Этот сайт использует файлы cookies. Продолжая просмотр страниц сайта, вы соглашаетесь с использованием файлов cookies. Если вам нужна дополнительная информация, пожалуйста, посетите страницу Политика файлов Cookie

Прямой эфир

Русский

English

Войти / Регистрация

Cryptocurrencies: 10239 / Markets: 91375

Market Cap: $ 3 333 980 781 223 / 24h Vol: $ 198 413 061 974 / BTC Dominance: 56.952750892725%

Н Новости

Работа с YOLOV8. Детекция, сегментация, трекинг объектов, а также подготовка собственного датасета и обучение

Если вам кажется, что начать работу с нейросетями - это сложно, то этот материал для вас!

Итак, YOLO (You Only Look Once) — нейронная сеть, предназначенная работы с объектами на изображениях и может решать следующие задачи:

Детекция - обнаружение объектов
Сегментация - разделение изображения на области, которые относятся к каждому объекту
Классификация - определение что же находится на изображении
Поиск ключевых точек тела - для определения позы человека
Трекинг объектов - потоковая обработка, при которой для каждого объекта возможно сохранять и использовать историю местоположения

Также в этой статье также будет рассмотрено:

Предсказание движения на основе трекинга
Создание собственного датасета для дообучения модели детекции новых объектов

Подготовка к работе с YOLO

Отличительной особенностью YOLO является подход, при котором вы можете начать использовать нейросеть имея минимальные навыки программирования на Python.
Для установки YOLO на ваш компьютер выполните к консоли:

pip install ultralyticsПосле этого все необходимы модули будут установлены и можно переходить к работе.

Детекция объектов на фото

Детекция объектов - определение местоположения объектов и их классов на изображении.

Для примера возьмем картинку и определим объекты на ней:

Множество разных объектов

Для использования нейросети YOLO напишем скрипт:

from ultralytics import YOLO
import cv2
import numpy as np
import os

# Загрузка модели YOLOv8
model = YOLO('yolov8n.pt')

# Список цветов для различных классов
colors = [
    (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255),
    (255, 0, 255), (192, 192, 192), (128, 128, 128), (128, 0, 0), (128, 128, 0),
    (0, 128, 0), (128, 0, 128), (0, 128, 128), (0, 0, 128), (72, 61, 139),
    (47, 79, 79), (47, 79, 47), (0, 206, 209), (148, 0, 211), (255, 20, 147)
]

# Функция для обработки изображения
def process_image(image_path):
    # Загрузка изображения
    image = cv2.imread(image_path)
    results = model(image)[0]
    
    # Получение оригинального изображения и результатов
    image = results.orig_img
    classes_names = results.names
    classes = results.boxes.cls.cpu().numpy()
    boxes = results.boxes.xyxy.cpu().numpy().astype(np.int32)

    # Подготовка словаря для группировки результатов по классам
    grouped_objects = {}

    # Рисование рамок и группировка результатов
    for class_id, box in zip(classes, boxes):
        class_name = classes_names[int(class_id)]
        color = colors[int(class_id) % len(colors)]  # Выбор цвета для класса
        if class_name not in grouped_objects:
            grouped_objects[class_name] = []
        grouped_objects[class_name].append(box)

        # Рисование рамок на изображении
        x1, y1, x2, y2 = box
        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
        cv2.putText(image, class_name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # Сохранение измененного изображения
    new_image_path = os.path.splitext(image_path)[0] + '_yolo' + os.path.splitext(image_path)[1]
    cv2.imwrite(new_image_path, image)

    # Сохранение данных в текстовый файл
    text_file_path = os.path.splitext(image_path)[0] + '_data.txt'
    with open(text_file_path, 'w') as f:
        for class_name, details in grouped_objects.items():
            f.write(f"{class_name}:\n")
            for detail in details:
                f.write(f"Coordinates: ({detail[0]}, {detail[1]}, {detail[2]}, {detail[3]})\n")

    print(f"Processed {image_path}:")
    print(f"Saved bounding-box image to {new_image_path}")
    print(f"Saved data to {text_file_path}")


process_image('test.png')

В начале кода — выбор модели (она скачается автоматически при первом запуске скрипта). Кроме минимальной yolov8n.pt, доступны еще несколько:
yolov8n.pt yolov8s.pt yolov8m.pt yolov8l.pt yolov8x.pt

Каждая следующая больше, работает медленнее, но и некоторые объекты определяет значительно лучше.

Сравнительные характеристики моделей, а также возможность их запуска на различных устройствах описаны в статье Степана Жданова:
https://habr.com/ru/articles/822917/

В результате работы нейросети получаем объект boxes, который содержит информацию о координатах, найденных на изображении объектов, а также принадлежности их к классу (person, car, bus, traffic light и т.д.)

Итак, после выполнения данного скрипта видим результат:

Кроме этого, для дополнительной обработки данные по объектам на изображении сохраняются в текстовый файл такого вида:

car:
Coordinates: (842, 681, 1180, 894)
Coordinates: (254, 849, 524, 971)
Coordinates: (49, 620, 425, 857)
stop sign:
Coordinates: (407, 560, 470, 626)
Coordinates: (267, 494, 341, 557)
traffic light:
Coordinates: (334, 157, 451, 426)
Coordinates: (938, 97, 1031, 312)
Coordinates: (86, 481, 130, 602)
person:
Coordinates: (578, 711, 710, 990)
Coordinates: (715, 723, 750, 798)
Coordinates: (715, 852, 864, 976)
Coordinates: (241, 897, 385, 1012)
truck:
Coordinates: (52, 620, 425, 859)

Детекция объектов в видео-потоке

После практики с одним изображением перейдем к обработке видео. В целом, оно не сильно сложнее, т.к. видео это всего лишь последовательность изображений! Отличительной особенностью следующего кода является только то, что для записи видео используются специальные кодеки для формата MP4.

from ultralytics import YOLO
import cv2
import numpy as np

# Загрузка модели YOLOv8
model = YOLO('yolov8n.pt')

# Список цветов для различных классов
colors = [
    (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255),
    (255, 0, 255), (192, 192, 192), (128, 128, 128), (128, 0, 0), (128, 128, 0),
    (0, 128, 0), (128, 0, 128), (0, 128, 128), (0, 0, 128), (72, 61, 139),
    (47, 79, 79), (47, 79, 47), (0, 206, 209), (148, 0, 211), (255, 20, 147)
]

# Открытие исходного видеофайла
input_video_path = 'input.mp4'
capture = cv2.VideoCapture(input_video_path)

# Чтение параметров видео
fps = int(capture.get(cv2.CAP_PROP_FPS))
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))

# Настройка выходного файла
output_video_path = 'detect.mp4'
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))

while True:
    # Захват кадра
    ret, frame = capture.read()
    if not ret:
        break

    # Обработка кадра с помощью модели YOLO
    results = model(frame)[0]

    # Получение данных об объектах
    classes_names = results.names
    classes = results.boxes.cls.cpu().numpy()
    boxes = results.boxes.xyxy.cpu().numpy().astype(np.int32)
    # Рисование рамок и подписей на кадре
    for class_id, box, conf in zip(classes, boxes, results.boxes.conf):
        if conf>0.5:
            class_name = classes_names[int(class_id)]
            color = colors[int(class_id) % len(colors)]
            x1, y1, x2, y2 = box
            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
            cv2.putText(frame, class_name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # Запись обработанного кадра в выходной файл
    writer.write(frame)

# Освобождение ресурсов и закрытие окон
capture.release()
writer.release()

Для детекции объектов при помощи веб камеры - достаточно в строке
capture = cv2.VideoCapture(input_video_path)указать индекс камеры для подключенияcapture = cv2.VideoCapture(0)Пример полного кода:
https://github.com/stepanburmistrov/YoloV8/blob/main/Detection_Camera.py

Сегментация изображения

Сегментация - разделение изображения на классы. Один из самых эффектных способов разобраться с этим процессом будет удаление фона вокруг человека с фото/видео.

Для начала, возьмем фотографию (кадр из предыдущего видео) и применим модель YoloV8 предназначенную для сегментации. Как и в случае с детекцией выбор есть:
yolov8n-seg.pt yolov8s-seg.pt yolov8m-seg.pt yolov8l-seg.pt yolov8x-seg.pt

import cv2
import numpy as np
from ultralytics import YOLO
import os

# Загрузка модели YOLOv8
model = YOLO('yolov8x-seg.pt')

colors = [
    (255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255),
    (255, 0, 255), (192, 192, 192), (128, 128, 128), (128, 0, 0), (128, 128, 0),
    (0, 128, 0), (128, 0, 128), (0, 128, 128), (0, 0, 128), (72, 61, 139),
    (47, 79, 79), (47, 79, 47), (0, 206, 209), (148, 0, 211), (255, 20, 147)
]

def process_image(image_path):
    # Проверка наличия папки для сохранения результатов
    if not os.path.exists('results'):
        os.makedirs('results')
    
    # Загрузка изображения
    image = cv2.imread(image_path)
    image_orig = image.copy()
    h_or, w_or = image.shape[:2]
    image = cv2.resize(image, (640, 640))
    results = model(image)[0]
    
    classes_names = results.names
    classes = results.boxes.cls.cpu().numpy()
    masks = results.masks.data.cpu().numpy()

    # Наложение масок на изображение
    for i, mask in enumerate(masks):
        color = colors[int(classes[i]) % len(colors)]
        
        # Изменение размера маски перед созданием цветной маски
        mask_resized = cv2.resize(mask, (w_or, h_or))
        
        # Создание цветной маски
        color_mask = np.zeros((h_or, w_or, 3), dtype=np.uint8)
        color_mask[mask_resized > 0] = color

        # Сохранение маски каждого класса в отдельный файл
        mask_filename = os.path.join('results', f"{classes_names[classes[i]]}_{i}.png")
        cv2.imwrite(mask_filename, color_mask)

        # Наложение маски на исходное изображение
        image_orig = cv2.addWeighted(image_orig, 1.0, color_mask, 0.5, 0)


    # Сохранение измененного изображения
    new_image_path = os.path.join('results', os.path.splitext(os.path.basename(image_path))[0] + '_segmented' + os.path.splitext(image_path)[1])
    cv2.imwrite(new_image_path, image_orig)
    print(f"Segmented image saved to {new_image_path}")

process_image('segmentation_test.png')

В результате получим вот такое изображение:

Сегментация изображения с помощью YoloV8

А также в папку results сохранятся маски каждого найденного класса, для удобного исследования

Теперь поработаем над удалением фона - для примера будем заменять фон на зеленый. В дальнейшем это поможет в других программах для редактирования фото или видео эффективно размыть края и получить весьма достойный результат!

Пример кода, решающего эту задачу:

import cv2
import numpy as np
from ultralytics import YOLO
import os

model = YOLO('yolov8n-seg.pt')

# Цвет для выделения объектов класса "person"
person_color = (0, 255, 0)  # Зеленый цвет

def process_image(image_path):
    frame = cv2.imread(image_path)
    if frame is None:
        print("Ошибка: не удалось загрузить изображение")
        return

    image_orig = frame.copy()
    h_or, w_or = frame.shape[:2]
    image = cv2.resize(frame, (640, 640))
    results = model(image)[0]

    classes = results.boxes.cls.cpu().numpy()
    masks = results.masks.data.cpu().numpy()

    # Создаем зеленый фон
    green_background = np.zeros_like(image_orig)
    green_background[:] = person_color

    # Наложение масок на изображение
    for i, mask in enumerate(masks):
        class_name = results.names[int(classes[i])]
        if class_name == 'person':
            color_mask = np.zeros((640, 640, 3), dtype=np.uint8)
            resized_mask = cv2.resize(mask, (640, 640), interpolation=cv2.INTER_NEAREST)
            color_mask[resized_mask > 0] = person_color

            color_mask = cv2.resize(color_mask, (w_or, h_or), interpolation=cv2.INTER_NEAREST)

            mask_resized = cv2.resize(mask, (w_or, h_or), interpolation=cv2.INTER_NEAREST)
            green_background[mask_resized > 0] = image_orig[mask_resized > 0]

    # Сохраняем обработанное изображение с добавлением суффикса '_segmented'
    base_name, ext = os.path.splitext(image_path)
    output_path = f"{base_name}_removed_BG{ext}"
    cv2.imwrite(output_path, green_background)
    print(f"Processed image saved to {output_path}")

    cv2.imshow('Processed Image', green_background)  # Показываем обработанное изображение
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Путь к изображению, которое необходимо обработать
image_path = 'test.jpg'
process_image(image_path)

Удаление фона вокруг человека с помощью нейросети YOLOV8

По коду есть важное дополнение, что для качественной и наиболее точной разметки изображения, перед "скармливанием" нейросети нужно привести к размеру 640*640 px.
В остальных случаях маски объектов могут получаться с небольшим смещением.

В качестве еще одного примера для использования — автоматическое создание стикеров:
https://github.com/stepanburmistrov/YoloV8/blob/main/Segmentation_Image2Sticker.py

Создание стикера при помощи YoloV8

Классификация

Процесс обработки изображения, при котором все изображение целиком будет относиться к определенному классу.

Детекция и классификация решают разные задачи и имеют свои особенности и области применения. Вот основные причины, почему не всегда следует использовать детекцию вместо классификации:

Сложность задачи:
- Классификация: Определяет, к какому классу относится весь объект или изображение целиком. Например, классификация фотографии как "собака" или "кошка".
- Детекция: Находит объекты внутри изображения и определяет их классы и местоположение (например, где на фотографии находится собака и где кошка).
Ресурсы и вычислительная мощность:
- Классификация: Обычно требует меньше вычислительных ресурсов, так как анализируется весь объект или изображение целиком без необходимости определения его частей.
- Детекция: Более вычислительно затратна, так как требует анализа изображения для поиска объектов и определения их границ.
Скорость выполнения:
- Классификация: Быстрее, так как выполняется одна операция определения класса для всего изображения.
- Детекция: Медленнее, так как требует многократного анализа изображения для поиска всех объектов и их классификации.
Сложность реализации:
- Классификация: Проще в реализации и настройке, так как обучается на меньших и более структурированных данных.
- Детекция: Более сложна в реализации, требует больше данных и времени на обучение, особенно если нужно обнаруживать объекты разного размера и формы.

Когда использовать классификацию

Целостное определение класса объекта: Если нужно определить класс всего объекта или изображения, а не его частей. Например, определить, что изображено на фотографии (собака или кошка).
Ограниченные вычислительные ресурсы: В условиях, когда ограничены ресурсы для вычислений и требуется быстрая обработка данных.
Более точное определение "подклассов". Например, можно находить буквы на изображении с помощью детекции, а затем более точно определять символ при помощи классификации!

Когда использовать детекцию

Множественные объекты на изображении: Если на изображении может быть несколько объектов разных классов, и нужно определить их местоположение и классы. Например, обнаружение автомобилей и пешеходов на улице.
Анализ сложных сцен: Когда нужно анализировать сложные сцены, где важно не только определить, какие объекты присутствуют, но и где они находятся.
Применение в реальном времени: В задачах, где необходимо отслеживать объекты в реальном времени, например, в системах видеонаблюдения.

Пример кода для одного изображения:

import cv2
import numpy as np
from ultralytics import YOLO
import os

model = YOLO('yolov8n-cls.pt')


def process_image(img):
    # Обработка кадра с помощью модели
    results = model(img)[0]

    # Отображение результатов классификации на изображении
    if results.probs is not None:
        # Доступ к вершинам классификации
        top1_idx = results.probs.top1  # Индекс класса с наивысшей вероятностью
        top1_conf = results.probs.top1conf.item()  # Вероятность для класса с наивысшей вероятностью
        class_name = results.names[top1_idx]  # Получаем имя класса по индексу

        # Отображаем класс и вероятность на кадре
        label = f"{class_name}: {top1_conf:.2f}"
        cv2.putText(img, label, (50, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 2,
                    (255, 0, 0), 3)

    return image

image = cv2.imread("dog.jpg")
image = process_image(image)
cv2.imwrite('result.jpg', image)

Не совсем колли, но похож! Модели для классификации есть куда учиться

Поиск ключевых точек тела - определения позы человека

Способой применения данной модели можно найти множество, вот некоторые из них:

Тренировки спортсменов: Помощь в анализе движений спортсменов для улучшения их техники
Реабилитация: Мониторинг и корректировка движений пациентов во время реабилитационных упражнений.
Обнаружение падений: Автоматическое обнаружение падений и других опасных ситуаций для пожилых людей или работников на производстве.
Анимация: Создание реалистичных движений для анимационных персонажей в фильмах и видеоиграх.
Виртуальная и дополненная реальность: Реалистичное отслеживание движений пользователей для создания интерактивных VR и AR приложений.
Анализ поведения клиентов: Изучение движения и поведения клиентов в магазинах для оптимизации выкладки товаров и улучшения обслуживания.
Цифровые зеркала: Виртуальная примерка одежды, позволяющая клиентам видеть, как они будут выглядеть в разных нарядах без необходимости физической примерки.

Пример кода для обработки изображения (а с помощью предыдущих примеров легко сделать обработку видео-файла или камеры):

from ultralytics import YOLO
import cv2
import numpy as np
import os

# Загрузка модели YOLOv8-Pose
model = YOLO('yolov8n-pose.pt')

# Словарь цветов для различных классов
colors = {
    'white': (255, 255, 255),
    'red': (0, 0, 255),
    'blue': (255, 0, 0)
}

def draw_skeleton(image, keypoints, confs, pairs, color):
    for (start, end) in pairs:
        if confs[start] > 0.5 and confs[end] > 0.5:
            x1, y1 = int(keypoints[start][0]), int(keypoints[start][1])
            x2, y2 = int(keypoints[end][0]), int(keypoints[end][1])
            if (x1, y1) != (0, 0) and (x2, y2) != (0, 0):  # Игнорирование точек в (0, 0)
                cv2.line(image, (x1, y1), (x2, y2), color, 2)

def process_image(image_path):
    # Загрузка изображения
    image = cv2.imread(image_path)
    if image is None:
        print("Ошибка: не удалось загрузить изображение")
        return

    # Обработка изображения с помощью модели
    results = model(image)[0]

    # Проверка на наличие обнаруженных объектов
    if hasattr(results, 'boxes') and hasattr(results.boxes, 'cls') and len(results.boxes.cls) > 0:
        classes_names = results.names
        classes = results.boxes.cls.cpu().numpy()
        boxes = results.boxes.xyxy.cpu().numpy().astype(np.int32)

        # Обработка ключевых точек
        if results.keypoints:
            keypoints = results.keypoints.data.cpu().numpy()
            confs = results.keypoints.conf.cpu().numpy()
            
            for i, (class_id, box, kp, conf) in enumerate(zip(classes, boxes, keypoints, confs)):
                draw_box=False
                if draw_box:
                    class_name = classes_names[int(class_id)]
                    color = colors['white']
                    x1, y1, x2, y2 = box
                    cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
                    cv2.putText(image, class_name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

                # Визуализация ключевых точек с номерами
                for j, (point, point_conf) in enumerate(zip(kp, conf)):
                    if point_conf > 0.5:  # Фильтрация по уверенности
                        x, y = int(point[0]), int(point[1])
                        if (x, y) != (0, 0):  # Игнорирование точек в (0, 0)
                            cv2.circle(image, (x, y), 5, colors['blue'], -1)
                            cv2.putText(image, str(j), (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors['blue'], 2)

                # Рисование скелета
                draw_skeleton(image, kp, conf, [(5, 7), (7, 9), (6, 8), (8, 10)], colors['white']) # Руки
                draw_skeleton(image, kp, conf, [(11, 13), (13, 15), (12, 14), (14, 16)], colors['red']) # Ноги
                draw_skeleton(image, kp, conf, [(5, 11), (6, 12)], colors['blue']) # Тело

    # Сохранение и отображение результатов
    output_path = os.path.splitext(image_path)[0] + "_pose_detected.jpg"
    cv2.imwrite(output_path, image)
    print(f"Сохранено изображение с результатами: {output_path}")

    cv2.imshow('YOLOv8-Pose Detection', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Путь к изображению для обработки
image_path = 'd.jpg'
process_image(image_path)

Трекинг объектов

Трекинг объектов — это процесс записи и анализа перемещений объектов в видео или последовательности изображений. Этот процесс включает присвоение уникальных идентификаторов каждому обнаруженному объекту и отслеживание его местоположения с течением времени. Благодаря этому подходу можно решать множество задач в различных областях.

Применения трекинга объектов:

Безопасность и видеонаблюдение: Обнаружение подозрительного поведения, автоматическое отслеживание людей для выявления подозрительных действий, определение и предупреждение о пропавших или оставленных без присмотра объектах.
Транспорт и логистика: Управление дорожным движением, мониторинг транспортных средств для оптимизации потока движения, автономные транспортные средства и предотвращение столкновений.
Розничная торговля: Анализ поведения покупателей для оптимизации расположения товаров, улучшения пользовательского опыта и противокражные системы для мониторинга подозрительных действий.
Спорт и анализ производительности: Анализ спортивных мероприятий, отслеживание игроков для детального анализа их действий и стратегии, использование трекинга для улучшения техники спортсменов.
Медицина и здравоохранение: Реабилитация, отслеживание движений пациентов для мониторинга их прогресса, анализ походки и других движений для выявления нарушений и заболеваний.
Робототехника и взаимодействие человек-компьютер: Навигация роботов, обеспечение безопасного передвижения роботов в динамической среде, жестовое управление устройствами и приложениями.

Пример кода для обработки видео:

from collections import defaultdict
import cv2
import numpy as np
from ultralytics import YOLO

# Загрузка модели YOLOv8
model = YOLO("yolov8x.pt")

# Открытие видео файла
video_path = "input.mp4"
cap = cv2.VideoCapture(video_path)

# Проверка успешного открытия видео
if not cap.isOpened():
    print(f"Ошибка открытия {video_path}")
    exit()


# Получение FPS видео
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

# Настройка VideoWriter для сохранения выходного видео
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('out.mp4', fourcc, fps, (width, height))

# Создание словаря для хранения истории треков объектов
track_history = defaultdict(lambda: [])

# Цикл для обработки каждого кадра видео
while cap.isOpened():
    # Считывание кадра из видео
    success, frame = cap.read()

    if not success:
        print("Конец видео")
        break

    # Применение YOLOv8 для отслеживания объектов на кадре, с сохранением треков между кадрами
    results = model.track(frame, persist=True)

    # Проверка на наличие объектов
    if results[0].boxes is not None and results[0].boxes.id is not None:
        # Получение координат боксов и идентификаторов треков
        boxes = results[0].boxes.xywh.cpu()  # xywh координаты боксов
        track_ids = results[0].boxes.id.int().cpu().tolist()  # идентификаторы треков

        # Визуализация результатов на кадре
        annotated_frame = results[0].plot()

        # Отрисовка треков
        for box, track_id in zip(boxes, track_ids):
            x, y, w, h = box  # координаты центра и размеры бокса
            track = track_history[track_id]
            track.append((float(x), float(y)))  # добавление координат центра объекта в историю
            if len(track) > 30:  # ограничение длины истории до 30 кадров
                track.pop(0)

            # Рисование линий трека
            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
            cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)

        # Отображение аннотированного кадра
        cv2.imshow("YOLOv8 Tracking", annotated_frame)
        out.write(annotated_frame)  # запись кадра в выходное видео
    else:
        # Если объекты не обнаружены, просто отображаем кадр
        cv2.imshow("YOLOv8 Tracking", frame)
        out.write(frame)  # запись кадра в выходное видео

    # Прерывание цикла при нажатии клавиши 'Esc'
    if cv2.waitKey(1) == 27:
        break

# Освобождение видеозахвата и закрытие всех окон OpenCV
cap.release()
out.release()  # закрытие выходного видеофайла
cv2.destroyAllWindows()

Теперь, когда трекинг работает, становится возможным анализировать собранные данные. Мы будем предсказывать местоположение объекта через заданное время:

Для этого будем использовать возможности библиотеки NumPy, которая позволяет найти линейную регрессию для заданных точек. Это поможет нам усреднить движение объекта и предсказать его будущие координаты.

Вот полный код функции predict_position, который использует метод наименьших квадратов для нахождения линии, лучше всего описывающей последние точки пути объекта, и экстраполяцию этой линии для предсказания будущего положения:

def predict_position(track, future_time, fps):
    if len(track) < 2:
        return track[-1]

    N = min(len(track), 25)
    track = np.array(track[-N:])

    times = np.arange(-N + 1, 1)

    A = np.vstack([times, np.ones(len(times))]).T
    k_x, b_x = np.linalg.lstsq(A, track[:, 0], rcond=None)[0]
    k_y, b_y = np.linalg.lstsq(A, track[:, 1], rcond=None)[0]

    future_frames = future_time * fps
    future_x = k_x * future_frames + b_x
    future_y = k_y * future_frames + b_y

Все тоже видео, но уже с обработкой предсказания:

Код для обработки видео с предсказанием

Создание собственного датасета

И теперь самое интересное — как же обучить нейросеть YOLOV8 обрабатывать не только автомобили и собачек, но и объекты с которыми необходимо работать. Для этого разработчиками предусмотрена возможность дообучить модель.
Почему дообучить? Потому что обучается не вся модель, а только последние слои нейронной сети. Это позволяет эффективно и быстро тренировать модель не ограниченном датасете, который реально разметить руками! Начнем!

Задача пройти полный путь и получить качественный результат, который в дальнейшем можно будет улучшать, увеличивая сложность объектов и условия их появления!
Представляю нашего героя - "Кубик" (который на самом деле прямоугольный параллелепипед). Именно его мы научим распознавать нашу модель!

— Сразу отвечу на вопрос: "А почему не OpenCV? Ведь ее тут достаточно".
— Да, более того, мы с помощью нее автоматически разметим датасет. А не ее, т.к. задача - обучить модель определять нужные объекты!

Снимаем видео с объектом, захватывая также кадры, где его нет, т.е. просто фон!

Следующим шагом разделяем видео на отдельные изображения

import cv2
import os
import time

# Путь к видеофайлу
video_path = '000.mp4'
# Папка для сохранения изображений
output_folder = 'output_images'
# Интервал между кадрами (каждый n-й кадр будет сохранен)
frame_interval = 1  # Можно изменить на 2, 5 и т.д.

os.makedirs(output_folder, exist_ok=True)

# Открытие видеофайла
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
    print(f"Ошибка открытия видеофайла: {video_path}")
    exit()

frame_count = 0
saved_count = 0

while True:
    ret, frame = cap.read()
    if not ret:
        break

    if frame_count % frame_interval == 0:
        # Получение текущего времени в виде временной метки
        timestamp = int(time.time() * 1000)  # Используем миллисекунды для большей точности
        output_path = os.path.join(output_folder, f'{timestamp}_frame_{saved_count:05d}.jpg')
        cv2.imwrite(output_path, frame)
        print(f"Сохранено: {output_path}")
        saved_count += 1

    frame_count += 1

cap.release()
print("Разделение видео на фотографии завершено.")

И получаем огромное количество фотографий. Сколько их нужно? Ответ на этот вопрос однозначно сложно дать, т.к. зависит от сложности объекта и условий, в которых он будет определяться.
Точно можно сказать, что чем разнообразнее будут кадры с окружающей обстановкой (если это, конечно, требуется в условиях дальнейшей эксплуатации), тем лучше потом будет работать обученная модель!

Теперь нужно разметить данные, т.е. указать места, где же находится искомый объект.

Автоматизированная разметка

Т.к. данная задача решается в "тепличных" условиях можем разметить автоматически, с применением OpenCV. Возьмем один из кадров нашего датасета и воспользуемся скриптом для подбора диапазонов в цветовой модели HSV, который позволит выделить кубик из фона:

import cv2
import numpy as np

def nothing(*arg):
    pass

cv2.namedWindow( "result" ) 
cv2.namedWindow( "settings" )


cv2.createTrackbar('h1', 'settings', 0, 180, nothing)
cv2.createTrackbar('s1', 'settings', 0, 255, nothing)
cv2.createTrackbar('v1', 'settings', 0, 255, nothing)
cv2.createTrackbar('h2', 'settings', 180, 180, nothing)
cv2.createTrackbar('s2', 'settings', 255, 255, nothing)
cv2.createTrackbar('v2', 'settings', 255, 255, nothing)

while True:
    img = cv2.imread('000.jpg')
    h,w,_=img.shape
    img=cv2.resize(img,(w//5,h//5))
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV )
 
    # считываем значения бегунков
    h1 = cv2.getTrackbarPos('h1', 'settings')
    s1 = cv2.getTrackbarPos('s1', 'settings')
    v1 = cv2.getTrackbarPos('v1', 'settings')
    h2 = cv2.getTrackbarPos('h2', 'settings')
    s2 = cv2.getTrackbarPos('s2', 'settings')
    v2 = cv2.getTrackbarPos('v2', 'settings')
    h_min = np.array((h1, s1, v1), np.uint8)
    h_max = np.array((h2, s2, v2), np.uint8)
    img_bin = cv2.inRange(hsv, h_min, h_max)
    cv2.imshow('result', img_bin)
    cv2.imshow('original', img)
    ch = cv2.waitKey(5)
    if ch == 27:
        break
cv2.destroyAllWindows()

Теперь необходимо записать полученные значения в формате:
lower_hsv = np.array([64, 54, 167]) upper_hsv = np.array([180, 255, 255])

Следующий скрипт применит данный фильтр ко всем изображениям и сохранит координаты найденного объекта.
ВАЖНО! Этот скрипт для решения простой задачи - где в кадре один объект одного класса, в хороших условиях. Для ручной разметки скрипт дальше!

import cv2
import os
import numpy as np

input_folder = 'output_images'
output_folder = 'dataset/train'
output_images_folder = os.path.join(output_folder, 'images')
output_labels_folder = os.path.join(output_folder, 'labels')

os.makedirs(output_images_folder, exist_ok=True)
os.makedirs(output_labels_folder, exist_ok=True)

lower_hsv = np.array([89, 71, 120])
upper_hsv = np.array([180, 255, 255])

def find_mask(image, lower_hsv, upper_hsv):
    hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv_image, lower_hsv, upper_hsv)
    return mask

def find_bounding_rect(mask):
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if contours:
        contour = max(contours, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(contour)
        return x, y, x + w, y + h
    else:
        return None

def normalize_coordinates(x1, y1, x2, y2, img_width, img_height):
    x_center = (x1 + x2) / 2 / img_width
    y_center = (y1 + y2) / 2 / img_height
    width = abs(x2 - x1) / img_width
    height = abs(y2 - y1) / img_height
    return x_center, y_center, width, height

for filename in os.listdir(input_folder):
    if filename.endswith(('.jpg', '.jpeg', '.png')):
        image_path = os.path.join(input_folder, filename)
        image = cv2.imread(image_path)
        resized_image = cv2.resize(image, (640,640))
        mask = find_mask(resized_image, lower_hsv, upper_hsv)
        bounding_rect = find_bounding_rect(mask)

        if bounding_rect is not None: 
            x1, y1, x2, y2 = bounding_rect
            x_center, y_center, width, height = normalize_coordinates(x1, y1, x2, y2, 640, 640)

            # Сохранение изображения
            output_image_path = os.path.join(output_images_folder, filename)
            cv2.imwrite(output_image_path, resized_image)

            # Сохранение 
            label_filename = os.path.splitext(filename)[0] + '.txt'
            label_file_path = os.path.join(output_labels_folder, label_filename)
            with open(label_file_path, 'w') as f:
                f.write(f"0 {x_center} {y_center} {width} {height}\n")

            print(f"Processed and saved {filename}")

print("Подготовка датасета завершена.")

В процессе обработки файлы изменяются до размера 640*640 пикселей для передачи в нейросеть. Также для каждого файла записывается TXT файл, в котором содержится информация о расположении объекта.
Важно, что координаты указываются не в пикселях, а в виде значений от 0 до 1, указывая отношение местоположение точки относительно размера кадра.

Структура папок на данном этапе выглядит следующим образом:

dataset/
├── train/
│   ├── images/
│   │   ├── img1.jpg
│   │   ├── img2.jpg
│   │   ├── ...
│   ├── labels/
│   │   ├── img1.txt
│   │   ├── img2.txt
│   │   ├── ...
├── output_images/
│   ├── img1.jpg
│   ├── img2.jpg
│   ├── ...
├── Dataset_video2images.py
└── Dataset_HSV_Markup.py

После разметки хорошо бы проверить, а как же качественно были размечены данные!

import cv2
import os

# Папки с изображениями и метками
images_path = 'dataset/train/images'
labels_path = 'dataset/train/labels'

# Папка для сохранения изображений с нарисованными прямоугольниками
output_folder = 'checked_images'
os.makedirs(output_folder, exist_ok=True)

# Цвета для классов (можно добавить больше цветов, если классов больше)
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)]

# Чтение всех файлов изображений и меток
images = [f for f in os.listdir(images_path) if f.endswith(('.jpg', '.jpeg', '.png'))]

# Функция для преобразования координат из нормализованных значений в пиксели
def denormalize_coordinates(x_center, y_center, width, height, img_width, img_height):
    x_center *= img_width
    y_center *= img_height
    width *= img_width
    height *= img_height
    x1 = int(x_center - width / 2)
    y1 = int(y_center - height / 2)
    x2 = int(x_center + width / 2)
    y2 = int(y_center + height / 2)
    return x1, y1, x2, y2

# Обработка изображений
for image_file in images:
    image_path = os.path.join(images_path, image_file)
    label_file = os.path.splitext(image_file)[0] + '.txt'
    label_path = os.path.join(labels_path, label_file)

    # Проверка наличия файла меток
    if not os.path.exists(label_path):
        print(f"Label file not found for image: {image_file}")
        continue

    # Загрузка изображения
    image = cv2.imread(image_path)
    img_height, img_width = image.shape[:2]

    # Чтение файла меток и рисование прямоугольников
    with open(label_path, 'r') as f:
        for line in f:
            cls, x_center, y_center, width, height = map(float, line.strip().split())
            x1, y1, x2, y2 = denormalize_coordinates(x_center, y_center, width, height, img_width, img_height)
            color = colors[int(cls) % len(colors)]
            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
            cv2.putText(image, f'class {int(cls)}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)

    # Сохранение изображения с нарисованными прямоугольниками
    output_path = os.path.join(output_folder, image_file)
    cv2.imwrite(output_path, image)
    print(f"Saved checked image: {output_path}")

print("Проверка разметки завершена.")

Проверяем и убеждаемся, что в папке checked_images лежат правильно размеченные данные. Эти рамки вокруг кубиков нарисованы на основе данных из labels, что гарантирует, попадание корректных данных в нейросеть для обучения.

Ручная разметка

Для работы с более сложными данными, множеством классов требуется ручная разметка. Существуют, конечно, онлайн-сервисы, но это не наш метод. Пишем сами:

import cv2
import os
import yaml
import shutil

# Путь к папке с исходными изображениями
full_images_path = 'output_images'
# Путь к папке для сохранения обработанных данных
dataset_path = 'dataset'
train_images_path = os.path.join(dataset_path, 'train', 'images')
train_labels_path = os.path.join(dataset_path, 'train', 'labels')
valid_images_path = os.path.join(dataset_path, 'valid', 'images')
valid_labels_path = os.path.join(dataset_path, 'valid', 'labels')
test_images_path = os.path.join(dataset_path, 'test', 'images')
test_labels_path = os.path.join(dataset_path, 'test', 'labels')
ready_images_path = os.path.join(full_images_path, 'ready')

os.makedirs(train_images_path, exist_ok=True)
os.makedirs(train_labels_path, exist_ok=True)
os.makedirs(valid_images_path, exist_ok=True)
os.makedirs(valid_labels_path, exist_ok=True)
os.makedirs(test_images_path, exist_ok=True)
os.makedirs(test_labels_path, exist_ok=True)
os.makedirs(ready_images_path, exist_ok=True)


window_name = 'Annotation Tool'
current_class = 0
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)]
annotations = []

# Функция для масштабирования изображения
def resize_image(image, size=(640, 640)):
    return cv2.resize(image, size)

# Обработка событий мыши
drawing = False
ix, iy = -1, -1

def draw_rectangle(event, x, y, flags, param):
    global ix, iy, drawing, annotations, current_class
    
    if event == cv2.EVENT_LBUTTONDOWN:
        drawing = True
        ix, iy = x, y
    elif event == cv2.EVENT_MOUSEMOVE:
        if drawing:
            image = param['original_image'].copy()
            for annotation in annotations:
                cls, x1, y1, x2, y2 = annotation
                cv2.rectangle(image, (x1, y1), (x2, y2), colors[cls], 2)
            cv2.rectangle(image, (ix, iy), (x, y), colors[current_class], 2)
            cv2.imshow(window_name, image)
    elif event == cv2.EVENT_LBUTTONUP:
        drawing = False
        annotations.append((current_class, ix, iy, x, y))
        image = param['original_image'].copy()
        for annotation in annotations:
            cls, x1, y1, x2, y2 = annotation
            cv2.rectangle(image, (x1, y1), (x2, y2), colors[cls], 2)
        cv2.imshow(window_name, image)
    elif event == cv2.EVENT_RBUTTONDOWN:  # Удаление последней рамки
        if annotations:
            removed_annotation = annotations.pop()
            image = param['original_image'].copy()  # Вернемся к оригинальному изображению
            for annotation in annotations:
                cls, x1, y1, x2, y2 = annotation
                cv2.rectangle(image, (x1, y1), (x2, y2), colors[cls], 2)
            cv2.imshow(window_name, image)
        else:
            image = param['original_image'].copy()
            cv2.imshow(window_name, image)

# Обновление файла data.yaml
def update_data_yaml():
    data_yaml_path = 'data.yaml'
    data = {
        'train': 'dataset/train/images',
        'val': 'dataset/valid/images',
        'test': 'dataset/test/images',
        'nc': 4,
        'names': ['class0', 'class1', 'class2', 'class3']
    }
    with open(data_yaml_path, 'w') as f:
        yaml.dump(data, f, default_flow_style=None, sort_keys=False)
    print(f"Updated {data_yaml_path}")

# Загрузка и обработка изображений
for filename in os.listdir(full_images_path):
    if filename.endswith(('.jpg', '.jpeg', '.png')):
        image_path = os.path.join(full_images_path, filename)
        image = cv2.imread(image_path)
        image = resize_image(image)
        original_image = image.copy()
        annotations = []

        cv2.namedWindow(window_name)
        cv2.setMouseCallback(window_name, draw_rectangle, param={'original_image': original_image})

        while True:
            image_with_annotations = original_image.copy()
            for annotation in annotations:
                cls, x1, y1, x2, y2 = annotation
                cv2.rectangle(image_with_annotations, (x1, y1), (x2, y2), colors[cls], 2)
            cv2.imshow(window_name, image_with_annotations)
            key = cv2.waitKey(1) & 0xFF

            if key == ord(' '):  # Нажатие пробела для сохранения
                # Сохранение изображения
                output_image_path = os.path.join(train_images_path, filename)
                cv2.imwrite(output_image_path, original_image)
                print(f"Saved image to {output_image_path}")

                # Сохранение текстовых данных
                label_filename = os.path.splitext(filename)[0] + '.txt'
                label_file_path = os.path.join(train_labels_path, label_filename)
                with open(label_file_path, 'w') as f:
                    for annotation in annotations:
                        cls, x1, y1, x2, y2 = annotation
                        x_center = (x1 + x2) / 2 / 640
                        y_center = (y1 + y2) / 2 / 640
                        width = abs(x2 - x1) / 640
                        height = abs(y2 - y1) / 640
                        f.write(f"{cls} {x_center} {y_center} {width} {height}\n")
                print(f"Saved labels to {label_file_path}")

                # Обновление data.yaml
                update_data_yaml()

                # Перемещение обработанного изображения
                ready_image_path = os.path.join(ready_images_path, filename)
                shutil.move(image_path, ready_image_path)
                print(f"Moved image to {ready_image_path}")
                break
            elif key == 27:  # Нажатие Esc для пропуска изображения
                print("Skipped image")
                break
            elif key in [ord(str(i)) for i in range(10)]:  # Выбор класса
                current_class = int(chr(key))
                print(f"Selected class: {current_class}")

        cv2.destroyAllWindows()

Краткая инструкция:
— Выбираете один из 4 классов с помощью цифр 0,1,2,3 на клавиатуре
— Рисуем рамки вокруг объектов
— Удалить последнюю рамку - правая кнопка мыши
— Перейти к следующему кадру - пробел

Теперь, когда все данные размечены осталось подготовить несколько файлов и можно запускать обучение!

Финальные приготовления

Для качественного обучения и проверки работы необходимо разделить все данные на 3 части:
— train (обучающие данные) - 70%
— test (тестовые данные, для проверки во время обучения) - 20 %
— valid (проверочные данные, для тестирования модели после обучения) - 10 %

Вручную делать это неудобно, поэтому автоматизируем процесс с помощью скрипта:

import os
import shutil
import random

# Параметры для разделения данных
test_percent = 0.2  # Процент данных для тестирования
valid_percent = 0.1  # Процент данных для проверки

# Путь к папке с данными
dataset_path = 'dataset'
train_images_path = os.path.join(dataset_path, 'train', 'images')
train_labels_path = os.path.join(dataset_path, 'train', 'labels')
valid_images_path = os.path.join(dataset_path, 'valid', 'images')
valid_labels_path = os.path.join(dataset_path, 'valid', 'labels')
test_images_path = os.path.join(dataset_path, 'test', 'images')
test_labels_path = os.path.join(dataset_path, 'test', 'labels')

os.makedirs(valid_images_path, exist_ok=True)
os.makedirs(valid_labels_path, exist_ok=True)
os.makedirs(test_images_path, exist_ok=True)
os.makedirs(test_labels_path, exist_ok=True)

# Получение всех файлов изображений и соответствующих меток
images = [f for f in os.listdir(train_images_path) if f.endswith(('.jpg', '.jpeg', '.png'))]
labels = [f for f in os.listdir(train_labels_path) if f.endswith('.txt')]

# Убедимся, что количество изображений и меток совпадает
images.sort()
labels.sort()

# Проверка на соответствие количества изображений и меток
if len(images) != len(labels):
    print("Количество изображений и меток не совпадает.")
    exit()

# Перемешивание данных
data = list(zip(images, labels))
random.shuffle(data)
images, labels = zip(*data)

# Разделение данных
num_images = len(images)
num_test = int(num_images * test_percent)
num_valid = int(num_images * valid_percent)
num_train = num_images - num_test - num_valid

# Перемещение данных в соответствующие папки
def move_files(file_list, source_image_dir, source_label_dir, dest_image_dir, dest_label_dir):
    for file in file_list:
        image_path = os.path.join(source_image_dir, file)
        label_path = os.path.join(source_label_dir, os.path.splitext(file)[0] + '.txt')
        shutil.move(image_path, os.path.join(dest_image_dir, file))
        shutil.move(label_path, os.path.join(dest_label_dir, os.path.splitext(file)[0] + '.txt'))

# Перемещение тестовых данных
move_files(images[:num_test], train_images_path, train_labels_path, test_images_path, test_labels_path)

# Перемещение валидационных данных
move_files(images[num_test:num_test + num_valid], train_images_path, train_labels_path, valid_images_path, valid_labels_path)

# Оставшиеся данные остаются в папке train

print(f"Перемещено {num_test} изображений в папку test.")
print(f"Перемещено {num_valid} изображений в папку valid.")
print(f"Осталось {num_train} изображений в папке train.")

Еще один важный этап - создание файла data.yaml, с информацией и папках, файлах и названиях классов в будущей модели. Структура файла выглядит так:

train: dataset/train/images 
val: dataset/valid/images 
test: dataset/test/images

nc: 4 
names: [class0, class1, class2, class3]

Теперь все готово, и структура проекта выглядит следующим образом:

dataset/
├── train/
│   ├── images/
│   │   ├── img1.jpg
│   │   ├── img2.jpg
│   │   ├── ...
│   ├── labels/
│   │   ├── img1.txt
│   │   ├── img2.txt
│   │   ├── ...
├── test/
│   ├── images/
│   │   ├── img3.jpg
│   │   ├── img4.jpg
│   │   ├── ...
│   ├── labels/
│   │   ├── img3.txt
│   │   ├── img4.txt
│   │   ├── ...
├── valid/
│   ├── images/
│   │   ├── img5.jpg
│   │   ├── img6.jpg
│   │   ├── ...
│   ├── labels/
│   │   ├── img5.txt
│   │   ├── img6.txt
│   │   ├── ...
├── output_images/
│   ├── img1.jpg
│   ├── img2.jpg
│   ├── ...
├── data.yaml
├── Dataset_video2images.py
└── Dataset_HSV_Markup.py

Обучение

Всего несколько параметров, с которыми можно и нужно работать в самом начале:
epochs = 500 — количество эпох обучения. Выбирается индивидуально для задачи. При обучении YOLO останавливается автоматически, если улучшения результатов не происходит несколько эпох.

batch = 64 - размер "пакета изображений" передаваемый за один раз в нейросеть. Изменяется в зависимости от количества доступной памяти.

imgsz = 640 - размер изображений

import os
from ultralytics import YOLO

current_dir = os.path.dirname(os.path.abspath(__file__))

data_path = os.path.join(current_dir, 'data.yaml')
model = YOLO(os.path.join(current_dir, 'yolov8n.pt'))


epochs = 500
batch = 64
imgsz = 640
if __name__ == '__main__':
    results = model.train(data=data_path,
                      epochs=epochs, 
                      batch=batch, 
                      imgsz=imgsz, 
                      name='red',
                      device='cuda')

Запускаем обучение и ждем!

После завершения процесса (время сильно может отличаться в зависимости от множества параметров) в папке runs будут лежать графики и прочие данные процесса обучения, а также файлы модели best.pt и last.pt

Осталось только в скрипте для детекции заменить файл модели на best.pt и УРА, можно пользоваться!

Источник

Теги

Категория

Новости

Дата

19 июн. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

Web-site. https://trustgeekshackexpert.com/ Tele-Gram, trustgeekshackexpert During the pandemic, I ventured into the world of cryptocurrency trading. My father loaned me $10,000, which I used to purchase my first bitcoins. With diligent research and some luck, I managed to grow my investment to over $350,000 in just a couple of years. I was thrilled with my success, but my excitement was short-lived when I decided to switch brokers and inadvertently fell victim to a phishing attack. While creating a new account, I received what seemed like a legitimate email requesting verification. Without second-guessing, I provided my information, only to realize later that I had lost access to my email and cryptocurrency wallets. Panic set in as I watched my hard-earned assets disappear before my eyes. Desperate to recover my funds, I scoured the internet for solutions. That's when I stumbled upon the Trust Geeks Hack Expert on the Internet. The service claimed to specialize in recovering lost crypto assets, and I decided to take a chance. Upon contacting them, the team swung into action immediately. They guided me through the entire recovery process with professionalism and efficiency. The advantages of using the Trust Geeks Hack Expert Tool became apparent from the start. Their team was knowledgeable and empathetic, understanding the urgency and stress of my situation. They employed advanced security measures to ensure my information was handled safely and securely. One of the key benefits of the Trust Geeks Hack Expert Tool was its user-friendly interface, which made a complex process much more manageable for someone like me, who isn't particularly tech-savvy. They also offered 24/7 support, so I never felt alone during recovery. Their transparent communication and regular updates kept me informed and reassured throughout. The Trust Geeks Hack Expert Tool is the best solution for anyone facing similar issues. Their swift response, expertise, and customer-centric approach set them apart from other recovery services. Thanks to their efforts, I regained access to my accounts and my substantial crypto assets. The experience taught me a valuable lesson about online security and showed me the incredible potential of the Trust Geeks Hack Expert Tool. Email:: trustgeekshackexpert{@}fastservice{.}com WhatsApp + 1.7.1.9.4.9.2.2.6.9.3
26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

Reach Out To Adrian Lamo Hacker via email: [email protected] / WhatsApp: ‪+1 (909) 739‑0269‬ Adrian Lamo Hacker is a formidable force in the realm of cybersecurity, offering a comprehensive suite of services designed to protect individuals and organizations from the pervasive threat of digital scams and fraud. With an impressive track record of recovering over $950 million, including substantial sums from high-profile scams such as a $600 million fake investment platform and a $1.5 million romance scam, Adrian Lamo Hacker has established itself as a leader in the field. One of the key strengths of Adrian Lamo Hacker lies in its unparalleled expertise in scam detection. The company leverages cutting-edge methodologies to defend against a wide range of digital threats, including phishing emails, fraudulent websites, and deceitful schemes. This proactive approach to identifying and neutralizing potential scams is crucial in an increasingly complex and interconnected digital landscape. Adrian Lamo Hacker's tailored risk assessments serve as a powerful tool for fortifying cybersecurity. By identifying vulnerabilities and potential points of exploitation, the company empowers its clients to take proactive measures to strengthen their digital defenses. This personalized approach to risk assessment ensures that each client receives targeted and effective protection against cyber threats. In the event of a security incident, Adrian Lamo Hacker's rapid incident response capabilities come into play. The company's vigilant monitoring and swift mitigation strategies ensure that any potential breaches or scams are addressed in real-time, minimizing the impact on its clients' digital assets and reputation. This proactive stance towards incident response is essential in an era where cyber threats can materialize with alarming speed and sophistication. In addition to its robust defense and incident response capabilities, Adrian Lamo Hacker is committed to empowering its clients to recognize and thwart common scam tactics. By fostering enlightenment in the digital realm, the company goes beyond simply safeguarding its clients; it equips them with the knowledge and awareness needed to navigate the digital landscape with confidence and resilience. Adrian Lamo Hacker services extend to genuine hacking, offering an additional layer of protection for its clients. This may include ethical hacking or penetration testing, which can help identify and address security vulnerabilities before malicious actors have the chance to exploit them. By offering genuine hacking services, Adrian Lamo Hacker demonstrates its commitment to providing holistic cybersecurity solutions that address both defensive and offensive aspects of digital protection. Adrian Lamo Hacker stands out as a premier provider of cybersecurity services, offering unparalleled expertise in scam detection, rapid incident response, tailored risk assessments, and genuine hacking capabilities. With a proven track record of recovering significant sums from various scams, the company has earned a reputation for excellence in combating digital fraud. Through its proactive and empowering approach, Adrian Lamo Hacker is a true ally for individuals and organizations seeking to navigate the digital realm with confidence.
04.07.24 04:49 ZionNaomi

For over twenty years, I've dedicated myself to the dynamic world of marketing, constantly seeking innovative strategies to elevate brand visibility in an ever-evolving landscape. So when the meteoric rise of Bitcoin captured my attention as a potential avenue for investment diversification, I seized the opportunity, allocating $20,000 to the digital currency. Witnessing my investment burgeon to an impressive $70,000 over time instilled in me a sense of financial promise and stability.However, amidst the euphoria of financial growth, a sudden and unforeseen oversight brought me crashing back to reality during a critical business trip—I had misplaced my hardware wallet. The realization that I had lost access to the cornerstone of my financial security struck me with profound dismay. Desperate for a solution, I turned to the expertise of Daniel Meuli Web Recovery.Their response was swift . With meticulous precision, they embarked on the intricate process of retracing the elusive path of my lost funds. Through their unwavering dedication, they managed to recover a substantial portion of my investment, offering a glimmer of hope amidst the shadows of uncertainty. The support provided by Daniel Meuli Web Recovery extended beyond mere financial restitution. Recognizing the imperative of fortifying against future vulnerabilities, they generously shared invaluable insights on securing digital assets. Their guidance encompassed crucial aspects such as implementing hardware wallet backups and fortifying security protocols, equipping me with recovered funds and newfound knowledge to navigate the digital landscape securely.In retrospect, this experience served as a poignant reminder of the critical importance of diligence and preparedness in safeguarding one's assets. Thanks to the expertise and unwavering support extended by Daniel Meuli Web Recovery, I emerged from the ordeal with renewed resilience and vigilance. Empowered by their guidance and fortified by enhanced security measures, I now approach the future with unwavering confidence.The heights of financial promise to the depths of loss and back again has been a humbling one, underscoring the volatility and unpredictability inherent in the digital realm. Yet, through adversity, I have emerged stronger, armed with a newfound appreciation for the importance of diligence, preparedness, and the invaluable support of experts like Daniel Meuli Web Recovery.As I persist in traversing the digital landscape, I do so with a judicious blend of vigilance and fortitude, cognizant that with adequate safeguards and the backing of reliable confidants, I possess the fortitude to withstand any adversity that may arise. For this, I remain eternally appreciative. Email Danielmeuliweberecovery @ email . c om WhatsApp + 393 512 013 528
13.07.24 21:13 michaelharrell825

In 2020, amidst the economic fallout of the pandemic, I found myself unexpectedly unemployed and turned to Forex trading in hopes of stabilizing my finances. Like many, I was drawn in by the promise of quick returns offered by various Forex robots, signals, and trading advisers. However, most of these products turned out to be disappointing, with claims that were far from reality. Looking back, I realize I should have been more cautious, but the allure of financial security clouded my judgment during those uncertain times. Amidst these disappointments, Profit Forex emerged as a standout. Not only did they provide reliable service, but they also delivered tangible results—a rarity in an industry often plagued by exaggerated claims. The positive reviews from other users validated my own experience, highlighting their commitment to delivering genuine outcomes and emphasizing sound financial practices. My journey with Profit Forex led to a net profit of $11,500, a significant achievement given the challenges I faced. However, my optimism was short-lived when I encountered obstacles trying to withdraw funds from my trading account. Despite repeated attempts, I found myself unable to access my money, leaving me frustrated and uncertain about my financial future. Fortunately, my fortunes changed when I discovered PRO WIZARD GIlBERT RECOVERY. Their reputation for recovering funds from fraudulent schemes gave me hope in reclaiming what was rightfully mine. With a mixture of desperation and cautious optimism, I reached out to them for assistance. PRO WIZARD GIlBERT RECOVERY impressed me from the start with their professionalism and deep understanding of financial disputes. They took a methodical approach, using advanced techniques to track down the scammers responsible for withholding my funds. Throughout the process, their communication was clear and reassuring, providing much-needed support during a stressful period. Thanks to PRO WIZARD GIlBERT RECOVERY's expertise and unwavering dedication, I finally achieved a resolution to my ordeal. They successfully traced and retrieved my funds, restoring a sense of justice and relief. Their intervention not only recovered my money but also renewed my faith in ethical financial services. Reflecting on my experience, I've learned invaluable lessons about the importance of due diligence and discernment in navigating the Forex market. While setbacks are inevitable, partnering with reputable recovery specialists like PRO WIZARD GIlBERT RECOVERY can make a profound difference. Their integrity and effectiveness have left an indelible mark on me, guiding my future decisions and reinforcing the value of trustworthy partnerships in achieving financial goals. I wholeheartedly recommend PRO WIZARD GIlBERT RECOVERY to anyone grappling with financial fraud or disputes. Their expertise and commitment to client satisfaction are unparalleled, offering a beacon of hope in challenging times. Thank you, PRO WIZARD GIlBERT RECOVERY, for your invaluable assistance in reclaiming what was rightfully mine. Your service not only recovered my funds but also restored my confidence in navigating the complexities of financial markets with greater caution and awareness. Email: prowizardgilbertrecovery(@)engineer.com Homepage: https://prowizardgilbertrecovery.xyz WhatsApp: +1 (516) 347‑9592
17.07.24 02:26 thompsonrickey

In the vast and often treacherous realm of online investments, I was entangled in a web of deceit that cost me nearly $45,000. It all started innocuously enough with an enticing Instagram profile promising lucrative returns through cryptocurrency investment. Initially, everything seemed promising—communications were smooth, and assurances were plentiful. However, as time passed, my optimism turned to suspicion. Withdrawal requests were met with delays and excuses. The once-responsive "investor" vanished into thin air, leaving me stranded with dwindling hopes and a sinking feeling in my gut. It became painfully clear that I had been duped by a sophisticated scheme designed to exploit trust and naivety. Desperate to recover my funds, I turned to online forums where I discovered numerous testimonials advocating for Muyern Trust Hacker. With nothing to lose, I contacted them, recounting my ordeal with a mixture of skepticism and hope. Their swift response and professional demeanor immediately reassured me that I had found a lifeline amidst the chaos. Muyern Trust Hacker wasted no time in taking action. They meticulously gathered evidence, navigated legal complexities, and deployed their expertise to expedite recovery. In what felt like a whirlwind of activity, although the passage of time was a blur amidst my anxiety, they achieved the seemingly impossible—my stolen funds were returned. The relief I felt was overwhelming. Muyern Trust Hacker not only restored my financial losses but also restored my faith in justice. Their commitment to integrity and their relentless pursuit of resolution were nothing short of remarkable. They proved themselves as recovery specialists and guardians against digital fraud, offering hope to victims like me who had been ensnared by deception. My gratitude knows no bounds for Muyern Trust Hacker. Reach them at muyerntrusted @ m a i l - m e . c o m AND Tele gram @ muyerntrusthackertech
18.07.24 20:13 austinagastya

I Testify For iBolt Cyber Hacker Alone - For Crypto Recovery Service I highly suggest iBolt Cyber Hacker to anyone in need of bitcoin recovery services. They successfully recovered my bitcoin from a fake trading scam with speed and efficiency. This crew is trustworthy, They kept me updated throughout the procedure. I thought my bitcoin was gone, I am so grateful for their help, If you find yourself in a similar circumstance, do not hesitate to reach out to iBolt Cyber Hacker for assistance. Thank you, iBOLT, for your amazing customer service! Please be cautious and contact them directly through their website. Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /
27.08.24 12:50 James889900

All you need is to hire an expert to help you accomplish that. If there’s any need to spy on your partner’s phone. From my experience I lacked evidence to confront my husband on my suspicion on his infidelity, until I came across ETHICALAHCKERS which many commend him of assisting them in their spying mission. So I contacted him and he provided me with access into his phone to view all text messages, call logs, WhatsApp messages and even her location. This evidence helped me move him off my life . I recommend you consult ETHICALHACKERS009 @ gmail.com OR CALL/TEXT ‪+1(716) 318-5536 or whatsapp +14106350697 if you need access to your partner’s phone
27.08.24 13:06 James889900

All you need is to hire an expert to help you accomplish that. If there’s any need to spy on your partner’s phone. From my experience I lacked evidence to confront my husband on my suspicion on his infidelity, until I came across ETHICALAHCKERS which many commend him of assisting them in their spying mission. So I contacted him and he provided me with access into his phone to view all text messages, call logs, WhatsApp messages and even her location. This evidence helped me move him off my life . I recommend you consult ETHICALHACKERS009 @ gmail.com OR CALL/TEXT ‪+1(716) 318-5536 or whatsapp +14106350697 if you need access to your partner’s phone
02.09.24 20:24 [email protected]

If You Need Hacker To Recover Your Bitcoin Contact Paradox Recovery Wizard Paradox Recovery Wizard successfully recovered $123,000 worth of Bitcoin for my husband, which he had lost due to a security breach. The process was efficient and secure, with their expert team guiding us through each step. They were able to trace and retrieve the lost cryptocurrency, restoring our peace of mind and financial stability. Their professionalism and expertise were instrumental in recovering our assets, and we are incredibly grateful for their service. Email: support@ paradoxrecoverywizard.com Email: paradox_recovery @cyberservices.com Wep: https://paradoxrecoverywizard.com/ WhatsApp: +39 351 222 3051.
06.09.24 01:35 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /
16.09.24 00:11 marcusaustin

Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /
23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

I lost about $876k few months ago trading on a fake binary option investment websites. I didn't knew they were fake until I tried to withdraw. Immediately, I realized these guys were fake. I contacted Sinew Claudia world recovery, my friend who has such experience before and was able to recover them, recommended me to contact them. I'm a living testimony of a successful recovery now. You can contact the legitimate recovery company below for help and assistance. [email protected] [email protected] WhatsApp: 6262645164
02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

The growth of WIN44 in Brazil is very interesting! If you're looking for more options for online betting and casino games, I recommend checking out Casinos in Brazil. It's a reliable platform that offers a wide variety of games and provides a safe and enjoyable experience for users. It's worth checking out! https://win44.vip
31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

In the world of Bitcoin recovery, Pro Wizard Gilbert truly represents the gold standard. My experience with Gilbert revealed just how exceptional his methods are and why he stands out as the premier authority in this critical field. When I first encountered the complexities of Bitcoin recovery, I was daunted by the technical challenges and potential risks. Gilbert’s approach immediately distinguished itself through its precision and effectiveness. His methods are meticulously designed, combining cutting-edge techniques with an in-depth understanding of the Bitcoin ecosystem. He tackled the recovery process with a level of expertise and thoroughness that was both impressive and reassuring. What sets Gilbert’s methods apart is not just their technical sophistication but also their strategic depth. He conducts a comprehensive analysis of each case, tailoring his approach to address the unique aspects of the situation. This personalized strategy ensures that every recovery effort is optimized for success. Gilbert’s transparent communication throughout the process was invaluable, providing clarity and confidence during each stage of the recovery. The results I achieved with Pro Wizard Gilbert’s methods were remarkable. His gold standard approach not only recovered my Bitcoin but did so with an efficiency and reliability that exceeded my expectations. His deep knowledge, innovative techniques, and unwavering commitment make him the definitive expert in Bitcoin recovery. For anyone seeking a benchmark in Bitcoin recovery solutions, Pro Wizard Gilbert’s methods are the epitome of excellence. His ability to blend technical prowess with strategic insight truly sets him apart in the industry. Call: for help. You may get in touch with them at ; Email: (prowizardgilbertrecovery(@)engineer.com) Telegram ; https://t.me/Pro_Wizard_Gilbert_Recovery Homepage ; https://prowizardgilbertrecovery.info
12.11.24 00:50 TERESA

Brigadia Tech Remikeable recovery has exhibited unparalleled strength in the realm of recovery. They stand out as the premier team to collaborate with if you encounter withdrawal difficulties from the platform where you’ve invested. Recently, I engaged with them to recover over a million dollars trapped in an investment platform I’d been involved with for months. I furnished their team with every detail of the investment, including accounts, names, and wallet addresses to which I sent the funds. This decision proved to be the best I’ve made, especially after realizing the company had scammed me. Brigadia Tech Remikeable recovery ensures exemplary service delivery and ensures the perpetrators face justice. They employ advanced techniques to ensure you regain access to your funds. Understandably, many individuals who have fallen victim to investment scams may still regret engaging in online services again due to the trauma of being scammed. However, I implore you to take action. Seek assistance from Brigadia Tech Remikeable Recovery Experts today and witness their remarkable capabilities. I am grateful that I resisted their enticements, and despite the time it took me to discover brigadia tech recovery, they ultimately fulfilled my primary objective. Without Brigadia Tech Recovery's intervention, I would have remained despondent and perplexed indefinitely. Also if you are looking for the best and safest investment company you can contact them, for wallet recovery, difficult withdrawal, etc. I am so happy to keep getting my daily BTC, all I do is keep 0.1 BTC in my mining wallet with the help of Brigadia Tech. They connected me to his mining stream and I earn 0.4 btc per day with this, my daily profit. I can get myself a new house and car. I can’t believe I have thousands of dollars in my bank account. Now you can get in. ([email protected]) Telegram +1 (323)-9 1 0 -1 6 0 5
17.11.24 09:31 Vivianlocke223

Have You Fallen Victim to Cryptocurrency Fraud? If your Bitcoin or other cryptocurrencies were stolen due to scams or fraudulent activities, Free Crypto Recovery Fixed is here to help you recover what’s rightfully yours. As a leading recovery service, we specialize in restoring lost cryptocurrency and assisting victims of fraud — no matter how long ago the incident occurred. Our experienced team leverages cutting-edge tools and expertise to trace and recover stolen assets, ensuring swift and secure results. Don’t let scammers jeopardize your financial security. With Free Crypto Recovery Fixed, you’re putting your trust in a reliable and dedicated team that prioritizes recovering your assets and ensuring their future protection. Take the First Step Toward Recovery Today! 📞 Text/Call: +1 407 212 7493 ✉️ Email: [email protected] 🌐 Website: https://freecryptorecovery.net Let us help you regain control of your financial future — swiftly and securely.
19.11.24 03:06 [email protected]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
19.11.24 03:07 [email protected]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
21.11.24 04:14 ronaldandre617

Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

Your Lost Bitcoins Are Not Gone Forever? Enquire From iBolt Cyber Hacker iBolt Cyber Hacker is a cybersecurity service that specializes in Bitcoin and cryptocurrency recovery. Even if your Bitcoin is locked away in a scammer inaccessible wallet, they have the tools and expertise to retrieve it. Many people, including seasoned cryptocurrency investors, face the daunting possibility of never seeing their lost funds again. iBolt cyber hacker service is a potential lifeline in these situations. I understand the concerns many people might have about trusting a third-party service to recover their Bitcoin. iBolt Cyber Hacker takes security seriously, implementing encryption and stringent privacy protocols. I was assured that no sensitive data would be compromised during the recovery process. Furthermore, their reputation in the cryptocurrency community, based on positive feedback from previous clients, gave me confidence that I was in good hands. Whtp +39, 351..105, 3619 Em.ail: ibolt @ cyber- wizard. co m
22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)
26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

Although recovering from the terrible effects of investment fraud can seem like an impossible task, it is possible to regain financial stability and go on with the correct assistance and tools. In my own experience with Wizard Web Recovery, a specialized company that assisted me in navigating the difficulties of recouping my losses following my fall prey to a sophisticated online fraud, that was undoubtedly the case. My life money had disappeared in an instant, leaving me in a state of shock when I first contacted Spartan Tech Group Retrieval through this Email: spartantechretrieval (@) g r o u p m a i l .c o m The compassionate and knowledgeable team there quickly put my mind at ease, outlining a clear and comprehensive plan of action. They painstakingly examined every aspect of my case, using their broad business contacts and knowledge to track the movement of my pilfered money. They empowered me to make knowledgeable decisions regarding the rehabilitation process by keeping me updated and involved at every stage. But what I valued most was their unrelenting commitment and perseverance; they persisted in trying every option until a sizable amount of my lost money had been successfully restored. It was a long and arduous journey, filled with ups and downs, but having Spartan Tech Group Retrieval in my corner made all the difference. Thanks to their tireless efforts, I was eventually able to rebuild my financial foundation and reclaim a sense of security and control over my life. While the emotional scars of investment fraud may never fully heal, working with this remarkable organization played a crucial role in my ability to move forward and recover. For proper talks, contact on WhatsApp:+1 (971) 4 8 7 - 3 5 3 8 and Telegram:+1 (581) 2 8 6 - 8 0 9 2 Thank you for your time reading as it will be of help.
27.11.24 00:39 [email protected]

Although recovering lost or inaccessible Bitcoin can be difficult and unpleasant, it is frequently possible to get back access to one's digital assets with the correct help and direction. Regarding the subject at hand, the examination of Trust Geeks Hack Expert Website www://trustgeekshackexpert.com/ assistance after an error emphasizes how important specialized services may be in negotiating the difficulties of Bitcoin recovery. These providers possess the technical expertise and resources necessary to assess the situation, identify the root cause of the issue, and devise a tailored solution to retrieve the lost funds. By delving deeper into the specifics of Trust Geeks Hack Expert approach, we can gain valuable insights into the nuances of this process. Perhaps they leveraged advanced blockchain analysis tools to trace the transaction history and pinpoint the location of the missing Bitcoins. Or they may have collaborated with the relevant parties, such as exchanges or wallet providers, to facilitate the recovery process. Equally important is the level of personalized support and communication that Trust Geeks Hack Expert likely provided, guiding the affected individual through each step of the recovery effort and offering reassurance during what can be an anxious and uncertain time. The success of their efforts, as evidenced by the positive outcome, underscores the importance of seeking out reputable and experienced service providers when faced with a Bitcoin-related mishap, as they possess the specialized knowledge and resources to navigate these challenges and restore access to one's digital assets. Email.. [email protected]
27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.