scripts/lamp_id_tracking.py

import csv
import re
from pathlib import Path

try:
    from PIL import Image
except ImportError:
    raise ImportError("Not found pillow. Try pip install pillow")

root_dir = Path('.')
labels_dir = root_dir / 'frames/labels'
images_dir = root_dir / 'frames/images'
output_csv = root_dir / 'lamp_tracks_15.csv'
summary_csv = root_dir / 'lamp_tracks_15_summary.csv'

if not labels_dir.exists():
    raise FileNotFoundError(f"Not found folder labels: {labels_dir.resolve()}")
if not images_dir.exists():
    raise FileNotFoundError(f"Not found folder images: {images_dir.resolve()}")

lamp_ranges = [
    (1, 8, 12),
    (2, 8, 18),
    (3, 16, 25),
    (4, 24, 33),
    (5, 33, 40),
    (6, 40, 48),
    (7, 53, 56),
    (8, 54, 63),
    (9, 60, 71),
    (10, 66, 76),
    (11, 70, 81),
    (12, 85, 94),
    (13, 85, 101),
    (14, 97, 111),
    (15, 105, 117),
]

image_exts = ['.jpg', '.jpeg', '.png', '.bmp', '.webp']
class_map = {0: 'lamp_on', 1: 'lamp_off', 2: 'lamp_occluded'}

def extract_frame_number(filename):
    stem = Path(filename).stem
    m = re.search(r'_(\d+)$', stem)
    if not m:
        return None
    return int(m.group(1))

def find_image(stem):
    for ext in image_exts:
        p = images_dir / f'{stem}{ext}'
        if p.exists():
            return p
    return None

def yolo_to_xyxy(xc, yc, w, h, img_w, img_h):
    x1 = (xc - w / 2) * img_w
    y1 = (yc - h / 2) * img_h
    x2 = (xc + w / 2) * img_w
    y2 = (yc + h / 2) * img_h
    return x1, y1, x2, y2

def center_of(box):
    x1, y1, x2, y2 = box
    return ((x1 + x2) / 2, (y1 + y2) / 2)

def active_lamps(frame_num):
    return [lid for lid, start, end in lamp_ranges if start <= frame_num <= end]

label_files = sorted(
    labels_dir.glob('*.txt'),
    key=lambda p: (extract_frame_number(p.name) is None, extract_frame_number(p.name) or 10**9, p.name)
)

rows = []
summary = []

for label_file in label_files:
    stem = label_file.stem
    frame_num = extract_frame_number(label_file.name)
    image_path = find_image(stem)

    if frame_num is None:
        print(f'Reading frame error in file: {label_file.name}')
        continue

    if image_path is None:
        print(f'Not found frame in file {label_file.name}')
        continue

    with Image.open(image_path) as img:
        img_w, img_h = img.size

    detections = []
    with open(label_file, 'r', encoding='utf-8') as f:
        for line_idx, line in enumerate(f, start=1):
            line = line.strip()
            if not line:
                continue
            parts = line.split()
            if len(parts) < 5:
                print(f'Format error {label_file.name}: {line}')
                continue

            cls = int(float(parts[0]))
            xc, yc, w, h = map(float, parts[1:5])
            box = yolo_to_xyxy(xc, yc, w, h, img_w, img_h)
            cx, cy = center_of(box)
            detections.append({
                'class_id': cls,
                'box': box,
                'center': (cx, cy),
                'line_idx': line_idx,
            })

    active = active_lamps(frame_num)
    dets_sorted = sorted(detections, key=lambda d: d['center'][0])
    lamps_sorted = sorted(active)

    if len(lamps_sorted) == 0:
        summary.append({
            'frame_number': frame_num,
            'file_name': image_path.name,
            'active_lamps': '',
            'assigned_track_ids': '',
            'num_detections': len(detections),
            'num_assigned': 0,
            'note': 'no_active_lamp_in_manual_range',
        })
        continue

    assigned = []
    if len(dets_sorted) <= len(lamps_sorted):
        for det, lid in zip(dets_sorted, lamps_sorted):
            assigned.append((lid, det))
    else:
        for i, det in enumerate(dets_sorted):
            lid = lamps_sorted[i % len(lamps_sorted)]
            assigned.append((lid, det))

    visible_ids = []
    for lid, det in assigned:
        x1, y1, x2, y2 = det['box']
        visible_ids.append(str(lid))
        rows.append({
            'frame_number': frame_num,
            'file_name': image_path.name,
            'label_file': label_file.name,
            'track_id': lid,
            'class_id': det['class_id'],
            'class_name': class_map.get(det['class_id'], f'class_{det["class_id"]}'),
            'x1': round(x1, 2),
            'y1': round(y1, 2),
            'x2': round(x2, 2),
            'y2': round(y2, 2),
            'cx': round(det['center'][0], 2),
            'cy': round(det['center'][1], 2),
            'bbox_w': round(x2 - x1, 2),
            'bbox_h': round(y2 - y1, 2),
        })

    summary.append({
        'frame_number': frame_num,
        'file_name': image_path.name,
        'active_lamps': ' '.join(map(str, lamps_sorted)),
        'assigned_track_ids': ' '.join(visible_ids),
        'num_detections': len(detections),
        'num_assigned': len(assigned),
        'note': '',
    })

fieldnames = [
    'frame_number', 'file_name', 'label_file', 'track_id', 'class_id', 'class_name',
    'x1', 'y1', 'x2', 'y2', 'cx', 'cy', 'bbox_w', 'bbox_h'
]
with open(output_csv, 'w', newline='', encoding='utf-8-sig') as f:
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(rows)

summary_fields = [
    'frame_number', 'file_name', 'active_lamps', 'assigned_track_ids',
    'num_detections', 'num_assigned', 'note'
]
with open(summary_csv, 'w', newline='', encoding='utf-8-sig') as f:
    writer = csv.DictWriter(f, fieldnames=summary_fields)
    writer.writeheader()
    writer.writerows(summary)

print(f'Created: {output_csv}')
print(f'Created: {summary_csv}')
print(f'Sum of detections: {len(rows)}')
print(f'Lamps count: {len(lamp_ranges)}')