├── LICENSE ├── README.md ├── __init__.py ├── __version__.py ├── assets ├── editor_example.jpg ├── editor_example_1.png ├── editor_example_2.png └── editor_example_3.jpg ├── js ├── editor_textDisplay.js ├── openpose_editor.js └── ui │ ├── OpenposeEditor.html │ └── assets │ ├── antd.dark-4fd6fdfe.css │ ├── index-22cc811d.css │ └── index-cd1939ba.js ├── openpose_editor_nodes.py ├── pyproject.toml ├── requirements.txt └── util.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # update 2 | 3 | -2025/06/03 -Added Pose Keypoints to Mask to mask node 4 | 5 | -2025/06/02 -Added translate X, Y and rotation. 6 | 7 | -2025/05/28 - Add a target keypoint. The scale entered by the user is multiplied by the scale of the target keypoint. 8 | 9 | https://github.com/user-attachments/assets/539cdcd7-520f-44d9-94ff-041b20e6e1cd 10 | 11 | -2025/05/28 - Add more scale options 12 | 13 | ![image](https://github.com/user-attachments/assets/22d7358b-2d83-498c-9536-c8c352033c99) 14 | 15 | ---------- 16 | 17 |
18 | 19 | # ComfyUI ultimate openpose editor 20 | 21 |
22 | 23 |

24 | 25 |

26 | 27 | 28 | 29 | This is an improved version of [ComfyUI-openpose-editor](https://github.com/huchenlei/ComfyUI-openpose-editor) 30 | in ComfyUI, enable input and output with flexible choices. Much more convenient and easier to use. It integrates the render function which you also can intall it separately from my [ultimate-openpose-render](https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render) repo or search in the Custom Nodes Manager 31 | 32 | If you like the project, please give me a star! ⭐ 33 | 34 | ## Installation 35 | 36 | - Manually install (**Recommend**), go to ComfyUI `/custom_nodes` directory 37 | ```bash 38 | git clone https://github.com/westNeighbor/ComfyUI-ultimate-openpose-editor 39 | cd ./ComfyUI-ultimate-openpose-editor 40 | pip install -r requirements.txt # if you use portable version, see below 41 | ``` 42 | if you use portable version, install requirement accordingly, for example, I have portable in my E: disk 43 | ```bash 44 | E:/ComfyUI_windows_portable/python_embeded/python.exe -m pip install -r requirements.txt 45 | ``` 46 | - You can install through the `Manager -> Custom Nodes Manger` search method, but probably you will meet problem to open the editor cause the **Manager** change the folder name, check the folder name after installation and change the folder name in file **_./js/openpose_editor.js_** in line 110 to the actual folder name in your computer. 47 | 48 | For example, if you have folder name **_westNeighbor_ComfyUI-ultimate-openpose-editor_** after installed, you need to change 49 | ```bash 50 | src: "extensions/ComfyUI-ultimate-openpose-editor/ui/OpenposeEditor.html", 51 | ``` 52 | to 53 | ```bash 54 | src: "extensions/westNeighbor_ComfyUI-ultimate-openpose-editor/ui/OpenposeEditor.html", 55 | ``` 56 | 57 | - Restart ComfyUI 58 | 59 | ## Usage 60 | - Insert node by `Right Click -> ultimate-openpose -> Openpose Editor Node` 61 | - `Right Click` on the node and select the `Open in Openpose Editor` to do the editting 62 |

63 | 64 |

65 | - send back after editting 66 |

67 | 68 |

69 | 70 | ## Features 71 | The node is very functional and rich features to fit all your needs. 72 | - It is totally local running, no internet requiring like the [ComfyUI-openpose-editor](https://github.com/huchenlei/ComfyUI-openpose-editor) 73 | - It can handle all kinds of situations 74 | 75 | - It can go without any input, you can get an empty image or add poses of persons in the editor ui. 76 | 77 | - It can accept POSE\_KEYPOINTS or poses in json format as input. 78 | 79 | - To edit poses, right click the node, and open the editor through `Open in Openpose Editor` menu. The send back poses will be shown in the pose json input area. Be ware that the edit priority is **POSE\_KEYPOINT > POSE\_JSON** 80 | 81 | - It can output pose images, or POSE\_KEYPOINTS or the json poses. Be ware that the output priority is **POSE\_JSON > POSE\_KEYPOINT** 82 | 83 | - It integrates the render options too, so you can use it as an render node too, or check my [ultimate-openpose-render](https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render) node. 84 | 85 |

86 | 87 |

88 | 89 | 90 | ## Credits 91 | - https://github.com/huchenlei/ComfyUI-openpose-editor 92 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .openpose_editor_nodes import OpenposeEditorNode, PoseToMaskNode 2 | 3 | 4 | WEB_DIRECTORY = "js" 5 | 6 | NODE_CLASS_MAPPINGS = { 7 | "OpenposeEditorNode": OpenposeEditorNode, 8 | "PoseToMaskNode": PoseToMaskNode, 9 | } 10 | 11 | NODE_DISPLAY_NAME_MAPPINGS = { 12 | "OpenposeEditorNode": "Openpose Editor Node", 13 | "PoseToMaskNode": "Pose Keypoints to Mask", 14 | } 15 | 16 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] 17 | -------------------------------------------------------------------------------- /__version__.py: -------------------------------------------------------------------------------- 1 | VERSION = '1.0.0' 2 | -------------------------------------------------------------------------------- /assets/editor_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toyxyz/ComfyUI-ultimate-openpose-editor/3bd76c5831c75eda1e3db5579aa90673028032dd/assets/editor_example.jpg -------------------------------------------------------------------------------- /assets/editor_example_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toyxyz/ComfyUI-ultimate-openpose-editor/3bd76c5831c75eda1e3db5579aa90673028032dd/assets/editor_example_1.png -------------------------------------------------------------------------------- /assets/editor_example_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toyxyz/ComfyUI-ultimate-openpose-editor/3bd76c5831c75eda1e3db5579aa90673028032dd/assets/editor_example_2.png -------------------------------------------------------------------------------- /assets/editor_example_3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toyxyz/ComfyUI-ultimate-openpose-editor/3bd76c5831c75eda1e3db5579aa90673028032dd/assets/editor_example_3.jpg -------------------------------------------------------------------------------- /js/editor_textDisplay.js: -------------------------------------------------------------------------------- 1 | import { app } from "../../../scripts/app.js"; 2 | import { ComfyWidgets } from "../../../scripts/widgets.js"; 3 | 4 | // Displays input text on a node 5 | app.registerExtension({ 6 | name: "EditOpenposeText", 7 | async beforeRegisterNodeDef(nodeType, nodeData, app) { 8 | if (nodeData.name === "OpenposeEditorNode") { 9 | function populate(text) { 10 | let removed = 0; 11 | if (this.widgets) { 12 | for (let i = 8; i < this.widgets.length; i++) { 13 | if (this.widgets[i].name === "display_only") { 14 | this.widgets[i].onRemove?.(); 15 | removed++; 16 | } 17 | } 18 | this.widgets.length = this.widgets.length - removed; 19 | } 20 | 21 | if (this.inputs?.[0].link || this.inputs?.[this.inputs.length-1].widget) { 22 | const v = [...text]; 23 | if (!v[0]) { 24 | v.shift(); 25 | } 26 | for (const list of v) { 27 | // const w = this.widgets[0]; 28 | // w.value = list; 29 | // if (this.inputs?.[this.inputs.length-1].widget) { 30 | 31 | const w = ComfyWidgets["STRING"](this, "display_only", ["STRING", { multiline: true }], app).widget; 32 | w.inputEl.readOnly = true; 33 | w.inputEl.style.opacity = 0.6; 34 | w.value = list; 35 | 36 | //} 37 | } 38 | } 39 | 40 | requestAnimationFrame(() => { 41 | const sz = this.computeSize(); 42 | if (sz[0] < this.size[0]) { 43 | sz[0] = this.size[0]; 44 | } 45 | if (sz[1] < this.size[1]) { 46 | sz[1] = this.size[1]; 47 | } 48 | this.onResize?.(sz); 49 | app.graph.setDirtyCanvas(true, false); 50 | }); 51 | } 52 | 53 | // When the node is executed we will be sent the input text, display this in the widget 54 | const onExecuted = nodeType.prototype.onExecuted; 55 | nodeType.prototype.onExecuted = function (message) { 56 | onExecuted?.apply(this, arguments); 57 | populate.call(this, message.POSE_JSON); 58 | }; 59 | 60 | const onConfigure = nodeType.prototype.onConfigure; 61 | nodeType.prototype.onConfigure = function () { 62 | onConfigure?.apply(this, arguments); 63 | if (this.widgets_values?.length) { 64 | // populate.call(this, this.widgets_values.slice(+this.widgets_values.length > 1)); 65 | } 66 | }; 67 | } 68 | }, 69 | }); 70 | -------------------------------------------------------------------------------- /js/openpose_editor.js: -------------------------------------------------------------------------------- 1 | import { app } from "../../scripts/app.js"; 2 | import { ComfyDialog, $el } from "../../scripts/ui.js"; 3 | import { ComfyApp } from "../../scripts/app.js"; 4 | 5 | 6 | function addMenuHandler(nodeType, cb) { 7 | const getOpts = nodeType.prototype.getExtraMenuOptions; 8 | nodeType.prototype.getExtraMenuOptions = function () { 9 | const r = getOpts.apply(this, arguments); 10 | cb.apply(this, arguments); 11 | return r; 12 | }; 13 | } 14 | 15 | class OpenposeEditorDialog extends ComfyDialog { 16 | static timeout = 5000; 17 | static instance = null; 18 | 19 | static getInstance() { 20 | if (!OpenposeEditorDialog.instance) { 21 | OpenposeEditorDialog.instance = new OpenposeEditorDialog(); 22 | } 23 | 24 | return OpenposeEditorDialog.instance; 25 | } 26 | 27 | constructor() { 28 | super(); 29 | this.element = $el("div.comfy-modal", { 30 | parent: document.body, 31 | style: { 32 | width: "80vw", 33 | height: "80vh", 34 | }, 35 | }, [ 36 | $el("div.comfy-modal-content", { 37 | style: { 38 | width: "100%", 39 | height: "100%", 40 | }, 41 | }, this.createButtons()), 42 | ]); 43 | this.is_layout_created = false; 44 | 45 | window.addEventListener("message", (event) => { 46 | if (event.source !== this.iframeElement.contentWindow) { 47 | return; 48 | } 49 | 50 | const message = event.data; 51 | if (message.modalId === 0) { 52 | const targetNode = ComfyApp.clipspace_return_node; 53 | const textAreaElement = targetNode.widgets[7].element; 54 | textAreaElement.value = JSON.stringify(event.data.poses); 55 | ComfyApp.onClipspaceEditorClosed(); 56 | this.close(); 57 | } 58 | }); 59 | } 60 | 61 | createButtons() { 62 | const closeBtn = $el("button", { 63 | type: "button", 64 | textContent: "Close", 65 | onclick: () => this.close(), 66 | }); 67 | return [ 68 | closeBtn, 69 | ]; 70 | } 71 | 72 | close() { 73 | super.close(); 74 | } 75 | 76 | async show() { 77 | if (!this.is_layout_created) { 78 | this.createLayout(); 79 | this.is_layout_created = true; 80 | await this.waitIframeReady(); 81 | } 82 | 83 | const targetNode = ComfyApp.clipspace_return_node; 84 | if (targetNode.inputs?.[0].link || targetNode.inputs?.[targetNode.inputs.length-1].widget){ 85 | const textAreaElement = targetNode.widgets[8].element; 86 | this.element.style.display = "flex"; 87 | this.setCanvasJSONString(textAreaElement.value.replace(/'/g, '"')); 88 | } else { 89 | const textAreaElement = targetNode.widgets[7].element; 90 | this.element.style.display = "flex"; 91 | if (textAreaElement.value === "") { 92 | let resolution_x = targetNode.widgets[3].value; 93 | let resolution_y = Math.floor(768*(resolution_x*1.0/512)); 94 | if (resolution_x < 64){ 95 | resolution_x = 512; 96 | resolution_y = 768; 97 | } 98 | 99 | let pose = `[{"people": [{"pose_keypoints_2d": [], "face_keypoints_2d": [], "hand_left_keypoints_2d": [], "hand_right_keypoints_2d": []}], "canvas_height": ${resolution_y}, "canvas_width": ${resolution_x}}]`; 100 | this.setCanvasJSONString(pose); 101 | } else { 102 | this.setCanvasJSONString(textAreaElement.value.replace(/'/g, '"')); 103 | } 104 | } 105 | } 106 | 107 | createLayout() { 108 | this.iframeElement = $el("iframe", { 109 | // Change to for local dev 110 | src: "extensions/ComfyUI-ultimate-openpose-editor/ui/OpenposeEditor.html", 111 | style: { 112 | width: "100%", 113 | height: "100%", 114 | border: "none", 115 | }, 116 | }); 117 | const modalContent = this.element.querySelector(".comfy-modal-content"); 118 | while (modalContent.firstChild) { 119 | modalContent.removeChild(modalContent.firstChild); 120 | } 121 | modalContent.appendChild(this.iframeElement); 122 | } 123 | 124 | waitIframeReady() { 125 | return new Promise((resolve, reject) => { 126 | const receiveMessage = (event) => { 127 | if (event.source !== this.iframeElement.contentWindow) { 128 | return; 129 | } 130 | if (event.data.ready) { 131 | window.removeEventListener("message", receiveMessage); 132 | clearTimeout(timeoutHandle); 133 | resolve(); 134 | } 135 | }; 136 | const timeoutHandle = setTimeout(() => { 137 | reject(new Error("Timeout")); 138 | }, OpenposeEditorDialog.timeout); 139 | 140 | window.addEventListener("message", receiveMessage); 141 | }); 142 | } 143 | 144 | setCanvasJSONString(jsonString) { 145 | this.iframeElement.contentWindow.postMessage({ 146 | modalId: 0, 147 | poses: JSON.parse(jsonString) 148 | }, "*"); 149 | } 150 | } 151 | 152 | app.registerExtension({ 153 | name: "OpenposeEditor", 154 | 155 | async beforeRegisterNodeDef(nodeType, nodeData) { 156 | if (nodeData.name === "OpenposeEditorNode") { 157 | addMenuHandler(nodeType, function (_, options) { 158 | options.unshift({ 159 | content: "Open in Openpose Editor", 160 | callback: () => { 161 | // `this` is the node instance 162 | ComfyApp.copyToClipspace(this); 163 | ComfyApp.clipspace_return_node = this; 164 | 165 | const dlg = OpenposeEditorDialog.getInstance(); 166 | dlg.show(); 167 | }, 168 | }); 169 | }); 170 | } 171 | } 172 | }); 173 | -------------------------------------------------------------------------------- /js/ui/OpenposeEditor.html: -------------------------------------------------------------------------------- 1 | 2 | 57 | 58 | 59 | Openpose Editor 60 | 61 | 62 | 63 | 64 |
ComfyUI-ULTIMATE-OPENPOSE-EDITOR
679
(SPACE / F) + Drag MouseHold key to pan the canvas
Mouse wheelZoom in/out
Right clickHide keypoint
Width
px
Height
px
88 | 89 | 93 | 94 | 95 | 96 | -------------------------------------------------------------------------------- /js/ui/assets/index-22cc811d.css: -------------------------------------------------------------------------------- 1 | .icon-switch[data-v-67f5f644]{cursor:pointer}.custom-svg[data-v-3df26ed3]{width:1rem;height:1rem;margin:3px}.custom-svg path[data-v-3df26ed3]{fill:var(--color-text);transition:fill .3s ease}.flipDisabled[data-v-1d917c1f]{opacity:50%}.hidden{opacity:50%;text-decoration:line-through}:root{--vt-c-white: #ffffff;--vt-c-white-soft: #f8f8f8;--vt-c-white-mute: #f2f2f2;--vt-c-black: #181818;--vt-c-black-soft: #222222;--vt-c-black-mute: #282828;--vt-c-indigo: #2c3e50;--vt-c-divider-light-1: rgba(60, 60, 60, .29);--vt-c-divider-light-2: rgba(60, 60, 60, .12);--vt-c-divider-dark-1: rgba(84, 84, 84, .65);--vt-c-divider-dark-2: rgba(84, 84, 84, .48);--vt-c-text-light-1: var(--vt-c-indigo);--vt-c-text-light-2: rgba(60, 60, 60, .66);--vt-c-text-dark-1: var(--vt-c-white);--vt-c-text-dark-2: rgba(235, 235, 235, .64)}:root{--color-background: var(--vt-c-white);--color-background-soft: var(--vt-c-white-soft);--color-background-mute: var(--vt-c-white-mute);--color-border: var(--vt-c-divider-light-2);--color-border-hover: var(--vt-c-divider-light-1);--color-heading: var(--vt-c-text-light-1);--color-text: var(--vt-c-text-light-1);--section-gap: 160px}body.dark-theme{--color-background: var(--vt-c-black);--color-background-soft: var(--vt-c-black-soft);--color-background-mute: var(--vt-c-black-mute);--color-border: var(--vt-c-divider-dark-2);--color-border-hover: var(--vt-c-divider-dark-1);--color-heading: var(--vt-c-text-dark-1);--color-text: var(--vt-c-text-dark-1)}@media (prefers-color-scheme: dark){:root{--color-background: var(--vt-c-black);--color-background-soft: var(--vt-c-black-soft);--color-background-mute: var(--vt-c-black-mute);--color-border: var(--vt-c-divider-dark-2);--color-border-hover: var(--vt-c-divider-dark-1);--color-heading: var(--vt-c-text-dark-1);--color-text: var(--vt-c-text-dark-2)}}*,*:before,*:after{box-sizing:border-box;margin:0;position:relative;font-weight:400}body{min-height:100vh;color:var(--color-text);background:var(--color-background);transition:color .5s,background-color .5s;line-height:1.6;font-family:Inter,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Fira Sans,Droid Sans,Helvetica Neue,sans-serif;font-size:15px;text-rendering:optimizeLegibility;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.visible-switch,.group-switch,.unjam-button,.lock-switch{padding:3px}.ant-collapse-header{align-items:center!important;justify-content:flex-start}.ant-list-item{justify-content:flex-start}.close-icon,.coords-group,.scale-ratio-input{margin-left:auto}.ant-col{padding:10px}.image-thumbnail{width:50px;height:50px;object-fit:cover;margin-right:8px}.uploaded-file-item>div{display:flex;justify-content:flex-start;align-items:center}.ant-space{margin-bottom:5px}li.keypoint-selected{background-color:#fdd;color:#b20000}.dark-theme li.keypoint-selected{color:#fdd;background-color:#5e0303}#control-panel{height:100vh;overflow-y:auto}#control-panel::-webkit-scrollbar{display:none}.ant-space{flex-wrap:wrap}.ant-input-number{max-width:100px} 2 | -------------------------------------------------------------------------------- /openpose_editor_nodes.py: -------------------------------------------------------------------------------- 1 | import json 2 | import torch 3 | import numpy as np 4 | import cv2 5 | from .util import draw_pose_json, draw_pose 6 | 7 | OpenposeJSON = dict 8 | 9 | class OpenposeEditorNode: 10 | @classmethod 11 | def INPUT_TYPES(s): 12 | return { 13 | "optional": { 14 | "show_body": ("BOOLEAN", {"default": True}), 15 | "show_face": ("BOOLEAN", {"default": True}), 16 | "show_hands": ("BOOLEAN", {"default": True}), 17 | "resolution_x": ("INT", {"default": -1, "min": -1, "max": 12800}), 18 | "use_ground_plane": ("BOOLEAN", {"default": True}), 19 | "pose_marker_size": ("INT", { "default": 4, "min": 0, "max": 100 }), 20 | "face_marker_size": ("INT", { "default": 3, "min": 0, "max": 100 }), 21 | "hand_marker_size": ("INT", { "default": 2, "min": 0, "max": 100 }), 22 | "pelvis_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 23 | "torso_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 24 | "neck_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 25 | # --- 머리 및 눈 관련 스케일 --- 26 | "head_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 27 | "eye_distance_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 28 | "eye_height": ("FLOAT", { "default": 0.0, "min": -100.0, "max": 100.0, "step": 0.1 }), 29 | "eyebrow_height": ("FLOAT", { "default": 0.0, "min": -100.0, "max": 100.0, "step": 0.1 }), # 눈썹 높이 조절 30 | "left_eye_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 31 | "right_eye_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 32 | "left_eyebrow_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 33 | "right_eyebrow_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 34 | "mouth_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 35 | "nose_scale_face": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 36 | "face_shape_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01 }), 37 | # --- 38 | "shoulder_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 39 | "arm_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 40 | "leg_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 41 | "hands_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 42 | "overall_scale": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01 }), 43 | "rotate_angle": ("FLOAT", {"default": 0.0, "min": -360.0, "max": 360.0, "step": 0.1}), 44 | "translate_x": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.1}), 45 | "translate_y": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.1}), 46 | "POSE_JSON": ("STRING", {"multiline": True}), 47 | "POSE_KEYPOINT": ("POSE_KEYPOINT",{"default": None}), 48 | "Target_pose_keypoint": ("POSE_KEYPOINT", {"default": None}), 49 | }, 50 | } 51 | 52 | RETURN_NAMES = ("POSE_IMAGE", "POSE_KEYPOINT", "POSE_JSON") 53 | RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT", "STRING") 54 | OUTPUT_NODE = True 55 | FUNCTION = "load_pose" 56 | CATEGORY = "ultimate-openpose" 57 | 58 | def load_pose(self, show_body, show_face, show_hands, resolution_x, use_ground_plane, 59 | pose_marker_size, face_marker_size, hand_marker_size, 60 | pelvis_scale, torso_scale, neck_scale, head_scale, eye_distance_scale, eye_height, eyebrow_height, 61 | left_eye_scale, right_eye_scale, left_eyebrow_scale, right_eyebrow_scale, 62 | mouth_scale, nose_scale_face, face_shape_scale, 63 | shoulder_scale, arm_scale, leg_scale, hands_scale, overall_scale, 64 | rotate_angle, translate_x, translate_y, 65 | POSE_JSON: str, POSE_KEYPOINT=None, Target_pose_keypoint=None) -> tuple[OpenposeJSON]: 66 | 67 | # 내부 함수인 process_pose에 Target_pose_keypoint를 전달하도록 수정 68 | def process_pose(pose_input_str_list, target_pose_obj=None, rot_angle=0, tr_x=0.0, tr_y=0.0): 69 | pose_imgs, final_keypoints_batch = draw_pose_json( 70 | pose_input_str_list, resolution_x, use_ground_plane, show_body, show_face, show_hands, 71 | pose_marker_size, face_marker_size, hand_marker_size, 72 | pelvis_scale, torso_scale, neck_scale, head_scale, eye_distance_scale, eye_height, eyebrow_height, 73 | left_eye_scale, right_eye_scale, left_eyebrow_scale, right_eyebrow_scale, 74 | mouth_scale, nose_scale_face, face_shape_scale, 75 | shoulder_scale, arm_scale, leg_scale, hands_scale, overall_scale, 76 | rot_angle, tr_x, tr_y, 77 | target_pose_keypoint_obj=target_pose_obj # util.py 함수로 Target_pose_keypoint 전달 78 | ) 79 | 80 | if not pose_imgs: return None, None, None 81 | 82 | pose_imgs_np = np.array(pose_imgs).astype(np.float32) / 255 83 | final_json_str = json.dumps(final_keypoints_batch, indent=4) 84 | return torch.from_numpy(pose_imgs_np), final_keypoints_batch, final_json_str 85 | 86 | input_json_str = "" 87 | # 팔 길이 비교를 위해 POSE_KEYPOINT가 우선순위를 갖도록 순서 조정 88 | if POSE_KEYPOINT is not None: 89 | normalized_json_data = json.dumps(POSE_KEYPOINT, indent=4).replace("'",'"').replace('None','[]') 90 | if not isinstance(POSE_KEYPOINT, list): 91 | input_json_str = f'[{normalized_json_data}]' 92 | else: 93 | input_json_str = normalized_json_data 94 | elif POSE_JSON: 95 | temp_json = POSE_JSON.replace("'",'"').replace('None','[]') 96 | try: 97 | parsed_json = json.loads(temp_json) 98 | input_json_str = f"[{temp_json}]" if not isinstance(parsed_json, list) else temp_json 99 | except json.JSONDecodeError: input_json_str = f"[{temp_json}]" 100 | 101 | if input_json_str: 102 | # process_pose 호출 시 Target_pose_keypoint 객체를 인자로 전달 103 | image_tensor, keypoint_obj_batch, json_str_batch = process_pose(input_json_str, Target_pose_keypoint, rotate_angle, translate_x, translate_y) 104 | if image_tensor is not None: 105 | return { "ui": {"POSE_JSON": [json_str_batch]}, "result": (image_tensor, keypoint_obj_batch, json_str_batch) } 106 | 107 | W, H = 512, 768 108 | blank_person = dict(pose_keypoints_2d=[], face_keypoints_2d=[], hand_left_keypoints_2d=[], hand_right_keypoints_2d=[]) 109 | blank_output_keypoints = [{"people": [blank_person], "canvas_width": W, "canvas_height": H}] 110 | W_scaled = resolution_x if resolution_x >= 64 else W 111 | H_scaled = int(H*(W_scaled*1.0/W)) 112 | blank_pose_for_draw = {"people": [blank_person]} 113 | pose_img = [draw_pose(blank_pose_for_draw, H_scaled, W_scaled, pose_marker_size, face_marker_size, hand_marker_size)] 114 | pose_img_np = np.array(pose_img).astype(np.float32) / 255 115 | return { "ui": {"POSE_JSON": [json.dumps(blank_output_keypoints)]}, "result": (torch.from_numpy(pose_img_np), blank_output_keypoints, json.dumps(blank_output_keypoints)) } 116 | 117 | 118 | class PoseToMaskNode: 119 | @classmethod 120 | def INPUT_TYPES(s): 121 | return { 122 | "required": { 123 | "pose_keypoint": ("POSE_KEYPOINT",), 124 | "line_thickness": ("INT", {"default": 30, "min": 1, "max": 1000, "step": 1, "display": "number"}), 125 | "finger_line_thickness": ("INT", {"default": 10, "min": 1, "max": 1000, "step": 1, "display": "number"}), 126 | "torso_thickness": ("INT", {"default": 30, "min": 1, "max": 1000, "step": 1, "display": "number"}), 127 | "head_size": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 1000.0, "step": 0.01, "display": "number"}), # 얼굴 마스크 크기 조절 128 | "confidence_threshold": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "display": "number"}), 129 | } 130 | # "optional": { # 캔버스 크기 오버라이드 옵션 (필요시 유지) 131 | # "override_canvas_width": ("INT", {"default": 0, "min": 0, "max": 8192}), 132 | # "override_canvas_height": ("INT", {"default": 0, "min": 0, "max": 8192}), 133 | # } 134 | } 135 | 136 | RETURN_TYPES = ("MASK",) 137 | RETURN_NAMES = ("MASK",) 138 | FUNCTION = "create_body_parts_mask" 139 | CATEGORY = "ultimate-openpose" 140 | 141 | # OpenPose 키포인트 인덱스 정의 (참고용) 142 | # Nose:0, Neck:1, RShoulder:2, RElbow:3, RWrist:4, LShoulder:5, LElbow:6, LWrist:7, 143 | # RHip:8, RKnee:9, RAnkle:10, LHip:11, LKnee:12, LAnkle:13 144 | 145 | # 팔, 다리, 목 연결선 정의 146 | LIMB_CONNECTIONS = [ 147 | (0, 1), # 코(0) - 목(1) 148 | (2, 3), (3, 4), # 오른쪽 팔 (RShoulder-RElbow, RElbow-RWrist) 149 | (5, 6), (6, 7), # 왼쪽 팔 (LShoulder-LElbow, LElbow-LWrist) 150 | (8, 9), (9, 10), # 오른쪽 다리 (RHip-RKnee, RKnee-RAnkle) 151 | (11, 12), (12, 13) # 왼쪽 다리 (LHip-LKnee, LAnkle) 152 | ] 153 | 154 | # 몸통을 구성하는 4개 꼭짓점 인덱스 (RShoulder, LShoulder, LHip, RHip 순서) 155 | TORSO_POLYGON_INDICES = [2, 5, 11, 8] 156 | 157 | # 손가락 연결선 정의 (OpenPose hand keypoints 기준) 158 | # 손목 (0)에서 시작하여 각 손가락의 마디를 연결 159 | # 출처: OpenPose GitHub 또는 문서에서 hand keypoint index 참조 160 | HAND_CONNECTIONS = [ 161 | # 엄지 (Thumb) 162 | (0, 1), (1, 2), (2, 3), (3, 4), 163 | # 검지 (Index Finger) 164 | (0, 5), (5, 6), (6, 7), (7, 8), 165 | # 중지 (Middle Finger) 166 | (0, 9), (9, 10), (10, 11), (11, 12), 167 | # 약지 (Ring Finger) 168 | (0, 13), (13, 14), (14, 15), (15, 16), 169 | # 새끼손가락 (Little Finger) 170 | (0, 17), (17, 18), (18, 19), (19, 20) 171 | ] 172 | 173 | # 얼굴 키포인트 인덱스 정의 174 | FACE_KEYPOINT_INDICES = list(range(17)) # 0~16번까지의 얼굴 키포인트 사용 175 | 176 | def _get_point(self, keypoints_list, index, confidence_threshold): 177 | """Helper function to get a valid point's coordinates if confidence is high enough.""" 178 | if not keypoints_list or (index * 3 + 2) >= len(keypoints_list): 179 | return None 180 | x, y, conf = keypoints_list[index * 3], keypoints_list[index * 3 + 1], keypoints_list[index * 3 + 2] 181 | # 유효한 좌표 (OpenPose에서 0,0은 종종 감지 안됨을 의미)이고 신뢰도가 문턱값 이상일 때 182 | if conf >= confidence_threshold and (x != 0 or y != 0): 183 | return (int(round(x)), int(round(y))) 184 | return None 185 | 186 | def create_body_parts_mask(self, pose_keypoint, line_thickness, finger_line_thickness, torso_thickness, head_size, confidence_threshold, override_canvas_width=0, override_canvas_height=0): 187 | if not pose_keypoint or not isinstance(pose_keypoint, list) or not pose_keypoint[0]: 188 | # 입력이 유효하지 않으면 기본 빈 마스크 반환 189 | h = override_canvas_height if override_canvas_height > 0 else 768 190 | w = override_canvas_width if override_canvas_width > 0 else 512 191 | mask = np.zeros((h, w), dtype=np.float32) 192 | return (torch.from_numpy(mask).unsqueeze(0),) 193 | 194 | frame_data = pose_keypoint[0] # 첫 번째 프레임의 데이터 사용 가정 195 | 196 | canvas_width = override_canvas_width if override_canvas_width > 0 else frame_data.get("canvas_width", 512) 197 | canvas_height = override_canvas_height if override_canvas_height > 0 else frame_data.get("canvas_height", 768) 198 | 199 | if canvas_width <= 0 or canvas_height <= 0: # 안전장치 200 | canvas_width = 512 201 | canvas_height = 768 202 | 203 | # 최종 마스크는 8비트 단일 채널 204 | mask_image = np.zeros((canvas_height, canvas_width), dtype=np.uint8) 205 | # 몸통 마스크를 위한 임시 마스크 206 | torso_mask_temp = np.zeros((canvas_height, canvas_width), dtype=np.uint8) 207 | 208 | 209 | for person_data in frame_data.get("people", []): 210 | body_keypoints = person_data.get("pose_keypoints_2d") 211 | hand_left_keypoints = person_data.get("hand_left_keypoints_2d", []) 212 | hand_right_keypoints = person_data.get("hand_right_keypoints_2d", []) 213 | face_keypoints = person_data.get("face_keypoints_2d", []) 214 | 215 | if not body_keypoints: 216 | continue 217 | 218 | # 1. 몸통 마스크 (채워진 사각형/다각형) - 임시 마스크에 그립니다. 219 | torso_points_for_poly = [] 220 | for idx in self.TORSO_POLYGON_INDICES: 221 | point = self._get_point(body_keypoints, idx, confidence_threshold) 222 | if point: 223 | # 좌표가 캔버스 범위 내에 있는지 확인 224 | if 0 <= point[0] < canvas_width and 0 <= point[1] < canvas_height: 225 | torso_points_for_poly.append(point) 226 | else: 227 | # 몸통 꼭짓점 중 하나라도 유효하지 않으면 몸통 마스크를 그리지 않음 228 | torso_points_for_poly = [] # 리스트 비우기 229 | break 230 | 231 | if len(torso_points_for_poly) == 4: # 4개의 꼭짓점이 모두 유효할 때만 그림 232 | np_torso_points = np.array([torso_points_for_poly], dtype=np.int32) 233 | cv2.fillConvexPoly(torso_mask_temp, np_torso_points, 255, lineType=cv2.LINE_AA) # 임시 마스크에 흰색(255)으로 채움 234 | 235 | # 몸통 마스크 확장 (dilate) - 임시 마스크에만 적용 236 | # torso_thickness 값에 따라 커널 크기 조정 (홀수로 유지) 237 | # torso_thickness가 1보다 작으면 1로 설정하여 최소한의 dilate 적용 238 | kernel_size_torso = max(1, torso_thickness // 2 * 2 + 1) 239 | kernel_torso = np.ones((kernel_size_torso, kernel_size_torso), np.uint8) 240 | torso_mask_temp = cv2.dilate(torso_mask_temp, kernel_torso, iterations=1) 241 | 242 | # 확장된 몸통 마스크를 최종 마스크에 추가 243 | mask_image = cv2.bitwise_or(mask_image, torso_mask_temp) 244 | 245 | # 2. 팔, 다리, 목 마스크 (두꺼운 선) - 최종 마스크에 직접 그립니다. 246 | for p1_idx, p2_idx in self.LIMB_CONNECTIONS: 247 | p1 = self._get_point(body_keypoints, p1_idx, confidence_threshold) 248 | p2 = self._get_point(body_keypoints, p2_idx, confidence_threshold) 249 | 250 | if p1 and p2: 251 | # 두 점이 모두 유효하고 캔버스 범위 내에 있을 경우 선 그리기 252 | if (0 <= p1[0] < canvas_width and 0 <= p1[1] < canvas_height and 253 | 0 <= p2[0] < canvas_width and 0 <= p2[1] < canvas_height): 254 | cv2.line(mask_image, p1, p2, 255, line_thickness, lineType=cv2.LINE_AA) 255 | 256 | # 3. 손가락 마스크 (새로 추가) - 최종 마스크에 직접 그립니다. 257 | for hand_keypoints in [hand_left_keypoints, hand_right_keypoints]: 258 | if not hand_keypoints: 259 | continue 260 | 261 | # 손가락 연결선 그리기 262 | for p1_idx, p2_idx in self.HAND_CONNECTIONS: 263 | p1 = self._get_point(hand_keypoints, p1_idx, confidence_threshold) 264 | p2 = self._get_point(hand_keypoints, p2_idx, confidence_threshold) 265 | 266 | if p1 and p2: 267 | if (0 <= p1[0] < canvas_width and 0 <= p1[1] < canvas_height and 268 | 0 <= p2[0] < canvas_width and 0 <= p2[1] < canvas_height): 269 | cv2.line(mask_image, p1, p2, 255, finger_line_thickness, lineType=cv2.LINE_AA) 270 | 271 | # 4. 얼굴 마스크 (타원) 272 | face_points = [] 273 | for idx in self.FACE_KEYPOINT_INDICES: 274 | point = self._get_point(face_keypoints, idx, confidence_threshold) 275 | if point: 276 | # 좌표가 캔버스 범위 내에 있는지 확인 277 | if 0 <= point[0] < canvas_width and 0 <= point[1] < canvas_height: 278 | face_points.append(point) 279 | 280 | if len(face_points) >= 5: # 타원을 그리기 위한 최소 점 개수 (최소 5개의 점이 필요함) 281 | np_face_points = np.array(face_points, dtype=np.int32) 282 | 283 | # 볼록 껍질(Convex Hull) 계산 284 | hull = cv2.convexHull(np_face_points) 285 | 286 | # 볼록 껍질을 감싸는 최소 크기 타원 계산 287 | # cv2.fitEllipse는 최소 5개의 점이 필요합니다. 288 | (x, y), (major_axis, minor_axis), angle = cv2.fitEllipse(hull) 289 | 290 | # head_size에 따라 타원 크기 조정 291 | major_axis *= head_size 292 | minor_axis *= head_size 293 | 294 | # 타원 그리기 295 | cv2.ellipse(mask_image, (int(x), int(y)), (int(major_axis / 2), int(minor_axis / 2)), int(angle), 0, 360, 255, cv2.FILLED, lineType=cv2.LINE_AA) 296 | 297 | 298 | # NumPy 배열을 PyTorch 텐서로 변환하고 정규화 (H, W) -> (1, H, W) 299 | mask_tensor = torch.from_numpy(mask_image.astype(np.float32) / 255.0).unsqueeze(0) 300 | 301 | return (mask_tensor,) 302 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "ComfyUI-ultimate-openpose-editor" 3 | description = "Enhanced features with flexible choice of inputs and outputs, fine control for pose plotting, freedom to composite poses and fast local pose editing." 4 | version = "1.0.0" 5 | license = { file = "LICENSE"} 6 | dependencies = ["torch", "polygraphy", "numpy", "opencv-python", "matplotlib"] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-editor" 10 | # Used by Comfy Registry https://comfyregistry.org 11 | 12 | [tool.comfy] 13 | PublisherId = "west-neighbor" 14 | DisplayName = "ComfyUI-ultimate-openpose-editor" 15 | Icon = "" 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | polygraphy 2 | matplotlib 3 | numpy 4 | opencv-python 5 | torch 6 | -------------------------------------------------------------------------------- /util.py: -------------------------------------------------------------------------------- 1 | import math 2 | import json 3 | import numpy as np 4 | import matplotlib 5 | import cv2 6 | from comfy.utils import ProgressBar 7 | 8 | eps = 0.01 9 | 10 | def scale(point, scale_factor, pivot): 11 | if not isinstance(point, np.ndarray): point = np.array(point) 12 | if not isinstance(pivot, np.ndarray): pivot = np.array(pivot) 13 | return pivot + (point - pivot) * scale_factor 14 | 15 | def draw_pose_json(pose_json_str, resolution_x, use_ground_plane, show_body, show_face, show_hands, 16 | pose_marker_size, face_marker_size, hand_marker_size, 17 | pelvis_scale, torso_scale, neck_scale, head_scale, eye_distance_scale, eye_height, eyebrow_height, 18 | left_eye_scale, right_eye_scale, left_eyebrow_scale, right_eyebrow_scale, 19 | mouth_scale, nose_scale_face, face_shape_scale, 20 | shoulder_scale, arm_scale, leg_scale, hands_scale, overall_scale, 21 | rotate_angle, translate_x, translate_y, 22 | target_pose_keypoint_obj=None): 23 | 24 | # 최종적으로 적용될 스케일 값을 초기화 25 | final_hands_scale = hands_scale 26 | final_torso_scale = torso_scale 27 | final_head_scale = head_scale 28 | final_neck_scale = neck_scale 29 | final_pelvis_scale = pelvis_scale 30 | final_shoulder_scale = shoulder_scale 31 | final_arm_scale = arm_scale 32 | final_leg_scale = leg_scale 33 | 34 | if target_pose_keypoint_obj and pose_json_str: 35 | try: 36 | source_pose_obj = json.loads(pose_json_str) 37 | 38 | # --- 공용 헬퍼 함수 정의 --- 39 | def get_point(kps_list, index): 40 | if index * 3 + 2 >= len(kps_list) or kps_list[index * 3 + 2] == 0: 41 | return None 42 | return np.array([kps_list[index * 3], kps_list[index * 3 + 1]]) 43 | 44 | def calculate_limb_length(kps_list, p1_idx, p2_idx): 45 | p1 = get_point(kps_list, p1_idx) 46 | p2 = get_point(kps_list, p2_idx) 47 | if p1 is not None and p2 is not None: 48 | return np.linalg.norm(p1 - p2) 49 | return 0.0 50 | 51 | # --- 팔 길이 계산 --- 52 | def get_max_arm_length(pose_obj): 53 | try: 54 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 55 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 56 | if not keypoints: return 0.0 57 | right_arm_len = calculate_limb_length(keypoints, 2, 3) + calculate_limb_length(keypoints, 3, 4) 58 | left_arm_len = calculate_limb_length(keypoints, 5, 6) + calculate_limb_length(keypoints, 6, 7) 59 | return max(left_arm_len, right_arm_len) 60 | except (IndexError, TypeError): return 0.0 61 | 62 | target_arm_len = get_max_arm_length(target_pose_keypoint_obj) 63 | source_arm_len = get_max_arm_length(source_pose_obj) 64 | 65 | if source_arm_len > 0 and target_arm_len > 0: 66 | final_arm_scale = arm_scale * (target_arm_len / source_arm_len) 67 | 68 | # --- 다리 길이 계산 --- 69 | def get_max_leg_length(pose_obj): 70 | try: 71 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 72 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 73 | if not keypoints: return 0.0 74 | right_leg_len = calculate_limb_length(keypoints, 8, 9) + calculate_limb_length(keypoints, 9, 10) 75 | left_leg_len = calculate_limb_length(keypoints, 11, 12) + calculate_limb_length(keypoints, 12, 13) 76 | return max(left_leg_len, right_leg_len) 77 | except (IndexError, TypeError): return 0.0 78 | 79 | target_leg_len = get_max_leg_length(target_pose_keypoint_obj) 80 | source_leg_len = get_max_leg_length(source_pose_obj) 81 | 82 | if source_leg_len > 0 and target_leg_len > 0: 83 | final_leg_scale = leg_scale * (target_leg_len / source_leg_len) 84 | 85 | # --- 어깨 너비 계산 --- 86 | def get_shoulder_width(pose_obj): 87 | try: 88 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 89 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 90 | if not keypoints: return 0.0 91 | width = calculate_limb_length(keypoints, 2, 5) 92 | return width 93 | except (IndexError, TypeError): return 0.0 94 | 95 | target_shoulder_width = get_shoulder_width(target_pose_keypoint_obj) 96 | source_shoulder_width = get_shoulder_width(source_pose_obj) 97 | 98 | if source_shoulder_width > 0 and target_shoulder_width > 0: 99 | final_shoulder_scale = shoulder_scale * (target_shoulder_width / source_shoulder_width) 100 | 101 | # --- 골반 너비 계산 --- 102 | def get_pelvis_width(pose_obj): 103 | try: 104 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 105 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 106 | if not keypoints: return 0.0 107 | width = calculate_limb_length(keypoints, 8, 11) 108 | return width 109 | except (IndexError, TypeError): return 0.0 110 | 111 | target_pelvis_width = get_pelvis_width(target_pose_keypoint_obj) 112 | source_pelvis_width = get_pelvis_width(source_pose_obj) 113 | 114 | if source_pelvis_width > 0 and target_pelvis_width > 0: 115 | final_pelvis_scale = pelvis_scale * (target_pelvis_width / source_pelvis_width) 116 | 117 | # --- 목 길이 계산 --- 118 | def get_neck_length(pose_obj): 119 | try: 120 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 121 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 122 | if not keypoints: return 0.0 123 | length = calculate_limb_length(keypoints, 1, 0) 124 | return length 125 | except (IndexError, TypeError): return 0.0 126 | 127 | target_neck_length = get_neck_length(target_pose_keypoint_obj) 128 | source_neck_length = get_neck_length(source_pose_obj) 129 | 130 | if source_neck_length > 0 and target_neck_length > 0: 131 | final_neck_scale = neck_scale * (target_neck_length / source_neck_length) 132 | 133 | # --- 머리 크기 계산 --- 134 | def get_head_size(pose_obj): 135 | try: 136 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 137 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 138 | if not keypoints: return 0.0 139 | 140 | head_indices = [0, 14, 15, 16, 17] 141 | valid_points = [p for i in head_indices if (p := get_point(keypoints, i)) is not None] 142 | 143 | if len(valid_points) < 3: return 0.0 144 | 145 | points_for_hull = np.array(valid_points, dtype=np.float32).reshape((-1, 1, 2)) 146 | hull = cv2.convexHull(points_for_hull) 147 | area = cv2.contourArea(hull) 148 | return area 149 | except (IndexError, TypeError): return 0.0 150 | 151 | target_head_size = get_head_size(target_pose_keypoint_obj) 152 | source_head_size = get_head_size(source_pose_obj) 153 | 154 | if source_head_size > 0 and target_head_size > 0: 155 | size_ratio = math.sqrt(target_head_size / source_head_size) 156 | final_head_scale = head_scale * size_ratio 157 | 158 | # --- 몸통 길이 계산 --- 159 | def get_torso_length(pose_obj): 160 | try: 161 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 162 | keypoints = pose_obj[0]['people'][0].get('pose_keypoints_2d', []) 163 | if not keypoints: return 0.0 164 | 165 | right_hip = get_point(keypoints, 8) 166 | left_hip = get_point(keypoints, 11) 167 | neck = get_point(keypoints, 1) 168 | 169 | if right_hip is None or left_hip is None or neck is None: return 0.0 170 | 171 | hip_midpoint = (right_hip + left_hip) / 2.0 172 | length = np.linalg.norm(neck - hip_midpoint) 173 | return length 174 | except (IndexError, TypeError): return 0.0 175 | 176 | target_torso_length = get_torso_length(target_pose_keypoint_obj) 177 | source_torso_length = get_torso_length(source_pose_obj) 178 | 179 | if source_torso_length > 0 and target_torso_length > 0: 180 | final_torso_scale = torso_scale * (target_torso_length / source_torso_length) 181 | 182 | # --- 손 크기 계산 (새로 추가된 부분) --- 183 | def get_hand_area(hand_kps_list): 184 | if not hand_kps_list: return 0.0 185 | valid_points = [] 186 | for i in range(0, len(hand_kps_list), 3): 187 | if hand_kps_list[i+2] > 0: 188 | valid_points.append([hand_kps_list[i], hand_kps_list[i+1]]) 189 | if len(valid_points) < 3: return 0.0 190 | points_for_hull = np.array(valid_points, dtype=np.float32).reshape((-1, 1, 2)) 191 | hull = cv2.convexHull(points_for_hull) 192 | return cv2.contourArea(hull) 193 | 194 | def get_max_hand_size(pose_obj): 195 | try: 196 | if not isinstance(pose_obj, list) or not pose_obj or 'people' not in pose_obj[0] or not pose_obj[0]['people']: return 0.0 197 | person = pose_obj[0]['people'][0] 198 | left_hand_kps = person.get('hand_left_keypoints_2d', []) 199 | right_hand_kps = person.get('hand_right_keypoints_2d', []) 200 | left_area = get_hand_area(left_hand_kps) 201 | right_area = get_hand_area(right_hand_kps) 202 | return max(left_area, right_area) 203 | except(IndexError, TypeError): return 0.0 204 | 205 | target_hand_size = get_max_hand_size(target_pose_keypoint_obj) 206 | source_hand_size = get_max_hand_size(source_pose_obj) 207 | 208 | if source_hand_size > 0 and target_hand_size > 0: 209 | size_ratio = math.sqrt(target_hand_size / source_hand_size) 210 | final_hands_scale = hands_scale * size_ratio 211 | 212 | 213 | except (json.JSONDecodeError, IndexError, TypeError): 214 | # 에러 발생 시 원래 값 유지 215 | final_hands_scale = hands_scale 216 | final_torso_scale = torso_scale 217 | final_head_scale = head_scale 218 | final_neck_scale = neck_scale 219 | final_pelvis_scale = pelvis_scale 220 | final_shoulder_scale = shoulder_scale 221 | final_arm_scale = arm_scale 222 | final_leg_scale = leg_scale 223 | 224 | pose_imgs = [] 225 | all_frames_keypoints_output = [] 226 | 227 | if pose_json_str: 228 | images_data_list = json.loads(pose_json_str) 229 | if not isinstance(images_data_list, list): images_data_list = [images_data_list] 230 | 231 | pbar = ProgressBar(len(images_data_list)) 232 | 233 | KP = { 234 | "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4, 235 | "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9, 236 | "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14, 237 | "LEye": 15, "REar": 16, "LEar": 17 238 | } 239 | 240 | FACE_KP_GROUPS_INDICES = { 241 | "Left_Eye": [42, 43, 44, 45, 46, 47, 69], 242 | "Right_Eye": [36, 37, 38, 39, 40, 41, 68], 243 | "Left_Eyebrow": [22, 23, 24, 25, 26], 244 | "Right_Eyebrow": [17, 18, 19, 20, 21], 245 | "Mouth": list(range(48, 68)), 246 | "Nose_Face": list(range(27, 36)), 247 | "Face_Shape": list(range(0, 17)) 248 | } 249 | 250 | INDIVIDUAL_FACE_SCALES = { 251 | "Left_Eye": left_eye_scale, "Right_Eye": right_eye_scale, 252 | "Left_Eyebrow": left_eyebrow_scale, "Right_Eyebrow": right_eyebrow_scale, 253 | "Mouth": mouth_scale, "Nose_Face": nose_scale_face, 254 | "Face_Shape": face_shape_scale 255 | } 256 | 257 | BODY_HEAD_PARTS = {KP["REye"], KP["LEye"], KP["REar"], KP["LEar"]} 258 | R_LEG_INDICES = {KP["RKnee"], KP["RAnkle"]} 259 | L_LEG_INDICES = {KP["LKnee"], KP["LAnkle"]} 260 | FEET_INDICES = {KP["RAnkle"], KP["LAnkle"]} 261 | 262 | for image_data in images_data_list: 263 | if 'people' not in image_data or not image_data['people']: 264 | pbar.update(1); continue 265 | 266 | figures = image_data['people'] 267 | H = image_data['canvas_height'] 268 | W = image_data['canvas_width'] 269 | 270 | current_image_people_data_for_output = [] 271 | all_scaled_candidates_for_drawing, all_scaled_faces_for_drawing, all_scaled_hands_for_drawing = [], [], [] 272 | final_subset_for_drawing = [[]] 273 | 274 | for fig_idx, figure in enumerate(figures): 275 | body_raw, face_raw, lhand_raw, rhand_raw = [figure.get(k, []) for k in ['pose_keypoints_2d', 'face_keypoints_2d', 'hand_left_keypoints_2d', 'hand_right_keypoints_2d']] 276 | 277 | if not body_raw or len(body_raw) < (KP["LEar"] + 1) * 3: continue 278 | 279 | initial_candidate = np.array([body_raw[i:i+2] for i in range(0, len(body_raw), 3)]) 280 | confidence_scores_body = [body_raw[i*3+2] for i in range(len(initial_candidate))] 281 | scaled_candidate_np = initial_candidate.copy() 282 | 283 | r_hip_orig, l_hip_orig = initial_candidate[KP["RHip"]], initial_candidate[KP["LHip"]] 284 | hip_center_orig = (r_hip_orig + l_hip_orig) / 2 285 | neck_orig, r_shoulder_orig, l_shoulder_orig, nose_orig = [initial_candidate[KP[k]] for k in ["Neck", "RShoulder", "LShoulder", "Nose"]] 286 | lwrist_orig, rwrist_orig = initial_candidate[KP["LWrist"]], initial_candidate[KP["RWrist"]] 287 | 288 | r_hip_final = scale(r_hip_orig, final_pelvis_scale, hip_center_orig) 289 | l_hip_final = scale(l_hip_orig, final_pelvis_scale, hip_center_orig) 290 | scaled_candidate_np[KP["RHip"]], scaled_candidate_np[KP["LHip"]] = r_hip_final, l_hip_final 291 | 292 | hip_center_final = (r_hip_final + l_hip_final) / 2 293 | neck_final = scale(neck_orig, final_torso_scale, hip_center_final) 294 | scaled_candidate_np[KP["Neck"]] = neck_final 295 | 296 | scaled_candidate_np[KP["RShoulder"]] = neck_final + (r_shoulder_orig - neck_orig) * final_shoulder_scale 297 | scaled_candidate_np[KP["LShoulder"]] = neck_final + (l_shoulder_orig - neck_orig) * final_shoulder_scale 298 | 299 | r_shoulder_final, l_shoulder_final = scaled_candidate_np[KP["RShoulder"]], scaled_candidate_np[KP["LShoulder"]] 300 | 301 | for i in [KP["RElbow"], KP["RWrist"]]: scaled_candidate_np[i] = r_shoulder_final + (initial_candidate[i] - r_shoulder_orig) * final_arm_scale 302 | for i in [KP["LElbow"], KP["LWrist"]]: scaled_candidate_np[i] = l_shoulder_final + (initial_candidate[i] - l_shoulder_orig) * final_arm_scale 303 | 304 | for i in R_LEG_INDICES: scaled_candidate_np[i] = r_hip_final + (initial_candidate[i] - r_hip_orig) * final_leg_scale 305 | for i in L_LEG_INDICES: scaled_candidate_np[i] = l_hip_final + (initial_candidate[i] - l_hip_orig) * final_leg_scale 306 | 307 | nose_body_final = neck_final + (nose_orig - neck_orig) * final_neck_scale 308 | scaled_candidate_np[KP["Nose"]] = nose_body_final 309 | 310 | effective_nose_translation = nose_body_final - nose_orig 311 | for i in BODY_HEAD_PARTS: 312 | part_moved_with_nose = initial_candidate[i] + effective_nose_translation 313 | scaled_candidate_np[i] = scale(part_moved_with_nose, final_head_scale, nose_body_final) 314 | 315 | face_points_scaled_current_fig = [] 316 | if face_raw: 317 | face_points_orig = [np.array(face_raw[i:i+2]) for i in range(0, len(face_raw), 3)] 318 | num_face_points = len(face_points_orig) 319 | 320 | face_points_positioned = [p + effective_nose_translation for p in face_points_orig] 321 | face_points_after_global_head_scale = [scale(p, final_head_scale, nose_body_final) for p in face_points_positioned] 322 | face_points_scaled_current_fig = list(face_points_after_global_head_scale) 323 | 324 | reye_pos_after_head_scale = scale(initial_candidate[KP["REye"]] + effective_nose_translation, final_head_scale, nose_body_final) 325 | leye_pos_after_head_scale = scale(initial_candidate[KP["LEye"]] + effective_nose_translation, final_head_scale, nose_body_final) 326 | eye_center = (reye_pos_after_head_scale + leye_pos_after_head_scale) / 2 327 | reye_pos_after_dist_scale = scale(reye_pos_after_head_scale, eye_distance_scale, eye_center) 328 | leye_pos_after_dist_scale = scale(leye_pos_after_head_scale, eye_distance_scale, eye_center) 329 | right_dist_translation = reye_pos_after_dist_scale - reye_pos_after_head_scale 330 | left_dist_translation = leye_pos_after_dist_scale - leye_pos_after_head_scale 331 | 332 | eye_height_offset = np.array([0.0, 0.0]) 333 | eyebrow_height_offset = np.array([0.0, 0.0]) 334 | direction_vector = nose_body_final - neck_final 335 | norm_direction = np.linalg.norm(direction_vector) 336 | if norm_direction > eps: 337 | unit_direction = direction_vector / norm_direction 338 | if abs(eye_height) > eps: eye_height_offset = unit_direction * eye_height 339 | if abs(eyebrow_height) > eps: eyebrow_height_offset = unit_direction * eyebrow_height 340 | 341 | group_translations = { 342 | "Right_Eye": right_dist_translation + eye_height_offset, 343 | "Left_Eye": left_dist_translation + eye_height_offset, 344 | "Right_Eyebrow": right_dist_translation + eyebrow_height_offset, 345 | "Left_Eyebrow": left_dist_translation + eyebrow_height_offset, 346 | } 347 | 348 | scaled_candidate_np[KP["REye"]] = reye_pos_after_dist_scale + eye_height_offset 349 | scaled_candidate_np[KP["LEye"]] = leye_pos_after_dist_scale + eye_height_offset 350 | 351 | for group_name, indices in FACE_KP_GROUPS_INDICES.items(): 352 | group_scale_modifier = INDIVIDUAL_FACE_SCALES.get(group_name, 1.0) 353 | valid_indices = [idx for idx in indices if idx < num_face_points] 354 | if not valid_indices: continue 355 | 356 | points_after_head_scale = [face_points_after_global_head_scale[idx] for idx in valid_indices] 357 | 358 | if group_name in group_translations: 359 | points_after_translation = [p + group_translations[group_name] for p in points_after_head_scale] 360 | else: 361 | points_after_translation = points_after_head_scale 362 | 363 | if abs(group_scale_modifier - 1.0) > eps: 364 | if group_name == "Face_Shape": 365 | pivot = nose_body_final 366 | direction_vector = neck_final - nose_body_final 367 | norm_direction = np.linalg.norm(direction_vector) 368 | if norm_direction > eps: 369 | unit_direction = direction_vector / norm_direction 370 | final_points = [] 371 | for p in points_after_translation: 372 | point_vector = p - pivot 373 | proj_length = np.dot(point_vector, unit_direction) 374 | parallel_component = proj_length * unit_direction 375 | perpendicular_component = point_vector - parallel_component 376 | scaled_parallel_component = parallel_component * group_scale_modifier 377 | new_point = pivot + scaled_parallel_component + perpendicular_component 378 | final_points.append(new_point) 379 | else: 380 | final_points = points_after_translation 381 | else: 382 | pivot = np.mean(points_after_translation, axis=0) 383 | final_points = [scale(p, group_scale_modifier, pivot) for p in points_after_translation] 384 | else: 385 | final_points = points_after_translation 386 | 387 | for i, idx in enumerate(valid_indices): 388 | face_points_scaled_current_fig[idx] = final_points[i] 389 | 390 | lwrist_final_calc, rwrist_final_calc = scaled_candidate_np[KP["LWrist"]], scaled_candidate_np[KP["RWrist"]] 391 | 392 | # hands_scale 대신 계산된 final_hands_scale을 사용하도록 수정 393 | lhand_scaled_current_fig = [(scale(np.array(lhand_raw[i:i+2]), final_hands_scale, lwrist_orig) + (lwrist_final_calc - lwrist_orig)) if lhand_raw[i+2] > 0 else np.array([0.0, 0.0]) for i in range(0, len(lhand_raw), 3)] if lhand_raw else [] 394 | rhand_scaled_current_fig = [(scale(np.array(rhand_raw[i:i+2]), final_hands_scale, rwrist_orig) + (rwrist_final_calc - rwrist_orig)) if rhand_raw[i+2] > 0 else np.array([0.0, 0.0]) for i in range(0, len(rhand_raw), 3)] if rhand_raw else [] 395 | 396 | scales_to_check = [leg_scale, torso_scale, overall_scale, pelvis_scale, head_scale] 397 | is_scaling_active = any(abs(s - 1.0) > 0.001 for s in scales_to_check) 398 | 399 | candidate_list_current_fig_np = scaled_candidate_np 400 | face_list_current_fig_np = np.array(face_points_scaled_current_fig) if face_points_scaled_current_fig else np.array([]) 401 | lhand_list_current_fig_np = np.array(lhand_scaled_current_fig) if lhand_scaled_current_fig else np.array([]) 402 | rhand_list_current_fig_np = np.array(rhand_scaled_current_fig) if rhand_scaled_current_fig else np.array([]) 403 | 404 | if use_ground_plane and is_scaling_active: 405 | ground_y_coord = H 406 | orig_feet_coords = [initial_candidate[i] for i in FEET_INDICES if i < len(initial_candidate)] 407 | orig_lowest_y = max(p[1] for p in orig_feet_coords) if orig_feet_coords else H 408 | orig_dist_to_ground = ground_y_coord - orig_lowest_y 409 | 410 | feet_coords_for_overall_pivot = [candidate_list_current_fig_np[i] for i in FEET_INDICES if i < len(candidate_list_current_fig_np)] 411 | 412 | if feet_coords_for_overall_pivot: 413 | feet_pos_pivot = np.mean(feet_coords_for_overall_pivot, axis=0) 414 | candidate_list_current_fig_np = np.array([scale(p, overall_scale, feet_pos_pivot) for p in candidate_list_current_fig_np]) 415 | if face_list_current_fig_np.size > 0: face_list_current_fig_np = np.array([scale(p, overall_scale, feet_pos_pivot) for p in face_list_current_fig_np]) 416 | if lhand_list_current_fig_np.size > 0: lhand_list_current_fig_np = np.array([scale(p, overall_scale, feet_pos_pivot) if np.sum(np.abs(p)) > eps else p for p in lhand_list_current_fig_np]) 417 | if rhand_list_current_fig_np.size > 0: rhand_list_current_fig_np = np.array([scale(p, overall_scale, feet_pos_pivot) if np.sum(np.abs(p)) > eps else p for p in rhand_list_current_fig_np]) 418 | 419 | final_feet_coords = [candidate_list_current_fig_np[i] for i in FEET_INDICES if i < len(candidate_list_current_fig_np)] 420 | if final_feet_coords: 421 | final_lowest_y = max(p[1] for p in final_feet_coords) 422 | desired_final_y = ground_y_coord - orig_dist_to_ground 423 | vertical_translation = desired_final_y - final_lowest_y 424 | 425 | candidate_list_current_fig_np = candidate_list_current_fig_np + np.array([0, vertical_translation]) 426 | if face_list_current_fig_np.size > 0: face_list_current_fig_np = face_list_current_fig_np + np.array([0, vertical_translation]) 427 | if lhand_list_current_fig_np.size > 0: lhand_list_current_fig_np = lhand_list_current_fig_np + np.array([0, vertical_translation]) 428 | if rhand_list_current_fig_np.size > 0: rhand_list_current_fig_np = rhand_list_current_fig_np + np.array([0, vertical_translation]) 429 | else: 430 | center_pivot = [W * 0.5, H * 0.5] 431 | candidate_list_current_fig_np = np.array([scale(p, overall_scale, center_pivot) for p in candidate_list_current_fig_np]) 432 | if face_list_current_fig_np.size > 0: face_list_current_fig_np = np.array([scale(p, overall_scale, center_pivot) for p in face_list_current_fig_np]) 433 | if lhand_list_current_fig_np.size > 0: lhand_list_current_fig_np = np.array([scale(p, overall_scale, center_pivot) if np.sum(np.abs(p)) > eps else p for p in lhand_list_current_fig_np]) 434 | if rhand_list_current_fig_np.size > 0: rhand_list_current_fig_np = np.array([scale(p, overall_scale, center_pivot) if np.sum(np.abs(p)) > eps else p for p in rhand_list_current_fig_np]) 435 | 436 | # Rotation Logic (after all scaling, before translation) 437 | if abs(rotate_angle) > eps: # Only rotate if angle is significant 438 | all_points_for_rotation_center = [] 439 | if candidate_list_current_fig_np.size > 0: 440 | # 유효한 body 포인트만 중심 계산에 사용 (confidence 기반으로 필터링하는 것이 더 정확할 수 있으나, 여기서는 모든 점 사용) 441 | all_points_for_rotation_center.extend(candidate_list_current_fig_np.tolist()) 442 | if face_list_current_fig_np.size > 0: 443 | all_points_for_rotation_center.extend(face_list_current_fig_np.tolist()) 444 | 445 | # 손 포인트 중 [0,0]이 아닌 유효한 포인트만 중심 계산에 사용 446 | if lhand_list_current_fig_np.size > 0: 447 | valid_lhand_points = [p.tolist() for p in lhand_list_current_fig_np if np.sum(np.abs(p)) > eps] 448 | if valid_lhand_points: 449 | all_points_for_rotation_center.extend(valid_lhand_points) 450 | if rhand_list_current_fig_np.size > 0: 451 | valid_rhand_points = [p.tolist() for p in rhand_list_current_fig_np if np.sum(np.abs(p)) > eps] 452 | if valid_rhand_points: 453 | all_points_for_rotation_center.extend(valid_rhand_points) 454 | 455 | if all_points_for_rotation_center: 456 | points_for_center_np = np.array(all_points_for_rotation_center) 457 | center_x = np.mean(points_for_center_np[:, 0]) 458 | center_y = np.mean(points_for_center_np[:, 1]) 459 | 460 | angle_rad = math.radians(rotate_angle) 461 | cos_a = math.cos(angle_rad) 462 | sin_a = math.sin(angle_rad) 463 | 464 | def apply_rotation_to_points(points_np, cx, cy, c_angle, s_angle): 465 | if points_np.size == 0: 466 | return points_np 467 | 468 | # 회전 적용할 포인트만 선택 (예: [0,0] 제외는 여기서 처리 안함, 모든 점 동일하게 회전) 469 | # 원본 포인트를 복사하여 사용 470 | rotated_points = points_np.copy() 471 | 472 | # 중심점으로 이동 473 | translated_x = rotated_points[:, 0] - cx 474 | translated_y = rotated_points[:, 1] - cy 475 | 476 | # 회전 477 | rotated_x = translated_x * c_angle - translated_y * s_angle 478 | rotated_y = translated_x * s_angle + translated_y * c_angle 479 | 480 | # 다시 원래 위치로 이동 (중심점 기준) 481 | rotated_points[:, 0] = rotated_x + cx 482 | rotated_points[:, 1] = rotated_y + cy 483 | return rotated_points 484 | 485 | if candidate_list_current_fig_np.size > 0: 486 | candidate_list_current_fig_np = apply_rotation_to_points(candidate_list_current_fig_np, center_x, center_y, cos_a, sin_a) 487 | if face_list_current_fig_np.size > 0: 488 | face_list_current_fig_np = apply_rotation_to_points(face_list_current_fig_np, center_x, center_y, cos_a, sin_a) 489 | if lhand_list_current_fig_np.size > 0: 490 | # [0,0] 점들도 회전 중심에 대해 상대적으로 회전됨 491 | lhand_list_current_fig_np = apply_rotation_to_points(lhand_list_current_fig_np, center_x, center_y, cos_a, sin_a) 492 | if rhand_list_current_fig_np.size > 0: 493 | # [0,0] 점들도 회전 중심에 대해 상대적으로 회전됨 494 | rhand_list_current_fig_np = apply_rotation_to_points(rhand_list_current_fig_np, center_x, center_y, cos_a, sin_a) 495 | 496 | 497 | if abs(translate_x) > eps or abs(translate_y) > eps: # 실제로 이동이 필요한 경우에만 연산 498 | translation_vector = np.array([translate_x, translate_y], dtype=np.float32) 499 | 500 | if candidate_list_current_fig_np.size > 0: 501 | candidate_list_current_fig_np = candidate_list_current_fig_np + translation_vector 502 | 503 | if face_list_current_fig_np.size > 0: 504 | face_list_current_fig_np = face_list_current_fig_np + translation_vector 505 | 506 | if lhand_list_current_fig_np.size > 0: 507 | lhand_list_current_fig_np = lhand_list_current_fig_np + translation_vector 508 | 509 | if rhand_list_current_fig_np.size > 0: 510 | rhand_list_current_fig_np = rhand_list_current_fig_np + translation_vector 511 | 512 | 513 | body_kps_out_current_fig = [item for i, p in enumerate(candidate_list_current_fig_np) for item in [p[0], p[1], confidence_scores_body[i]]] 514 | face_kps_out_current_fig = [item for p in face_list_current_fig_np for item in [p[0], p[1], 1.0]] if face_list_current_fig_np.size > 0 else [] 515 | 516 | original_lhand_confidences = [lhand_raw[i+2] for i in range(0, len(lhand_raw), 3)] if lhand_raw else [] 517 | original_rhand_confidences = [rhand_raw[i+2] for i in range(0, len(rhand_raw), 3)] if rhand_raw else [] 518 | 519 | lhand_kps_out_current_fig = [item for i, p in enumerate(lhand_list_current_fig_np) for item in [p[0], p[1], original_lhand_confidences[i]]] if lhand_list_current_fig_np.size > 0 else [] 520 | rhand_kps_out_current_fig = [item for i, p in enumerate(rhand_list_current_fig_np) for item in [p[0], p[1], original_rhand_confidences[i]]] if rhand_list_current_fig_np.size > 0 else [] 521 | 522 | current_image_people_data_for_output.append({ 523 | "pose_keypoints_2d": body_kps_out_current_fig, "face_keypoints_2d": face_kps_out_current_fig, 524 | "hand_left_keypoints_2d": lhand_kps_out_current_fig, "hand_right_keypoints_2d": rhand_kps_out_current_fig, 525 | }) 526 | 527 | all_scaled_candidates_for_drawing.extend(candidate_list_current_fig_np.tolist()) 528 | if face_list_current_fig_np.size > 0: all_scaled_faces_for_drawing.extend(face_list_current_fig_np.tolist()) 529 | if lhand_list_current_fig_np.size > 0: all_scaled_hands_for_drawing.append(lhand_list_current_fig_np.tolist()) 530 | if rhand_list_current_fig_np.size > 0: all_scaled_hands_for_drawing.append(rhand_list_current_fig_np.tolist()) 531 | 532 | if fig_idx == 0 and not final_subset_for_drawing[0]: 533 | final_subset_for_drawing[0].extend([i if body_raw[i*3+2]>0 else -1 for i in range(len(candidate_list_current_fig_np))]) 534 | else: 535 | prev_candidate_count = len(all_scaled_candidates_for_drawing) - len(candidate_list_current_fig_np) 536 | final_subset_for_drawing.append([prev_candidate_count+i if body_raw[i*3+2]>0 else -1 for i in range(len(candidate_list_current_fig_np))]) 537 | 538 | current_frame_keypoint_object = { "people": current_image_people_data_for_output, "canvas_width": W, "canvas_height": H } 539 | all_frames_keypoints_output.append(current_frame_keypoint_object) 540 | 541 | candidate_norm, faces_norm = all_scaled_candidates_for_drawing, all_scaled_faces_for_drawing 542 | hands_norm_for_drawing = all_scaled_hands_for_drawing 543 | 544 | if candidate_norm: 545 | candidate_np_norm = np.array(candidate_norm).astype(float); candidate_np_norm[...,0] /= float(W); candidate_np_norm[...,1] /= float(H) 546 | candidate_norm = candidate_np_norm.tolist() 547 | if faces_norm: 548 | faces_np_norm = np.array(faces_norm).astype(float); 549 | if faces_np_norm.size > 0: faces_np_norm[...,0] /= float(W); faces_np_norm[...,1] /= float(H) 550 | faces_norm = faces_np_norm.tolist() 551 | 552 | hands_final_norm_for_drawing = [] 553 | if hands_norm_for_drawing: 554 | for hand_kps_list in hands_norm_for_drawing: 555 | current_normalized_hand = [] 556 | for point_list in hand_kps_list: 557 | if not isinstance(point_list, (list, np.ndarray)) or len(point_list) != 2: continue 558 | norm_point = np.array(point_list).astype(float) 559 | if norm_point[0] > eps or norm_point[1] > eps: 560 | norm_point[0] /= float(W) 561 | norm_point[1] /= float(H) 562 | current_normalized_hand.append(norm_point.tolist()) 563 | if current_normalized_hand : hands_final_norm_for_drawing.append(current_normalized_hand) 564 | 565 | bodies = dict(candidate=candidate_norm, subset=final_subset_for_drawing) 566 | original_face_exists = any(fig.get('face_keypoints_2d') for fig in figures) 567 | original_lhand_exists = any(fig.get('hand_left_keypoints_2d') for fig in figures) 568 | original_rhand_exists = any(fig.get('hand_right_keypoints_2d') for fig in figures) 569 | 570 | pose = dict( 571 | bodies=bodies if show_body else {'candidate':[], 'subset':[]}, 572 | faces=faces_norm if show_face and original_face_exists else [], 573 | hands=hands_final_norm_for_drawing if show_hands and (original_lhand_exists or original_rhand_exists) else [] 574 | ) 575 | W_scaled = resolution_x if resolution_x >= 64 else W 576 | H_scaled = int(H*(W_scaled*1.0/W)) 577 | pose_imgs.append(draw_pose(pose, H_scaled, W_scaled, pose_marker_size, face_marker_size, hand_marker_size)) 578 | pbar.update(1) 579 | 580 | return pose_imgs, all_frames_keypoints_output 581 | 582 | def draw_pose(pose, H, W, pose_marker_size, face_marker_size, hand_marker_size): 583 | canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) 584 | body_render_info = pose.get('bodies', {}) 585 | candidate = body_render_info.get('candidate', []) 586 | subset = body_render_info.get('subset', []) 587 | faces_data = pose.get('faces', []) 588 | hands_data = pose.get('hands', []) 589 | 590 | if candidate and subset and np.array(candidate).size > 0 : canvas = draw_bodypose(canvas, np.array(candidate), np.array(subset), pose_marker_size) 591 | if hands_data and np.array(hands_data).size > 0 : canvas = draw_handpose(canvas, hands_data, hand_marker_size) 592 | if faces_data and np.array(faces_data).size > 0 : canvas = draw_facepose(canvas, faces_data, face_marker_size) 593 | return canvas 594 | 595 | def draw_bodypose(canvas, candidate, subset, pose_marker_size): 596 | H, W, C = canvas.shape 597 | limbSeq = [[1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [8, 9], [9, 10], [1, 11], [11, 12], [12, 13], [1, 0], [0, 14], [14, 16], [0, 15], [15, 17]] 598 | colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] 599 | if candidate.ndim != 2 or candidate.shape[1] != 2: return canvas 600 | for i in range(len(limbSeq)): 601 | for n in range(len(subset)): 602 | limb = limbSeq[i] 603 | if max(limb) >= subset.shape[1]: continue 604 | index = subset[n][np.array(limb)].astype(int) 605 | if -1 in index or max(index) >= len(candidate): continue 606 | Y, X = candidate[index, 0] * float(W), candidate[index, 1] * float(H) 607 | mX, mY = np.mean(X), np.mean(Y) 608 | length = np.linalg.norm(np.array([X[0], Y[0]]) - np.array([X[1], Y[1]])) 609 | angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) 610 | if length < 1: continue 611 | polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), pose_marker_size), int(angle), 0, 360, 1) 612 | cv2.fillConvexPoly(canvas, polygon, colors[i % len(colors)]) 613 | for n in range(len(subset)): 614 | for i in range(subset.shape[1]): 615 | index = int(subset[n][i]) 616 | if index == -1 or index >= len(candidate): continue 617 | x, y = candidate[index][0:2] 618 | x, y = int(x * W), int(y * H) 619 | cv2.circle(canvas, (x, y), pose_marker_size, colors[i % len(colors)], thickness=-1) 620 | return canvas 621 | 622 | def draw_handpose(canvas, all_hand_peaks, hand_marker_size): 623 | H, W, C = canvas.shape 624 | edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] 625 | for peaks_list_for_one_hand in all_hand_peaks: 626 | peaks_np = np.array(peaks_list_for_one_hand) 627 | if peaks_np.ndim != 2 or peaks_np.shape[1] != 2: continue 628 | for ie, e in enumerate(edges): 629 | if e[0] >= len(peaks_np) or e[1] >= len(peaks_np): continue 630 | x1_coord, y1_coord = peaks_np[e[0]] 631 | x2_coord, y2_coord = peaks_np[e[1]] 632 | if x1_coord < eps and y1_coord < eps or x2_coord < eps and y2_coord < eps: continue 633 | x1, y1 = int(x1_coord * W), int(y1_coord * H) 634 | x2, y2 = int(x2_coord * W), int(y2_coord * H) 635 | if x1 > eps and y1 > eps and x2 > eps and y2 > eps: 636 | cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=max(1, hand_marker_size)) 637 | for i, keyponit in enumerate(peaks_np): 638 | x_coord, y_coord = keyponit 639 | x, y = int(x_coord * W), int(y_coord * H) 640 | if x > eps and y > eps: cv2.circle(canvas, (x, y), max(1, hand_marker_size) + 1, (0, 0, 255), thickness=-1) 641 | return canvas 642 | 643 | def draw_facepose(canvas, all_lmks, face_marker_size): 644 | H, W, C = canvas.shape 645 | lmks_np = np.array(all_lmks) 646 | if lmks_np.ndim != 2 or lmks_np.shape[1] != 2: return canvas 647 | for lmk in lmks_np: 648 | x_coord, y_coord = lmk 649 | x, y = int(x_coord * W), int(y_coord * H) 650 | if x > eps and y > eps: cv2.circle(canvas, (x, y), face_marker_size, (255, 255, 255), thickness=-1) 651 | return canvas 652 | --------------------------------------------------------------------------------