utils/get_features.py

import layoutparser as lp
from PIL import Image
import tensorflow as tf
import numpy as np
import torch
import torchvision.ops.boxes as box_ops
from typing import List, Tuple
from .split_image import split_image
from .get_unique_values import get_unique_values

def get_vectors(*,
    predicted_bboxes: List[Tuple[int, int, int, int]],
    predicted_scores: List[float],
    predicted_labels: List[str],
    label_names: List[str],
    sub_images_bboxes: List[Tuple[int, int, int, int]],
    index_start: int = 0.17,
    index_end: int = 1,
    weighted_jaccard_index = False):
  bboxes_tensor: torch.Tensor = torch.tensor(predicted_bboxes)
  labels_nonce = { value:key for key, value in zip(get_unique_values(start = index_start, end = index_end, count = len(label_names)), list(label_names)) }
  
  def get_vector(bbox: Tuple[int, int, int, int], region_nonce: int):
    # bbox: Expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
    bbox_tensor: torch.Tensor = torch.tensor([bbox])
    [jaccard_indexes] = box_ops.box_iou(bbox_tensor, bboxes_tensor)
    '''
      Either get the index of bounding box with largest jaccard_index (Intersection Over Union) or
      get the index of bounding box with largest jaccard_index (Intersection Over Union) multiplied by the score.
      By doing this we strike a balance between accuracy and relative position.
    '''
    index_of_jaccard_index = jaccard_indexes.argmax() if not weighted_jaccard_index else np.multiply(jaccard_indexes, predicted_scores).argmax()
    jaccard_index = jaccard_indexes[index_of_jaccard_index]
    jaccard_index_bbox_label__nonce = labels_nonce[predicted_labels[index_of_jaccard_index]]
    jaccard_index_bbox_score = predicted_scores[index_of_jaccard_index]
    vector = region_nonce * jaccard_index * jaccard_index_bbox_label__nonce * jaccard_index_bbox_score
    return vector.item()
  sub_images_nonces = get_unique_values(start = index_start, end = index_end, count = len(sub_images_bboxes))
  for sub_image_bbox, region_nonce in zip(sub_images_bboxes, sub_images_nonces):
      yield get_vector(sub_image_bbox, region_nonce)

def get_predictions(
    image: Image.Image,
    model: lp.Detectron2LayoutModel,
    predictions_reducer = lambda *args: args):
  layout_predicted = model.detect(image)
  if len(layout_predicted) > 0:
    predicted_bboxes = [block.coordinates for block in layout_predicted]
    predicted_scores = [block.score for block in layout_predicted]
    predicted_labels = [block.type for block in layout_predicted]
    [predicted_bboxes, predicted_scores, predicted_labels] = predictions_reducer(
        predicted_bboxes,
        predicted_scores,
        predicted_labels)
    return {
        'predicted_bboxes': predicted_bboxes,
        'predicted_scores': predicted_scores,
        'predicted_labels': predicted_labels,
    }
  else:
    return {
        'predicted_bboxes': [],
        'predicted_scores': [],
        'predicted_labels': [],
    }

def predictions_reducer(
    predicted_bboxes: List[Tuple[int, int, int, int]],
    predicted_scores: List[float],
    predicted_labels: List[str]):
  selected_indices = tf.image.non_max_suppression(
    boxes = predicted_bboxes,
    scores = predicted_scores ,
    max_output_size = len(predicted_bboxes),
    iou_threshold = 0.01)
  return {
      'predicted_bboxes': tf.gather(predicted_bboxes, selected_indices).numpy().tolist(), # List[List[int, int, int, int]]
      'predicted_scores': tf.gather(predicted_scores, selected_indices).numpy().astype(float).tolist(),
      'predicted_labels': tf.gather(predicted_labels, selected_indices).numpy().astype(str).tolist()
    }

def get_features(image: Image.Image, model: lp.Detectron2LayoutModel, label_names: List[str], width_parts = 100, height_parts = 100):
  predictions = get_predictions(image, model)
  reduced_predictions = predictions_reducer(**predictions)
  sub_images_bboxes = list(split_image(np.array(image), width_parts, height_parts, result = 'bboxes'))

  vectors = get_vectors(
    sub_images_bboxes = sub_images_bboxes,
    label_names = label_names,
    weighted_jaccard_index = False,
    **predictions)
  
  weighted_vectors = get_vectors(
    sub_images_bboxes = sub_images_bboxes,
    label_names = label_names,
    weighted_jaccard_index = True,
    **predictions)
  
  reduced_vectors = get_vectors(
    sub_images_bboxes = sub_images_bboxes,
    label_names = label_names,
    weighted_jaccard_index = False,
    **reduced_predictions)
  
  reduced_weighted_vectors = get_vectors(
    sub_images_bboxes = sub_images_bboxes,
    label_names = label_names,
    weighted_jaccard_index = True,
    **reduced_predictions)

  return {
      'predicted_bboxes': predictions['predicted_bboxes'],
      'predicted_scores': predictions['predicted_scores'],
      'predicted_labels': predictions['predicted_labels'],
      'vectors': list(vectors),
      'weighted_vectors': list(weighted_vectors),

      'reduced_predicted_bboxes': reduced_predictions['predicted_bboxes'],
      'reduced_predicted_scores': reduced_predictions['predicted_scores'],
      'reduced_predicted_labels': reduced_predictions['predicted_labels'],
      'reduced_vectors': list(reduced_vectors),
      'weighted_reduced_vectors': list(reduced_weighted_vectors),
  }