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434b0b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | from __future__ import annotations
import os
import os.path as osp
from collections import defaultdict
import time
from mmpose.apis.inference import batch_inference_pose_model
import numpy as np
import torch
import torch.nn as nn
import scipy.signal as signal
from ultralytics import YOLO
from mmpose.apis import (
init_pose_model,
get_track_id,
vis_pose_result,
)
ROOT_DIR = osp.abspath(f"{__file__}/../../")
VIT_DIR = osp.join(ROOT_DIR, "third-party/ViTPose")
VIS_THRESH = 0.5
BBOX_CONF = 0.5
TRACKING_THR = 0.1
MINIMUM_FRMAES = 15
MINIMUM_JOINTS = 6
class DetectionModel(object):
def __init__(self, pose_model_ckpt, device, with_tracker=True):
# ViTPose
pose_model_cfg = osp.join(
VIT_DIR,
"configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_huge_wholebody_256x192.py",
)
#'vitpose-h-multi-coco.pth')
self.pose_model = init_pose_model(
pose_model_cfg, pose_model_ckpt, device=device
)
# YOLO
bbox_model_ckpt = osp.join(ROOT_DIR, "checkpoints", "yolov8x.pt")
if with_tracker:
self.bbox_model = YOLO(bbox_model_ckpt)
else:
self.bbox_model = None
self.device = device
self.initialize_tracking()
def initialize_tracking(
self,
):
self.next_id = 0
self.frame_id = 0
self.pose_results_last = []
self.tracking_results = {
"id": [],
"frame_id": [],
"bbox": [],
}
def xyxy_to_cxcys(self, bbox, s_factor=1.05):
cx, cy = bbox[[0, 2]].mean(), bbox[[1, 3]].mean()
scale = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 200 * s_factor
return np.array([[cx, cy, scale]])
def compute_bboxes_from_keypoints(self, s_factor=1.2):
X = self.tracking_results["keypoints"].copy()
mask = X[..., -1] > VIS_THRESH
bbox = np.zeros((len(X), 3))
for i, (kp, m) in enumerate(zip(X, mask)):
bb = [kp[m, 0].min(), kp[m, 1].min(), kp[m, 0].max(), kp[m, 1].max()]
cx, cy = [(bb[2] + bb[0]) / 2, (bb[3] + bb[1]) / 2]
bb_w = bb[2] - bb[0]
bb_h = bb[3] - bb[1]
s = np.stack((bb_w, bb_h)).max()
bb = np.array((cx, cy, s))
bbox[i] = bb
bbox[:, 2] = bbox[:, 2] * s_factor / 200.0
self.tracking_results["bbox"] = bbox
def compute_bbox(self, img):
bboxes = (
self.bbox_model.predict(
img,
device=self.device,
classes=0,
conf=BBOX_CONF,
save=False,
verbose=False,
)[0]
.boxes.xyxy.detach()
.cpu()
.numpy()
)
bboxes = [{"bbox": bbox} for bbox in bboxes]
imgs = [img for _ in range(len(bboxes))]
return bboxes, imgs
def batch_detection(self, bboxes, imgs, batch_size=32):
all_poses = []
all_bboxes = []
for i in range(0, len(bboxes), batch_size):
poses, bbox_xyxy = batch_inference_pose_model(
self.pose_model,
imgs[i : i + batch_size],
bboxes[i : i + batch_size],
return_heatmap=False,
)
all_poses.append(poses)
all_bboxes.append(bbox_xyxy)
all_poses = np.concatenate(all_poses)
all_bboxes = np.concatenate(all_bboxes)
return all_poses, all_bboxes
def track(self, img, fps, length):
# bbox detection
bboxes = (
self.bbox_model.predict(
img,
device=self.device,
classes=0,
conf=BBOX_CONF,
save=False,
verbose=False,
)[0]
.boxes.xyxy.detach()
.cpu()
.numpy()
)
pose_results = [{"bbox": bbox} for bbox in bboxes]
pose_results, self.next_id = get_track_id(
pose_results,
self.pose_results_last,
self.next_id,
use_oks=False,
tracking_thr=TRACKING_THR,
use_one_euro=True,
fps=fps,
)
for pose_result in pose_results:
_id = pose_result["track_id"]
xyxy = pose_result["bbox"]
bbox = xyxy # self.xyxy_to_cxcys(xyxy)
self.tracking_results["id"].append(_id)
self.tracking_results["frame_id"].append(self.frame_id)
self.tracking_results["bbox"].append(bbox)
self.frame_id += 1
self.pose_results_last = pose_results
def process(self, fps):
for key in ["id", "frame_id", "bbox"]:
self.tracking_results[key] = np.array(self.tracking_results[key])
# self.compute_bboxes_from_keypoints()
output = defaultdict(lambda: defaultdict(list))
ids = np.unique(self.tracking_results["id"])
for _id in ids:
idxs = np.where(self.tracking_results["id"] == _id)[0]
for key, val in self.tracking_results.items():
if key == "id":
continue
output[_id][key] = val[idxs]
# Smooth bounding box detection
ids = list(output.keys())
for _id in ids:
if len(output[_id]["bbox"]) < MINIMUM_FRMAES:
del output[_id]
continue
kernel = int(int(fps / 2) / 2) * 2 + 1
smoothed_bbox = np.array(
[signal.medfilt(param, kernel) for param in output[_id]["bbox"].T]
).T
output[_id]["bbox"] = smoothed_bbox
return output
def visualize(self, img, pose_results):
vis_img = vis_pose_result(
self.pose_model,
img,
pose_results,
dataset=self.pose_model.cfg.data["test"]["type"],
dataset_info=None, # self.pose_model.cfg.data['test'].get('dataset_info', None),
kpt_score_thr=0.3,
radius=4,
thickness=1,
show=False,
)
return vis_img
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