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

# Download an image
image_path = 'path/to/your/image.jpg'  # Replace with your image path
image = load_image(image_path)

# Define box annotations for the image (example format)
boxes = np.array([[x_min, y_min, x_max, y_max, class_id], ...])  # Replace with your boxes

# Image augmentation pipeline
image_augmentation = pr.Sequential([
    pr.RandomContrast(0.2),
    pr.RandomBrightness(0.2),
    pr.RandomSaturation(0.2),
    pr.RandomHue(0.1),
])

# Box augmentation pipeline
box_augmentation = pr.Sequential([
    pr.ToImageBoxCoordinates(),
    pr.Expand(max_expand_ratio=1.5),
    pr.RandomSampleCrop(),
    pr.RandomFlipBoxesLeftRight(),
])

# Preprocessing pipeline for boxes
preprocess_boxes = pr.Sequential([
    pr.MatchBoxesToAnchors(anchors, num_classes),  # Define your anchors and num_classes
    pr.EncodeBoxes(),
    pr.ExpandDims(axis=0),  # For batch dimension
    pr.ToOneHot(num_classes),
])

# Drawing boxes pipeline
draw_boxes = pr.DrawBoxes2D(class_names, colors=None)  # Define your class_names

# Putting everything together in a single processor
class AugmentBoxesAndImage(pr.Processor):
    def __init__(self, image_augmentation, box_augmentation, preprocess_boxes, draw_boxes):
        super(AugmentBoxesAndImage, self).__init__()
        self.image_augmentation = image_augmentation
        self.box_augmentation = box_augmentation
        self.preprocess_boxes = preprocess_boxes
        self.draw_boxes = draw_boxes

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

# Instantiate the processor
processor = AugmentBoxesAndImage(image_augmentation, box_augmentation, preprocess_boxes, draw_boxes)

# Process an image and boxes
augmented_image, augmented_boxes = processor([image, boxes])

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

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

    def __getitem__(self, idx):
        batch_x = self.images[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.boxes[idx * self.batch_size:(idx + 1) * self.batch_size]
        return np.array([
            self.processor([image, boxes]) for image, boxes in zip(batch_x, batch_y)
        ])

# Example usage
# images = [load_image(path) for path in list_of_image_paths]  # Define your list_of_image_paths
# boxes = [your_boxes_for_each_image]  # Define your boxes for each image
# sequence = DataSequence(images, boxes, processor, batch_size=8)
# for batch in sequence:
#     # Process each batch
```