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import cv2
import numpy as np
eps = 0.01
def draw_bodypose(canvas, candidate, subset, score=None, conf_threshold=0.3):
"""Draw body pose with optional confidence filtering
Args:
canvas: canvas to draw on
candidate: pose candidate
subset: pose subset
score: confidence scores (optional)
conf_threshold: confidence threshold for filtering (default: 0.3)
"""
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
# Add confidence filtering (like ControlNeXt)
if score is not None:
conf = score[n][np.array(limbSeq[i]) - 1]
if conf[0] < conf_threshold or conf[1] < conf_threshold:
continue
coords = candidate[index.astype(int)]
if np.any(coords <= eps):
continue
Y = coords[:, 0] * float(W)
X = coords[:, 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), stickwidth), 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
# Add confidence filtering for keypoints
if score is not None:
conf = score[n][i]
if conf < conf_threshold:
continue
x, y = candidate[index][0:2]
x = int(x * W)
y = int(y * H)
cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
return canvas
def draw_handpose(canvas, all_hand_peaks, all_hand_scores=None, conf_threshold=0.3):
"""Draw hand pose with optional confidence filtering
Args:
canvas: canvas to draw on
all_hand_peaks: hand keypoints
all_hand_scores: confidence scores (optional)
conf_threshold: confidence threshold for filtering (default: 0.3)
"""
import matplotlib
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],
]
# (person_number*2, 21, 2)
for i in range(len(all_hand_peaks)):
peaks = all_hand_peaks[i]
peaks = np.array(peaks)
scores = all_hand_scores[i] if all_hand_scores is not None else None
for ie, e in enumerate(edges):
x1, y1 = peaks[e[0]]
x2, y2 = peaks[e[1]]
# Add confidence filtering
if scores is not None:
score1 = scores[e[0]]
score2 = scores[e[1]]
if score1 < conf_threshold or score2 < conf_threshold:
continue
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=2,
)
for idx, keyponit in enumerate(peaks):
x, y = keyponit
# Add confidence filtering for keypoints
if scores is not None and scores[idx] < conf_threshold:
continue
x = int(x * W)
y = int(y * H)
if x > eps and y > eps:
cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
return canvas
def draw_facepose(canvas, all_lmks, all_scores=None, conf_threshold=0.3):
"""Draw face pose with optional confidence filtering
Args:
canvas: canvas to draw on
all_lmks: face landmarks
all_scores: confidence scores (optional)
conf_threshold: confidence threshold for filtering (default: 0.3)
"""
H, W, C = canvas.shape
for i, lmks in enumerate(all_lmks):
lmks = np.array(lmks)
scores = all_scores[i] if all_scores is not None else None
for idx, lmk in enumerate(lmks):
# Add confidence filtering
if scores is not None and scores[idx] < conf_threshold:
continue
x, y = lmk
x = int(x * W)
y = int(y * H)
if x > eps and y > eps:
cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
return canvas
def draw_pose(pose, height: int, width: int, include_face: bool = True, include_hands: bool = True, conf_threshold: float = 0.3) -> np.ndarray:
"""Draw pose with confidence filtering
Args:
pose: pose data dictionary
height: canvas height
width: canvas width
include_face: whether to draw face
include_hands: whether to draw hands
conf_threshold: confidence threshold for filtering (default: 0.3)
"""
canvas = np.zeros(shape=(height, width, 3), dtype=np.uint8)
# Handle bodies
candidate = pose["bodies"]
# For openpose format, subset is typically derived from body_scores
# Create a simple subset array for single person (18 keypoints)
body_scores_data = pose.get("body_scores", None)
if body_scores_data is not None:
# Create subset: reshape scores to be compatible
subset = body_scores_data.reshape(1, -1) if len(body_scores_data.shape) == 1 else body_scores_data
else:
# No scores available, create dummy subset
subset = np.zeros((1, 18))
canvas = draw_bodypose(canvas, candidate, subset, score=subset, conf_threshold=conf_threshold)
if include_face:
faces = pose.get("faces", [])
face_scores = pose.get("faces_scores", None)
if len(faces) > 0:
canvas = draw_facepose(canvas, faces, all_scores=face_scores, conf_threshold=conf_threshold)
if include_hands:
hands = pose.get("hands", [])
hand_scores = pose.get("hands_scores", None)
if len(hands) > 0:
canvas = draw_handpose(canvas, hands, all_hand_scores=hand_scores, conf_threshold=conf_threshold)
return canvas
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