638 lines
23 KiB
Python
638 lines
23 KiB
Python
"""
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OpenPose keypoint parsing, torso detection, 3D rotation, and skeleton rendering.
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"""
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from __future__ import annotations
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import math
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from typing import Any
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import cv2
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import numpy as np
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# Body keypoint indices (0-based, OpenPose COCO format)
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# 0=nose, 1=neck, 2=r_shoulder, 3=r_elbow, 4=r_wrist, 5=l_shoulder, 6=l_elbow, 7=l_wrist,
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# 8=mid_hip, 9=r_hip, 10=r_knee, 11=r_ankle, 12=l_hip, 13=l_knee, 14=l_ankle,
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# 15=r_eye, 16=l_eye, 17=r_ear, 18=l_ear
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TORSO_INDICES = [1, 2, 5, 8, 9, 12] # neck, shoulders, mid_hip, hips
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# Face/head keypoints (hidden when rotated >90° - back of head visible)
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FACE_INDICES = {0, 15, 16, 17, 18}
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# Limb-specific depth scales (OpenPose COCO body indices 0-18)
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# Torso compact; arms/legs extend further; head moderate
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# Based on typical human proportions in frontal view
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BODY_DEPTH_SCALES: dict[int, float] = {
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0: 0.30, # nose - head
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1: 0.20, # neck - torso center
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2: 0.35, # r_shoulder
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3: 0.50, # r_elbow - arm extends
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4: 0.55, # r_wrist
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5: 0.35, # l_shoulder
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6: 0.50, # l_elbow
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7: 0.55, # l_wrist
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8: 0.22, # mid_hip - torso
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9: 0.30, # r_hip
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10: 0.45, # r_knee - leg extends
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11: 0.50, # r_ankle
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12: 0.30, # l_hip
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13: 0.45, # l_knee
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14: 0.50, # l_ankle
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15: 0.28, # r_eye
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16: 0.28, # l_eye
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17: 0.30, # r_ear
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18: 0.30, # l_ear
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}
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# Default depth scale for hands (21 keypoints each - no per-index in OpenPose hand spec)
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HAND_DEPTH_SCALE = 0.45
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# Static limb definitions: (a, b) connection, draw layer (0=torso first, 4=feet last), color index
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# Connections never change; draw order is anatomical (torso -> arms -> legs)
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BODY_LIMBS = [
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((0, 1), 0, 0), # nose - neck (head)
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((1, 2), 0, 0), # neck - r_shoulder
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((1, 5), 0, 0), # neck - l_shoulder
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((2, 3), 1, 2), # r_shoulder - r_elbow
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((3, 4), 2, 3), # r_elbow - r_wrist
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((5, 6), 1, 4), # l_shoulder - l_elbow
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((6, 7), 2, 5), # l_elbow - l_wrist
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((1, 8), 0, 6), # neck - mid_hip (spine)
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((8, 9), 3, 7), # mid_hip - r_hip
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((9, 10), 3, 8), # r_hip - r_knee
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((10, 11), 4, 9), # r_knee - r_ankle
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((8, 12), 3, 10), # mid_hip - l_hip
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((12, 13), 3, 11),# l_hip - l_knee
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((13, 14), 4, 12),# l_knee - l_ankle
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]
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# ControlNet OpenPose standard colors (BGR) - matches expected figure appearance
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# Red chest -> orange/yellow arms -> green spine -> teal/blue/purple legs
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BODY_COLORS = [
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(0, 0, 255), # red - neck-shoulders (chest line)
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(0, 0, 255),
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(0, 85, 255), # orange - r upper arm
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(0, 170, 255), # yellow - r forearm
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(0, 255, 85), # green - l upper arm
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(85, 255, 0), # lime green - l forearm
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(0, 255, 0), # green - spine (neck to mid_hip)
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(0, 255, 170), # teal - r thigh
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(0, 255, 255), # cyan - r knee
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(170, 255, 0), # light blue - r shin
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(255, 0, 0), # blue - l thigh
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(255, 85, 0), # dark blue - l knee
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(255, 0, 170), # purple - l shin
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]
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# Static joint definitions: index -> (draw layer, color index)
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# Draw order is anatomical (head -> torso -> arms -> legs)
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JOINT_LAYERS = {
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0: (0, 0), # nose - head
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1: (0, 0), 2: (1, 2), 3: (1, 2), 4: (1, 3), 5: (1, 4), 6: (1, 4), 7: (1, 5), 8: (0, 6),
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9: (2, 7), 10: (2, 8), 11: (3, 9), 12: (2, 10), 13: (2, 11), 14: (3, 12),
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}
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# Valid main-body keypoint indices for limb drawing. Explicitly excludes face (15-18)
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# to prevent spurious connections when formats vary. Includes 0 (nose) for head-neck limb.
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MAIN_BODY_INDICES = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}
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# Hand edges (0-indexed, 21 keypoints per hand) - used when hands enabled
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HAND_EDGES = [
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(0, 1), (1, 2), (2, 3), (3, 4), (0, 5), (5, 6), (6, 7), (7, 8),
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(0, 9), (9, 10), (10, 11), (11, 12), (0, 13), (13, 14), (14, 15), (15, 16),
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(0, 17), (17, 18), (18, 19), (19, 20),
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]
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def _keypoints_to_openpose_dict(
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keypoints: dict[str, list[tuple[float, float, float]]],
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) -> dict[str, list[float]]:
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"""Convert internal keypoint dict to OpenPose/ComfyUI format (flat x,y,c arrays)."""
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body = keypoints.get("body", [])
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hand_left = keypoints.get("hand_left", [])
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hand_right = keypoints.get("hand_right", [])
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face = keypoints.get("face", [])
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def flatten(pts: list[tuple[float, float, float]]) -> list[float]:
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result = []
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for x, y, c in pts:
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result.extend([float(x), float(y), float(c)])
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return result
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person = {
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"pose_keypoints_2d": flatten(body),
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"hand_left_keypoints_2d": flatten(hand_left),
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"hand_right_keypoints_2d": flatten(hand_right),
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"face_keypoints_2d": flatten(face),
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}
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return {"people": [person]}
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def _extract_keypoint_array(flat: list[float] | None) -> list[tuple[float, float, float]]:
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"""Convert flat [x,y,c, x,y,c, ...] to list of (x, y, conf)."""
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if not flat or len(flat) < 3:
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return []
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points = []
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for i in range(0, len(flat), 3):
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if i + 2 < len(flat):
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x, y, c = flat[i], flat[i + 1], flat[i + 2]
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points.append((float(x), float(y), float(c)))
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return points
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def _body_from_numpy(arr: np.ndarray) -> list[tuple[float, float, float]]:
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"""Convert body array (N,2) or (N,3) to list of (x,y,conf)."""
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if arr is None or arr.size == 0:
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return []
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arr = np.asarray(arr)
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if arr.ndim != 2:
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return []
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points = []
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for i in range(arr.shape[0]):
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if arr.shape[1] >= 3:
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points.append((float(arr[i, 0]), float(arr[i, 1]), float(arr[i, 2])))
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elif arr.shape[1] >= 2:
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points.append((float(arr[i, 0]), float(arr[i, 1]), 1.0))
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else:
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break
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return points
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def _scale_keypoints_to_image(
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keypoints: dict[str, list[tuple[float, float, float]]],
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width: int,
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height: int,
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) -> dict[str, list[tuple[float, float, float]]]:
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"""
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Scale keypoints to image dimensions.
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- Normalized [0,1] coords: scale by width, height
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- Pixel coords from smaller resolution (e.g. DWPose at 384): scale to match image
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"""
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all_x, all_y = [], []
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for pts in keypoints.values():
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for x, y, c in pts:
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if c > 0:
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all_x.append(x)
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all_y.append(y)
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if not all_x or not all_y:
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return keypoints
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max_x, max_y = max(all_x), max(all_y)
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min_x, min_y = min(all_x), min(all_y)
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# Normalized coords in [0,1] (ComfyUI POSE_KEYPOINT may use this)
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if max_x <= 1.5 and max_y <= 1.5 and min_x >= -0.1 and min_y >= -0.1:
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scale_x, scale_y = width, height
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# Pixel coords from smaller resolution (e.g. DWPose at 384)
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# Only scale when max is clearly from a smaller resolution; avoid scaling when
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# the figure is already at image size but doesn't fill the frame (causes ~40% blow-up)
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elif max_x < width * 0.76 and max_y < height * 0.76:
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scale_x = width / max(max_x, 1)
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scale_y = height / max(max_y, 1)
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if abs(scale_x - 1.0) < 0.01 and abs(scale_y - 1.0) < 0.01:
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return keypoints
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else:
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return keypoints
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result: dict[str, list[tuple[float, float, float]]] = {}
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for part, pts in keypoints.items():
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scaled = [
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(x * scale_x, y * scale_y, c) if c > 0 else (x, y, c)
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for x, y, c in pts
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]
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result[part] = scaled
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return result
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def parse_pose_keypoint(pose_keypoint: Any) -> dict[str, list[tuple[float, float, float]]] | None:
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"""
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Parse POSE_KEYPOINT from ComfyUI (OpenPose/DWPose format) into normalized structure.
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Returns dict with keys: body, hand_left, hand_right, face; each value is list of (x,y,conf).
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Returns None if invalid or empty.
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"""
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if pose_keypoint is None:
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return None
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# POSE_KEYPOINT can be a list (one per image) or single dict
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data = pose_keypoint
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if isinstance(pose_keypoint, list):
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if not pose_keypoint:
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return None
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data = pose_keypoint[0]
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if not isinstance(data, dict):
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return None
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# Structure 1: {"people": [{"pose_keypoints_2d": [...], "hand_left_keypoints_2d": [...], ...}]}
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people = data.get("people", [])
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if people:
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person = people[0]
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body = _extract_keypoint_array(person.get("pose_keypoints_2d"))
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hand_left = _extract_keypoint_array(person.get("hand_left_keypoints_2d"))
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hand_right = _extract_keypoint_array(person.get("hand_right_keypoints_2d"))
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face = _extract_keypoint_array(person.get("face_keypoints_2d"))
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if body:
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return {"body": body, "hand_left": hand_left, "hand_right": hand_right, "face": face}
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# Structure 2: {"person_0": {"body": np.array, "hands": np.array, "face": np.array}}
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for key in ("person_0", "person"):
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if key in data:
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person = data[key]
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body = person.get("body")
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if body is not None:
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body = _body_from_numpy(np.asarray(body))
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else:
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body = []
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hands = person.get("hands", np.zeros((2, 21, 2)))
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hands = np.asarray(hands)
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hand_left = _body_from_numpy(hands[0]) if hands.shape[0] > 0 else []
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hand_right = _body_from_numpy(hands[1]) if hands.shape[0] > 1 else []
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face = _body_from_numpy(np.asarray(person.get("face", [])))
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if body:
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return {"body": body, "hand_left": hand_left, "hand_right": hand_right, "face": face}
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# Structure 3: flat {"pose_keypoints_2d": [...], ...} at top level (no "people")
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body = _extract_keypoint_array(data.get("pose_keypoints_2d"))
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if body:
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hand_left = _extract_keypoint_array(data.get("hand_left_keypoints_2d"))
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hand_right = _extract_keypoint_array(data.get("hand_right_keypoints_2d"))
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face = _extract_keypoint_array(data.get("face_keypoints_2d"))
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return {"body": body, "hand_left": hand_left, "hand_right": hand_right, "face": face}
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return None
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def extract_keypoints_from_image(image: np.ndarray) -> dict[str, list[tuple[float, float, float]]] | None:
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"""
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Run DWPose on image to extract keypoints. Requires comfyui-controlnet-aux.
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Returns parsed keypoint dict or None on failure.
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"""
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try:
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from custom_controlnet_aux.dwpose import DwposeDetector
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import comfy.model_management as model_management
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except ImportError:
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return None
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try:
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model = DwposeDetector.from_pretrained(
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"yzd-v/DWPose",
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"yzd-v/DWPose",
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det_filename="yolox_l.onnx",
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pose_filename="dw-ll_ucoco_384.onnx",
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torchscript_device=model_management.get_torch_device(),
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)
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# DWPose expects HWC uint8; annotators often expect BGR
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if image.dtype != np.uint8:
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image = (np.clip(image, 0, 1) * 255).astype(np.uint8)
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if len(image.shape) == 2:
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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elif image.shape[2] == 3:
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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pose_img, openpose_dict = model(
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image,
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include_hand=True,
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include_face=True,
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include_body=True,
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detect_resolution=min(image.shape[0], image.shape[1], 512),
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)
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del model
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return parse_pose_keypoint(openpose_dict)
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except Exception:
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return None
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def compute_torso_pivot(keypoints: dict[str, list[tuple[float, float, float]]]) -> tuple[float, float] | None:
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"""
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Compute torso center from body keypoints. Uses neck, shoulders, mid_hip, hips.
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Supports "no torso segment" variants: derives neck from shoulders and mid_hip from hips
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when those keypoints are missing (c<=0) but shoulders/hips are present.
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Returns (cx, cy) or None if insufficient keypoints.
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"""
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body = keypoints.get("body", [])
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if len(body) < 2:
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return None
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def get_pt(idx: int) -> tuple[float, float, float] | None:
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if idx >= len(body):
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return None
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x, y, c = body[idx]
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return (x, y, c) if c > 0 else None
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xs, ys, total_conf = 0.0, 0.0, 0.0
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for idx in TORSO_INDICES:
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pt = get_pt(idx)
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if pt is not None:
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x, y, c = pt
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xs += x * c
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ys += y * c
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total_conf += c
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# Fallback for "no torso segment" / stick-figure variants: derive missing points
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# Neck (1) from shoulders (2, 5); mid_hip (8) from hips (9, 12)
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if get_pt(1) is None:
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r_sh = get_pt(2)
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l_sh = get_pt(5)
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if r_sh is not None and l_sh is not None:
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nx = (r_sh[0] + l_sh[0]) / 2
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ny = (r_sh[1] + l_sh[1]) / 2
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xs += nx
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ys += ny
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total_conf += 1.0
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if get_pt(8) is None:
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r_hip = get_pt(9)
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l_hip = get_pt(12)
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if r_hip is not None and l_hip is not None:
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mx = (r_hip[0] + l_hip[0]) / 2
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my = (r_hip[1] + l_hip[1]) / 2
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xs += mx
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ys += my
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total_conf += 1.0
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if total_conf <= 0:
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return None
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return (xs / total_conf, ys / total_conf)
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def _compute_adaptive_depth_scale(
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keypoints: dict[str, list[tuple[float, float, float]]],
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pivot: tuple[float, float],
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) -> float:
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"""
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Compute adaptive depth scale from body proportions (shoulder width).
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Normalizes to typical pose so rotation looks consistent across image sizes.
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"""
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body = keypoints.get("body", [])
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if len(body) < 6:
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return 0.4
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x2, _, c2 = body[2] # r_shoulder
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x5, _, c5 = body[5] # l_shoulder
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if c2 <= 0 or c5 <= 0:
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return 0.4
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shoulder_width = abs(x2 - x5)
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if shoulder_width < 1:
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return 0.4
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# Typical shoulder width ~100-150px in normalized poses; scale so base 0.4 holds
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return 0.4 * (120.0 / shoulder_width)
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def rotate_keypoints_3d(
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keypoints: dict[str, list[tuple[float, float, float]]],
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pivot: tuple[float, float],
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degrees: float,
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direction: str,
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depth_scale: float | None = None,
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) -> dict[str, list[tuple[float, float, float]]]:
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"""
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Rotate all keypoints around Y-axis through pivot. Left = positive angle (CCW), right = negative.
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Uses limb-specific depth inference per OpenPose COCO body indices for 3D-style projection.
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"""
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theta_deg = degrees if direction == "left" else -degrees
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theta = math.radians(theta_deg)
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cx, cy = pivot
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if depth_scale is None:
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depth_scale = _compute_adaptive_depth_scale(keypoints, pivot)
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cos_t = math.cos(theta)
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sin_t = math.sin(theta)
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def rotate_point(
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x: float, y: float, scale: float
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) -> tuple[float, float]:
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# Side-aware: (x - cx) gives correct sign for left/right body sides
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z = (x - cx) * scale
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x_new = (x - cx) * cos_t - z * sin_t + cx
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y_new = y
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return (x_new, y_new)
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result: dict[str, list[tuple[float, float, float]]] = {}
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for part, points in keypoints.items():
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rotated: list[tuple[float, float, float]] = []
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for i, (x, y, c) in enumerate(points):
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if c <= 0:
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rotated.append((x, y, c))
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continue
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scale = BODY_DEPTH_SCALES.get(i, HAND_DEPTH_SCALE) if part == "body" else HAND_DEPTH_SCALE
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nx, ny = rotate_point(x, y, depth_scale * scale)
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rotated.append((nx, ny, c))
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result[part] = rotated
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# Face visibility: when rotated >90°, hide face/head keypoints (back of head)
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if abs(theta_deg) >= 90:
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if "body" in result:
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body = result["body"]
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for idx in FACE_INDICES:
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if idx < len(body):
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x, y, c = body[idx]
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body[idx] = (x, y, 0.0)
|
|
# Also hide separate face keypoints (OpenPose face mesh)
|
|
if "face" in result:
|
|
result["face"] = [(x, y, 0.0) for x, y, c in result["face"]]
|
|
|
|
return result
|
|
|
|
|
|
def _fit_and_center_keypoints_on_canvas(
|
|
keypoints: dict[str, list[tuple[float, float, float]]],
|
|
width: int,
|
|
height: int,
|
|
padding: float = 0.05,
|
|
) -> dict[str, list[tuple[float, float, float]]]:
|
|
"""
|
|
Scale keypoints to fit within canvas (if they exceed bounds), then center.
|
|
Prevents feet/head from being cut off when the figure is taller than the image.
|
|
"""
|
|
all_x, all_y, total_conf = 0.0, 0.0, 0.0
|
|
for pts in keypoints.values():
|
|
for x, y, c in pts:
|
|
if c > 0:
|
|
all_x += x * c
|
|
all_y += y * c
|
|
total_conf += c
|
|
|
|
if total_conf <= 0:
|
|
return keypoints
|
|
|
|
# Bounding box of visible keypoints
|
|
xs = [x for pts in keypoints.values() for x, y, c in pts if c > 0]
|
|
ys = [y for pts in keypoints.values() for x, y, c in pts if c > 0]
|
|
if not xs or not ys:
|
|
return keypoints
|
|
|
|
min_x, max_x = min(xs), max(xs)
|
|
min_y, max_y = min(ys), max(ys)
|
|
|
|
# Centroid (stays fixed when scaling around it)
|
|
cx = all_x / total_conf
|
|
cy = all_y / total_conf
|
|
|
|
# Max extent from centroid in each direction (we center the centroid, so we must fit
|
|
# the larger of the two sides; using bbox_h/2 fails when centroid is off-center)
|
|
extent_x = max(cx - min_x, max_x - cx)
|
|
extent_y = max(cy - min_y, max_y - cy)
|
|
|
|
# Usable canvas with padding (half-extent each side of center)
|
|
pad_w = width * padding
|
|
pad_h = height * padding
|
|
usable_half_w = (width - 2 * pad_w) / 2.0
|
|
usable_half_h = (height - 2 * pad_h) / 2.0
|
|
|
|
# Scale down so extent from centroid fits within usable half-canvas
|
|
scale = 1.0
|
|
if extent_x > usable_half_w and usable_half_w > 0:
|
|
scale = min(scale, usable_half_w / extent_x)
|
|
if extent_y > usable_half_h and usable_half_h > 0:
|
|
scale = min(scale, usable_half_h / extent_y)
|
|
target_x = width / 2.0
|
|
target_y = height / 2.0
|
|
dx = target_x - cx
|
|
dy = target_y - cy
|
|
|
|
result: dict[str, list[tuple[float, float, float]]] = {}
|
|
for part, pts in keypoints.items():
|
|
result[part] = [
|
|
(cx + (x - cx) * scale + dx, cy + (y - cy) * scale + dy, c)
|
|
for x, y, c in pts
|
|
]
|
|
return result
|
|
|
|
|
|
def join_broken_segments(
|
|
keypoints: dict[str, list[tuple[float, float, float]]],
|
|
threshold: float,
|
|
) -> dict[str, list[tuple[float, float, float]]]:
|
|
"""
|
|
Optional: join broken segments when one endpoint visible, other missing.
|
|
Currently a no-op (disabled) per plan.
|
|
"""
|
|
return keypoints
|
|
|
|
|
|
def render_openpose_image(
|
|
keypoints: dict[str, list[tuple[float, float, float]]],
|
|
width: int,
|
|
height: int,
|
|
) -> np.ndarray:
|
|
"""
|
|
Draw OpenPose skeleton on blank canvas. Black background (matches ControlNet pose images).
|
|
Uses static connection and draw-order definitions; no depth computation.
|
|
"""
|
|
canvas = np.zeros((height, width, 3), dtype=np.uint8)
|
|
|
|
body = keypoints.get("body", [])
|
|
|
|
# Build limb list from static definitions; filter by validity
|
|
max_limb_ratio = 0.5 # Max limb length vs image diagonal; filters bad connections
|
|
diag = (width**2 + height**2) ** 0.5
|
|
max_limb_len = diag * max_limb_ratio
|
|
|
|
limb_data: list[tuple[int, int, int, int, float]] = []
|
|
for i, ((a, b), layer, color_idx) in enumerate(BODY_LIMBS):
|
|
if a >= len(body) or b >= len(body):
|
|
continue
|
|
if a not in MAIN_BODY_INDICES or b not in MAIN_BODY_INDICES:
|
|
continue
|
|
x1, y1, c1 = body[a]
|
|
x2, y2, c2 = body[b]
|
|
if c1 <= 0 or c2 <= 0:
|
|
continue
|
|
limb_len = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
|
|
if limb_len > max_limb_len:
|
|
continue
|
|
mean_x = (x1 + x2) / 2
|
|
limb_data.append((a, b, layer, color_idx, mean_x))
|
|
|
|
# Draw limbs: anatomical layer first, then left-to-right (back-to-front for rotated view)
|
|
limb_data.sort(key=lambda t: (t[2], t[4]))
|
|
for a, b, _, color_idx, _ in limb_data:
|
|
x1, y1, c1 = body[a]
|
|
x2, y2, c2 = body[b]
|
|
color = BODY_COLORS[color_idx % len(BODY_COLORS)]
|
|
cv2.line(canvas, (int(x1), int(y1)), (int(x2), int(y2)), color, 4)
|
|
|
|
# Draw joints: anatomical layer first, then left-to-right (back-to-front for rotated view)
|
|
joint_data = [
|
|
(i, x, y, c)
|
|
for i, (x, y, c) in enumerate(body)
|
|
if c > 0 and i in JOINT_LAYERS
|
|
]
|
|
joint_data.sort(key=lambda t: (JOINT_LAYERS.get(t[0], (0, 0))[0], t[1]))
|
|
for i, x, y, c in joint_data:
|
|
_, color_idx = JOINT_LAYERS.get(i, (0, 0))
|
|
color = BODY_COLORS[color_idx]
|
|
cv2.circle(canvas, (int(x), int(y)), 4, color, -1)
|
|
|
|
# Hands and face rendering - commented out to focus on main body composition
|
|
# for hand_key, hand_pts in [("hand_left", keypoints.get("hand_left", [])),
|
|
# ("hand_right", keypoints.get("hand_right", []))]:
|
|
# if len(hand_pts) < 2:
|
|
# continue
|
|
# ...
|
|
# cv2.line(canvas, ...)
|
|
# cv2.circle(canvas, ...)
|
|
|
|
return canvas
|
|
|
|
|
|
def rotate_openpose(
|
|
image: np.ndarray,
|
|
pose_keypoint: Any | None,
|
|
direction: str,
|
|
degrees: float,
|
|
image_index: int = 0,
|
|
) -> tuple[np.ndarray, bool, dict[str, list[tuple[float, float, float]]] | None]:
|
|
"""
|
|
Main pipeline: extract/parse keypoints, detect torso, rotate, render.
|
|
Returns (output_image, success, rotated_keypoints). On failure, returns (input_image, False, None).
|
|
rotated_keypoints is internal format: {"body": [(x,y,c),...], "hand_left": [...], ...}
|
|
image_index: used for console logging when processing batches.
|
|
"""
|
|
h, w = image.shape[:2]
|
|
|
|
# Get keypoints
|
|
if pose_keypoint is not None:
|
|
keypoints = parse_pose_keypoint(pose_keypoint)
|
|
else:
|
|
keypoints = extract_keypoints_from_image(image)
|
|
|
|
if keypoints is None:
|
|
return image, False, None
|
|
|
|
# Scale normalized coords to image size (ComfyUI may pass 0-1 range)
|
|
keypoints = _scale_keypoints_to_image(keypoints, w, h)
|
|
|
|
# Console: pre-rotated figure (before rotation, after scaling)
|
|
pre_dict = _keypoints_to_openpose_dict(keypoints)
|
|
print(f"[OpenPose Rotator] Image {image_index} PRE-ROTATED (direction={direction}, degrees={degrees}):")
|
|
print(f" body: {keypoints.get('body', [])}")
|
|
print(f" pose_keypoints_2d (flat): {pre_dict.get('people', [{}])[0].get('pose_keypoints_2d', [])}")
|
|
|
|
# Torso pivot
|
|
pivot = compute_torso_pivot(keypoints)
|
|
if pivot is None:
|
|
return image, False, None
|
|
|
|
# Rotate keypoints around torso pivot
|
|
rotated = rotate_keypoints_3d(keypoints, pivot, degrees, direction)
|
|
rotated = join_broken_segments(rotated, 0.0)
|
|
|
|
# Scale to fit and center figure (prevents feet/head cutoff, fixes right/left bias)
|
|
rotated = _fit_and_center_keypoints_on_canvas(rotated, w, h)
|
|
|
|
# Console: post-rotated figure (after rotation, fit, and center)
|
|
post_dict = _keypoints_to_openpose_dict(rotated)
|
|
print(f"[OpenPose Rotator] Image {image_index} POST-ROTATED (direction={direction}, degrees={degrees}):")
|
|
print(f" body: {rotated.get('body', [])}")
|
|
print(f" pose_keypoints_2d (flat): {post_dict.get('people', [{}])[0].get('pose_keypoints_2d', [])}")
|
|
|
|
# Render using static connection and draw-order definitions
|
|
rendered = render_openpose_image(rotated, w, h)
|
|
return rendered, True, rotated
|