| import os |
| import glob |
| import yaml |
| import numpy as np |
| from ultralytics import YOLO |
|
|
| with open('detectors/llen/data.yaml') as f: |
| cfg = yaml.safe_load(f) |
|
|
| root = cfg['path'] |
| img_dir = os.path.join(root, cfg['val']) |
| lbl_dir = img_dir.replace('images', 'labels') |
|
|
| print(f"root: {root}") |
| print(f"img_dir: {img_dir}") |
| print(f"lbl_dir: {lbl_dir}") |
| print(f"img_dir exists: {os.path.isdir(img_dir)}") |
| print(f"lbl_dir exists: {os.path.isdir(lbl_dir)}") |
|
|
| img_paths = sorted(glob.glob(os.path.join(img_dir, '*'))) |
| lbl_paths = sorted(glob.glob(os.path.join(lbl_dir, '*'))) |
| print(f"\nfiles found in img_dir: {len(img_paths)}") |
| print(f"files found in lbl_dir: {len(lbl_paths)}") |
| print(f"first 3 img files: {[os.path.basename(p) for p in img_paths[:3]]}") |
| print(f"first 3 lbl files: {[os.path.basename(p) for p in lbl_paths[:3]]}") |
|
|
| if not img_paths: |
| print("\nNo images found -- stop here, the glob pattern or path is wrong.") |
| raise SystemExit |
|
|
| |
| test_img = img_paths[0] |
| fname = os.path.splitext(os.path.basename(test_img))[0] |
| lbl_path = os.path.join(lbl_dir, fname + '.txt') |
|
|
| print(f"\n--- Inspecting: {fname} ---") |
| print(f"expected label path: {lbl_path}") |
| print(f"label file exists: {os.path.exists(lbl_path)}") |
|
|
| if os.path.exists(lbl_path): |
| with open(lbl_path) as f: |
| content = f.read() |
| print(f"raw label file content:\n{content[:500]}") |
| else: |
| print("Label file does NOT exist at that path -- this is likely the bug. " |
| "Check what's actually inside lbl_dir below:") |
| print(os.listdir(lbl_dir)[:10] if os.path.isdir(lbl_dir) else "lbl_dir doesn't exist") |
|
|
| |
| print(f"\n--- Model prediction on same image ---") |
| model = YOLO('runs/detect/runs/detect/domain_detectors/llen_detector/weights/best.pt') |
| r = list(model.predict(source=test_img, conf=0.001, iou=0.7, verbose=False))[0] |
| print(f"r.path: {r.path}") |
| print(f"r.orig_shape: {r.orig_shape}") |
| if r.boxes is not None and len(r.boxes) > 0: |
| print(f"num predictions: {len(r.boxes)}") |
| print(f"first 3 xyxyn (normalized): \n{r.boxes.xyxyn[:3].cpu().numpy()}") |
| print(f"first 3 xyxy (pixel): \n{r.boxes.xyxy[:3].cpu().numpy()}") |
| print(f"first 3 conf: {r.boxes.conf[:3].cpu().numpy()}") |
| print(f"first 3 cls: {r.boxes.cls[:3].cpu().numpy()}") |
| else: |
| print("No predictions at all for this image (unexpected at conf=0.001).") |
|
|
