```python
import numpy as np
from paz import processors as pr
from paz.backend.image import load_image
from paz.pipelines import DetectMinimal

# Define image augmentation pipeline
image_augmentation = pr.Sequential([
    pr.RandomContrast((0.5, 1.5)),
    pr.RandomBrightness((0.5, 1.5)),
    pr.RandomSaturation((0.5, 1.5)),
    pr.RandomHue(0.25),
])

# Define box augmentation pipeline
box_augmentation = pr.Sequential([
    pr.ConvertBoxesToImageBoxes(),
    pr.Expand(max_expand_ratio=2.0, probability=0.5),
    pr.RandomSampleCrop(min_aspect_ratio=0.5, max_aspect_ratio=2.0, probability=0.5),
    pr.RandomFlipBoxesLeftRight(),
])

# Define preprocessing pipeline for boxes
preprocess_boxes = pr.Sequential([
    pr.MatchBoxesToAnchors(),
    pr.EncodeBoxes(),
    pr.ExpandDims(axis=0),  # For batch dimension
    pr.ToOneHot(num_classes=21),  # Assuming VOC dataset for example
])

# Define a pipeline for drawing boxes
draw_boxes = pr.DrawBoxes2D(class_names=['background', 'class1', 'class2'])  # Example class names

# Define a complete processor that puts everything together
class CompleteProcessor(pr.Processor):
    def __init__(self):
        super(CompleteProcessor, self).__init__()
        self.image_augmentation = image_augmentation
        self.box_augmentation = box_augmentation
        self.draw_boxes = draw_boxes
        self.preprocess_boxes = preprocess_boxes

    def call(self, inputs):
        image, boxes = inputs
        image = self.image_augmentation(image)
        boxes = self.box_augmentation(boxes)
        image_with_boxes = self.draw_boxes([image, boxes])
        preprocessed_boxes = self.preprocess_boxes(boxes)
        return image_with_boxes, preprocessed_boxes

# Example usage
processor = CompleteProcessor()
image_path = 'path/to/your/image.jpg'
image = load_image(image_path)
boxes = np.array([[0, 0.1, 0.1, 0.2, 0.2], [1, 0.3, 0.3, 0.4, 0.4]])  # Example boxes [class, x_min, y_min, x_max, y_max]
image_with_boxes, preprocessed_boxes = processor([image, boxes])

# Define a sequence generator for processing batches of data
class DataSequence(pr.Sequence):
    def __init__(self, image_paths, boxes, batch_size=32):
        self.image_paths = image_paths
        self.boxes = boxes
        self.batch_size = batch_size
        self.processor = CompleteProcessor()

    def __len__(self):
        return int(np.ceil(len(self.image_paths) / float(self.batch_size)))

    def __getitem__(self, idx):
        batch_x = self.image_paths[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.boxes[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_images = []
        batch_boxes = []
        for path, box in zip(batch_x, batch_y):
            image = load_image(path)
            image, box = self.processor([image, box])
            batch_images.append(image)
            batch_boxes.append(box)
        return np.array(batch_images), np.array(batch_boxes)
```