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import numpy as np
import cv2 as cv
def iou_obb_pair(i, j, bboxes1, bboxes2):
"""
Compute IoU for the rotated rectangles at index i and j in the batches `bboxes1`, `bboxes2` .
"""
rect1 = bboxes1[int(i)]
rect2 = bboxes2[int(j)]
(cx1, cy1, w1, h1, angle1) = rect1[0:5]
(cx2, cy2, w2, h2, angle2) = rect2[0:5]
r1 = ((cx1, cy1), (w1, h1), angle1)
r2 = ((cx2, cy2), (w2, h2), angle2)
# Compute intersection
ret, intersect = cv.rotatedRectangleIntersection(r1, r2)
if ret == 0 or intersect is None:
return 0.0 # No intersection
# Calculate intersection area
intersection_area = cv.contourArea(intersect)
# Calculate union area
area1 = w1 * h1
area2 = w2 * h2
union_area = area1 + area2 - intersection_area
# Compute IoU
return intersection_area / union_area if union_area > 0 else 0.0
class AssociationFunction:
def __init__(self, w, h, asso_mode="iou"):
"""
Initializes the AssociationFunction class with the necessary parameters for bounding box operations.
The association function is selected based on the `asso_mode` string provided during class creation.
Parameters:
w (int): The width of the frame, used for normalizing centroid distance.
h (int): The height of the frame, used for normalizing centroid distance.
asso_mode (str): The association function to use (e.g., "iou", "giou", "centroid", etc.).
"""
self.w = w
self.h = h
self.asso_mode = asso_mode
self.asso_func = self._get_asso_func(asso_mode)
@staticmethod
def iou_batch(bboxes1, bboxes2) -> np.ndarray:
bboxes2 = np.expand_dims(bboxes2, 0)
bboxes1 = np.expand_dims(bboxes1, 1)
xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
wh = w * h
o = wh / (
(bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) +
(bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) -
wh
)
return o
@staticmethod
def iou_batch_obb(bboxes1, bboxes2) -> np.ndarray:
N, M = len(bboxes1), len(bboxes2)
def wrapper(i, j):
return iou_obb_pair(i, j, bboxes1, bboxes2)
iou_matrix = np.fromfunction(np.vectorize(wrapper), shape=(N, M), dtype=int)
return iou_matrix
@staticmethod
def hmiou_batch(bboxes1, bboxes2):
"""
Compute a modified Intersection over Union (hIoU) between two batches of bounding boxes,
incorporating a vertical overlap ratio.
Parameters:
- bboxes1: (N, 4) array of bounding boxes [x1, y1, x2, y2]
- bboxes2: (M, 4) array of bounding boxes [x1, y1, x2, y2]
Returns:
- hmiou: (N, M) array where hmiou[i, j] is the modified IoU between bboxes1[i] and bboxes2[j]
"""
# Expand dimensions for broadcasting
bboxes1 = np.expand_dims(bboxes1, axis=1) # Shape: (N, 1, 4)
bboxes2 = np.expand_dims(bboxes2, axis=0) # Shape: (1, M, 4)
# Compute vertical overlap ratio 'o'
intersect_y1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
intersect_y2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
intersection_height = np.maximum(0.0, intersect_y2 - intersect_y1)
union_y1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
union_y2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
union_height = np.maximum(1e-10, union_y2 - union_y1)
o = intersection_height / union_height
# Compute standard IoU
inter_x1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
inter_y1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
inter_x2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
inter_y2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
inter_w = np.maximum(0.0, inter_x2 - inter_x1)
inter_h = np.maximum(0.0, inter_y2 - inter_y1)
inter_area = inter_w * inter_h
area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) # Shape: (N, 1)
area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) # Shape: (1, M)
union_area = area1 + area2 - inter_area
iou = inter_area / (union_area + 1e-10)
# Modify IoU with vertical overlap ratio
hmiou = iou * o
return hmiou
@staticmethod
def giou_batch(bboxes1, bboxes2) -> np.ndarray:
"""
:param bboxes1: predict of bbox(N,4)(x1,y1,x2,y2)
:param bboxes2: groundtruth of bbox(N,4)(x1,y1,x2,y2)
:return:
"""
# Ensure predict's bbox form
bboxes2 = np.expand_dims(bboxes2, 0)
bboxes1 = np.expand_dims(bboxes1, 1)
xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
wh = w * h # Intersection area
# Compute areas of individual boxes
area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
# Union area
union_area = area1 + area2 - wh
iou = wh / union_area
xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
wc = xxc2 - xxc1
hc = yyc2 - yyc1
assert (wc > 0).all() and (hc > 0).all()
area_enclose = wc * hc # Area of the smallest enclosing box
# Corrected GIoU computation
giou = iou - (area_enclose - union_area) / area_enclose
giou = (giou + 1.0) / 2.0 # Resize from (-1,1) to (0,1)
return giou
def centroid_batch(self, bboxes1, bboxes2) -> np.ndarray:
centroids1 = np.stack(((bboxes1[..., 0] + bboxes1[..., 2]) / 2,
(bboxes1[..., 1] + bboxes1[..., 3]) / 2), axis=-1)
centroids2 = np.stack(((bboxes2[..., 0] + bboxes2[..., 2]) / 2,
(bboxes2[..., 1] + bboxes2[..., 3]) / 2), axis=-1)
centroids1 = np.expand_dims(centroids1, 1)
centroids2 = np.expand_dims(centroids2, 0)
distances = np.sqrt(np.sum((centroids1 - centroids2) ** 2, axis=-1))
norm_factor = np.sqrt(self.w ** 2 + self.h ** 2)
normalized_distances = distances / norm_factor
return 1 - normalized_distances
def centroid_batch_obb(self, bboxes1, bboxes2) -> np.ndarray:
centroids1 = np.stack((bboxes1[..., 0], bboxes1[..., 1]),axis=-1)
centroids2 = np.stack((bboxes2[..., 0], bboxes2[..., 1]),axis=-1)
centroids1 = np.expand_dims(centroids1, 1)
centroids2 = np.expand_dims(centroids2, 0)
distances = np.sqrt(np.sum((centroids1 - centroids2) ** 2, axis=-1))
norm_factor = np.sqrt(self.w ** 2 + self.h ** 2)
normalized_distances = distances / norm_factor
return 1 - normalized_distances
@staticmethod
def ciou_batch(bboxes1, bboxes2) -> np.ndarray:
"""
Calculate Complete Intersection over Union (CIoU) for batches of bounding boxes.
:param bboxes1: Predicted bounding boxes of shape (N, 4) as (x1, y1, x2, y2)
:param bboxes2: Ground truth bounding boxes of shape (N, 4) as (x1, y1, x2, y2)
:return: CIoU scores scaled between 0 and 1
"""
epsilon = 1e-7 # Small value to prevent division by zero
# Expand dimensions for broadcasting
bboxes2 = np.expand_dims(bboxes2, 0) # Shape: (1, M, 4)
bboxes1 = np.expand_dims(bboxes1, 1) # Shape: (N, 1, 4)
# Calculate the intersection box
xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
wh = w * h
# Calculate IoU
area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])
area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])
iou = wh / (area1 + area2 - wh + epsilon)
# Calculate center points
centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0
centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0
centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0
centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0
# Calculate squared center distance
inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2
# Calculate smallest enclosing box diagonal
xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2 + epsilon
# Calculate aspect ratio consistency
w1 = bboxes1[..., 2] - bboxes1[..., 0]
h1 = bboxes1[..., 3] - bboxes1[..., 1]
w2 = bboxes2[..., 2] - bboxes2[..., 0]
h2 = bboxes2[..., 3] - bboxes2[..., 1]
# Prevent division by zero
h2 = h2 + epsilon
h1 = h1 + epsilon
arctan_diff = np.arctan(w2 / h2) - np.arctan(w1 / h1)
v = (4 / (np.pi ** 2)) * (arctan_diff ** 2)
# Calculate alpha
S = 1 - iou
alpha = v / (S + v + epsilon)
# Compute CIoU
ciou = iou - (inner_diag / outer_diag) + (alpha * v)
# Scale CIoU to [0, 1]
return (ciou + 1) / 2.0
def diou_batch(bboxes1, bboxes2) -> np.ndarray:
"""
:param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)
:param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)
:return:
"""
# for details should go to https://arxiv.org/pdf/1902.09630.pdf
# ensure predict's bbox form
bboxes2 = np.expand_dims(bboxes2, 0)
bboxes1 = np.expand_dims(bboxes1, 1)
# calculate the intersection box
xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])
yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
wh = w * h
iou = wh / (
(bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) +
(bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) -
wh
)
centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0
centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0
centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0
centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0
inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2
xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])
yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])
xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])
yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])
outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2
diou = iou - inner_diag / outer_diag
return (diou + 1) / 2.0
@staticmethod
def run_asso_func(self, bboxes1, bboxes2):
"""
Runs the selected association function (based on the initialization string) on the input bounding boxes.
Parameters:
bboxes1: First set of bounding boxes.
bboxes2: Second set of bounding boxes.
"""
return self.asso_func(bboxes1, bboxes2)
def _get_asso_func(self, asso_mode):
"""
Returns the corresponding association function based on the provided mode string.
Parameters:
asso_mode (str): The association function to use (e.g., "iou", "giou", "centroid", etc.).
Returns:
function: The appropriate function for the association calculation.
"""
ASSO_FUNCS = {
"iou": AssociationFunction.iou_batch,
"iou_obb": AssociationFunction.iou_batch_obb,
"hmiou": AssociationFunction.hmiou_batch,
"giou": AssociationFunction.giou_batch,
"ciou": AssociationFunction.ciou_batch,
"diou": AssociationFunction.diou_batch,
"centroid": self.centroid_batch, # only not being staticmethod
"centroid_obb": self.centroid_batch_obb
}
if self.asso_mode not in ASSO_FUNCS:
raise ValueError(f"Invalid association mode: {self.asso_mode}. Choose from {list(ASSO_FUNCS.keys())}")
return ASSO_FUNCS[self.asso_mode]
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