Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

support coco-wholebody visualization in pose_tracker python demo #2450

Merged
merged 2 commits into from
Sep 21, 2023
Merged

support coco-wholebody visualization in pose_tracker python demo #2450

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
fd12849
update
RunningLeon Sep 21, 2023
93e7740
update
RunningLeon Sep 21, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
183 changes: 160 additions & 23 deletions demo/python/pose_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import os

import cv2
import numpy as np
from mmdeploy_runtime import PoseTracker


Expand All @@ -18,39 +19,173 @@ def parse_args():
help='path of mmdeploy SDK model dumped by model converter')
parser.add_argument('video', help='video path or camera index')
parser.add_argument('--output_dir', help='output directory', default=None)
parser.add_argument(
'--skeleton',
default='coco',
choices=['coco', 'coco_wholebody'],
help='skeleton for keypoints')

args = parser.parse_args()
if args.video.isnumeric():
args.video = int(args.video)
return args


def visualize(frame, results, output_dir, frame_id, thr=0.5, resize=1280):
skeleton = [(15, 13), (13, 11), (16, 14), (14, 12), (11, 12), (5, 11),
(6, 12), (5, 6), (5, 7), (6, 8), (7, 9), (8, 10), (1, 2),
(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6)]
palette = [(255, 128, 0), (255, 153, 51), (255, 178, 102), (230, 230, 0),
(255, 153, 255), (153, 204, 255), (255, 102, 255),
(255, 51, 255), (102, 178, 255),
(51, 153, 255), (255, 153, 153), (255, 102, 102), (255, 51, 51),
(153, 255, 153), (102, 255, 102), (51, 255, 51), (0, 255, 0),
(0, 0, 255), (255, 0, 0), (255, 255, 255)]
link_color = [
0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16
]
point_color = [16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]
VISUALIZATION_CFG = dict(
coco=dict(
skeleton=[(15, 13), (13, 11), (16, 14), (14, 12), (11, 12), (5, 11),
(6, 12), (5, 6), (5, 7), (6, 8), (7, 9), (8, 10), (1, 2),
(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6)],
palette=[(255, 128, 0), (255, 153, 51), (255, 178, 102), (230, 230, 0),
(255, 153, 255), (153, 204, 255), (255, 102, 255),
(255, 51, 255), (102, 178, 255), (51, 153, 255),
(255, 153, 153), (255, 102, 102), (255, 51, 51),
(153, 255, 153), (102, 255, 102), (51, 255, 51), (0, 255, 0),
(0, 0, 255), (255, 0, 0), (255, 255, 255)],
link_color=[
0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16
],
point_color=[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,
0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
]),
coco_wholebody=dict(
skeleton=[
(15, 13),
(13, 11),
(16, 14),
(14, 12),
(11, 12),
(5, 11),
(6, 12),
(5, 6),
(5, 7),
(6, 8),
(7, 9),
(8, 10),
(1, 2),
(0, 1),
(0, 2),
(1, 3),
(2, 4),
(3, 5),
(4, 6),
(15, 17),
(15, 18),
(15, 19),
(16, 20),
(16, 21),
(16, 22),
(91, 92),
(92, 93),
(93, 94),
(94, 95),
(91, 96),
(96, 97),
(97, 98),
(98, 99),
(91, 100),
(100, 101),
(101, 102),
(102, 103),
(91, 104),
(104, 105),
(105, 106),
(106, 107),
(91, 108),
(108, 109),
(109, 110),
(110, 111),
(112, 113),
(113, 114),
(114, 115),
(115, 116),
(112, 117),
(117, 118),
(118, 119),
(119, 120),
(112, 121),
(121, 122),
(122, 123),
(123, 124),
(112, 125),
(125, 126),
(126, 127),
(127, 128),
(112, 129),
(129, 130),
(130, 131),
(131, 132),
],
palette=[
(51, 153, 255),
(0, 255, 0),
(255, 128, 0),
(255, 255, 255),
(255, 153, 255),
(102, 178, 255),
(255, 51, 51),
],
RunningLeon marked this conversation as resolved.
Show resolved Hide resolved
link_color=[
1, 1, 2, 2, 0, 0, 0, 0, 1, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 1, 1, 1,
1, 2, 2, 2, 2, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 1, 1, 1, 1
],
point_color=[
0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 2, 2, 2, 2, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 1, 1,
1, 1, 3, 2, 2, 2, 2, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 1, 1, 1, 1
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072,
0.062, 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089, 0.068,
0.066, 0.066, 0.092, 0.094, 0.094, 0.042, 0.043, 0.044, 0.043,
0.040, 0.035, 0.031, 0.025, 0.020, 0.023, 0.029, 0.032, 0.037,
0.038, 0.043, 0.041, 0.045, 0.013, 0.012, 0.011, 0.011, 0.012,
0.012, 0.011, 0.011, 0.013, 0.015, 0.009, 0.007, 0.007, 0.007,
0.012, 0.009, 0.008, 0.016, 0.010, 0.017, 0.011, 0.009, 0.011,
0.009, 0.007, 0.013, 0.008, 0.011, 0.012, 0.010, 0.034, 0.008,
0.008, 0.009, 0.008, 0.008, 0.007, 0.010, 0.008, 0.009, 0.009,
0.009, 0.007, 0.007, 0.008, 0.011, 0.008, 0.008, 0.008, 0.01,
0.008, 0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024,
0.035, 0.018, 0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032,
0.02, 0.019, 0.022, 0.031, 0.029, 0.022, 0.035, 0.037, 0.047,
0.026, 0.025, 0.024, 0.035, 0.018, 0.024, 0.022, 0.026, 0.017,
0.021, 0.021, 0.032, 0.02, 0.019, 0.022, 0.031
]))


def visualize(frame,
results,
output_dir,
frame_id,
thr=0.5,
resize=1280,
skeleton_type='coco'):

skeleton = VISUALIZATION_CFG[skeleton_type]['skeleton']
palette = VISUALIZATION_CFG[skeleton_type]['palette']
link_color = VISUALIZATION_CFG[skeleton_type]['link_color']
point_color = VISUALIZATION_CFG[skeleton_type]['point_color']

scale = resize / max(frame.shape[0], frame.shape[1])
keypoints, bboxes, _ = results
scores = keypoints[..., 2]
keypoints = (keypoints[..., :2] * scale).astype(int)
bboxes *= scale
img = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
for kpts, score, bbox in zip(keypoints, scores, bboxes):
show = [0] * len(kpts)
show = [1] * len(kpts)
for (u, v), color in zip(skeleton, link_color):
if score[u] > thr and score[v] > thr:
cv2.line(img, kpts[u], tuple(kpts[v]), palette[color], 1,
cv2.LINE_AA)
show[u] = show[v] = 1
else:
show[u] = show[v] = 0
for kpt, show, color in zip(kpts, show, point_color):
if show:
cv2.circle(img, kpt, 1, palette[color], 2, cv2.LINE_AA)
Expand All @@ -64,7 +199,7 @@ def visualize(frame, results, output_dir, frame_id, thr=0.5, resize=1280):

def main():
args = parse_args()

np.set_printoptions(precision=4, suppress=True)
video = cv2.VideoCapture(args.video)

tracker = PoseTracker(
Expand All @@ -73,12 +208,9 @@ def main():
device_name=args.device_name)

# optionally use OKS for keypoints similarity comparison
coco_sigmas = [
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
]
sigmas = VISUALIZATION_CFG[args.skeleton]['sigmas']
state = tracker.create_state(
det_interval=1, det_min_bbox_size=100, keypoint_sigmas=coco_sigmas)
det_interval=1, det_min_bbox_size=100, keypoint_sigmas=sigmas)

if args.output_dir:
os.makedirs(args.output_dir, exist_ok=True)
Expand All @@ -89,7 +221,12 @@ def main():
if not success:
break
results = tracker(state, frame, detect=-1)
if not visualize(frame, results, args.output_dir, frame_id):
if not visualize(
frame,
results,
args.output_dir,
frame_id,
skeleton_type=args.skeleton):
break
frame_id += 1

Expand Down
Loading