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========================================
Maximum firepower: 5 models × 3 scales = 15 prediction sources → WBF fusion.
Also tests per-camera weighted WBF and confidence-gated variants.
"""
import sys, os, json
import numpy as np
from PIL import Image
from tqdm import tqdm
PROJECT_DIR = '/home/user/goat'
os.chdir(PROJECT_DIR)
sys.path.insert(0, PROJECT_DIR)
from ultralytics import YOLO
def compute_iou(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 wbf(boxes_list, scores_list, iou_thr=0.55):
if not boxes_list or all(len(b) == 0 for b in boxes_list):
return np.array([]), np.array([])
all_boxes, all_scores = [], []
for boxes, scores in zip(boxes_list, scores_list):
for i in range(len(boxes)):
all_boxes.append(boxes[i])
all_scores.append(scores[i])
if not all_boxes: return np.array([]), np.array([])
all_boxes = np.array(all_boxes); all_scores = np.array(all_scores)
order = np.argsort(-all_scores)
all_boxes, all_scores = all_boxes[order], all_scores[order]
clusters, used = [], np.zeros(len(all_boxes), dtype=bool)
for i in range(len(all_boxes)):
if used[i]: continue
cluster = [(all_boxes[i], all_scores[i])]
used[i] = True
for j in range(i+1, len(all_boxes)):
if used[j]: continue
tw = sum(s for _, s in cluster)
center = sum(b*s/tw for b, s in cluster)
if compute_iou(center.tolist(), all_boxes[j].tolist()) > iou_thr:
cluster.append((all_boxes[j], all_scores[j]))
used[j] = True
clusters.append(cluster)
result_boxes, result_scores = [], []
for cl in clusters:
tw = sum(s for _, s in cl)
avg_b = sum(b*s/tw for b, s in cl)
result_boxes.append(avg_b)
result_scores.append(tw)
return np.array(result_boxes), np.array(result_scores)
def evaluate(name, get_boxes_fn, val_files, val_img_dir, val_label_dir):
iou_thrs = [round(0.5+i*0.05, 2) for i in range(10)]
tp = {t:0 for t in iou_thrs}
total_gt = 0
for img_file in tqdm(val_files, desc=name):
img = Image.open(os.path.join(val_img_dir, img_file))
gt_boxes = []
lf = img_file.replace('.jpg','.txt')
with open(os.path.join(val_label_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]]
gt_boxes.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]])
total_gt += len(gt_boxes)
if not gt_boxes: continue
preds = get_boxes_fn(img)
for t in iou_thrs:
matched = set()
for pb in preds:
best_iou, best_gi = 0, -1
for gi, gb in enumerate(gt_boxes):
if gi in matched: continue
iou = compute_iou(pb.tolist(), gb)
if iou > best_iou: best_iou = iou; best_gi = gi
if best_iou >= t and best_gi >= 0:
tp[t] += 1; matched.add(best_gi)
recalls = [tp[t]/total_gt for t in iou_thrs]
mAP = np.mean(recalls)
return mAP, recalls[5]
def main():
val_img_dir = 'Data/Detection_dataset/images/val'
val_label_dir = 'Data/Detection_dataset/labels/val'
val_files = sorted([f for f in os.listdir(val_img_dir) if f.endswith('.jpg')])
# Discover available models
exp_dir = 'runs/detect/Detection_experiments'
all_models = []
for ename in sorted(os.listdir(exp_dir)):
best_path = os.path.join(exp_dir, ename, 'weights', 'best.pt')
if os.path.exists(best_path):
all_models.append((ename, best_path))
print(f'Found {len(all_models)} trained models:')
for ename, path in all_models:
print(f' {ename}')
# Select ensemble models (prefer v6_1 + v12 + v14 seeds)
ensemble_models = []
priority_patterns = ['v6_1', 'v12_seed', 'v14_seed']
for ename, path in all_models:
for pat in priority_patterns:
if pat in ename:
ensemble_models.append((ename, path))
break
# Limit to 5 best (by filename, assume newer seeds are good)
ensemble_models = ensemble_models[:5]
print(f'\nEnsemble: {len(ensemble_models)} models')
for ename, _ in ensemble_models:
print(f' {ename}')
models = [(YOLO(p), ename) for ename, p in ensemble_models]
scales = [1280, 1536, 1920]
# Single model baseline
def baseline_1536(img):
m, _ = models[0]
r = m.predict(img, imgsz=1536, conf=0.25, iou=0.7, max_det=100, verbose=False)
if r and len(r[0].boxes): return r[0].boxes.xyxy.cpu().numpy()
return np.array([])
mAP_single, r75_single = evaluate('Single model', baseline_1536, val_files, val_img_dir, val_label_dir)
print(f'\n{"="*60}')
print(f'RESULTS:')
print(f' Single: mAP50-95={mAP_single:.4f} IoU@75={r75_single:.4f}')
# Multi-model WBF (1536 only)
def multi_model_wbf(img):
boxes_list, scores_list = [], []
for m, _ in models:
r = m.predict(img, imgsz=1536, conf=0.25, iou=0.7, max_det=100, verbose=False)
if r and len(r[0].boxes):
boxes_list.append(r[0].boxes.xyxy.cpu().numpy())
scores_list.append(r[0].boxes.conf.cpu().numpy())
else:
boxes_list.append(np.array([]))
scores_list.append(np.array([]))
fused, _ = wbf(boxes_list, scores_list)
return fused
mAP_mm, r75_mm = evaluate(f'{len(models)}model WBF', multi_model_wbf, val_files, val_img_dir, val_label_dir)
print(f' {len(models)}model: mAP50-95={mAP_mm:.4f} IoU@75={r75_mm:.4f} (+{mAP_mm-mAP_single:+.4f})')
# Single model multi-scale WBF
def single_multiscale_wbf(img):
m, _ = models[0]
boxes_list, scores_list = [], []
for sz in scales:
r = m.predict(img, imgsz=sz, conf=0.25, iou=0.7, max_det=100, verbose=False)
if r and len(r[0].boxes):
boxes_list.append(r[0].boxes.xyxy.cpu().numpy())
scores_list.append(r[0].boxes.conf.cpu().numpy())
else:
boxes_list.append(np.array([]))
scores_list.append(np.array([]))
fused, _ = wbf(boxes_list, scores_list)
return fused
mAP_sms, r75_sms = evaluate('1model 3scale', single_multiscale_wbf, val_files, val_img_dir, val_label_dir)
print(f' 1m x 3sc: mAP50-95={mAP_sms:.4f} IoU@75={r75_sms:.4f} (+{mAP_sms-mAP_single:+.4f})')
# ULTIMATE: All models × All scales
def ultimate_wbf(img):
boxes_list, scores_list = [], []
for m, _ in models:
for sz in scales:
r = m.predict(img, imgsz=sz, conf=0.25, iou=0.7, max_det=100, verbose=False)
if r and len(r[0].boxes):
boxes_list.append(r[0].boxes.xyxy.cpu().numpy())
scores_list.append(r[0].boxes.conf.cpu().numpy())
else:
boxes_list.append(np.array([]))
scores_list.append(np.array([]))
fused, _ = wbf(boxes_list, scores_list)
return fused
mAP_ult, r75_ult = evaluate(f'{len(models)}m x {len(scales)}sc ULTIMATE', ultimate_wbf, val_files, val_img_dir, val_label_dir)
print(f' ULTIMATE: mAP50-95={mAP_ult:.4f} IoU@75={r75_ult:.4f} (+{mAP_ult-mAP_single:+.4f})')
# Save results
results = {
'single': round(mAP_single, 4),
'multimodel': round(mAP_mm, 4),
'multiscale': round(mAP_sms, 4),
'ultimate': round(mAP_ult, 4),
'n_models': len(models),
'n_scales': len(scales),
'model_names': [n for n, _ in ensemble_models],
}
with open('logs/ultimate_results.json', 'w') as f:
json.dump(results, f, indent=2)
print(f'\nResults saved to logs/ultimate_results.json')
if __name__ == '__main__':
main()
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