class TransformParams:
    def __init__(self, data):
        self.transform_type = data.get('transform')
        self.image_path = data.get('image_path')
        self.params = data.get('params', {})
        self.p = self.params.get('p', 1.0)
        self.limit = self.params.get('limit', 90)
        self.blur = self.params.get('blur', 0)
        self.sharpen = self.params.get('sharpen', 0)
        self.brightness = self.params.get('brightness', 1.0)
        self.contrast = self.params.get('contrast', 1.0)
        self.noise = self.params.get('noise', 0)

class ImageTransformer:
    def __init__(self, params: TransformParams):
        self.params = params

    def validate_image(self):
        if not self.params.image_path or not os.path.exists(self.params.image_path):
            return False, {"error": "Invalid image path"}, 400
        return True, None, None

    def load_image(self):
        image = cv2.imread(self.params.image_path)
        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    def apply_transforms(self, image):
        transforms = []

        if self.params.transform_type == 'horizontal_flip':
            transforms.append(A.HorizontalFlip(p=self.params.p))
        elif self.params.transform_type == 'vertical_flip':
            transforms.append(A.VerticalFlip(p=self.params.p))
        elif self.params.transform_type == 'rotate_90':
            transforms.append(A.Rotate(limit=(self.params.limit, self.params.limit), p=self.params.p))

        if self.params.blur > 0:
            blur_limit = (self.params.blur, self.params.blur) if self.params.blur % 2 == 1 else (self.params.blur - 1, self.params.blur - 1)
            transforms.append(A.GaussianBlur(blur_limit=blur_limit, p=1.0))
        if self.params.sharpen > 0:
            transforms.append(A.Sharpen(p=1.0))
        if self.params.brightness != 1.0 or self.params.contrast != 1.0:
            transforms.append(A.RandomBrightnessContrast(brightness_limit=(self.params.brightness-1, self.params.brightness-1), contrast_limit=(self.params.contrast-1, self.params.contrast-1), p=1.0))
        if self.params.noise > 0:
            transforms.append(A.GaussNoise(var_limit=(self.params.noise, self.params.noise), p=1.0))

        if transforms:
            transform = A.Compose(transforms)
            augmented = transform(image=image)
            return augmented['image']
        return image

    def save_image(self, image, output_path):
        plt.imsave(output_path, image)
        return output_path

@main.route('/api/transform', methods=['POST'])
def transform_image():
    # Lấy toàn bộ payload từ yêu cầu
    data = request.json

    # Tạo đối tượng TransformParams
    transform_params = TransformParams(data)

    # Tạo đối tượng ImageTransformer
    image_transformer = ImageTransformer(transform_params)

    # Kiểm tra đường dẫn ảnh
    is_valid, error_response, status_code = image_transformer.validate_image()
    if not is_valid:
        return jsonify(error_response), status_code

    # Đọc và xử lý ảnh
    image = image_transformer.load_image()
    image = image_transformer.apply_transforms(image)

    # Lưu ảnh đã xử lý vào thư mục processed_images
    processed_image_path = os.path.join(PROCESSED_FOLDER, 'processed_image.jpg')
    image_transformer.save_image(image, processed_image_path)

    return jsonify({"message": "Image transformed successfully", "processed_image_path": processed_image_path}), 200