File size: 8,229 Bytes
6a5bb7e | 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 | """Stereo-constrained WBF: cross-camera consistency from East shed pairs.
Learns positional mapping EastLeft<->EastRight from 197 stereo pairs,
then uses it to weight predictions during WBF.
"""
import sys,os,json,gc,numpy as np
from PIL import Image
from tqdm import tqdm
from collections import defaultdict
PROJECT_DIR='/home/user/goat'
os.chdir(PROJECT_DIR);sys.path.insert(0,PROJECT_DIR)
from ultralytics import YOLO
import torch
def iou_fn(b1,b2):
x1,y1=max(b1[0],b2[0]),max(b1[1],b2[1])
x2,y2=min(b1[2],b2[2]),min(b1[3],b2[3])
inter=max(0,x2-x1)*max(0,y2-y1)
a1=(b1[2]-b1[0])*(b1[3]-b1[1]);a2=(b2[2]-b2[0])*(b2[3]-b2[1])
return inter/(a1+a2-inter+1e-8)
def learn_stereo_prior():
"""Learn size ratios and positional offsets between EastLeft/EastRight."""
img_dir='Data/Detection_dataset/images/train'
lbl_dir='Data/Detection_dataset/labels/train'
# Find matched pairs
pairs={}
files=sorted(os.listdir(img_dir))
for f in files:
if not f.endswith('.jpg'): continue
parts=f.split('_',1)
if len(parts)<2: continue
cam,ts=parts[0],parts[1]
if cam in ['EastLeft','EastRight']:
pairs.setdefault(ts,{})[cam]=f
# Collect size/position stats
l_sizes,r_sizes=[],[]
l_centers,r_centers=[],[]
for ts,data in pairs.items():
if 'EastLeft' not in data or 'EastRight' not in data: continue
lf,rf=data['EastLeft'],data['EastRight']
for side,fname in [('L',lf),('R',rf)]:
lp=os.path.join(lbl_dir,fname.replace('.jpg','.txt'))
if not os.path.exists(lp): continue
with open(lp) as fh:
for line in fh:
p=line.strip().split()
if len(p)>=5:
cx,cy,w,h=float(p[1]),float(p[2]),float(p[3]),float(p[4])
if side=='L':
l_sizes.append(np.sqrt(w*h))
l_centers.append([cx,cy])
else:
r_sizes.append(np.sqrt(w*h))
r_centers.append([cx,cy])
if len(l_sizes)<10: return None
# Compute stats
prior={
'size_ratio': float(np.mean(r_sizes)/np.mean(l_sizes)) if l_sizes else 1.0,
'cx_offset': float(np.mean([r[0] for r in r_centers])-np.mean([l[0] for l in l_centers])),
'cy_mean_diff': float(np.mean([r[1] for r in r_centers])-np.mean([l[1] for l in l_centers])),
}
print(f'Stereo prior: size_ratio={prior["size_ratio"]:.3f}, cx_offset={prior["cx_offset"]:.4f}')
return prior
def wbf_fn(bl,sl,thr=0.55,weights=None):
if not bl or all(len(b)==0 for b in bl): return np.array([])
ab,as_=[],[]
for i,(bx,sx) in enumerate(zip(bl,sl)):
w=weights[i] if weights else np.ones(len(bx))
for j in range(len(bx)):
ab.append(bx[j]);as_.append(sx[j]*w[j])
if not ab: return np.array([])
ab=np.array(ab);as_=np.array(as_)
o=np.argsort(-as_);ab=ab[o];as_=as_[o]
cl,us=[],np.zeros(len(ab),dtype=bool)
for i in range(len(ab)):
if us[i]: continue
c=[(ab[i],as_[i])];us[i]=True
for j in range(i+1,len(ab)):
if us[j]: continue
tw=sum(s for _,s in c)
ct=sum(b*s/tw for b,s in c)
if iou_fn(ct.tolist(),ab[j].tolist())>thr: c.append((ab[j],as_[j]));us[j]=True
cl.append(c)
rb,rs=[],[]
for c in cl:
tw=sum(s for _,s in c)
rb.append(sum(b*s/tw for b,s in c));rs.append(tw)
return np.array(rb)
def pred_fn(model,img,sz,flip=False,bright=1.0):
ia=img
if bright!=1.0: ia=ImageEnhance.Brightness(ia).enhance(bright)
if flip: ia=ia.transpose(Image.FLIP_LEFT_RIGHT)
r=model.predict(ia,imgsz=sz,conf=0.25,iou=0.7,max_det=100,verbose=False)
if not r or len(r[0].boxes)==0: return np.array([]),np.array([])
b=r[0].boxes.xyxy.cpu().numpy();s=r[0].boxes.conf.cpu().numpy()
if flip: w=img.size[0];b[:,[0,2]]=w-b[:,[2,0]]
return b
def main():
val_dir='Data/Detection_dataset/images/val'
lbl_dir='Data/Detection_dataset/labels/val'
vfs=sorted([f for f in os.listdir(val_dir) if f.endswith('.jpg')])
iou_thrs=[round(0.5+i*0.05,2) for i in range(10)]
prior=learn_stereo_prior()
def eval_boxes(name,boxes_per_img):
tp={t:0 for t in iou_thrs};tg=0
for idx,vf in enumerate(vfs):
img=Image.open(os.path.join(val_dir,vf))
gb=[]
lf=vf.replace('.jpg','.txt')
with open(os.path.join(lbl_dir,lf)) as f:
for line in f:
p=line.strip().split()
if len(p)>=5:
cx,cy,w,h=[float(x) for x in p[1:5]]
gb.append([(cx-w/2)*img.size[0],(cy-h/2)*img.size[1],(cx+w/2)*img.size[0],(cy+h/2)*img.size[1]])
tg+=len(gb)
if not gb: continue
for t in iou_thrs:
mt=set()
for pb in boxes_per_img[idx]:
if len(pb)==0: continue
bi,bg=0,-1
for gi,gt in enumerate(gb):
if gi in mt: continue
ii=iou_fn(pb.tolist(),gt)
if ii>bi: bi=ii;bg=gi
if bi>=t and bg>=0: tp[t]+=1;mt.add(bg)
rec=[tp[t]/tg for t in iou_thrs]
mAP=np.mean(rec)
print('{}: mAP50-95={:.4f}'.format(name,mAP))
return mAP
# Use v6_1 for quick test
m=YOLO('runs/detect/Detection_experiments/v6_1_s_refined/weights/best.pt')
# Baseline
bp=[]
for vf in tqdm(vfs,desc='Baseline'):
img=Image.open(os.path.join(val_dir,vf))
b,_=pred_fn(m,img,1536);bp.append(b)
mAP_base=eval_boxes('Baseline',bp)
# Multi-scale WBF without stereo
mp=[]
for vf in tqdm(vfs,desc='MS-WBF'):
img=Image.open(os.path.join(val_dir,vf))
bl,sl=[],[]
for sz in [1280,1536,1920]:
for fl in [False,True]:
for br in [1.0,1.2]:
b,s=pred_fn(m,img,sz,fl,br)
if len(b)>0: bl.append(b);sl.append(s)
mp.append(wbf_fn(bl,sl))
mAP_ms=eval_boxes('MS-WBF',mp)
# Stereo-constrained: apply size consistency check to East shed images
if prior:
sp=[]
for vf in tqdm(vfs,desc='StereoWBF'):
img=Image.open(os.path.join(val_dir,vf));iw,ih=img.size
cam=vf.split('_2025')[0]
is_east=cam.startswith('East')
bl,sl,weights=[],[],[]
for sz in [1280,1536,1920]:
for fl in [False,True]:
for br in [1.0,1.2]:
b,s=pred_fn(m,img,sz,fl,br)
if len(b)>0:
bl.append(b);sl.append(s)
w=np.ones(len(b))
if is_east and prior:
# Check size plausibility via stereo prior
for i in range(len(b)):
pred_w, pred_h = b[i][2]-b[i][0], b[i][3]-b[i][1]
pred_sz = np.sqrt(pred_w*pred_h) / max(iw,ih)
# Expected opposite-camera size
if cam=='EastLeft':
exp_sz = pred_sz * prior['size_ratio']
else:
exp_sz = pred_sz / prior['size_ratio']
# If size is unusual for its camera, slight penalty
z = abs(pred_sz - exp_sz) / (exp_sz + 1e-8)
if z > 0.3: w[i] = 0.7
weights.append(w)
sp.append(wbf_fn(bl,sl,weights=weights))
mAP_stereo=eval_boxes('StereoWBF',sp)
print('\nBaseline: {:.4f}'.format(mAP_base))
print('MS-WBF: {:.4f} (+{:.4f})'.format(mAP_ms,mAP_ms-mAP_base))
print('Stereo: {:.4f} (+{:.4f})'.format(mAP_stereo,mAP_stereo-mAP_base))
del m;gc.collect();torch.cuda.empty_cache()
if __name__=='__main__':
main()
|