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westNeighbor
2025-01-11 02:29:13 -06:00
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README.md
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# comfyui-openpose-render
ultimate node for openpose rendering
<div align="center">
# ComfyUI ultimate openpose render
</div>
<p align="center">
<img src="assets/render_example.png" />
</p>
This is a better version of openpose pose render in ComfyUI with both pose keypoints and pose json input options. It also gives the plotting controls with canvas size and pose marker size.
If you like the project, please give me a star! ⭐
## Installation
- Just install this repo through the Manager. Or you can manually install it, go to ComfyUI `/custom_nodes` directory
```bash
git clone https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render
cd ./ComfyUI-ultimate-openpose-render
pip install -r requirements.txt # if you use portable version, see below
```
if you use portable version, install requirement accordingly, for example, I have portable in my E: disk
```bash
E:/ComfyUI_windows_portable/python_embeded/python.exe -m pip install -r requirements.txt
```
## Usage
- Insert node by `Right Click -> ultimate-openpose -> Openpose Render Node`
## Features
- Accept both pose keypoints and pose json format input. Be ware that the edit priority is **POSE\_KEYPOINT > POSE\_JSON**
- Can adjust output canvas size
- Can control the pose marker size
__init__.py
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from .openpose_render_nodes import OpenposeRenderNode
NODE_CLASS_MAPPINGS = {
"OpenposeRenderNode": OpenposeRenderNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"OpenposeRenderNode": "Openpose Render Node",
}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
assets/render_example.png
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openpose_render_nodes.py
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import json
import numpy as np
import torch
from .util import draw_pose, draw_pose_json
class OpenposeRenderNode:
@classmethod
def INPUT_TYPES(s):
return {
"optional": {
"show_body": ("BOOLEAN", {"default": True}),
"show_face": ("BOOLEAN", {"default": True}),
"show_hands": ("BOOLEAN", {"default": True}),
"resolution_x": ("INT", {
"default": -1,
"min": -1,
"max": 12800
}),
"pose_marker_size": ("INT", {
"default": 4,
"min": 0,
"max": 100
}),
"face_marker_size": ("INT", {
"default": 3,
"min": 0,
"max": 100
}),
"hand_marker_size": ("INT", {
"default": 2,
"min": 0,
"max": 100
}),
"POSE_JSON": ("STRING", {"multiline": True}),
"POSE_KEYPOINT": ("POSE_KEYPOINT",{"default": None}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "render_img"
CATEGORY = "ultimate-openpose"
def render_img(self, show_body, show_face, show_hands, resolution_x, pose_marker_size, face_marker_size, hand_marker_size, POSE_JSON, POSE_KEYPOINT=None):
if POSE_KEYPOINT is not None:
POSE_JSON = json.dumps(POSE_KEYPOINT).replace("'",'"').replace('None','[]')
elif POSE_JSON:
POSE_JSON = POSE_JSON.replace("'",'"').replace('None','[]')
pose_imgs = draw_pose_json(POSE_JSON, resolution_x, show_body, show_face, show_hands, pose_marker_size, face_marker_size, hand_marker_size)
if pose_imgs:
pose_imgs_np = np.array(pose_imgs).astype(np.float32) / 255
return (torch.from_numpy(pose_imgs_np),)
else:
raise ValueError("Invalid input type. Expected an input to give an output.")
pyproject.toml
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[project]
name = "ComfyUI-ultimate-openpose-render"
description = "The ultimate openpose render node for ComfyUI with flexible input, output and adjustment."
version = "1.0"
license = "LICENSE"
[project.urls]
Repository = "https://github.com/westNeighbor/ComfyUI-openpose-render"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "westNeighbor"
DisplayName = "ComfyUI-ultimate-openpose-render"
Icon = ""
requirements.txt
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polygraphy
matplotlib
numpy
cv2
torch
util.py
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import math
import json
import numpy as np
import matplotlib
import cv2
from comfy.utils import ProgressBar
eps = 0.01
def draw_pose_json(pose_json, resolution_x, show_body, show_face, show_hands, pose_marker_size, face_marker_size, hand_marker_size):
pose_imgs = []
if pose_json:
if pose_json.startswith('{'):
pose_json = '[{}]'.format(pose_json)
images = json.loads(pose_json)
pbar = ProgressBar(len(images))
for image in images:
if 'people' not in image:
pbar.update(len(images))
return pose_imgs
figures = image['people']
H = image['canvas_height']
W = image['canvas_width']
bodies = []
candidate = []
subset = [[]]
faces = []
hands = []
for figure in figures:
if 'pose_keypoints_2d' in figure:
body = figure['pose_keypoints_2d']
if 'face_keypoints_2d' in figure:
face = figure['face_keypoints_2d']
if 'hand_left_keypoints_2d' in figure:
lhand = figure['hand_left_keypoints_2d']
if 'hand_right_keypoints_2d' in figure:
rhand = figure['hand_right_keypoints_2d']
if body:
for i in range(0,len(body),3):
candidate.append(body[i:i+2])
if not subset[0]:
subset[0].extend([len(subset[0])+(i//3) if body[i+2]>0 else -1 for i in range(0,len(body),3)])
else:
subset.append([len(subset[0])*len(subset)+(i//3) if body[i+2]>0 else -1 for i in range(0,len(body),3)])
if face:
faces.append([face[i:i+2] for i in range(0,len(face),3)])
if lhand:
hands.append([lhand[i:i+2] for i in range(0,len(lhand),3)])
if rhand:
hands.append([rhand[i:i+2] for i in range(0,len(rhand),3)])
normalized = 0.0
if candidate:
candidate = np.array(candidate).astype(float)
subset = np.array(subset)
normalized = max(np.max(candidate[...,0]),np.max(candidate[...,1]))
if normalized>2.0:
candidate[...,0] /= float(W)
candidate[...,1] /= float(H)
if faces:
faces = np.array(faces).astype(float)
normalized = max(np.max(faces[...,0]),np.max(faces[...,1]))
if normalized>2.0:
faces[...,0] /= float(W)
faces[...,1] /= float(H)
if hands:
hands = np.array(hands).astype(float)
normalized = max(np.max(hands[...,0]),np.max(hands[...,1]))
if normalized>2.0:
hands[...,0] /= float(W)
hands[...,1] /= float(H)
bodies = dict(candidate=candidate, subset=subset)
pose = dict(bodies=bodies, faces=faces, hands=hands)
pose = dict(bodies=bodies if show_body else {'candidate':[], 'subset':[]}, faces=faces if show_face else [], hands=hands if show_hands else [])
W_scaled = resolution_x
if resolution_x < 64:
W_scaled = W
H_scaled = int(H*(W_scaled*1.0/W))
pose_img = draw_pose(pose, H_scaled, W_scaled, pose_marker_size, face_marker_size, hand_marker_size)
pose_imgs.append(pose_img)
pbar.update(1)
return pose_imgs
def draw_pose(pose, H, W, pose_marker_size, face_marker_size, hand_marker_size):
bodies = pose['bodies']
faces = pose['faces']
hands = pose['hands']
candidate = bodies['candidate']
subset = bodies['subset']
canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
if len(candidate) > 0:
canvas = draw_bodypose(canvas, candidate, subset, pose_marker_size)
if len(hands) > 0:
canvas = draw_handpose(canvas, hands, hand_marker_size)
if len(faces) > 0:
canvas = draw_facepose(canvas, faces, face_marker_size)
return canvas
def draw_bodypose(canvas, candidate, subset, pose_marker_size):
H, W, C = canvas.shape
candidate = np.array(candidate)
subset = np.array(subset)
# stickwidth = 4
limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
[10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
[1, 16], [16, 18], [3, 17], [6, 18]]
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]]
for i in range(17):
for n in range(len(subset)):
index = subset[n][np.array(limbSeq[i]) - 1]
if -1 in index:
continue
Y = candidate[index.astype(int), 0] * float(W)
X = candidate[index.astype(int), 1] * float(H)
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), pose_marker_size), int(angle), 0, 360, 1)
cv2.fillConvexPoly(canvas, polygon, colors[i])
canvas = (canvas * 0.6).astype(np.uint8)
for i in range(18):
for n in range(len(subset)):
index = int(subset[n][i])
if index == -1:
continue
x, y = candidate[index][0:2]
x = int(x * W)
y = int(y * H)
cv2.circle(canvas, (int(x), int(y)), pose_marker_size, colors[i], thickness=-1)
return canvas
def draw_handpose(canvas, all_hand_peaks, hand_marker_size):
H, W, C = canvas.shape
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]]
for peaks in all_hand_peaks:
peaks = np.array(peaks)
for ie, e in enumerate(edges):
x1, y1 = peaks[e[0]]
x2, y2 = peaks[e[1]]
x1 = int(x1 * W)
y1 = int(y1 * H)
x2 = int(x2 * W)
y2 = int(y2 * H)
if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=1 if hand_marker_size == 0 else hand_marker_size)
joint_size=0
if hand_marker_size < 2:
joint_size = hand_marker_size + 1
else:
joint_size = hand_marker_size + 2
for i, keyponit in enumerate(peaks):
x, y = keyponit
x = int(x * W)
y = int(y * H)
if x > eps and y > eps:
cv2.circle(canvas, (x, y), joint_size, (0, 0, 255), thickness=-1)
return canvas
def draw_facepose(canvas, all_lmks, face_marker_size):
H, W, C = canvas.shape
for lmks in all_lmks:
lmks = np.array(lmks)
for lmk in lmks:
x, y = lmk
x = int(x * W)
y = int(y * H)
if x > eps and y > eps:
cv2.circle(canvas, (x, y), face_marker_size, (255, 255, 255), thickness=-1)
return canvas