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"""
Reference drawing function from the MusePose
https://github.com/TMElyralab/MusePose/blob/main/pose/script/dwpose.py
"""
import math
import cv2
import numpy as np
eps = 0.01
def smart_width(d):
if d < 5:
return 1
elif d < 10:
return 2
elif d < 20:
return 3
elif d < 40:
return 4
elif d < 80:
return 5
elif d < 160:
return 6
elif d < 320:
return 7
else:
return 8
def draw_bodypose(canvas, candidate, subset):
H, W, C = canvas.shape
candidate = np.array(candidate)
subset = np.array(subset)
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]))
width = smart_width(length)
polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), width), 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)
radius = 4
cv2.circle(canvas, (int(x), int(y)), radius, colors[i], thickness=-1)
return canvas
def draw_handpose(canvas, all_hand_peaks):
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)
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:
length = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
width = smart_width(length)
cv2.line(
canvas,
(x1, y1),
(x2, y2),
matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
thickness=width,
)
for _, keyponit in enumerate(peaks):
x, y = keyponit
x = int(x * W)
y = int(y * H)
if x > eps and y > eps:
radius = 3
cv2.circle(canvas, (x, y), radius, (0, 0, 255), thickness=-1)
return canvas
def draw_facepose(canvas, all_lmks):
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:
radius = 3
cv2.circle(canvas, (x, y), radius, (255, 255, 255), thickness=-1)
return canvas
# Calculate the resolution
def size_calculate(h, w, resolution):
H = float(h)
W = float(w)
# resize the short edge to the resolution
k = float(resolution) / min(H, W) # short edge
H *= k
W *= k
# resize to the nearest integer multiple of 64
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
return H, W
def warpAffine_kps(kps, M):
a = M[:, :2]
t = M[:, 2]
kps = np.dot(kps, a.T) + t
return kps
def draw_pose(pose, height, width, draw_face):
# bodies = pose["bodies"]
# only the most significant person
faces = pose["faces"][:1]
hands = pose["hands"][:2]
# candidate = bodies["candidate"][:18]
# subset = bodies["subset"][:1]
bodies = pose["bodies"][:18]
body_scores = pose["body_scores"][:1]
# draw
canvas = np.zeros(shape=(height, width, 3), dtype=np.uint8)
canvas = draw_bodypose(canvas, bodies, body_scores)
canvas = draw_handpose(canvas, hands)
if draw_face == True:
canvas = draw_facepose(canvas, faces)
return canvas
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