lichen/detecttracking
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

box select in a track and feat simi modify in tracker

Browse Source
This commit is contained in:
王庆刚
2025-01-14 19:00:59 +08:00
parent 744fb7b7b2
commit bfe7bc0fd5
11 changed files with 157 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
__pycache__/track_reid.cpython-39.pyc
View File

Binary file not shown.

BIN
contrast/feat_extract/__pycache__/config.cpython-39.pyc
View File

Binary file not shown.

BIN
contrast/feat_extract/__pycache__/inference.cpython-39.pyc
View File

Binary file not shown.

66
pipeline.py
View File

@ -10,6 +10,7 @@ import cv2
import pickle
import numpy as np
from pathlib import Path
from scipy.spatial.distance import cdist
from track_reid import yolo_resnet_tracker
from tracking.dotrack.dotracks_back import doBackTracks
@ -19,13 +20,45 @@ from utils.getsource import get_image_pairs, get_video_pairs
from tracking.utils.read_data import read_similar
def save_subimgs(imgdict, boxes, spath, ctype):
def save_subimgs(imgdict, boxes, spath, ctype, featdict = None):
'''
当前 box 特征和该轨迹前一个 box 特征的相似度，可用于和跟踪序列中的相似度进行比较
'''
boxes = boxes[np.argsort(boxes[:, 7])]
for i in range(len(boxes)):
fid, bid = int(boxes[i, 7]), int(boxes[i, 8])
if f"{fid}_{bid}" in imgdict.keys():
img = imgdict[f"{fid}_{bid}"]
imgpath = spath / f"{ctype}_{fid}_{bid}.png"
cv2.imwrite(imgpath, img)
simi = None
tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
if i>0:
_, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys():
feat0 = featdict[f"{fid0}_{bid0}"]
feat1 = featdict[f"{fid}_{bid}"]
simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0]
img = imgdict[f"{fid}_{bid}"]
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
if simi is not None:
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png"
cv2.imwrite(imgpath, img)
def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None):
'''
当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中，
采用该方式记录特征相似度
'''
for i in range(len(boxes)):
tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
key = f"{fid}_{bid}"
img = imgdict[key]
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
if simidict is not None and key in simidict.keys():
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png"
cv2.imwrite(imgpath, img)
def pipeline(
@ -177,15 +210,18 @@ def pipeline(
yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
ctype = 0
imgdict = {}
imgdict, featdict, simidict = {}, {}, {}
for y in yolos:
imgdict.update(y["imgs"])
featdict.update(y["feats"])
simidict.update(y["featsimi"])
for track in vts.Residual:
if isinstance(track, np.ndarray):
save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype)
save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict)
else:
save_subimgs(imgdict, track.boxes, savepath_pipeline_subimgs, ctype)
save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs, ctype, featdict)
'''轨迹显示模块'''
@ -243,14 +279,14 @@ def main():
if item.is_dir():
# item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
parmDict["eventpath"] = item
# pipeline(**parmDict)
pipeline(**parmDict)
try:
pipeline(**parmDict)
except Exception as e:
errEvents.append(str(item))
# try:
# pipeline(**parmDict)
# except Exception as e:
# errEvents.append(str(item))
k+=1
if k==1:
if k==2:
break
errfile = os.path.join(parmDict["savepath"], f'error_events.txt')

31
track_reid.py
View File

@ -38,6 +38,7 @@ import glob
import numpy as np
import pickle
import torch
from scipy.spatial.distance import cdist
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
@ -222,7 +223,19 @@ def yolo_resnet_tracker(
这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
'''
det_tracking = Boxes(det, im0.shape).cpu().numpy()
tracks = tracker.update(det_tracking, im0)
tracks, outfeats = tracker.update(det_tracking, im0)
simdict, simdict1 = {}, {}
for fid, bid, mfeat, cfeat, features in outfeats:
if mfeat is not None and cfeat is not None:
simi = 1 - np.maximum(0.0, cdist(mfeat[None, :], cfeat[None, :], "cosine"))[0][0]
simdict.update({f"{int(frameId)}_{int(bid)}":simi})
if cfeat is not None and len(features)>=2:
mfeat = features[-2]
simi = 1 - np.maximum(0.0, cdist(mfeat[None, :], cfeat[None, :], "cosine"))[0][0]
simdict1.update({f"{int(frameId)}_{int(bid)}":simi})
if len(tracks) > 0:
tracks[:, 7] = frameId
@ -239,7 +252,10 @@ def yolo_resnet_tracker(
"bboxes": det,
"tboxes": tracks,
"imgs": imgdict,
"feats": featdict}
"feats": featdict,
"featsimi": simdict, # 当前 box 特征和该轨迹 smooth_feat 特征的相似度
"featsimi1": simdict1 # 当前 box 特征和该轨迹前一个 box 特征的相似度
}
yoloResnetTracker.append(frameDict)
# imgs, features = inference_image(im0, tracks)
@ -248,7 +264,14 @@ def yolo_resnet_tracker(
'''================== 2. 提取手势位置 ==================='''
for *xyxy, id, conf, cls, fid, bid in reversed(tracks):
name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)]
label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
if f"{int(frameId)}_{int(bid)}" in simdict.keys():
sim = simdict[f"{int(frameId)}_{int(bid)}"]
label = f"{name} {sim:.2f}"
else:
label = None if hide_labels else name
# label = None if hide_labels else (name if hide_conf else f'{name} {conf:.1f}')
if id >=0 and cls==0:
color = colors(int(cls), True)
@ -489,7 +512,7 @@ def run(
'''
det_tracking = Boxes(det, im0.shape).cpu().numpy()
tracks = tracker.update(det_tracking, im0)
tracks, outfeats = tracker.update(det_tracking, im0)
if len(tracks) == 0:
continue

BIN
tracking/dotrack/__pycache__/dotracks.cpython-39.pyc
View File

Binary file not shown.

71
tracking/dotrack/dotracks.py
View File

@ -22,6 +22,37 @@ class MoveState:
FreeMove = 3
Unknown = -1
def bbox_ioa(box1, box2, iou=False, eps=1e-7):
"""
Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.
Args:
box1 (np.array): A numpy array of shape (n, 4) representing n bounding boxes.
box2 (np.array): A numpy array of shape (m, 4) representing m bounding boxes.
iou (bool): Calculate the standard iou if True else return inter_area/box2_area.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
Returns:
(np.array): A numpy array of shape (n, m) representing the intersection over box2 area.
"""
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
# Intersection area
inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
# box2 area
area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
if iou:
box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
area = area + box1_area[:, None] - inter_area
# Intersection over box2 area
return inter_area / (area + eps)
class ShoppingCart:
def __init__(self, bboxes):
@ -90,6 +121,7 @@ class Track:
self.boxes = boxes
self.features = features
self.slt_boxes = self.select_boxes()
self.tid = int(boxes[0, 4])
self.cls = int(boxes[0, 6])
@ -139,6 +171,43 @@ class Track:
if self.cls == 0:
self.extract_hand_features()
def select_boxes(self):
slt_boxes = []
idx = np.argsort(self.boxes[:, 7])
boxes = self.boxes[idx]
features = self.features[idx]
for i in range(len(boxes)):
simi = None
box, tid, fid, bid = boxes[i, :4], int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
if i == 0:
slt_boxes.append(boxes[i, :])
continue
if len(boxes)!=len(features):
print("check!")
continue
box0, tid0, fid0, bid0 = boxes[i-1, :4], int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
# 当前 box 和轨迹上一个 box 的iou
iou = bbox_ioa(box[None, :], box0[None, :])
# 当前 box 和轨迹上一个 box 的 feat similarity
feat0 = features[i, :][None, :]
feat1 = features[i-1, :][None, :]
simi = 1 - np.maximum(0.0, cdist(feat0, feat1, "cosine"))[0][0]
if iou > 0.85 and simi>0.85:
continue
slt_boxes.append(boxes[i, :])
return np.array(slt_boxes)
def compute_cornpoints(self):
'''
@ -417,6 +486,8 @@ class doTracks:
self.FreeMove = [] # subset of self.Residual
def array2list(self):
'''
将 bboxes 变换为 track 列表

BIN
tracking/trackers/__pycache__/bot_sort.cpython-39.pyc
View File

Binary file not shown.

BIN
tracking/trackers/__pycache__/byte_tracker.cpython-39.pyc
View File

Binary file not shown.

2
tracking/trackers/bot_sort.py
View File

@ -172,7 +172,7 @@ class BOTSORT(BYTETracker):
'''1. reid 相似度阈值，低于该值的两 boxes 图像不可能是同一对象，需要确定一个合理的可信阈值
2. iou 的约束为若约束，故 iou_dists 应设置为较大的值
'''
emb_dists_mask = (emb_dists > 0.9)
emb_dists_mask = (emb_dists > 0.8)
iou_dists[emb_dists_mask] = 1
emb_dists[iou_dists_mask] = 1

7
tracking/trackers/byte_tracker.py
View File

@ -375,7 +375,12 @@ class BYTETracker:
output = np.asarray(output1 + output2, dtype=np.float32)
return output
out_feat1 = [(x.frame_id, x.idx, x.smooth_feat, x.curr_feat, x.features) for x in self.tracked_stracks if x.is_activated]
out_feat2 = [(x.frame_id, x.idx, x.smooth_feat, x.curr_feat, x.features) for x in first_finded if x.first_find]
return output, out_feat1 + out_feat2
def get_result(self):