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# SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import numpy.typing as npt
from typing import Dict, List, Tuple, Literal
def get_overlaps(
boxes: npt.NDArray[np.float64],
other_boxes: npt.NDArray[np.float64],
normalize: Literal["box_only", "all"] = "box_only",
) -> npt.NDArray[np.float64]:
"""
Checks if a box overlaps with any other box.
Boxes are expeceted in format (x0, y0, x1, y1)
Args:
boxes (np array [4] or [n x 4]): Boxes.
other_boxes (np array [m x 4]): Other boxes.
Returns:
np array [n x m]: Overlaps.
"""
if boxes.ndim == 1:
boxes = boxes[None, :]
x0, y0, x1, y1 = (
boxes[:, 0][:, None],
boxes[:, 1][:, None],
boxes[:, 2][:, None],
boxes[:, 3][:, None],
)
areas = (y1 - y0) * (x1 - x0)
x0_other, y0_other, x1_other, y1_other = (
other_boxes[:, 0][None, :],
other_boxes[:, 1][None, :],
other_boxes[:, 2][None, :],
other_boxes[:, 3][None, :],
)
areas_other = (y1_other - y0_other) * (x1_other - x0_other)
# Intersection
inter_y0 = np.maximum(y0, y0_other)
inter_y1 = np.minimum(y1, y1_other)
inter_x0 = np.maximum(x0, x0_other)
inter_x1 = np.minimum(x1, x1_other)
inter_area = np.maximum(0, inter_y1 - inter_y0) * np.maximum(0, inter_x1 - inter_x0)
# Overlap
if normalize == "box_only": # Only consider box included in other box
overlaps = inter_area / areas
elif (
normalize == "all"
): # Consider box included in other box and other box included in box
overlaps = inter_area / np.minimum(areas, areas_other[:, None])
else:
raise ValueError(f"Invalid normalization: {normalize}")
return overlaps
def get_distances(
title_boxes: npt.NDArray[np.float64], other_boxes: npt.NDArray[np.float64]
) -> npt.NDArray[np.float64]:
"""
Computes the distances between title and table/chart boxes.
Distance is computed as the sum of the vertical and horizontal distances.
Horizontal distance uses min(boxes center dist, boxes left dist).
Vertical distance uses min(top_title to bottom_other dists, bottom_title to top_other dists).
Args:
title_boxes (np array [n_titles x 4]): Title boxes.
other_boxes (np array [n_other x 4]): Other boxes.
Returns:
np array [n_titles x n_other]: Distances between titles and other boxes.
"""
x0_title, xc_title, y0_title, y1_title = (
title_boxes[:, 0],
(title_boxes[:, 0] + title_boxes[:, 2]) / 2,
title_boxes[:, 1],
title_boxes[:, 3],
)
x0_other, xc_other, y0_other, y1_other = (
other_boxes[:, 0],
(other_boxes[:, 0] + other_boxes[:, 2]) / 2,
other_boxes[:, 1],
other_boxes[:, 3],
)
x_dists = np.min(
[
np.abs(
xc_title[:, None] - xc_other[None, :]
), # Title center to other center
np.abs(x0_title[:, None] - x0_other[None, :]), # Title left to other left
],
axis=0,
)
y_dists = np.min(
[
np.abs(y1_title[:, None] - y0_other[None, :]), # Title above other
np.abs(y0_title[:, None] - y1_other[None, :]), # Title below other
],
axis=0,
)
dists = y_dists + x_dists / 2
return dists
def find_titles(
title_boxes: npt.NDArray[np.float64],
table_boxes: npt.NDArray[np.float64],
chart_boxes: npt.NDArray[np.float64],
max_dist: float = 0.1,
) -> Dict[int, Tuple[str, int]]:
"""
Associates titles to tables and charts.
Args:
title_boxes (np array [n_titles x 4]): Title boxes.
table_boxes (np array [n_tables x 4]): Table boxes.
chart_boxes (np array [n_charts x 4]): Chart boxes.
max_dist (float, optional): Maximum distance between title and table/chart. Defaults to 0.1.
Returns:
dict: Dictionary of assigned titles.
- Keys are the indices of the titles,
- Values are tuples of:
- str: Whether the title is assigned to a "chart" or "table"
- int: index of the assigned table/chart
"""
if not len(title_boxes) or not (len(table_boxes) or len(chart_boxes)):
return {}
# print(title_boxes.shape, table_boxes.shape, chart_boxes.shape)
# Get distances
chart_distances = np.ones((len(title_boxes), 0))
if len(chart_boxes):
chart_distances = get_distances(title_boxes, chart_boxes)
chart_overlaps = get_overlaps(title_boxes, chart_boxes, normalize="box_only")
# print(chart_overlaps, "chart_overlaps", chart_overlaps.shape)
# print(chart_distances, "chart_distances", chart_distances.shape)
chart_distances = np.where(chart_overlaps > 0.25, 0, chart_distances)
# print(chart_distances)
table_distances = np.ones((len(title_boxes), 0))
if len(table_boxes):
table_distances = get_distances(title_boxes, table_boxes)
if len(chart_boxes): # Penalize table titles that are inside charts
table_distances = np.where(
chart_overlaps.max(1, keepdims=True) > 0.25,
table_distances * 10,
table_distances,
)
# print(table_distances, "table_distances")
# Assign to tables
assigned_titles = {}
for i, table in enumerate(table_boxes):
best_match = np.argmin(table_distances[:, i])
if table_distances[best_match, i] < max_dist:
assigned_titles[best_match] = ("table", i)
table_distances[best_match] = np.inf
chart_distances[best_match] = np.inf
# Assign to charts
for i, chart in enumerate(chart_boxes):
best_match = np.argmin(chart_distances[:, i])
if chart_distances[best_match, i] < max_dist:
assigned_titles[best_match] = ("chart", i)
chart_distances[best_match] = np.inf
return assigned_titles
def postprocess_included(
boxes: npt.NDArray[np.float64],
labels: npt.NDArray[np.int_],
confs: npt.NDArray[np.float64],
class_: str = "title",
classes: List[str] = ["table", "chart", "title", "infographic"],
) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.int_], npt.NDArray[np.float64]]:
"""
Post process title predictions.
- Remove titles that are included in other boxes
Args:
boxes (numpy.ndarray [N, 4]): Array of bounding boxes.
labels (numpy.ndarray [N]): Array of labels.
confs (numpy.ndarray [N]): Array of confidences.
class_ (str, optional): Class to postprocess. Defaults to "title".
classes (list, optional): Classes. Defaults to ["table", "chart", "title", "infographic"].
Returns:
boxes (numpy.ndarray): Array of bounding boxes.
labels (numpy.ndarray): Array of labels.
confs (numpy.ndarray): Array of confidences.
"""
boxes_to_pp = boxes[labels == classes.index(class_)]
confs_to_pp = confs[labels == classes.index(class_)]
order = np.argsort(confs_to_pp) # least to most confident for NMS
boxes_to_pp, confs_to_pp = boxes_to_pp[order], confs_to_pp[order]
if len(boxes_to_pp) == 0:
return boxes, labels, confs
# other_boxes = boxes[labels != classes.index("title")]
inclusion_classes = ["table", "infographic", "chart"]
if class_ in ["header_footer", "title"]:
inclusion_classes.append("text")
other_boxes = boxes[np.isin(labels, [classes.index(c) for c in inclusion_classes])]
# Remove boxes included in other_boxes
kept_boxes, kept_confs = [], []
for i, b in enumerate(boxes_to_pp):
# # Inclusion NMS
# if i < len(titles) - 1:
# overlaps_titles = get_overlaps(t, titles[i + 1:], normalize="all")
# if overlaps_titles.max() > 0.9:
# continue
# print(t)
# print(other_boxes)
if len(other_boxes) > 0:
overlaps = get_overlaps(b, other_boxes, normalize="box_only")
if overlaps.max() > 0.9:
continue
kept_boxes.append(b)
kept_confs.append(confs_to_pp[i])
# Aggregate
kept_boxes = np.stack(kept_boxes) if len(kept_boxes) else np.empty((0, 4))
kept_confs = np.stack(kept_confs) if len(kept_confs) else np.empty(0)
boxes_pp = np.concatenate([boxes[labels != classes.index(class_)], kept_boxes])
confs_pp = np.concatenate([confs[labels != classes.index(class_)], kept_confs])
labels_pp = np.concatenate(
[
labels[labels != classes.index(class_)],
np.ones(len(kept_boxes)) * classes.index(class_),
]
)
return boxes_pp, labels_pp, confs_pp
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