import cv2
import numpy as np
import xml.etree.ElementTree as ET


# Функция для извлечения настроек из XMP файла
def extract_xmp_settings(xmp_data):
    settings = {}
    root = ET.fromstring(xmp_data)
    ns = {'crs': 'http://ns.adobe.com/camera-raw-settings/1.0/'}

    for elem in root.findall('.//crs:*', ns):
        key = elem.tag.split('}')[-1]
        value = elem.text.strip()  # Удаление пробелов и переносов строк

        if value:
            try:
                # Попытка преобразовать значение в float или int
                settings[key] = float(value) if '.' in value else int(value)
            except ValueError:
                # Обработка исключения, если преобразование не удалось
                print(f"Не удалось преобразовать значение '{value}' для настройки '{key}'")

    return settings


# Функция для применения настроек к изображению
def apply_adjustments(image, settings):
    if 'Exposure2012' in settings:
        exposure = settings['Exposure2012']
        image = cv2.add(image, np.array([exposure * 255]).astype(np.uint8))

    if 'Contrast2012' in settings:
        contrast = settings['Contrast2012']
        image = cv2.convertScaleAbs(image, alpha=1 + contrast / 100)

    if 'Highlights2012' in settings:
        highlights = settings['Highlights2012']
        image = apply_highlights_adjustment(image, highlights)

    if 'Shadows2012' in settings:
        shadows = settings['Shadows2012']
        image = apply_shadows_adjustment(image, shadows)

    if 'Temperature' in settings:
        temperature = settings['Temperature']
        image = apply_temperature_adjustment(image, temperature)

    if 'Saturation' in settings:
        saturation = settings['Saturation']
        image = apply_saturation_adjustment(image, saturation)

    if 'Texture' in settings:
        texture = settings['Texture']
        image = apply_texture_adjustment(image, texture)

    if 'Dehaze' in settings:
        # Применение настройки устранения тумана
        dehaze = settings['Dehaze']
        image = apply_dehaze_adjustment(image, dehaze)

    if 'Vibrance' in settings:
        # Применение настройки вибрантности
        vibrance = settings['Vibrance']
        image = apply_vibrance_adjustment(image, vibrance)

    return image


def apply_vibrance_adjustment(image, vibrance):
    hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv_image)

    increase_function = np.vectorize(lambda x: x + (1 - x / 255.0) * vibrance)
    s = increase_function(s).clip(0, 255).astype(np.uint8)

    final_hsv = cv2.merge((h, s, v))
    vibrant_image = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)

    return vibrant_image


def apply_dehaze_adjustment(image, dehaze):
    # Увеличение контраста
    contrast_enhanced = cv2.convertScaleAbs(image, alpha=1.0 + dehaze / 100)

    # Уменьшение серого оттенка (простая коррекция цвета)
    # Можно настроить коэффициенты для более точной коррекции
    gray_world = np.mean(contrast_enhanced, axis=(0, 1))
    gray_world_correction = contrast_enhanced * (gray_world / 255.0)
    corrected_image = np.clip(gray_world_correction, 0, 255).astype(np.uint8)

    return corrected_image


def apply_saturation_adjustment(image, saturation):
    # Реализация применения насыщенности
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    s = cv2.convertScaleAbs(s, alpha=1 + saturation / 100)
    final_hsv = cv2.merge((h, s, v))
    return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)


def apply_highlights_adjustment(image, highlights):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    highlight_mask = cv2.inRange(v, 150, 255)
    v = cv2.add(v, np.array([highlights], dtype=np.uint8), mask=highlight_mask)
    final_hsv = cv2.merge((h, s, v))
    return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)


def apply_shadows_adjustment(image, shadows):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    shadows_mask = cv2.inRange(v, 0, 105)
    v = cv2.add(v, np.array([shadows], dtype=np.uint8), mask=shadows_mask)
    final_hsv = cv2.merge((h, s, v))
    return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)


def apply_temperature_adjustment(image, temperature):
    adjustment = (temperature - 5000) / 10000.0
    b, g, r = cv2.split(image)
    if adjustment > 0:
        r = cv2.addWeighted(r, 1.0 + adjustment, r, 0, 0)
    else:
        b = cv2.addWeighted(b, 1.0 - adjustment, b, 0, 0)
    return cv2.merge((b, g, r))


def apply_saturation_adjustment(image, saturation):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    s = cv2.convertScaleAbs(s, alpha=1 + saturation / 100)
    final_hsv = cv2.merge((h, s, v))
    return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)


def apply_texture_adjustment(image, texture):
    # Базовая реализация эффекта текстуры
    kernel_size = 3  # Размер ядра может быть настроен
    blurred = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
    texture_effect = cv2.addWeighted(image, 1.5, blurred, -0.5, 0)
    texture_effect = cv2.addWeighted(texture_effect, 1 + texture / 100, image, 0, 0)
    return texture_effect

# Чтение XMP файла
with open('settings/@danillainspiredpresetsbyiqbaal.xmp', 'r') as file:
    xmp_content = file.read()

# Извлечение настроек из XMP
xmp_settings = extract_xmp_settings(xmp_content)

# Чтение изображения
image = cv2.imread('example.jpg')

# Применение настроек
processed_image = apply_adjustments(image, xmp_settings)

# Сохранение результата
cv2.imwrite('processed_example.jpg', processed_image)