from os import path, replace, remove, listdir, makedirs
from os.path import abspath
import shutil
from datetime import datetime
from pathlib import Path
from ultralytics import YOLO
from constants import processed_images_dir, processed_labels_dir, annotation_classes, prefix, date_format, datasets_dir, models_dir

latest_model = path.join(models_dir, f'{prefix}latest.pt')
today_folder = f'{prefix}{datetime.now():{date_format}}'
today_dataset = path.join(datasets_dir, today_folder)
train_set = 70
valid_set = 20
test_set = 10


def form_dataset():
    makedirs(today_dataset, exist_ok=True)
    images = listdir(processed_images_dir)

    train_size = int(len(images) * train_set / 100.0)
    valid_size = int(len(images) * valid_set / 100.0)

    move_annotations(images[:train_size], 'train')
    move_annotations(images[train_size:train_size + valid_size], 'valid')
    move_annotations(images[train_size + valid_size:], 'test')

    create_yaml()


def move_annotations(images, folder):
    destination_images = path.join(today_dataset, folder, 'images')
    makedirs(destination_images, exist_ok=True)
    destination_labels = path.join(today_dataset, folder, 'labels')
    makedirs(destination_labels, exist_ok=True)
    for image_name in images:
        image_path = path.join(processed_images_dir, image_name)
        label_name = f'{Path(image_name).stem}.txt'
        label_path = path.join(processed_labels_dir, label_name)
        if not check_label(label_path):
            remove(image_path)
        else:
            replace(image_path, path.join(destination_images, image_name))
            replace(label_path, path.join(destination_labels, label_name))


def check_label(label_path):
    lines_edited = False
    if not path.exists(label_path):
        return False
    with open(label_path, 'r') as f:
        lines = f.readlines()
        for line in lines:
            for val in line.split(' ')[1:]:
                if float(val) > 1:
                    lines.remove(line)
                    lines_edited = True
    if len(lines) == 0:
        return False
    if not lines_edited:
        return True

    with open(label_path, 'w') as label_write:
        label_write.writelines(lines)
        label_write.close()
    return True


def create_yaml():
    lines = ['names:']
    for c in annotation_classes:
        lines.append(f'- {annotation_classes[c].name}')
    lines.append(f'nc: {len(annotation_classes)}')

    lines.append(f'test: test/images')
    lines.append(f'train: train/images')
    lines.append(f'val: valid/images')
    lines.append('')

    today_yaml = abspath(path.join(today_dataset, 'data.yaml'))
    with open(today_yaml, 'w', encoding='utf-8') as f:
        f.writelines([f'{line}\n' for line in lines])


def revert_to_processed_data(date):
    def revert_dir(src_dir, dest_dir):
        for file in listdir(src_dir):
            s = path.join(src_dir, file)
            d = path.join(dest_dir, file)
            replace(s, d)
    date_dataset = path.join(datasets_dir, f'{prefix}{date}')
    makedirs(processed_images_dir, exist_ok=True)
    makedirs(processed_labels_dir, exist_ok=True)
    for subset in ['test', 'train', 'valid']:
        revert_dir(path.join(date_dataset, subset, 'images'), processed_images_dir)
        revert_dir(path.join(date_dataset, subset, 'labels'), processed_labels_dir)
    shutil.rmtree(date_dataset)


if __name__ == '__main__':
    # form_dataset()

    model_name = latest_model if path.isfile(latest_model) else 'yolov8m.yaml'
    print(f'Initial model: {model_name}')
    model = YOLO(model_name)
    
    # cur_folder = path.join(datasets_dir, f'{prefix}2024-06-18')

    cur_folder = today_dataset
    yaml = abspath(path.join(cur_folder, 'data.yaml'))
    results = model.train(data=yaml, epochs=100, batch=55, imgsz=640, save_period=1)

    shutil.copy(f'{results.save_dir}/weights/best.pt', latest_model)
    shutil.copytree(results.save_dir, path.join(models_dir, cur_folder))
    shutil.rmtree('runs')
    shutil.rmtree(path.join(models_dir, f'{prefix}latest'))