grmchnandClaude
a2e991ad8d
- Set show_face parameter default from True to False in render_nodes.py - Provides cleaner pose visualization by default without face keypoints - Users can still enable face rendering manually when needed 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
92 lines
4.5 KiB
Python
92 lines
4.5 KiB
Python
"""
|
|
ProportionChanger render node classes
|
|
Contains nodes for rendering POSE_KEYPOINT data using DWPose styling
|
|
"""
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
# Import rendering utilities from our utils package
|
|
from ..utils import draw_dwpose_render
|
|
import os
|
|
import logging
|
|
|
|
# Debug mode control via environment variable (local implementation to avoid imports)
|
|
DEBUG_MODE = os.getenv('PROPORTION_CHANGER_DEBUG', 'false').lower() in ('true', '1', 'yes')
|
|
log = logging.getLogger(__name__)
|
|
|
|
def debug_log(message):
|
|
"""Conditional debug logging - only outputs if DEBUG_MODE is enabled"""
|
|
if DEBUG_MODE:
|
|
log.debug(f"🔍 {message}")
|
|
|
|
|
|
class ProportionChangerDWPoseRender:
|
|
"""
|
|
DWPose Render Node with 25-point keypoint support including toe keypoints.
|
|
Compatible with ultimate-openpose-render parameters but using DWPose rendering algorithms.
|
|
Resolves coordinate misalignment issues when displaying ProportionChanger outputs.
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"pose_keypoint": ("POSE_KEYPOINT", {"tooltip": "POSE_KEYPOINT data to render"}),
|
|
},
|
|
"optional": {
|
|
"show_body": ("BOOLEAN", {"default": True, "tooltip": "Draw body keypoints"}),
|
|
"show_face": ("BOOLEAN", {"default": False, "tooltip": "Draw face keypoints"}),
|
|
"show_hands": ("BOOLEAN", {"default": True, "tooltip": "Draw hand keypoints"}),
|
|
"show_feet": ("BOOLEAN", {"default": True, "tooltip": "Draw toe keypoints (DWPose 25-point feature)"}),
|
|
"resolution_x": ("INT", {"default": -1, "min": -1, "max": 12800, "tooltip": "Output width (-1 for original)"}),
|
|
"pose_marker_size": ("INT", {"default": 4, "min": 0, "max": 100, "tooltip": "Body keypoint marker size"}),
|
|
"face_marker_size": ("INT", {"default": 3, "min": 0, "max": 100, "tooltip": "Face keypoint marker size"}),
|
|
"hand_marker_size": ("INT", {"default": 2, "min": 0, "max": 100, "tooltip": "Hand keypoint marker size"}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "render_img"
|
|
CATEGORY = "ProportionChanger"
|
|
|
|
def render_img(self, pose_keypoint, show_body=True, show_face=True, show_hands=True, show_feet=True,
|
|
resolution_x=-1, pose_marker_size=4, face_marker_size=3, hand_marker_size=2):
|
|
|
|
if pose_keypoint is None:
|
|
raise ValueError("pose_keypoint input is required")
|
|
|
|
# Debug: Print pose_keypoint structure
|
|
debug_log(f"Debug pose_keypoint type: {type(pose_keypoint)}")
|
|
if isinstance(pose_keypoint, list) and len(pose_keypoint) > 0:
|
|
debug_log(f"Debug pose_keypoint length: {len(pose_keypoint)}")
|
|
first_frame = pose_keypoint[0]
|
|
debug_log(f"Debug first frame keys: {first_frame.keys() if isinstance(first_frame, dict) else 'Not a dict'}")
|
|
if isinstance(first_frame, dict) and 'people' in first_frame:
|
|
debug_log(f"Debug people count: {len(first_frame['people'])}")
|
|
if len(first_frame['people']) > 0:
|
|
person = first_frame['people'][0]
|
|
if 'pose_keypoints_2d' in person:
|
|
keypoints = person['pose_keypoints_2d']
|
|
debug_log(f"Debug pose_keypoints_2d length: {len(keypoints)}")
|
|
debug_log(f"Debug first 9 keypoints: {keypoints[:9]}")
|
|
# Check for actual coordinate values
|
|
non_zero_coords = [(i//3, keypoints[i], keypoints[i+1], keypoints[i+2])
|
|
for i in range(0, min(54, len(keypoints)), 3)
|
|
if keypoints[i] != 0 or keypoints[i+1] != 0]
|
|
debug_log(f"Debug non-zero coordinates (first 5): {non_zero_coords[:5]}")
|
|
elif isinstance(pose_keypoint, dict):
|
|
debug_log(f"Debug single frame keys: {pose_keypoint.keys()}")
|
|
|
|
# Render using DWPose algorithms
|
|
pose_imgs = draw_dwpose_render(
|
|
pose_keypoint, resolution_x, show_body, show_face, show_hands, show_feet,
|
|
pose_marker_size, face_marker_size, hand_marker_size
|
|
)
|
|
|
|
if pose_imgs:
|
|
# Convert to ComfyUI tensor format
|
|
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.") |