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8baa7cf 7bf8a91 8baa7cf 7bf8a91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import numpy as np
import numpy.typing as npt
from typing import Tuple, List
def bb_iou_array(
boxes: npt.NDArray[np.float64], new_box: npt.NDArray[np.float64]
) -> npt.NDArray[np.float64]:
"""
Calculates the Intersection over Union (IoU) between a box and an array of boxes.
Args:
boxes (numpy.ndarray): Array of bounding boxes with shape (N, 4).
new_box (numpy.ndarray): A single bounding box [x_min, y_min, x_max, y_max].
Returns:
numpy.ndarray: Array of IoU values between the new_box and each box in the array.
"""
# bb interesection over union
xA = np.maximum(boxes[:, 0], new_box[0])
yA = np.maximum(boxes[:, 1], new_box[1])
xB = np.minimum(boxes[:, 2], new_box[2])
yB = np.minimum(boxes[:, 3], new_box[3])
interArea = np.maximum(xB - xA, 0) * np.maximum(yB - yA, 0)
# compute the area of both the prediction and ground-truth rectangles
boxAArea = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
boxBArea = (new_box[2] - new_box[0]) * (new_box[3] - new_box[1])
iou = interArea / (boxAArea + boxBArea - interArea)
return iou
def expand_boxes(
boxes: npt.NDArray[np.float64],
r_x: Tuple[float, float] = (1, 1),
r_y: Tuple[float, float] = (1, 1),
size_agnostic: bool = True,
) -> npt.NDArray[np.float64]:
"""
Expands bounding boxes by a specified ratio.
Expected box format is normalized [x_min, y_min, x_max, y_max].
Args:
boxes (numpy.ndarray): Array of bounding boxes with shape (N, 4).
r_x (tuple, optional): Left, right expansion ratios. Defaults to (1, 1) (no expansion).
r_y (tuple, optional): Up, down expansion ratios. Defaults to (1, 1) (no expansion).
size_agnostic (bool, optional): Expand independently of the box shape. Defaults to True.
Returns:
numpy.ndarray: Adjusted bounding boxes clipped to the [0, 1] range.
"""
old_boxes = boxes.copy()
if not size_agnostic:
h = boxes[:, 3] - boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
else:
h, w = 1, 1
boxes[:, 0] -= w * (r_x[0] - 1) # left
boxes[:, 2] += w * (r_x[1] - 1) # right
boxes[:, 1] -= h * (r_y[0] - 1) # up
boxes[:, 3] += h * (r_y[1] - 1) # down
boxes = np.clip(boxes, 0, 1)
# Enforce non-overlapping boxes
for i in range(len(boxes)):
for j in range(i + 1, len(boxes)):
iou = bb_iou_array(boxes[i][None], boxes[j])[0]
old_iou = bb_iou_array(old_boxes[i][None], old_boxes[j])[0]
# print(iou, old_iou)
if iou > 0.05 and old_iou < 0.1:
if boxes[i, 1] < boxes[j, 1]: # i above j
boxes[j, 1] = min(old_boxes[j, 1], boxes[i, 3])
if old_iou > 0:
boxes[i, 3] = max(old_boxes[i, 3], boxes[j, 1])
else:
boxes[i, 1] = min(old_boxes[i, 1], boxes[j, 3])
if old_iou > 0:
boxes[j, 3] = max(old_boxes[j, 3], boxes[i, 1])
return boxes
def retrieve_title(
boxes: npt.NDArray[np.float64],
labels: npt.NDArray[np.int_],
classes: List[str],
) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.int_], npt.NDArray[np.float64]]:
"""
Retrieves missed captions by using the biggest `other` box.
If no chart_title is detected, this function finds the largest box
labeled as 'other' (with width > 0.3) and relabels it as 'chart_title'.
Args:
boxes (numpy.ndarray): Array of bounding boxes with shape (N, 4).
labels (numpy.ndarray): Array of labels with shape (N,).
classes (list): List of class labels.
Returns:
numpy.ndarray [N]: Array of labels.
"""
if classes.index("chart_title") not in labels:
widths = boxes[:, 2] - boxes[:, 0]
scores = widths * (labels == classes.index("other")) * (widths > 0.3)
replaced = np.argmax(scores) if max(scores) > 0 else None
if replaced is not None:
labels[replaced] = classes.index("chart_title")
return labels
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