_tensor + +def pillow_image_from_path(image_path: str) -> Image.Image: + """ + 从指定路径加载图像为 Pillow 图像。 + :param image_path: 图像文件的路径 + :return: 加载后的 Pillow 图像 + """ + image = Image.open(image_path) + # print(f"图像 {image_path} 已从文件加载为 Pillow 图像。 | Image {image_path} has been loaded from file as a Pillow image.") + return image + +def pillow_image_from_tensor(tensor: torch.Tensor): + # 先将张量移到 CPU 并转换为 numpy 数组 + numpy_array = tensor.cpu().numpy() + + # 确保数组的维度和数据类型正确 + if numpy_array.ndim == 4: + # 如果是批量数据，取第一个图像 + numpy_array = numpy_array[0] + + # 调整维度顺序，从 (C, H, W) 到 (H, W, C) + if numpy_array.shape[0] in [3, 4]: + numpy_array = np.transpose(numpy_array, (1, 2, 0)) + + # 将数据类型转换为 uint8 + if numpy_array.dtype != np.uint8: + # 如果数据范围是 [0, 1]，将其转换为 [0, 255] + if numpy_array.max() <= 1.0: + numpy_array = (numpy_array * 255).astype(np.uint8) + else: + numpy_array = numpy_array.astype(np.uint8) + + # 创建 Pillow 图像 + image = Image.fromarray(numpy_array) + return image + + + +def pillow_image_get_shape(image:Image.Image)-> tuple[int,int]: + width, height = image.size + return tuple(width, height) + +def pillow_image_to_gray(image:Image.Image)->Image.Image: + return image.convert('L') + +def pillow_image_to_pillow_mask(image:Image.Image, channel:str): + """ + 将 Pillow 图像转换为 Pillow 掩码。 + + :param pillow_image: 输入的 Pillow 图像对象 + :param channel: 选择的通道，字符串类型，可选值为 'red', 'green', 'blue', 'alpha' + :return: 转换后的 Pillow 掩码图像对象 + """ + channels = ["red", "green", "blue", "alpha"] + if channel not in channels: + raise ValueError(f"无效的通道名称 '{channel}'，请选择 'red', 'green', 'blue' 或 'alpha'。") + + # 将 Pillow 图像转换为 numpy 数组 + + image_array = np.array(image) + + # 根据通道名称确定索引 + channel_index = channels.index(channel) + if len(image_array.shape) == 3: # 彩色图像 + mask_array = image_array[:, :, channel_index] + elif len(image_array.shape) == 2: # 灰度图像 + if channel != 'alpha': + mask_array = image_array + else: + raise ValueError("灰度图像没有 alpha 通道。") + else: + raise ValueError("输入的图像数组维度不符合要求。") + + # 将 numpy 数组转换回 Pillow 图像 + mask = Image.fromarray(mask_array) + return mask + + +def pillow_image_from_input(input: Union[str, torch.Tensor,Image.Image], is_mask: bool = False) -> Image.Image: + """ + 从图像路径或图像张量加载Pillow图像或遮罩。 + + Args: + input (Union[str, torch.Tensor]): 输入可以是图像/遮罩的文件路径（字符串）， + 或者是一个图像/遮罩张量（torch.Tensor）。 + is_mask (bool): 如果为True，则表示加载的是遮罩；否则为图像。默认值为False。 + + Returns: + Image.Image: 加载后的Pillow图像或遮罩对象。 + + Raises: + ValueError: 如果输入既不是字符串也不是torch.Tensor类型。 + FileNotFoundError: 如果输入是字符串但文件不存在。 + """ + + if isinstance(input, str): + image = pillow_image_from_path(input) + if is_mask: + image = pillow_image_to_gray(image) + return image + + if isinstance(input, torch.Tensor): + image =pillow_image_from_tensor(input) + if is_mask: + image = pillow_image_to_gray(image) + return image + + if isinstance(input, Image.Image): + image =input + if is_mask: + image = pillow_image_to_gray(image) + return image + + + +def create_shape_mask(shape: str, width: int, height: int) -> Image.Image: + """ + 创建指定形状的遮罩 + + Args: + shape (str): 形状名称，如 "square", "triangle", "circle", "heart", "star", "diamond" + width (int): 遮罩的宽度 + height (int): 遮罩的高度 + + Returns: + Image.Image: 创建好的遮罩图像 + """ + mask = Image.new('L', (width, height), 0) + draw = ImageDraw.Draw(mask) + if shape == "square": + draw.rectangle((0, 0, width, height), fill=255) + elif shape == "triangle": + draw.polygon([(0, height), (width // 2, 0), (width, height)], fill=255) + elif shape == "circle": + draw.ellipse((0, 0, width, height), fill=255) + elif shape == "heart": + points = [] + for t in range(0, 360, 10): + rad = math.radians(t) + x_coord = 16 * math.sin(rad) ** 3 + y_coord = -(13 * math.cos(rad) - 5 * math.cos(2 * rad) - 2 * math.cos(3 * rad) - math.cos(4 * rad)) + x_scaled = int(x_coord * width / 32 + width / 2) + y_scaled = int(y_coord * height / 32 + height / 2) + points.append((x_scaled, y_scaled)) + draw.polygon(points, fill=255) + elif shape == "star": + outer_radius = min(width, height) // 2 + inner_radius = outer_radius // 2 + center_x = width // 2 + center_y = height // 2 + points = [] + for i in range(5): + angle1 = 2 * math.pi * i / 5 + angle2 = 2 * math.pi * (i + 0.5) / 5 + x1 = center_x + outer_radius * math.cos(angle1) + y1 = center_y + outer_radius * math.sin(angle1) + x2 = center_x + inner_radius * math.cos(angle2) + y2 = center_y + inner_radius * math.sin(angle2) + points.extend([(x1, y1), (x2, y2)]) + draw.polygon(points, fill=255) + elif shape == "diamond": + draw.polygon([(width // 2, 0), (width, height // 2), (width // 2, height), (0, height // 2)], fill=255) + return mask + +def get_shard_dimensions(x: int, y: int, shard_size: int, width: int, height: int) -> tuple: + """ + 计算单个碎片的实际尺寸 + + Args: + x (int): 碎片的起始 x 坐标 + y (int): 碎片的起始 y 坐标 + shard_size (int): 碎片的大小 + width (int): 原始图片的宽度 + height (int): 原始图片的高度 + + Returns: + tuple: 碎片的实际宽度和高度 + """ + actual_width = min(shard_size, width - x) + actual_height = min(shard_size, height - y) + return actual_width, actual_height + +def extract_shard_image(original_img: Image.Image, x: int, y: int, actual_width: int, actual_height: int) -> Image.Image: + """ + 从原始图片中提取单个碎片图像 + + Args: + original_img (Image.Image): 原始图片 + x (int): 碎片的起始 x 坐标 + y (int): 碎片的起始 y 坐标 + actual_width (int): 碎片的实际宽度 + actual_height (int): 碎片的实际高度 + + Returns: + Image.Image: 提取的碎片图像 + """ + return original_img.crop((x, y, x + actual_width, y + actual_height)) + +def apply_shard_mask(shard_img: Image.Image, shard_shape: str, shard_mask_image: Union[str, torch.Tensor], actual_width: int, actual_height: int) -> Image.Image: + """ + 为碎片图像应用遮罩形状和遮罩图像 + + Args: + shard_img (Image.Image): 碎片图像 + shard_shape (str): 碎片的形状 + shard_mask_image (Union[str, torch.Tensor]): 碎片的遮罩图像 + actual_width (int): 碎片的实际宽度 + actual_height (int): 碎片的实际高度 + + Returns: + Image.Image: 应用遮罩后的碎片图像 + """ + if shard_shape: + mask = create_shape_mask(shard_shape, actual_width, actual_height) + shard_img.putalpha(mask) + if shard_mask_image is not None: + # Boolean value of Tensor with more than one value is ambiguous + mask = pillow_image_from_input(shard_mask_image, is_mask=True) + mask = mask.resize((actual_width, actual_height)) + shard_img.putalpha(mask) + return shard_img + +def generate_shard_movement_params(speed: int, rotation: int, frame_count: int, fade_start: int) -> dict: + """ + 生成单个碎片的运动参数 + + Args: + speed (int): 碎片的运动速度 + rotation (int): 碎片的旋转速度 + frame_count (int): 总帧数 + fade_start (int): 碎片开始消失的帧数 + + Returns: + dict: 碎片的运动参数 + """ + angle = random.uniform(0, 2 * math.pi) + shard_speed = random.uniform(0.5, 1.5) * speed + rot_speed = random.uniform(-1, 1) * rotation + fade = random.randint(frame_count // 2, frame_count - 5) if max(0, fade_start) < 1 else random.randint(max(0, fade_start - 5), min(frame_count - 1, fade_start + 5)) + return { + 'angle': angle, + 'speed': shard_speed, + 'rotation': 0, + 'rotation_speed': rot_speed, + 'fade_start': fade, + 'opacity': 1.0 + } + +def create_shard_info(shard_img: Image.Image, x: int, y: int, speed: int, rotation: int, frame_count: int, fade_start: int) -> dict: + """ + 创建单个碎片的完整信息 + + Args: + shard_img (Image.Image): 碎片图像 + x (int): 碎片的起始 x 坐标 + y (int): 碎片的起始 y 坐标 + speed (int): 碎片的运动速度 + rotation (int): 碎片的旋转速度 + frame_count (int): 总帧数 + fade_start (int): 碎片开始消失的帧数 + + Returns: + dict: 碎片的完整信息 + """ + movement_params = generate_shard_movement_params(speed, rotation, frame_count, fade_start) + return { + 'img': shard_img, + 'x': x, + 'y': y, + 'width': shard_img.width, + 'height': shard_img.height, + **movement_params + } + +def update_shard_position_and_rotation(shard: dict, frame: int): + """ + 更新单个碎片的位置和旋转 + + Args: + shard (dict): 碎片的完整信息 + frame (int): 当前帧数 + """ + if frame > 0: + shard['x'] += math.cos(shard['angle']) * shard['speed'] + shard['y'] += math.sin(shard['angle']) * shard['speed'] + shard['rotation'] += shard['rotation_speed'] + +def update_shard_opacity(shard: dict, frame: int): + """ + 更新单个碎片的透明度 + + Args: + shard (dict): 碎片的完整信息 + frame (int): 当前帧数 + """ + if frame >= shard['fade_start']: + shard['opacity'] = max(0, shard['opacity'] - 0.1) + +def rotate_and_apply_opacity(shard: dict) -> Image.Image: + """ + 旋转碎片图像并应用透明度 + + Args: + shard (dict): 碎片的完整信息 + + Returns: + Image.Image: 旋转并应用透明度后的碎片图像 + """ + rotated_shard = shard['img'].rotate(shard['rotation'], expand=True) + if shard['opacity'] < 1.0: + alpha = rotated_shard.split()[3] + alpha = alpha.point(lambda p: p * shard['opacity']) + rotated_shard.putalpha(alpha) + return rotated_shard + +def text_padd(text: str, action: str, length: int, filled: str = ' ') -> str: + """ + 对字符串进行填充操作 + + Args: + text (str): 要填充的字符串 + action (str): 填充方向，'padd-start' 或 'padd-end' + length (int): 填充后的总长度 + filled (str): 填充字符，默认为空格 + + Returns: + str: 填充后的字符串 + """ + res = text + if 'padd-start' in action: + res = text.rjust(length, filled) + elif 'padd-end' in action: + res = text.ljust(length, filled) + return res + +# 编写一个函数，将 false,true转为 bool。忽略大小写。支持传入 字符或者布尔。添加函数提示和注释。 +# 支持 enable,disable,1,0 +# 如果输入为None,第二个参数为默认值，支持设置默认值。 +# 如果输入为None或者空字符,第二个参数为默认值，支持设置默认值。 +# 函数注释由中文/英文 双语展示 +def to_bool(value: str | bool | None, default: bool = False) -> bool: + """ + 此函数用于将输入值转换为布尔类型。 + 支持布尔值输入，也支持 'true'、'false'、'enable'、'disable'、'1'、'0' 字符串输入（忽略大小写）。 + 若输入为 None 或者空字符串，则返回默认值。 + + 参数: + value (str | bool | None): 要转换为布尔值的输入，可以是字符串、布尔值或 None。 + default (bool): 当输入为 None 或者空字符串时返回的默认值，默认为 False。 + + 返回: + bool: 转换后的布尔值。 + + 异常: + ValueError: 若输入既不是有效的布尔值，也不是支持的字符串，会抛出此异常。 + + This function is used to convert the input value to a boolean type. + It supports boolean input and string inputs such as 'true', 'false', 'enable', 'disable', '1', '0' (case-insensitive). + If the input is None or an empty string, the default value will be returned. + + Parameters: + value (str | bool | None): The input to be converted to a boolean value, which can be a string, a boolean, or None. + default (bool): The default value to be returned when the input is None or an empty string, with a default of False. + + Returns: + bool: The converted boolean value. + + Exceptions: + ValueError: If the input is neither a valid boolean value nor a supported string, this exception will be thrown. + """ + if value is None or (isinstance(value, str) and value.strip() == ""): + return default + if isinstance(value, bool): + return value + if isinstance(value, str): + # 去除字符串首尾空白并转换为小写 + # Remove leading and trailing whitespace from the string and convert it to lowercase + value = value.strip().lower() + if value in ['true', 'enable', '1']: + return True + elif value in ['false', 'disable', '0']: + return False + raise ValueError(f"无法将 '{value}' 转换为布尔值。 Unable to convert '{value}' to a boolean value.") + + +# def tensor_list_to_batch(tensor_list:list[ torch.Tensor]): +# """ +# 将张量列表转换为一个批次（batch）。 + +# 参数: +# tensor_list (list): 张量列表。 + +# 返回: +# torch.Tensor: 转换后的批次张量。 +# """ +# return torch.stack(tensor_list, dim=0) + + +# def pillow_image_to_tensor(image: Image.Image): +# # 步骤 1: 将 PIL 图像对象转换为 numpy 数组 +# # PIL 图像对象在内存中的存储格式不便于直接进行深度学习计算 +# # 使用 np.array() 函数将其转换为 numpy 数组，数组的形状通常为 (高度, 宽度, 通道数) +# image_np = np.array(image) + +# # 步骤 2: 将 numpy 数组转换为 PyTorch 张量 +# # 使用 torch.tensor() 函数将 numpy 数组转换为 PyTorch 张量 +# # 同时指定数据类型为 torch.float32，这是深度学习中常用的数据类型 +# image_tensor = torch.tensor(image_np, dtype=torch.float32) + +# # 步骤 3: 调整张量的维度 +# # 在 PyTorch 中，图像张量的常见形状是 (通道数, 高度, 宽度) +# # 而 numpy 数组的形状是 (高度, 宽度, 通道数)，所以需要调整维度 +# # 使用 permute() 函数对维度进行重新排列，参数 (2, 0, 1) 表示将原数组的第 2 维放到第 0 位，第 0 维放到第 1 位，第 1 维放到第 2 位 +# image_tensor = image_tensor.permute(2, 0, 1) + +# # 步骤 4: 归一化像素值 +# # 在深度学习中，通常需要将像素值归一化到 [0, 1] 范围 +# # 因为图像的像素值通常在 [0, 255] 之间，所以将每个像素值除以 255 来实现归一化 +# image_tensor = image_tensor / 255 + +# # 步骤 5: 移除可能存在的批量维度为 1 的情况 +# # 如果张量的维度是 4（通常表示 (批量大小, 通道数, 高度, 宽度)） +# # 并且批量大小为 1，那么使用 squeeze(0) 方法移除这个批量维度 +# if image_tensor.ndim == 4 and image_tensor.shape[0] == 1: +# # print(f"[shatter] 当前张量维度为 {image_tensor.dim()}，自动调整为3维。") +# image_tensor = image_tensor.squeeze(0) + +# # if image_tensor.dim() < 4: +# # print(f"[shatter] 当前张量维度为 {image_tensor.dim()}，自动调整为4维。") +# # # 自动调整维度 +# # while image_tensor.dim() < 4: +# # image_tensor = image_tensor.unsqueeze(0) + +# return image_tensor + + +def pillow_image_to_tensor(image: Image.Image) -> torch.Tensor: + # 步骤 1: 将 PIL 图像对象转换为 numpy 数组 + # PIL 图像对象在内存中的存储格式不便于直接进行深度学习计算 + # 使用 np.array() 函数将其转换为 numpy 数组，数组的形状通常为 (高度, 宽度, 通道数) + np_array = np.array(image) + # 步骤 2: 将 numpy 数组转换为 PyTorch 张量 + tensor = torch.from_numpy(np_array).permute(2, 0, 1).float() / 255.0 # 转换为 (C, H, W) 形状并归一化到 [0, 1] + return tensor + +def tensor_list_to_batch(tensor_list: list[torch.Tensor]) -> torch.Tensor: + """ + 将张量列表转换为一个批次（batch）。 + + 参数: + tensor_list (list): 张量列表。 + + 返回: + torch.Tensor: 转换后的批次张量。 + """ + # 检查列表是否为空 + if not tensor_list: + raise ValueError("输入的张量列表为空。") + # 检查所有张量的形状是否一致 + first_shape = tensor_list[0].shape + for tensor in tensor_list: + if tensor.shape != first_shape: + raise ValueError("张量列表中的所有张量形状必须一致。") + # 将张量列表堆叠成一个批次张量 + batch_tensor = torch.stack(tensor_list) + return batch_tensor + + +# def pillow_images_to_tensor(images: list[Image.Image]) -> torch.Tensor: +# """ +# 将Pillow图像列表转换为图像张量。 + +# 参数: +# images (list[Image.Image]): 包含Pillow图像对象的列表。 + +# 返回: +# torch.Tensor: 形状为 (N, C, H, W) 的图像张量，其中 N 是图像数量，C 是通道数，H 是高度，W 是宽度。 +# """ +# transform = transforms.Compose([ +# transforms.ToTensor() +# ]) +# tensors = [transform(img) for img in images] +# return torch.stack(tensors) + + +def tensor_common_upscale(tensor: torch.Tensor, height: int, width: int, mode: str = "bilinear", align_corners: bool = False) -> torch.Tensor: + """ + 自定义的上采样函数，实现图像的上采样操作。 + Custom upsampling function to perform image upsampling. + + 参数: + :param tensor: 输入的图像张量 + The input image tensor + :param height: 目标高度 + The target height + :param width: 目标宽度 + The target width + :param mode: 上采样模式，默认为双线性插值 + Upsampling mode, default is bilinear interpolation + :param align_corners: 是否对齐角点，默认为 False + Whether to align corners, default is False + + 返回: + :return: 上采样后的图像张量 + The upsampled image tensor + """ + # 调整张量形状为 (B, C, H, W) + if tensor.dim() == 3: + tensor = tensor.unsqueeze(0) + # 使用 PyTorch 的 interpolate 函数进行上采样 + upsampled = torch.nn.functional.interpolate(tensor, size=(height, width), mode=mode, align_corners=align_corners) + # 移除批量维度（如果只有一个样本） + if upsampled.shape[0] == 1: + upsampled = upsampled.squeeze(0) + return upsampled + + +def tensor_image_and_mask_from_path_or_pillow(image_input: str | Image.Image) -> tuple[torch.Tensor, torch.Tensor, bool]: + """ + 处理单张图像，将其转换为 RGB 图像张量和掩码张量。 + Process a single image and convert it into an RGB image tensor and a mask tensor. + + 参数: + :param image_input: 图像文件的路径或者 PIL.Image.Image 对象。 + The path of the image file or a PIL.Image.Image object. + + 返回: + :return: 处理后的图像张量、掩码张量和是否有非空掩码的标志。 + The processed image tensor, mask tensor, and a flag indicating whether there is a non-empty mask. + """ + if isinstance(image_input, str): + # 如果输入是字符串，当作文件路径处理 + i = Image.open(image_input) + elif isinstance(image_input, Image.Image): + # 如果输入是 PIL.Image.Image 对象，直接使用 + i = image_input + else: + raise ValueError("输入必须是字符串类型的文件路径或者 PIL.Image.Image 对象。") + + i = ImageOps.exif_transpose(i) + # # 转换为 RGB 模式 + # image = i.convert("RGB") + # 转换为 RGBA 模式 + image = i.convert("RGBA") + + # 将图像转换为 numpy 数组并归一化 + image = np.array(image).astype(np.float32) / 255.0 + # 转换为 PyTorch 张量并调整形状 + image = torch.from_numpy(image)[None, ] + + # 检查图像是否有透明度通道 + has_non_empty_mask = False + if 'A' in i.getbands(): + # 获取透明度通道并转换为 numpy 数组，然后归一化 + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + # 转换为 PyTorch 张量并取反 + mask = 1. - torch.from_numpy(mask) + has_non_empty_mask = True + else: + # 如果没有透明度通道，创建全零掩码 + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + return image, mask, has_non_empty_mask + + +def tensor_image_and_mask_batch_from_list(images: list[torch.Tensor], masks: list[torch.Tensor], has_non_empty_mask: bool) -> tuple[torch.Tensor, torch.Tensor, int]: + """ + 根据图像和掩码列表处理并拼接图像和掩码张量。 + Process and concatenate image and mask tensors based on the image and mask lists. + + 参数: + :param images: 图像张量列表 + A list of image tensors + :param masks: 掩码张量列表 + A list of mask tensors + :param has_non_empty_mask: 是否有非空掩码的标志 + A flag indicating whether there is a non-empty mask + + 返回: + :return: 拼接后的图像张量、掩码张量和图像数量 + The concatenated image tensor, mask tensor, and the number of images + """ + if len(images) == 1: + return (images[0], masks[0], 1) + elif len(images) > 1: + # 获取第一张图像的张量 + image1 = images[0] + mask1 = None + + # 拼接图像张量 + for image2 in images[1:]: + if image1.shape[1:] != image2.shape[1:]: + # 如果图像形状不一致，进行上采样 + image2 = tensor_common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1) + image1 = torch.cat((image1, image2), dim=0) + + # 拼接掩码张量 + for mask2 in masks[1:]: + if has_non_empty_mask: + if image1.shape[1:3] != mask2.shape: + # 如果掩码形状与图像不一致，进行插值 + mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False) + mask2 = mask2.squeeze(0) + else: + mask2 = mask2.unsqueeze(0) + else: + mask2 = mask2.unsqueeze(0) + + if mask1 is None: + mask1 = mask2 + else: + mask1 = torch.cat((mask1, mask2), dim=0) + + return (image1, mask1, len(images)) + else: + return (torch.tensor([]), torch.tensor([]), 0) + +def create_shatter_effect(params: dict): + """ + 创建图片破碎效果并保存序列帧 + + 参数: + params (dict): 包含所有配置参数的字典 + + 如果传入 shards_area_mask 碎化粒子只在该区域初始化 + + 如果传入 save_image 则保存图片，否则不保存图片 + """ + + input_image_path = params.get('input_image_path') + # print(f"input_image_path type: {type(input_image_path)}, value: {input_image_path}") # 添加调试输出 + # 其他代码保持不变 + + input_image_path = params.get('input_image_path') + output_folder = params.get('output_folder') + frame_count = params.get('frame_count', 20) + shard_size = params.get('shard_size', 30) + speed = params.get('speed', 10) + rotation = params.get('rotation', 5) + fade_start = params.get('fade_start', 0) + name_prefix = params.get('name_prefix', "frame_") + name_id_length = params.get('name_id_length', 3) + frame_start_index = params.get('frame_start_index', 1) + shard_shape = params.get('shard_shape', None) + shard_mask_image = params.get('shard_mask_image', None) + shards_area_mask = params.get('shards_area_mask', None) + save_image = to_bool(params.get('save_image', True)) + show_image = to_bool(params.get('show_image', False)) + + frame_count,shard_size=tuple([int(item) for item in [frame_count,shard_size]]) + fade_start,name_id_length,frame_start_index=tuple([int(item) for item in [fade_start,name_id_length,frame_start_index]]) + speed,rotation=tuple([float(item) for item in [speed,rotation]]) + # name_prefix=tuple([int(item) for item in [name_prefix]]) + + # 创建输出文件夹 + if save_image: + os.makedirs(output_folder, exist_ok=True) + + # 加载原始图片 + original_img = pillow_image_from_input(input_image_path, False) + width, height = original_img.size + + + # 转换为RGBA模式以便处理透明度 + original_img = original_img.convert("RGBA") + + # 创建碎片网格 + # cols = math.ceil(width / shard_size) + # rows = math.ceil(height / shard_size) + + # shards_area_mask = pillow_image_from_input(shards_area_mask, is_mask=True) + # if shards_area_mask.getpixel((x, y)) != 0: + + # 为每个碎片生成随机运动参数 + # shards = [] + # for row in range(rows): + # for col in range(cols): + # x = col * shard_size + # y = row * shard_size + # actual_width, actual_height = get_shard_dimensions(x, y, shard_size, width, height) + # if actual_width <= 0 or actual_height <= 0: + # continue + # shard_img = extract_shard_image(original_img, x, y, actual_width, actual_height) + # shard_img = apply_shard_mask(shard_img, shard_shape, shard_mask_image, actual_width, actual_height) + # shard_info = create_shard_info(shard_img, x, y, speed, rotation, frame_count, fade_start) + # shards.append(shard_info) + + + shards = [] + + if shards_area_mask is not None: + shards_area_mask = pillow_image_from_input(shards_area_mask, is_mask=True) + + # 遍历原始图像的每个位置 + for y in range(0, height, shard_size): + for x in range(0, width, shard_size): + + # 检查遮罩图像对应位置是否为有效位置 + if shards_area_mask is not None and shards_area_mask.getpixel((x, y)) == 0: + continue + + # 计算碎片的实际尺寸 + actual_width, actual_height = get_shard_dimensions(x, y, shard_size, width, height) + if actual_width <= 0 or actual_height <= 0: + continue + + # 从原始图片中提取单个碎片图像 + shard_img = extract_shard_image(original_img, x, y, actual_width, actual_height) + # 为碎片图像应用遮罩形状和遮罩图像 + shard_img = apply_shard_mask(shard_img, shard_shape, shard_mask_image, actual_width, actual_height) + # 生成单个碎片的运动参数 + movement_params = generate_shard_movement_params(speed, rotation, frame_count, fade_start) + # 创建单个碎片的完整信息 + shard_info = create_shard_info(shard_img, x, y, speed, rotation, frame_count, fade_start) + shards.append(shard_info) + + filelist:list[str]=[] + image_list = [] + # 生成每一帧 + for frame in range(frame_count): + # 创建透明背景 + frame_img = Image.new("RGBA", (width, height), (0, 0, 0, 0)) + draw = ImageDraw.Draw(frame_img) + + # 绘制所有碎片 + for shard in shards: + update_shard_position_and_rotation(shard, frame) + update_shard_opacity(shard, frame) + rotated_shard = rotate_and_apply_opacity(shard) + draw_x = shard['x'] - (rotated_shard.width - shard['width']) / 2 + draw_y = shard['y'] - (rotated_shard.height - shard['height']) / 2 + frame_img.paste(rotated_shard, (int(draw_x), int(draw_y)), rotated_shard) + + + # get file path + name_id = text_padd(str(frame + frame_start_index), 'padd-start', name_id_length, '0') + filepath = str(Path(f"{output_folder}/{name_prefix}{name_id}.png").as_posix()) + filelist.append(filepath) + print(f"已生成帧 {frame + 1}/{frame_count}") + + # 保存帧 + if save_image: + frame_img.save(filepath) + + if show_image: + image_list.append(frame_img) + + + images=[] + masks=[] + has_non_empty_mask = False + if show_image: + for img in image_list: + image,mask,has_mask = tensor_image_and_mask_from_path_or_pillow(img) + images.append(image) + masks.append(mask) + if has_mask: + has_non_empty_mask = True + images,masks,has_non_empty_mask=tensor_image_and_mask_batch_from_list(images, masks, has_non_empty_mask) + return (filelist,images,masks,has_non_empty_mask) +